Zhiguo Zeng

Enrico Zio
email: enrico.zio@ecp.fr

An integrated modeling framework for quantitative business continuity assessment

Keywords: Business continuity management, risk analysis, event tree, event consequence diagram, semi-Markovian model

Systems are increasingly exposed to threats of disruptive events, e.g., failures, natural disasters, terrorist attacks, etc. A proactive approach is needed to protect the business and reduce the potential losses caused by these disruptive events. Business Continuity Management (BCM) is a way to integrate the recovery process within the preventive framework of risk assessment. Such integrated risk management strategy offers great potential benefits. However, the complexity of applying it in practice is such that existing BCM strategies are mainly based on qualitative methods only, which limits the potential added values. To support quantitative BCM, in this paper we define a set of quantitative business continuity metrics. The defined metrics are based on the estimated losses incurred by the disruptive event in the whole business process. For this, the business process is divided into four sequential phases, i.e., protection phase, mitigation phase, emergency phase and recovery phase. For each phase, a specific modeling method is developed and an integrated modeling framework is constructed for the business continuity. Simulation can, then, be used to quantify the business continuity metrics. The developed methods are applied to assess the business continuity of an oil storage tank farm.

al. [START_REF] Parise | Switching procedures and business continuity management: The flock logic of multiple source systems[END_REF] discussed methods that are used to ensure the business continuity of a safety-related power supply system. Torabi et al. [START_REF] Torabi | An enhanced risk assessment framework for business continuity management systems[END_REF] developed an enhanced risk assessment framework to support business continuity management. Rabbani et al. [START_REF] Rabbani | Developing a two-step fuzzy costbenefit analysis for strategies to continuity management and disaster recovery[END_REF] presented a fuzzy cost-benefit analysis method for planning BCM strategies.

As a holistic, integrated risk management strategy, BCM offers great potential benefits but the complexity of the problem is such that most currently existing BCM strategies are based on qualitative methods only, and this limits practical application. Very few works concern the quantitative modeling and analysis of business continuity. Boehmer et al. [START_REF] Boehmer | Evaluation of a business continuity plan using process algebra and modal logic[END_REF] presented an approach, which is based on process algebra and modal logic, to model the system behavior in the business continuity process. Similar models have been applied in Brandt et al. [START_REF] Brandt | Modeling and reconfiguration of critical business processes for the purpose of a business continuity management respecting security, risk and compliance requirements at credit suisse using algebraic graph transformation[END_REF] to describe the business continuity process of a credit card company. A multi-layer model is developed in Asnar [START_REF] Asnar | Analyzing business continuity through a multi-layers model[END_REF] to model the business continuity of a loan originating process. Bonafede et al. [START_REF] Bonafede | Statistical models for business continuity management[END_REF] combined Cox's model and Bayesian networks to model the business continuity process. Tan and Takakuwa [START_REF] Tan | Use of simulation in a factory for business continuity planning[END_REF] developed a simulation model to investigate the business continuity of a company considering the outbreak of a pandemic disease, where the business continuity is characterized by the operation rate and the plant-utilization rate. These models describe the post-crisis behavior of the system. However, no clearly-defined business metrics have been defined from these models, which prohibits the quantitative analysis of business continuity based on these models and therefore, limits their application in practice.

This paper focuses on the modeling and quantitative analysis of business continuity. The contributions of this paper are summarized as follows.

• Four quantitative metrics for business continuity are defined, based on the potential losses caused by the disruptive events.

• An integrated modeling framework is developed to model the whole business process.

• A simulation-based method is presented to calculate the defined business continuity metrics based on the integrated model.

The rest of the paper is organized as follows. In Section II, we present a four-phase conceptual model for business processes. Based on the conceptual model, the definition of the quantitative business continuity metrics are presented in Section III. An integrated modeling framework is developed in Section IV for the modeling and assessment of business continuity. The developed methods are applied in Section V to assess the business continuity of an oil storage tank farm. Finally, the paper is concluded in Section VI with a discussion on potential future works.

II. A CONCEPTUAL MODEL FOR BUSINESS CONTINUITY PROCESS

In this section, we present a conceptual model that describes the business continuity process and identifies its major contributing factors. Business continuity is defined as the capability of an organization to continue delivery of products or services at acceptable levels following disruptive events [START_REF]Societal security -business continuity management systems[END_REF]. It measures the capability of an organization to remain at or quickly recover to operational states after being affected by disruptive events.

Usually, the business process of an organization can be characterized by a performance indicator, denoted by PPI B , whose value reflects the degree to which the objective of the organization is satisfied. For example, for an oil refinery, the PPI B is its daily production yield; for a manufacturing factory, the PPI B is the products produced per day. The values of PPI B are determined by the operation state of the organization: the PPI B December 31, 2016 DRAFT remains at its nominal value when the system is under normal operation and drops to a degraded value when the normal operation of the system is disrupted. In practice, an organization is susceptible to various disruptive events, which might jeopardize its business continuity. Commonly encountered disruptive events include

• technological disruptions, caused by component or system failures;

• natural disruptions, caused by natural disasters, e.g., floods, earthquakes, lightening, etc.;

• social disruptions, caused by social movements, e.g., terrorist attacks, strikes, supply chain disruptions, etc.

When one or some of these disruptive events occur, the normal operation is disrupted and PPI B drops to a degraded value, as a consequence of the disruptive events. The organization and its stakeholders, then, suffer from losses caused by the business interruption. To reduce such losses, various business continuity measures can be taken to guarantee the continuity of the business process. Generally speaking, those measures can be divided into four categories, i.e.,

• protection measures, for defending the system from the disruptive events and preventing them from damaging the system. If protection measures succeed, the business process is not interrupted;

• mitigation measures, which are automatically activated when the protection measures fail and initial damage has been caused by the disruptive events. The aim of the mitigation measures is to contain the evolution of the disruptive events at the early stages of development, so that damages can be mitigated;

• emergency measures, which happen when the mitigation measures fail to contain the damage, and often require significant human intervention;

• recovery measures, which aim at re-establishing normal operation.

For example, lightning is a severe threat to oil & gas systems [START_REF] Chang | A study of storage tank accidents[END_REF]. Often, a lightening protection mast is installed at oil and gas tank farms as a protection measure against the threat of lightning [START_REF] Necci | Accident scenarios triggered by lightning strike on atmospheric storage tanks[END_REF]. If the protection mast fails to protect the system, the oil storage tank might catch fire [START_REF] Necci | A model for process equipment damage probability assessment due to lightning[END_REF]. Mitigation measures, such as the automatic fire extinguishing system, are automatically activated to fight the fire in order to prevent it from spreading to other tanks, causing a domino effect [START_REF] Necci | A model for process equipment damage probability assessment due to lightning[END_REF]. Emergency measures, e.g., the intervention of a fire brigade, are needed when the mitigation measures fail to stop the propagation of the accident [START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF]. Then, recovery measures, e.g., the repair and restoration of the affected tanks, are carried out to recover operation and minimize the losses caused by the business interruption.

Figure 1 presents a conceptual model that schematically illustrates the evolution of a business process under a disruptive event. The business process is divided into four phases: protection, mitigation, emergency and recovery. Each phase is associated with the corresponding business continuity measure. As shown in Figure 1, the PPI B of an actual business process might deviate from its nominal value due to the presence of various disruptive events. The severity and duration of the business interruption caused by the disruptive event can be controlled by implementing business continuity measures in the different phases. Among them, protection measures affect the resistance of the system to disruptive events. Mitigation and emergency measures determine how much system performance is degraded from the damage caused by the disruptive event. Recovery measures influence how quickly the system can recover to normal operation. In this section, we define a set of quantitative business continuity metrics to cover the whole business continuity process. Evidently, business (performance) continuity is inversely related to the losses caused by the disruptive event, i.e., a higher value of business continuity indicates less losses caused by the business interruption. Therefore, we define the business continuity metrics as a function of the losses due to the disruptive event.

Two kinds of losses are considered in this paper, i.e., direct losses and indirect losses. Direct losses, denoted by L D , are those caused directly by the disruptive event. For example, when a lightning strike hits an oil storage tank, L D might include the structural damage of the tank, the loss of the oil contained in the tank due to leakages in the damaged structure, and other possible losses that occur right after the lightning strikes.

Indirect losses, denoted by L I , are those incurred during the recovery process. In the previous example of the storage tank, the tank needs to be shutdown or at least operated at degraded performances during the recovery process, which causes revenue losses to the organization. Besides, L I also includes the costs of replacing or restoring the affected tank, e.g., the costs of spare parts, maintenance personnel, etc.

Let L i denote the losses caused by the ith disruptive event. The loss L i includes direct loss and indirect loss.

Therefore, we have

L i = L D,i + L I,i , (1)
where L D,i and L I,i are the direct loss and indirect loss caused by the ith disruptive event, respectively. The indirect loss, L I,i , measures the lost profits due to the degraded performance and the potential system downtime in the recovery process. To further quantify L I,i , it is assumed that

L I,i = k • t recv,i • (PPI B,N -PPI B,i ), (2)
where k is the indirect loss caused by the disruptive event per unit time per unit PPI B ; t recv,i is the time that is taken to recover the business process to its nominal performance; PPI B,N is the nominal PPI B for the business process and PPI B,i is the performance indicator of the business process after the damage of the ith disruptive event.

We are interested in the business continuity over a time interval [0, T ] (e.g., the life of the system). Suppose that the number of disruptive events that occur in [0, T ] is n(T ). From (1), the loss due to the disruptive events that occur in [0, T ], denoted by L T , is

L T = n(T ) i=1 (L D,i + L I,i ) , (3)
where L D,i and L I,i are the direct loss and indirect loss of the ith disruptive event, respectively, and L I,i is calculated using [START_REF] Hameed | A risk-based shutdown inspection and maintenance interval estimation considering human error[END_REF]. It should be noted that due to various uncertainties, the n(T ), L D,i and L I,i are random variables; therefore, L T is also a random variable. Based on (3), we can define quantitative metrics for business continuity.

Definition 1. We define the Business Continuity Value (BCV) as

BCV = 1 - L T L tol (4)
where L tol is the maximum loss that an organization can withstand.

It is easy to verify that that BCV ∈ (-∞, 1] and a high value of BCV indicates high business continuity. The physical meaning of ( 4) is the relative difference between the losses caused by the disruptive events and the maximum losses that an organization could stand. Since L tol represents the maximum tolerable losses for an organization, BCV = 0 is a borderline case: a BCV whose value less than zero indicates that the organization might have trouble in recovering after the occurrence of the disruptive events.

The relationship between BCV and PPI B is that, the BCV is defined based on the expected losses L T , which includes both direct losses and indirect losses, as shown in [START_REF] Meng | Method to analyze the regional life loss risk by airborne chemicals released after devastating earthquakes: A simulation approach[END_REF]. The indirect losses L I,i are, then, determined by the value of PPI B , according to [START_REF] Hameed | A risk-based shutdown inspection and maintenance interval estimation considering human error[END_REF]. The physical meaning of ( 2) is to measure the revenue losses due to the potential partial operation period of the system (i.e., operating at a reduced capacity) after the strike of the disruptive events.

The BCV defined in ( 4) is conceptually similar to a widely applied resilience metric, the resilience triangle:

if we let k = 1 and t recv,i = 1 in (2), the L T in ( 4) is, then, equivalent to the resilience triangle metric defined in [START_REF] Bruneau | A framework to quantitatively assess and enhance the seismic resilience of communities[END_REF]. Although seems similar, the BCV differs from resilience since the concept of resilience mainly focuses on the performance degradation and recovery process, but does not relate the process to the losses suffered by the organization. The BCV, on the other hand, explicitly considers the losses and therefore, allows to evaluate the possibility of recovery from financial perspectives.

Since BCV is a random variable, for practical applicability, four numerical metrics, i.e., Expected Business Continuity Value (EBCV), Standard Deviation of Business Continuity Value (SDBCV), P BI and P BF , are derived based on the probability distribution of BCV. EBCV is calculated as the expected value of BCV.

SDBCV is calculated as the standard deviation of BCV, to measure the variability in the BCV values.

December 31, 2016 DRAFT

The probability that disruptive events causes business interruption in the considered time interval, denoted by P BI , is the third numerical metrics defined in this paper:

P BI = P r (BCV < 1) . (5)
It measures the likelihood of business interruption caused by the disruptive events.

When the losses caused by the disruptive events are beyond tolerable, the organization might have trouble recovering: this situation is called Business Failure (BF), in this paper. The probability of business failure, denoted by P BF , is calculated by

P BF = P r (BCV < 0) . (6)
The value of P BF measures the capability that an organization cannot recover from potential disruptive events.

It should be noted that the four numerical metrics can be explained in financial terms using monetary values (e.g., in U.S. dollars). For example, suppose that the organization objective of the analysis can withstand financial losses up to $100, 000 (i.e., L T = $100, 000) and EBCV = 0.8, SDBCV = 0.01, P BI = 0.8, P BF = 0.1:

• EBCV = 0.8 indicates that the organization can accept to suffer on average a financial loss of 100, 000 ×

(1 -0.8) = $20, 000;

• SDBCV measures the variations in the acceptable expected losses: a larger SDBCV indicates that the expected losses are subject to large variations and vice versa;

• P BI = 0.8 indicates that with probability 0.8, the organization could suffer financial losses;

• P BF = 0.1 indicates that with probability 0.1, the organization could suffer financial losses larger than $100, 000.

IV. AN INTEGRATED MODELING FRAMEWORK FOR BUSINESS CONTINUITY ASSESSMENT

In this section, we first present an integrated modeling framework for business continuity (Section IV-A).

Then, a simulation-based method is developed to calculate the defined business continuity metrics (Section IV-B).

A. The integrated modeling framework

To model the business process, we make the following assumptions to capture its characteristics:

1) The occurrence of disruptive events follows a homogeneous Poisson process with a rate parameter λ DE .

2) The consequences of the disruptive event are classified into n discrete states with increasing degree of severity, denoted by

C DE,0 , C DE,1 , • • • , C DE,n
, where C DE,0 represents the consequence state that no damage is caused by the disruptive event.

3) The times it takes to recover the business process from state C DE,i to C DE,i-1 , denoted by t recv,i , i = 1, 2, • • • , n are independent and identically distributed random variables, with probability density functions f recv,i (t).

4) The reliability of the business continuity measures does not change with time.

As shown in Figure 1, the business process comprises of the protection, mitigation, emergency and recovery phases. Since each phase has its own characteristics, different modelling approaches are needed for different phase to capture the phase-specific characteristics. An example of integrated modeling framework is presented in Figure 2. The protection, mitigation and emergency phases determine the consequences of the disruptive event. Event trees can be used to model these phases [START_REF] Zio | An introduction to the basics of reliability and risk analysis[END_REF]. In an event tree model, the probabilities of the intermediate events represent the reliabilities/uncertainties of the allocated business continuity measures. The outcomes of protection phase and mitigation phase depend on the reliabilities of the protection and mitigation measures, respectively, which, according to Assumptions 4, do not depend on time. Then, fault tree models can be used to calculate these intermediate probabilities [START_REF] Zio | An introduction to the basics of reliability and risk analysis[END_REF]. The result of the emergency phase, on the other hand, involves the modeling of a sequence of activities by the emergency response team. Models capable of considering the sequential dynamics of such activities should be used for the emergency phase, e.g., the event sequence diagram [START_REF] Zhou | Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects[END_REF]. The recovery process also involves the dynamics of maintenance actions, which can be effectively described in terms of the process of transitions between system states, e.g., by semi-Markovian models [START_REF] Compare | Semi-markov model for the oxidation degradation mechanism in gas turbine nozzles[END_REF].

Fig. 2. An integrated model for business continuity assessment 1) Event tree model for the disruptive events and consequences: Event tree (see [START_REF] Zio | An introduction to the basics of reliability and risk analysis[END_REF]) is used to model the disruptive events and consequences. In the event tree, the initial event is the disruptive event, which is assumed to follow a Poisson process with a rate parameter λ DE . Each intermediate event represents the success or failure of a business continuity measure. The end events in the event tree represents the consequences of the disruptive events and are expressed as combinations of the states (success or failure) of the business continuity measures.

The occurrence probabilities of the intermediate events associated with the protection and mitigation phases are determined by the reliability of these measures. Reliability models, e.g., Fault Tree Analysis (FTA) [START_REF] Zio | An introduction to the basics of reliability and risk analysis[END_REF],

can be used to calculate these probabilities. The occurrence probabilities of the intermediate events associated 2) Event sequence diagram for emergency measures: ESD is a graphical visualization tool to describe the sequence of related events [START_REF] Zhou | Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects[END_REF]. It has been applied widely to model emergency actions and assess the risk of domino effects after the initial damage event [START_REF] Necci | Assessment of domino effect: State of the art and research needs[END_REF][START_REF] Abdolhamidzadeh | Anatomy of a domino accident: Roots, triggers and lessons learnt[END_REF][START_REF] Swaminathan | The event sequence diagram framework for dynamic probabilistic risk assessment[END_REF]. In ESD, events are classified into four categories [START_REF] Swaminathan | The event sequence diagram framework for dynamic probabilistic risk assessment[END_REF]:

initial events, which are the starting point of the evolution of the emergency process, e.g., a tank catches fire;

delay events, which are used to model the time required to finish a certain job in the emergency response, e.g., the arrival of the fire brigade at the fire spot. Delay events can be further distinguished as deterministic delay events, which are used to describe a job with fixed working time, and random delay events, which are used to describe a job with random working time; comment events, which describe the development of an event sequence; and terminate events, which indicate the termination of an ESD, e.g., the occurrence /non-occurrence of domino effects.

Usually, ESD is used to model the escalation of accidents, i.e., domino effects, considering the presence of emergency response activities [START_REF] Zhou | Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects[END_REF]. In ESD, domino effects are expressed by an event chain, starting from initial events to terminate events. The events are connected by logic gates to describe the emergency response activities. Four types of logic gates are considered in ESD, i.e., Output AND Gate, Input AND Gate, Output OR gate, Input OR gate [START_REF] Zhou | Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects[END_REF]. The times associated with the output of the gates can be determined by the input times of the gate, as shown in Figure 3 [START_REF] Zhou | Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects[END_REF]. The probability of the domino effect, can, then, be calculated based on analytical or simulation procedures, as summarized in [START_REF] Zhou | Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects[END_REF], which also determines the occurrence probability of the intermediate events associated with emergency measures in the event tree model.

3) Semi-Markovian model for the recovery process: The PPI B of the business process is degraded by the disruptive events and recovers as a result of the recovery measures. From Assumption 3, the transition rates associated with the recovery process are not constant, since the recovery times do not follow exponential distribution. Hence, the evolution of the PPI B is modeled by a semi-Markovian model [START_REF] Ross | Introduction to probability models[END_REF], as shown in Figure 2. In the semi-Markovian model, the states C DE,i , i = 0, 1, • • • , n correspond to the consequences of the disruptive events, obtained from the event tree analysis. The failure rates, λ ij , 0 ≤ i ≤ n -1, i < j, are December 31, 2016 DRAFT Fig. 4. A simulation-based method to calculate the loss caused by a single disruptive event

• direct puncturing of the tank wall, which leads to leakage and possible ignition of the leaked crude oil (pool fire);

• rim-seal fire, which is caused by the ignition of the flammable vapors by the lightning (full surface fire).

Both scenarios can escalate to cause more severe consequences and losses if not contained properly and promptly, as shown in Figure 6. For a detailed discussion on the escalation of the accident scenarios depicted in Figure 6, readers can refer to [START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF] and [START_REF] Necci | Accident scenarios triggered by lightning strike on atmospheric storage tanks[END_REF]. Protection, mitigation, emergency and recovery measures are designed to ensure the business continuity of the tank farm, as shown in Table I [START_REF] Necci | Accident scenarios triggered by lightning strike on atmospheric storage tanks[END_REF][START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF].

B. Event tree model for the disruptive events and consequences

An event tree model is constructed in Figure 7 for a single tank throughout the protection, mitigation and emergency phases. In Figure 7, the initial event, a lightning stricking the tank farm, is assumed to follow a

Poisson process with rate λ L ; P pro is the probability that the protection mast successfully prevents the lightning from damaging the tank; P DD is the probability that the arrived lightning strike results in a direct damage to the tank; P F V is the probability that flammable vapors exist in the rim area. According to [START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF], flammable vapors are always present in the rim area of EFR tanks; therefore, we assume P F V = 1; P AF ES is the probability that the automatic rim seal fire extinguishing system operates normally so that a rim-seal fire could be extinguished promptly; P F F S is the probability that the fixed foam system operates normally so that a full surface fire could Fig. 5. Layout of the crude oil storage tank farm [START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF] Fig. 6. Escalation of accident scenarios be extinguished promptly; P F B is the probability that the fire brigade arrives in time and works correctly so that the fires can be controlled before domino effects develop.

1) Determination of λ L : According to [START_REF]Protection against lightning[END_REF], the arrival rate of the lightning strikes on a single tank can be calculated by

λ S = N G • A D • C D × 10 -6 . (7)
In [START_REF] Yang | Risk assessment of rare events[END_REF], N G is the arrival rate of the lightning strikes per year per kilometer (km -2 • y -1 ); A D is the equivalent receiving area (m 2 ) for the storage tank. Suppose R and H denote the radius and height of the tank, respectively.

Then A D is calculated by

A D = π(R + 3H) 2 (8)
The value of C D , which represents the location factor of the building, can be determined from Table II [START_REF]Protection against lightning[END_REF].

In this case study, the N G of Tianjin is estimated to be 2.82/km

Mitigation measures

Automatic rim seal fire extinguishing system Automatic rim seal fire extinguishing system can detect and automatically fight against the rim-seal fire. 7), λ S = 9.842 × 10 -4 (y -1 ). Finally, λ L is determined by

λ L = n • λ S ,
where n is the number of working tanks. Therefore, we have λ S = 9.842 × 10 -3 (y -1 ).

2) Determination of P pro : Reference [START_REF] Necci | A model for process equipment damage probability assessment due to lightning[END_REF] develops a "capture model" to simulate the number of lightning strikes captured by a storage tank with known geometry. Based on the "capture model" and Monte Carlo simulations, the reduction rate of the captured lightning strikes after the implementation of the protection masts can be assessed, which is equivalent to the P pro in this paper [START_REF] Necci | Reduction of natech risk due to lightning by the use of protection systems[END_REF][START_REF] Necci | Quantification of risk reduction due to the installation of different lightning protection solutions for large atmospheric storage tanks[END_REF]. Based on their results, given the geometry of the storage tanks and the design information of the protection masts, the value of P pro is P pro = 0.996 [START_REF] Necci | Quantification of risk reduction due to the installation of different lightning protection solutions for large atmospheric storage tanks[END_REF].

3) Determination of P DD : Direct damage of the tank is caused by the heat generated by the lightning strikes, which primarily depends on the peak current of the captured lightning strike. The generated heat would lead to the formation of hemispherical grooves on the walls or roofs of the tank [START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF]. If the amount of the generated heat is large enough, the radius of the grooves might be greater than the thickness of the walls or roofs, and leads to the perforation of them. Reference [START_REF] Dehn | Lightening protection guide[END_REF] points out that to cause direct perforation, the peak current of the captured lightning strike needs to exceed a threshold value, denoted by I th , which is dependent on the thickness of the tank wall or roof, as shown in Table III. Reference [START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF] proposes an empirical relationship to calculate the probability that the peak current I peak is greater than I,

lgP (I peak > I) = - I 88 . (9)
In this case study, the thickness of the tank wall is 35 (mm) and the thickness of the roof is 5 (mm). Since the thickness of the tank wall is much higher than that of the roof, we only consider the possibility of the perforation of the roof in this paper. From Table III, I th = 158 (kA); substituting it into (9), we have P DD = 1.6 × 10 -2

for the tanks in this case study.

4) Determination of P AF ES and P F F S : The failure probabilities of similar AFES and FFS systems have been analyzed by Necci in [START_REF] Necci | Accident scenarios triggered by lightning strike on atmospheric storage tanks[END_REF], using fault tree analysis. Here, we use the reference values of P AF ES and P F F S from [START_REF] Necci | Accident scenarios triggered by lightning strike on atmospheric storage tanks[END_REF], i.e., P AF ES = 0.976, P F F S = 0.993.

5) Determination of P F B : Fire brigade is the last safety barrier to prevent the fire from spreading to nearby tanks. If fire brigade fails to respond promptly and correctly, domino effects might arise, causing severe damages and losses. According to [START_REF] Zhou | Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects[END_REF], the value of P F B can be determined by the total response time of the fire brigade, denoted by T res , which accounts for the whole duration from the occurrence of the initial fire to its extinguishment by the fire brigade. Cozzani et al. [START_REF] Cozzani | The assessment of risk caused by domino effect in quantitative area risk analysis[END_REF] proposed that to stop the full surface fire from spreading to the nearby tanks, the fire needs to be put off within T th = 17 (min).

An ESD similar to that in [START_REF] Zhou | Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects[END_REF] is constructed to describe the sequential behavior of the fire brigade, as shown in Figure 8. According to the survey in [START_REF] Chen | The statistics law of fire response time and its correlation with the scale of urban fire[END_REF], the T i , i = 1, 2, • • • , 5 follow lognormal distributions with the parameters summarized in Table IV, where e and m are parameters of the lognormal distribution, whose probability density function is

f (x) =      1 √ 2πσx e -(ln(x)-e) 2 2m 2
, x > 0 0, x ≤ 0. 6) Calculate the probability of the consequences: Following the rules of event tree analysis [START_REF] Zio | An introduction to the basics of reliability and risk analysis[END_REF], the conditional probability of each consequence given the occurrence of the initial event can be calculated, as shown in Table V.

C. Semi-Markovian model for the recovery process

The consequences C 1 , C Damages are caused to the tank where the fire started and the four neighboring tanks.

P 5 = (1 -Ppro)(1 -P DD )P F V (1 -P AF ES )(1 - P F F S (1 -P F B )) C 6
Normal functioning No damages are caused to any tank.

P 6 = Ppro + (1 -Ppro)(1 -P DD )(1 -P F V )
is affected. When the consequence of the lightning strike is C 2 or C 5 , the fire caused by the lightning spreads to the nearby tanks and domino effects are caused. In this paper, we assume that if domino effects occur, the number of affected tanks is 5. The number of available tanks is used as the PPI B of the business process for the tank farm. When a tank is hit by the lightning or affected by domino effects, it becomes unavailable until it is repaired. Recovery measures should be performed to replace or restore the affected tank, so that it could return to normal operations. The behavior of the PPI B is described in the state diagram in Figure 9, where the states represent the number of available tanks. As shown in of the recovery process are not constant while the transition rates of the degradation process are, the dynamics of the PPI B , as described in Figure 9, follow a Semi-Markovian process. Simulation-based methods, such as those in [START_REF] Compare | Semi-markov model for the oxidation degradation mechanism in gas turbine nozzles[END_REF], can be exploited to investigate the time-dependent behavior of the PPI B .

D. Loss modeling and business continuity assessment

The losses an organization might suffer from the disruptive events include the direct losses and the indirect losses. In this case study, the direct losses depend on the number of affected tanks, denoted by n:

L D,i = n • k L D , (10)
where k L D is the average direct damage per tank caused by lightning and domino effect. In this case study, for illustrative purposes, we assume that k L D = 6 (in arbitrary units) per tank.

The indirect losses are calculated using [START_REF] Hameed | A risk-based shutdown inspection and maintenance interval estimation considering human error[END_REF], where the number of available tanks is used as the PPI B of the tank farm and the nominal PPI B is 10. The indirect constant k is assumed to be 0.25 units per day per tank.

That is, if n tanks are unavailable for N days, the organization would suffer 0.25 × (10 -n) × N units of indirect losses. The value of L tol , which represents the maximum loss that an organization could withstand, should be determined considering the financial conditions of the organization. For the tank farm, we assume L tol = 15 units for illustrative purposes. The simulation-based methods that are developed in Section IV-B are used to calculate the quantitative business continuity metrics, with a sample size of 10 6 . The results and discussions are given in the next subsection.

E. Results and discussions

The results of the simulation are summarized in Figure 10, which displays the empirical distribution of BCV.

BCV values are manly distributed in four regions (Region 1 -Region 4 in Figure 10):

• Most of the samples have a BCV equal to 1 (Region 1 in Figure 10), which indicates that no business interruption has been caused by the lightning (either no lightning strike hits the tank farm or the consequence of the lightning strike has been blocked by the protection measures so that no damage is caused).

• A closer examination at the samples that fall in Region 2 in Figure 10 reveals that for these samples, the lightning strike hits a tank and makes it catch fire. The fire is controlled promptly so that no domino effect results, which corresponds to state S 2 in Table VI. Moreover, the recovery process is smooth: no lightning The scatter of this region is due to the variability of the recovery time.

• A similar analysis is done to the samples in Region 3. Most of the samples in this region suffer from domino effects (the state S 3 in Table VI). Therefore, the affected number of the storage tanks is 5. Moreover, the recovery process is smooth: no lightning strike hits the fire tank during the recovery process. Hence, the mean value of BCV in this region is 1 -5 × 6 + 5 × 0.25 × 30 15 = -3.5.

The scatter of this region is again due to the variability of the recovery time.

• The samples that fall in Region 4 suffer from more severe damages. For example, two or more lightning strikes hit the tank farm and cause domino effects. Therefore, there samples have much lower and more spread BCV values than in the previous cases.

The four numerical business continuity metrics can be calculated based on the simulation results: EBCV = 0.9783, SDBCV = 0.2203, P BI = 1.78 × 10 -2 , P BF = 4.10 × 10 -3 .

For this case study, given the related assumptions and numerical values used for the parameters, we can conclude with high confidence that the tank farm would demonstrate high business continuity under the threat of lightning.

VI. CONCLUSION

This paper focuses on the quantitative modeling and assessment of business continuity. A conceptual model that divides the business process into four sequential phases, protection, mitigation, emergency and recovery, is proposed as a foundation of the metrics and models for business continuity assessment. An integrated framework is developed to model business continuity. In the integrated model, the protection, mitigation and emergency phases are modeled by event tree models, where fault trees and event sequence diagrams are exploited for the characterization of the probabilities associated with the intermediate events. The recovery phase is modeled by a semi-Markovian model. A simulation-based method is developed to calculate the business continuity metrics based on the developed models. A case study on a crude oil storage tank farm demonstrates the applicability of the modeling framework.

In this paper, we only considered the system subject to a single disruptive event. In the future, the framework should be extended to multiple disruptive events. Moreover, we rely on event trees to describe the protection, mitigation and emergency phases. Since the event tree is a static model, it cannot capture the time-dependent dynamics and the dependencies among the events. In the future, more advanced models, such as Bayesian Networks, can be integrated into the framework to more realistically model some aspects of the business continuity. A framework can also be developed to identify major contributors to business continuity and determine optimal design solutions, so that the required business continuity can be achieved under the constraint of time and resources.

Fig. 1 .

 1 Fig. 1. A conceptual model for the business continuity process

  with the emergency phase, on the other hand, are calculated by the Event Sequence Diagram (ESD)[START_REF] Zhou | Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects[END_REF], since the modeling of the emergency phase involves a sequence of activities taken by the emergency response team.As a result of the event tree modeling, the consequences of the disruptive event,C DE,0 , C DE,1 , • • • , C DE,n ,and the probabilities of these consequences, denoted by P C0 , P C1 , • • • , P Cn , can be obtained. Based on the rule of the decomposition of Poisson processes[START_REF] Ross | Introduction to probability models[END_REF], each possible consequence, C DE,i , i = 0, 1, • • • , n, can December 31, 2016 DRAFT be characterized by a Poisson process with the rate λ DE • P Ci .

Fig. 3 .

 3 Fig.3. Output times of the logic gates[START_REF] Zhou | Application of event sequence diagram to evaluate emergency response actions during fire-induced domino effects[END_REF]

  Fixed foam systemFixed foam system is automatically activated if the pool fire develops to full surface fire and aims at extinguishing full surface fires.Emergency measuresFire brigade Fire brigade is the last defensive barrier to control the fire and prevent it from escalating to other tanks.Recovery measuresRestore and / or replace the damaged tanksThe storage capability of the tank farm can be recovered by restoring or replacing the damaged tanks.

Fig. 7 .

 7 Fig. 7. Event tree model for the protection, mitigation and emergency phases

Fig. 8 .

 8 Fig. 8. Event sequence diagram of the emergency responses

P 1 = ( 1 - 2 P 2 = ( 1 - 3 P 3 = ( 1 - 4 4 = ( 1 - 5

 112213314415 Ppro)P DD P F B C Domino effect caused by pool fire Fire spread to the neighboring tanks; Damages are caused to the tank where the fire started and the four neighboring tanks. Ppro)P DD (1 -P F B ) C Rim seal fire extinguished A rim seal fire has occurred and extinguished; Local damage is caused to the tank where the incident occurs. Ppro)(1 -P DD )P F V P AF ES C Full surface fire extinguished A full surface fire has occurred and extinguished; Local damage is caused to the tank where the incident occurs. P Ppro)(1 -P DD )P F V (1 -P AF ES )(P F F S + (1 -P F F S P F B )) C Domino effect caused by full surface fire Fire spread to the neighboring tanks;

Fig. 9 .

 9 Fig. 9. Behavior of the PPI B under the threat of lightning

Fig. 10 .

 10 Fig. 10. Histogram of the BCV

TABLE I THE

 I

		BUSINESS CONTINUITY MEASURES OF THE TANK FARM
	Category	Measures	Descriptions
	Protection measures	Lightning protection mast	Lightning protection mast is used to protect the oil storage tank from damages caused by lightning.

TABLE III I

 III th FOR DIFFERENT THICKNESSES OF TANK WALLS[START_REF] Dehn | Lightening protection guide[END_REF]

	Thickness / mm	4	5	6	7	8	9	10
	I th / kA	95	158	273	442	666	943	1276

TABLE IV THE

 IV DISTRIBUTION OF THE EVENTS IN THE EVENT SEQUENCE DIAGRAM[START_REF] Chen | The statistics law of fire response time and its correlation with the scale of urban fire[END_REF]

	Event	e	m
	T 1	1.30	0.40
	T 2	0.88	0.78
	T 3	1.56	0.31
	T 4 / T 5	1.20	0.34

  [START_REF] Hameed | A risk-based shutdown inspection and maintenance interval estimation considering human error[END_REF]

  , • • • , C 6 , listed in Table V, can be further grouped into three categories, based on the number of affected storage tanks, as shown in Table VI. When the consequence of the lightning strike is C 6 , there is no storage tank affected by the lightning. When the consequence of the lightning strike is C 1 , C 3 or C 4 , there are local damages to the hit tank, without spreading to domino effects: therefore, only one tank

  Table VI, the PPI B might degrade in two manners, i.e., either by S 2 or by S 3 . Since the arrival of lightning strikes follows a homogeneous Poisson process with a rate λ L , the transition rates associated with S 2 and S 3 are determined by the decomposition of Poisson processes [39], i.e.,

λ 2 = P S2 λ L , λ 3 = P S3 λ L . According to

Table V and VI, P S2 = P 1 + P 3 + P 4 , P S3 = P 2 + P 5 . In this paper, for brevity, we just assume that only one tank can be repaired at a time and the recovery time t recv follows a LogNormal distribution with a mean value 30 (d) and a standard deviation 5 (d). Since the transition rates December 31, 2016 DRAFT

[START_REF] Zio | The Monte Carlo simulation method for system reliability and risk analysis[END_REF] DRAFT

Then, the value of P F B is calculated by Monte Carlo simulations, i.e., P F B = 0.693. December 31, 2016 DRAFT

determined based on the conditional probabilities of the consequences given the occurrence of the initial event of the event tree, which can also be yielded by the event tree analysis. According to Assumption 3, the recovery process is characterized by the probability density distribution of the recovery times, which should be estimated based on historical data or expert judgements.

Based on the developed models, the evolution of the PPI B in the entire business process, which covers the protection, mitigation, emergency and recovery phases, can be simulated using Monte Carlo simulations [START_REF] Zio | The Monte Carlo simulation method for system reliability and risk analysis[END_REF].

The business continuity metrics can, then, be estimated by further considering the losses incurred in the business process, as described in (1) -(3). Poisson process with rate parameter λ DE . Therefore, t disp is generated by simulating the next arrival time of a Poisson process with the parameter λ DE . When a simulated disruptive event arrives, its consequence is determined based on the consequence probabilities calculated from event tree analysis. Based on the direct damage corresponding to each consequence, the values of P P I B,cur and L D are updated. From Assumption 3, the recovery time t recv can be generated from the probability density function f recv,i (t). When a recovery event occurs, the P P I B,cur is updated first, based on the effectiveness of the recovery process. Then, the indirect loss, L I , is updated by considering the time spent in the previous degraded state.

B. A simulation-based method for business continuity assessment

The algorithm in Figure 4 is repeated N times and the empirical distribution of the BCV can be estimated based on these samples using (4). The numerical business continuity metrics defined in Section III can, then, be calculated from the empirical distribution of the BCV.

V. APPLICATION

A. System descriptions

In this section, we consider the crude oil storage tank farm originally reported in [START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF] as a case study, and apply the developed modeling framework to assess its business continuity. The tank farm is located in Tianjin, China, and its layout is illustrated in Figure 5 [START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF]. Ten External Floating Roof (EFR) storage tanks make up the tank farm, which is used to store crude oil. Each tank is 21.8 (m) in height and has a diameter of 80 (m).

The main material of the floating roof and tank wall is #16 steel, whose thicknesses are 5 (mm) and 35 mm, respectively. The capacity of a single tank is 1 × 10 5 (m 3 ) and therefore, the tank farm has a total capacity of 1 × 10 6 (m 3 ) [START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF]. To remain operational at full capacity, all of the ten tanks must be in working state.

Therefore, the number of working tanks is used as the PPI B of the tank farm.

The business continuity assessment starts from the identification of potential disruptive events. According to [START_REF] Chang | A study of storage tank accidents[END_REF][START_REF] Renni | Industrial accidents triggered by lightning[END_REF], lightning is the most frequently encountered disruptive event that could damage storage tanks. Hence, for illustrative purposes, we only consider lightning as the potential disruptive event in this paper. For an EFR tank that stores crude oil, two typical accident scenarios might be caused by lightning [START_REF] Wu | Quantitative risk assessment of fire accidents of large-scale oil tanks triggered by lightning[END_REF]:


Jean Mickael Lebreton
email: jean.lebreton@univ-reunion.fr

Nour Murad

Richard Lorion

Real-Time Radio Signal Mapping using an Autonomous Robot

A measurement technique for an indoor/outdoor radio signal mapping based on remote sensing through a mobile robot is provided. The robot is autonomous and able to provide its actual location in relation to its initial position. The data are sent in real time to the base station using wireless transmission and the electromagnetic mapping is obtained in real time as well. A real case scenario is considered by measuring the electromagnetic field from our department network. The automated system gives consistent results since it shows highest energy levels next to the sources. This papers shows that a robot can remotely make a precise radio signal mapping of an unknown environment.

I. INTRODUCTION

Radio signal strength mapping is generally realized in wireless site surveys in order to monitor the coverage of a wireless network. Systems and methods for surveying a wireless network site are presented in [START_REF] Hrastar | Systems and methods for wireless network site survey[END_REF]. The radio frequency (RF) coverage is generally based on measurement done by a mobile human user. The measurements consist of repetitive tasks which are time consuming and can lead to measuring errors regarding the coordinate information. Other measurement campaigns are done with sensor nodes placed on transport vehicles such as [START_REF] Hasenfratz | Spatially Resolved Monitoring of Radio-Frequency Electromagnetic Field[END_REF], however this method does not enable a controllable path by the sensor node itself.

This article proposes a measurement technique to avoid these disadvantages by using an autonomous mobile robot. Besides, measurements are done for different positions since the robot can move inside an environment and record its path. The data are transferred in real-time to the base station (BS) using a wireless transmission. Then the user can watch in real time the actual position of the robot and the data exploitation. The advantages of this method are the fact that measurements are done in an autonomous way and the mapping is done in real time. The user's presence on the site is only needed at the beginning of the measurement process.

Moreover, electromagnetic mapping can be used to find and identify the different electromagnetic sources available in the environment. This automated system can then be exploited for other projects which aim at harvesting RF energy. Jabbar presents the overview and progress achieved in RF energy harvesting field in [START_REF] Jabbar | RF energy harvesting system and circuits for charging of mobile devices[END_REF]. The automated system can also produce RSSI fingerprinting of an area, which is used for WiFi localisation in some studies [START_REF] Biswas | Wifi localization and navigation for autonoum indoor mobile robots[END_REF], [START_REF] Husen | Indoor Human Localization with Orientation using WiFi Fingerprinting[END_REF]. Our main contribution is the automation of the RF mapping by a robot and the RF distribution can be viewed in real time.

II. PRINCIPLE AND ALGORITHM

The RF mapping principle is to let the robot move within a defined area and to frequently make a scan of the available RF signals. The algorithm used by the robot is defined in figure 1.

III. RESULTS AND DISCUSSION

A RF mapping of our department has been done. The floor plan is illustrated in Figure 2 and the access points locations are represented as well. The network deployment and configuration have been done by the department and no specific change is done for the measurement process in order to study the network state. The results are presented in figure 3, which shows the mapping obtained after 45 minutes of operating time. The robot moved along the corridor of the building and the path is represented in the upper-left subfigure. At each point, the robot measured the received signal strength (RSSI) from five access points. The measured data Besides, the robot travelled within an area of 40 meters by 10 meters. It was located upstairs, so the measurements could not be done in the middle of the area. The signal strengths are only represented in the corridor even if an extrapolation can be done to show the global evolution of the RSSI in the total area. The RSSI distribution related to the different sources is represented in figure 3. It shows a reliable mapping for first three sources, which were the closest acess points to the robot path. For all of them, the RSSI distribution is consistent since high signal strengths are observed next to the related access point locations. The same signal strength levels are also obtained from these sources, which is consistent with their location toward the robot path. Then, some data have been collected for two other sources, but they were not close enough to get a reliable map. Therefore, the robot can carry out a good RF mapping of a network in a real case study. The entire mapping process can be viewed in [START_REF] Lebreton | Real time visualization of the RF mapping in the R&T department (32x speed)[END_REF].

IV. APPLICATION TO PROPAGATION MODEL

CHARACTERIZATION

The radio signal propagation is studied according to the log-normal path loss model [START_REF] Vallet | On the sensitivity of RSS based localization using the log-normal model: An empirical study[END_REF]:

P dBm = P 0 -10n log(||X -X s ||) + Z σ (1)
where P 0 is the received power at 1 meter from the source, n is the path loss exponent, X and X s are respectively the positions of the receiver and transceiver, Z σ is a normally distributed variable with a standard deviation σ and a zero-mean value, which represents the random shadowing effects. A least-square method is applied to the signal strength model and the measures from the radio signal mapping in order to estimate P 0 and n. We are then able to calculate the standard deviation σ by using equation 1. The results are given in table I.

V. CONCLUSION AND PERSPECTIVE

This paper shows that a precise electromagnetic mapping can be achieved by an autonomous robot. As our project is concerned by RF energy harvesting, the map provided by the robot depicts the location where the most energy is available. It is useful for a future network deployment of nodes with energy harvesting capabilities. Besides, the electromagnetic mapping is done in real time and with an automated system.

Fig. 1 .

 1 Fig. 1. Flowchart of the algorithm used by the robot to perform a RF mapping of an area.

Fig. 2 .

 2 Fig. 2. Floor plan of the department with the access points location.

Fig. 3 .

 3 Fig. 3. RF mapping of the department obtained after 45 minutes of measurement.


Jean Lebreton
email: jean.lebreton@univ-reunion.fr

Nour Murad

Somasekhar Kandukuri

Richard Lorion

An Energy-Efficient Addressing Mechanism for Wake-up Radio Systems

Wake-up Radio receivers (WuRx) must be energyefficient for their integration into Wireless Sensor Networks (WSNs). The most energy-hungry component is the address decoding hardware and it is generally powered by the node battery. In this paper, an addressing mechanism is proposed and the decoding process is explained in detail, on the contrary of previous works. This paper also shows the energy reduction of the address decoding stage while performed by a microcontroller, in comparison with one of the best correlator approach. The energy consumption and latency are also well investigated with real measurements, which is often omitted in previous works. Our approach is also flexible since a trade-off between energy consumption and wake-up latency is proposed. While decoding the address, the most energy-efficient mode consumes only 23.8 μA with a wake-up latency of 8.88 ms.

I. INTRODUCTION

Wireless Sensor Networks (WSNs) are considered as one of the most promising technologies. Energy efficiency is very crucial in WSN since nodes are usually battery operated devices. In most application scenarios, the major source of energy depletion is the radio module and many research works tends to reduce its energy consumption [START_REF] Rault | Energy efficiency in wireless sensor networks: A top-down survey[END_REF].

Wake-up Radio (WuR) systems have been proposed during the last decade to improve the energy efficiency of Wireless Sensor Networks (WSNs). WuR systems tend to replace duty-cycled Medium Access Control (MAC) protocols, since they reduce significantly the problem of idle listening and overhearing, which are the major sources of energy waste in WSNs [START_REF] Oller | Has Time Come to Switch From Duty-Cycled MAC Protocols to Wake-Up Radio for Wireless Sensor Networks?[END_REF]. In order to eliminate idle listening, the Wakeup Radio receiver (WuRx) enables the nodes to wake up on demand by a particular radio message, namely a Wake-up Call (WuC). Upon reception of a WuC, the node turns on its main MicroController Unit (MCU) and its main radio card for sending measured data.

Addressing mechanisms can also be integrated into WuRx for selecting a node to wake up, thus reducing overhearing. The addressing functionality requires additional power consumption for the WuRx when it is decoding the address. This energy consumption is often not mentioned in earlier works, but it is crucial for realistic WSN simulations involving WuRx scenarios such in [START_REF] Lebreton | Implementation of a Wake-up Radio Cross-Layer Protocol in OMNeT++/MiXiM[END_REF] In this paper, we present a semi-passive WuRx based on the combination of a compact rectenna array [START_REF] Riviere | Impact of the coupling effect and the configuration on a compact rectenna array[END_REF] and a MCU. The MCU is essentially used to decode the address included in the WuC. Moreover, additional information can be added in the WuC for developing more sophisticated wake-up schemes. We also measured and analysed the MCU energy consumption while processing the WuC signal under different oscillator configurations.

Main contributions of this paper are the following:

-we present in detail the addressing mechanism and in particular how the address is decoded by the WuRx;

-we experimentally measure the energy consumption and wake-up latency of the proposed WuRx regarding considering configurations of the MCU;

-we make a comparative analysis of our addressing mechanism with up-to-date approaches.

The rest of this article is organized as follows: in section II we describes related work. Section III presents the hardware design related to the WuR system and also the software developed for the addressing mechanism. The method for measuring the energy consumption and wake-up latency is then described in section IV. Results and discussion are given in section V for different configurations of the MCU. Section VI presents a comparative analysis regarding the WuRx power consumption. Finally, conclusion and perspectives are given in section VII.

II. RELATED WORKS

Gu and Stankovic proposed the first wake-up radio design with a basic radio-triggered circuit in [START_REF] Gu | Radio-Triggered Wake-Up Capability for Sensor Networks[END_REF]. Several wake-up receivers have been proposed in the literature to reduce the current consumption of the wake-up radio circuit, and they are classified in different types such as RFID, heterodyne, MCU or correlator.

Many papers proposing a WuRx supporting an addressing mechanism generally focus on the hardware and they give little or no information on the address decoding stage, especially how it is performed and how much it consumes [START_REF] Jelicic | Analytic comparison of wake-up receivers for wsns and benefits over the wake-on radio scheme[END_REF]. In this paper, the addressing mechanism is explained in details with the communication protocol used to decode the address. Also, the energy consumption for decoding the address is provided here.

While some previous works used the same modulation technique as our work (OOK modulation) and the same hardware for address decoding (MCU), the decoding technique is still not detailed. For instance, a MCU is used to sample data in [START_REF] Magno | An ultra low power high sensitivity wakeup radio receiver with addressing capability[END_REF]- [START_REF] Ansari | Radio-triggered Wake-ups with Addressing Capabilities for Extremely Low Power Sensor Network Applications[END_REF], which means they would read the digital port and reconstruct the signal after that. To the best of our knowledge, there is no paper proposing a standard or implementing existing communication protocols such as UART into the WuRx for the addressing decoding. Our decoding method is more efficient and also easier to reproduce for further implementations.

For decoding the address, WuRx available in the literature either have a dedicated component or the task is processed by the MCU. While using a dedicated component such as correlator in [START_REF] Oller | Performance Evaluation and Comparative Analysis of SubCarrier Modulation Wake-up Radio Systems for Energy-Efficient Wireless Sensor Networks[END_REF], the power consumption is well investigated. However, the MCU power consumption is lightly considered while being used for the address decoding, such in [START_REF] Magno | An ultra low power high sensitivity wakeup radio receiver with addressing capability[END_REF], [START_REF] Ansari | Radio-triggered Wake-ups with Addressing Capabilities for Extremely Low Power Sensor Network Applications[END_REF]. In general, a high clock frequency (typically from 1 to 16 MHz) is used and only the current consumption in sleeping mode is given, which is not sufficient. This paper shows the combined effect of the clock frequency and the WuC bit rate on the MCU power consumption. Only the work from [START_REF] Van Der Doorn | A prototype low-cost wakeup radio for the 868 MHz band[END_REF] presented its MCU energy consumption but the approach showed a relatively high power consumption.

III. HARDWARE DESIGN AND SOFTWARE DEVELOPMENT

The hardware and software tools implemented for this work are described in this section. The WuRx design is presented first and the related addressing mechanism is then discussed. Finally, our Wake-up Transmitter (WuTx) in charge of transmitting the WuC is presented.

A. Semi-passive WuRx design

The considered node structure is first described here with the block diagram depicted in figure 1. A semi-passive WuRx that can harvest energy from received signals is connected to both main radio and main MCU. These two components can be separated devices or they can be integrated into one device as a wireless mote such as TelosB, Xbee or Waspmote. Both of them can be waken up by the WuRx through an interrupt signal.

Our semi-passive WuRx is composed of three main blocks as illustrated in figure 2. The RF energy harvesting circuit converts the RF signal into a DC signal. In this work, we use a compact rectenna array presented in [START_REF] Riviere | Impact of the coupling effect and the configuration on a compact rectenna array[END_REF] which can harvest energy from received signals in the 2.4 GHz band. A comparator is added to the output of the RF energy harvesting circuit, which then produces a digital "high" if the output DC voltage is greater than a predefined threshold. In our case, a nano-power comparator from Texas Instruments [START_REF]TLV3691 Datasheet[END_REF] is considered. The comparator output voltage is then connected to a low-power MCU, a PIC24F16KA102 from Microchip [START_REF]PIC24f16ka102 Family Data Sheet[END_REF] in our case. The MCU purpose is essentially to process the address decoding function, and also to generate the wake-up interrupt accordingly for waking up the main MCU and main radio of the node. The WuRx prototype is depicted in figure 3, it was developed and tested in a breadboard. The realization of a PCB (Printed Circuit Board) is currently under consideration.

B. Energy Efficient Addressing Mechanism

In this paper, addressing mechanism refers to the modulation technique and the communication protocol used for transmitting and decoding the address in the WuC. As the WuRx architecture is relatively simple compared to traditional wireless receiver, there is less possibilities for the addressing mechanism. The simple architecture is also preferred for its low energy consumption since a more complex circuit often require more power consumption.

The most appropriate modulation technique for WuRx is certainly the On-Off Keying (OOK) and it is also the most employed technique in WuRx. OOK is actually the simplest form of Amplitude-Shift Keying (ASK) modulation where digital data are represented by the presence or absence of a carrier wave. In general, the absence of a carrier wave corresponds to a binary 0 and the presence of a carrier wave corresponds to a binary 1. The WuRx circuit is compatible with such modulation since the RF energy harvesting circuit converts RF signals into a DC voltage, meaning that the absence of a carrier wave corresponds to a zero voltage while the presence of a carrier wave corresponds to non-zero voltage. The comparator is then employed to reconstruct a digital signal for the MCU.

Some serial communication protocols are already implemented in the MCU for communicating with other peripheral devices such as SPI (Serial Peripheral Interface), UART (Universal Asynchronous Receiver Transmitter) or I 2 C (Inter-Integrated Circuit). A UART communication is chosen here due to its asynchronous aspect and the fact that one signal is sufficient for communicating. We then configured the UART communication using one start bit, one stop bit, 8 bits of data and no parity. As the destination address field is composed of 8 bits, the network size limit is 256 nodes. This number of nodes is sufficient for medium size WSNs in which WuR systems are involved. The bit rate, also known as baud rate, is variable since we consider different bit rates for the energy consumption analysis.

Finally, the WuC message is composed of a preamble, a start bit, a destination address field and a stop bit, as illustrated in figure 4. The MCU wakes up from deep sleep mode after detection of the preamble, which must be long enough to cover the MCU oscillator start-up time.

C. WuTx design

The challenge is then to develop a Wake-up Transmitter (WuTx) capable of transmitting the WuC according to an OOK modulation and compliant with the UART protocol. Our WuTx is actually a USRP-2922 (Universal Software Radio Peripheral) from National Instruments, which enables the development of a specific physical layer. We implemented a Virtual Instrument (VI) under LabVIEW for prototyping the physical layer communication. The VI main input parameters are the preamble size, the 8-bit destination address and the transmission gain. The VI configures the USRP to transmit the defined message using an OOK modulation with specified parameters such as the carrier frequency fixed at 2.45 GHz and the bit rate.

The VI under LabVIEW and the USRP form together a Software Defined Radio (SDR). The implementation described here refers to the physical layer of the WuTx, but a MAC layer has also been implemented in the same VI for designing the base station related to our network, since the 8-bit address can be dynamically varied in the program. Multiple nodes can then be interrogated by the WuTx and accordingly to the MAC layer specifications, which is not the topic of this paper.

IV. MEASUREMENT METHOD

The measurement system consisted of a variable resistor connected in series with a DC power supply and the MCU. An oscilloscope along with three probes were used to capture the signals related to our wake-up procedure. The first probe (yellow wire) was connected to the variable resistor terminals for measuring the current consumption. The second probe (green wire) was connected to the output voltage of the rectenna for capturing the WuC reception. The third probe Fig. 6. Signals measured by the oscilloscope upon reception of a WuC. The horizontal sweep is 2 ms/div, the vertical deflection is 50 mV/div for channel 1 and 2 V/div for channel 2-3.

(blue wire) was connected to the digital port RA0, which is programmed to be driven high upon a WuC validation. The circuit schematic is depicted in figure 5.

The measurement method was carried out for five MCU configurations. A special attention was paid to the system clock source since three internal oscillators are available in the PIC24F. The oscillators frequency can be selected by a programmable clock divider with a nominal 8 MHz output for the Fast internal RC (FRC) oscillator, a nominal 500 kHz for the Lower Power FRC (LPFRC) oscillator and a fixed 32 kHz for the Low-Power internal RC (LPRC) oscillator.

The measurement method is presented for the configuration using the LPRC oscillator at 32 kHz. The signals were captured by the oscilloscope upon reception of a WuC, as shown in figure 6. The MCU exits from deep sleep mode immediately after detecting the preamble. A long preamble of 20 bits is chosen here to match with the oscillator startup time and the number of instructions within the program initialization. After the address decoding and validation, the digital pin RA0 is driven high to trigger the node wake-up. The MCU enters deep sleep mode after this interrupt, with a noticeable current peak depicted in figure 6.

The wake-up delay is defined as the duration between the first bit of preamble received and the interrupt sent by the MCU. Considering this definition, the wake-up delay is directly measured through the oscilloscope.

The MCU energy consumption is calculated by integrating its power consumption over time, which is expressed by equation ( 1).

E = Tact 0 (U Gen -U (t)) × U (t) R v dt ( 1
)
where T act is the MCU active period, R v is the variable resistance, U (t) is the voltage across the resistor and U Gen is the voltage of the DC power supply.

The resistance R v is selected accordingly to get a voltage U (t) negligible compared to U Gen . The equation ( 1) is then Fig. 5. Energy measurement system applied to the wake-up radio simplified into equation ( 2) assuming that U (t) << U Gen .

E = U Gen R v Tact 0 U (t)dt (2)
The oscilloscope computed the numerical integration of U (t) over the active period T act with a high resolution of 2.5 million samples per second.

V. RESULTS AND DISCUSSION

For each configuration, we empirically determined the minimum preamble size. For smaller size of preamble, the MCU does not process the address decoding correctly. The minimum preamble size is then selected in each configuration for energyefficiency purpose. The bit-rate is often very limited in WuRx systems. Our rectifying circuit would theoretically be able to reach a bit-rate of 10.4 kbps, but a practical limit of 8 kbps was noticed. We varied the bit-rate between 4 and 8 kbps, which is the same order as other WuRx systems. The experimental results are given in table I.

The first two scenarios show the clear impact of the bit rate on both energy consumption and wake-up delay while using the FRC oscillator. A higher bit rate gives a lower wake-up delay and lower energy consumption. In this case, the energy consumption is reduced because of the lower wake-up delay since the current consumption is almost same for both scenarios.

Two other scenarios were considered while using the LPFRC oscillator, in which they differ from the frequency selection. This case is particularly interesting because it presents a trade-off between energy consumption and wake-up delay. The MCU consumes 1.79 μJ with the LPFRC oscillator set to 500 kHz while it consumes 1.71 μJ with the LPFRC oscillator set to 62.5 kHz. However, the wake-up delay is only equal to 1.94 ms with the 500 kHz oscillator while it is equal to 3.42 ms with the 62.5 kHz oscillator. Even if our main motivation is energy-efficiency, we notice an energy reduction of 5% with a wake-up delay increased by 76%. This trade-off is not advantageous for applications involving in both energy and latency constraints.

The LPRC oscillator is selected in the last scenario. The maximum bit rate of the serial communication is equal to one eighth of the oscillator frequency. The optimal bit rate is then considered for this configuration. The energy consumption is lower than the other configurations but the wake-up delay is longer, as it consumes only 1.01 μJ with a wake-up delay of 8.88 ms.

For our wake-up system, we obviously pick the 32 kHz LPRC oscillator since it is the most energy-efficient. If a trade-off between energy and latency is required, the 500 kHz LPFRC is certainly the best choice. Compared to the LPRC oscillator, it reduces the wake-up delay by a factor of 4.5 while only increasing the energy consumption by a factor of 0.8.

VI. COMPARATIVE ANALYSIS

Hardware correlators are often presented as one of the most energy-efficient component for addressing purpose and several WuRx in the literature are based on correlator. The WuRx in [START_REF] Oller | Performance Evaluation and Comparative Analysis of SubCarrier Modulation Wake-up Radio Systems for Energy-Efficient Wireless Sensor Networks[END_REF] was presented as one of best WuRx in the literature. Its power consumption was well studied, allowing an accurate comparison with real measures. A MCU running at 8MHz was used in [START_REF] Magno | An ultra low power high sensitivity wakeup radio receiver with addressing capability[END_REF] but the energy consumption for address decoding was not provided, our measures are then exploited for comparison. Our MCU approach using the 500 kHz LPFRC oscillator or the 32 kHZ LPRC oscillator are then compared to their approach.

The comparative analysis is based on the average current consumption of the MCU or correlator. The average current consumption Ī is highly dependent on the frequency of WuC reception, which corresponds to the network traffic. Let us define the WuC inter arrival time T w as the duration between two consecutive WuC receptions. The average current consumptions are calculated through equation 3 with the II.

Ī = T w -T act T w I idle + T act T w I act ( 3
)
where I idle is the current consumption in idle mode and I act is the current consumption in active mode for decoding the address. The evolution of the average current consumption Ī is depicted in figure 7 for the four considered approaches. Figure 7 shows that the MCU approach can perform better than the correlator approach from an energy perspective. In fact, the correlator approach consumes less current when the WuC inter arrival time is low, which corresponds to a WuRx requested very frequently. As the correlator consumes less current in its active mode compared to the MCU, the average current consumption in then lower for this scenario. However, WuRx are preferably used in low duty-cycle WSN where nodes are most of the time in deep sleep mode. For this reason, the MCU approach is more energy-efficient than the correlator approach due to its very low idle current. The average current consumption is also reduced by using the MCU low-power modes for address decoding, compared to the 8 Mhz mode in [START_REF] Magno | An ultra low power high sensitivity wakeup radio receiver with addressing capability[END_REF].

VII. CONCLUSIONS AND PERSPECTIVES

An energy-efficient addressing mechanism is proposed for WuRx in WSNs. Even though this addressing mechanism was applied to our WuRx, it can be integrated in any WuRx of the literature for reducing their energy consumption. The energyefficiency is obtained by optimizing the MCU configuration for the specific task of decoding the address included in the WuC. The energy consumption can also be trade off for latency with this MCU approach, since different power modes are available.

The presented addressing mechanism can also be used to send additional data directly from the WuTx to the WuRx, WuC Inter Arrival Time (s) which might be useful for a more complex WuR protocol.

As the first block of our WuRx design is an energy harvesting circuit, this work can be a supportive tool for the growing field of Simultaneous Wireless Information and Power Transfert (SWIPT).

Fig. 1 .

 1 Fig. 1. Block diagram of the node with an additional WuRx circuit.

Fig. 2 .Fig. 3 .

 23 Fig. 2. Block diagram of the semi-passive WuRx.

Fig. 4 .

 4 Fig. 4. Frame structure of the WuC with an example in binary representation, destination address is the symbol '5' in ASCII table.

Fig. 7 .

 7 Fig. 7. Average current consumption vs. WuC inter arrival time.

TABLE I WAKE

 I -UP DELAY AND ENERGY CONSUMPTION FOR ADDRESS DECODING USING DIFFERENT CONFIGURATIONS OF THE MCU.

	Oscillator	FRC		LPFRC		LPRC
	Frequency	8 MHz		500 kHz 62.5 kHz 32 kHz
	Preamble size (bits)	2	2	5	10	20
	Bit rate (kbps)	4	8	8	8	4
	Wake-up delay (ms)	2.94	1.59	1.94	3.42	8.88
	Current consumption (μA) 3196 2970 181	95.5	23.8
	Energy consumption (μJ)	32.7	16.6	1.79	1.71	1.01
	parameter values given in table


Somasekhar Kandukuri
email: somasekhar.kandukuri@univ-reunion.fr

Jean-Mickaël Lebreton

Nour Murad

Richard Lorion

Jean-Daniel Lan

Sun Luk

Energy-Efficient Cluster-Based Protocol using An Adaptive Data Aggregative Window Function (ADAWF) for Wireless Sensor Networks

published or not. The documents may come

I. INTRODUCTION

In WSNs, sensor nodes lifetime always rely on each other. In most of the application scenarios, sensor nodes (SNs) are used as battery powered devices which have limited storages and processing capabilities. Periodically, sensors can spend a lot of energy to transmit or receive the sensor readings which shortens their lifetime as well as their network lifetime. Since data aggregation or data collection has grown as one of the promising area in energy-efficient WSNs for maximizing the network lifetime, and several well-known data aggregation techniques have been studied in the literature [START_REF] Anastasi | Energy conservation in wireless sensor networks: A survey[END_REF][START_REF] Yoon | Exploiting spatial correlation towards an energy efficient clustered aggregation technique (cag)[wireless sensor network applications[END_REF][START_REF] Sharaf | TiNA: a scheme for temporal coherency-aware in-network aggregation[END_REF][START_REF] Madden | TinyDB: an acquisitional query processing system for sensor networks[END_REF][START_REF] Tsiftes | Enabling large-scale storage in sensor networks with the coffee file system[END_REF][START_REF] Tan | Adaptive calibration for fusion-based wireless sensor networks[END_REF][START_REF] De Paola | Predictive models for energy saving in wireless sensor networks[END_REF][START_REF] Wang | Data gathering in wireless sensor networks through intelligent compressive sensing[END_REF], however they are limited to their specificity. Especially, earlier literature works are designed based on the homogeneous application scenarios than heterogeneous, since many deployment scenarios proved that nodes have their own specific application tasks [START_REF] Taysi | Tinyears: spying on house appliances with audio sensor nodes[END_REF] rather than homogeneous tasks which are rarely used in practice. Having a better aggregation mechanism not only reduces redundant transmissions, it also saves a huge amount of receiver's energy of all other three modules such as radio receiving or listening, computation, and processing procedures of receiver nodes. Hence, it is necessary to use an effective data redundancy technique or simple prediction methods in order to avoid the redundant data transmissions to ensure the reliability in network applications.

In this paper, we primarily designed our proposition based on the homogeneous application scenarios, and then heterogeneous. In this regard, we present two sorts of data filtration, one performs in sensor nodes for finding temporal data redundancies (TDRs) using both relative variation (RV) and aggregative window functions, and the another one uses in CH nodes for exploiting spatial data redundancies (SDRs) using both RV and A-DAWF. This paper is presented as work in progress, and the designed implementations of our work are discussed in section III. In section IV, we show the preliminary results to demonstrate that the proposed mechanism can suppress a huge amount of data transmissions in both time and space. II. RELATED WORK Energy-efficienct strategies are a widely studied area in WSNs, and a taxonomy of various approaches are presented in the literature [START_REF] Anastasi | Energy conservation in wireless sensor networks: A survey[END_REF].

TiNA [START_REF] Sharaf | TiNA: a scheme for temporal coherency-aware in-network aggregation[END_REF] used a clause condition for specifying the differed ranges, if the differed range is greater than the specified range between any two values, then the differed result can be reported, otherwise ignored. TiNA is more related to our work, as we also used the RV function to find TDRs between every two window stored phenomena among the individual nodes. CAG [START_REF] Yoon | Exploiting spatial correlation towards an energy efficient clustered aggregation technique (cag)[wireless sensor network applications[END_REF] proposed a cluster-based technique which reports only spatial correlations of cluster nodes by a CH to the BS, and ignores the individual nodes temporal data. The authors in [START_REF] De Paola | Predictive models for energy saving in wireless sensor networks[END_REF] proposed another cluster-based method like CAG to build a predictive model on CH nodes instead of individual sensor nodes and let complete computational burden on header nodes itself. In the contrary, the authors in [START_REF] Madden | TinyDB: an acquisitional query processing system for sensor networks[END_REF] proposed a TinyDB and Cougar data collection models for maintaining node as a small database query engine to the BS, and the other recent works in [START_REF] Tsiftes | Enabling large-scale storage in sensor networks with the coffee file system[END_REF][START_REF] Tsiftes | A database in every sensor[END_REF] enables a data storage with coffee file system (CFS), and a dynamic Antelope database system in every sensor node.

Unlike previous works, our proposal avoids the consideration of building either rich spatial compression-based or temporal prediction-based mathematical models, and designed a simple data redundancy algorithm based on the Intel datasets. The basic window principle of this work has been approached from one of our earlier research works [START_REF] Kandukuri | Power Control Mechanisms on WARP Boards[END_REF]. In our considerations, normal nodes sense the environmental phenomena by using A-DAWF mechanism for exploiting TDRs in every node, and SDRs in CH nodes as well.

III. A-DAWF PRELIMINARIES

In this paper, we exploit both temporal and spatial correlations through the attribute window functions in order to suppress the redundant data information at a fixed window time intervals r and R. We consider a cluster-based sensor network with n SNs, which continuously forward the uncorrelated set of data attributes to the CH, T DR(t) = (p 1 , p 2 , ...., p M ) generates the sensed physical phenomena at different time instances t, and s CHs as super nodes, which receive spatial data messages of nodes at given time instances τ , SDR(τ ) = (q 1 , q 2 , ...., q N ) and their own sensor readings. The attribute detection nature of environmental phenomena p M , may be the attributes being sensed by nodes as temperature or humidity or may be the result of any application phenomena. If the sensor monitoring attributes are continuous, we consider A-DAWF mechanism to monitor the redundant data.

A. A-DAWF Implementation

A-DAWF is an aggregative window function which can reduce the redundant data transmissions for individual sensor nodes in both time and space. In this approach, we present two sorts of scenarios to do the filtration in both sensor nodes and CHs. In this work, we assume that the network model is single-hop clustering as shown in the figure 1, and the sensor nodes are purely distributed, which can compute and process the obtained environmental data at different time instances t by using A-DAWF mechanism. Since computation is the second highest energy consumer after the communication, although for computing the simple prediction-based models does not require much energy to make a comparison between the sensed readings through the window function. Thus, for suppressing TDRs, the following constraints must be satisfied in sensor nodes.

T R count = if |pi+1-pi| pi > η, T R count = 0 otherwise, T R count = 1 (1)
µ K = M i=1 p i+KM M (2)
According to the first scenario, the RV function of Eq.( 1), where T R count represents the temporal count of RV. If RV is greater than the threshold η then the T R count will be 0, otherwise T R count will be reported as 1. Therefore, we set the threshold η value as 0.05 and it can also be varied as per the physical phenomena or different application physical quantities. In the second scenario, A-DAWF evaluates the stored readings by Eq.( 2) where µ K is the mean value of window at K times which starts from zero. And i is an index of environmental property being sensed by sensor node as either temperature or humidity at given t time instances, and r is the round time interval of w M . And, M is a window size that can either be fixed or vary based on the nodes computational resources or flash memory. During every w M of a sensor node, it can compare its current value p i+1 with the previous value of p i through the window stored readings by Eq.( 1). In general applications, most of the obtained environmental data are either redundant or correlated. We assume that the observed nodes data are highly correlated. However, comparing current data with previous data values for finding redundant data does not show the greatest impacts of data transmission reductions, because of the instant deletion of previous data records after a successful transmission to the CH. While considering this, we proposed an attribute window concept w M in every sensor node. However, w M keeps the record of every 10 detected values during the observed time interval r and cleared the values on the basis of FIFO approach after each successful data window transmission to the CH node. Furthermore, we also implement the A-DAWF mechanism in CHs for finding both SDRs and TDRs, since CHs receive the data messages of SDR(τ ) among sensor nodes as well as their own sensor readings of T DR(t). In CH nodes, we assume that the window w Q size is 50, which can also be varied based on the CHs computational constraints for monitoring nodes spatial data and their own readings. In this case, if sensor nodes have the same application monitoring tasks at different periods, then there will be several SDRs over nodes. Hence, for reducing SDRs among nodes, CH keeps the spatial data records of its corresponding sensor nodes during every round time interval of R. In order to find SDRs, the following expressions Eq.( 3) and ( 4) must be satisfied in CH nodes, and the parameter considerations and their ranges of SDRs are used almost same as like sensor nodes of Eq.( 1) and ( 2) with exception of the window sizes, but the CH A-DAWF threshold η value is fixed at 0.05 and may vary since CHs window constraints are different than the SNs window constraints. According to the Eq.( 3), SR count represents a spatial correlations count of RV in CH windows.

SR count = if |qj+1-qj | qj > η, SR count = 0 otherwise, SR count = 1 (3) µ L = Q j=1 q j+LQ Q (4)

B. A-DAWF Prediction-Based Mechanism

The iterative Algorithm 1 starts by initializing all the variables and their parameters. The core of this algorithm, consists of a data redundancy loop that predicts the node redundant or correlated data with the previous data values. If the current readings are differ from the previous readings, then only node adds/counts the sorted data into its corresponding window packet before fowarding it to the cluster-head, otherwise the data can be flushed itself.

The entire algorithm iteratively stored the received data of their corresponding nodes into their attribute window functions, which can comput and update the data packets before forwarding to the CH node. In this algorithm, A-DAWF has two time intervals; one as nodes received data time intervals t and another one as window forwarding time intervals r. Using RV function (Algorithm 1, line 4) A-DAWF mechanism examines the window stored data of nodes, whether they are redundant and correlated, if RV is greater than the threshold then it counts and forwards the non-correlated data towards the BS. If the window contains the redundant information then (Algorithm, line 8) A-DAWF utilizes its mean averaging function to send one appropriate data value rather than the all window redundant messages. Moreover this pre-filtration method helps to reduce their redundant data transmissions, which then also reduces the total communication burden on nodes and maximizes the overall nodes lifetime as well as the network lifetime.

Moreover, we have also been developing the further considerations and improvements of the propositions in COOJA/Contiki simulator for both various metric and comparative analysis with several well known protocols. Since the simulator uses its own developed software's and can be uploaded directly on any recommended real motes. For

IV. EXPERIMENTAL STUDY AND PRELIMINARY RESULTS

In this section, we presented the preliminary results of cluster topology with the help of real Intel Data sets from [START_REF] Madden | Intel Lab Data[END_REF]. According to this online repository, the data values are captured every 31 seconds, and the readings (temperature, relative humidity and light) are collected from the indoor network deployment of 54 sensor nodes at Intel-Berkeley Research Labs, between February 28 and April 5, 2004. The preliminary study of this network simulations are permformed in MATLAB. In this experimental study case, we considered the temperature datasets for preliminary results, and we only used one day and night measures of three sensor nodes. However, while developing our proposition, we notice huge TDRs and SDRs with A-DAWF compared to the datasets sampling rate system as shown in Figure 2 and Figure 5. readings compared to the datasets sampling rate. In order to calculate the transmission costs (T X cost ) in every node, we used a T X cost metric of Eq.( 5) to evaluate the performances of A-DAWF as shown in figure 3. However, we also conduct several simulation tests using different η and w M parameters over the T X cost and MAE metrics to explore the performances at both varying thresholds and window sizes as shown in figure 3 and figure 4. Figure 3 describes the total amount of saved transmissions over various window and threshold parameters, where a minimum window of 10 saves 66%, and a maximum window of 100 reduces more than 90% of the required transmissions . In figure 4, we present the mean absolute prediction error of sensor nodes at head node, which then shows the attained error over the various window sizes for minimum to maximum prediction error of the thresholds η are from 0.001 to 0.1. Following the minimum to maximum thresholds of MAE is less than one percent (<1%) in all cases. Figure 5 demonstrates the spatial correlations of data detection by a considered cluster nodes to the CH at different given time instances t from [START_REF] Madden | Intel Lab Data[END_REF]. Which then figure 5 clearly shows that there are still large amount of spatial redundant data among the nodes, in which CH A-DAWF can still suppress a large amount of redundant data at a various thresholds and at a various window sizes of 10, 50 and 100. This experimental study help us to understand the differences and how often faulty readings generate the correlated values and their interactions between each other. Moreover, we also notice that there is no variation in TDRs and SDRs after η of 0.05 at a fixed window intervals, thus whatever the higher values are given then the A-DAWF deliverd data remain constant, which is obvious since RV only does the redundant data comparison between every two window data values, and process the nonredundant or mean averaging data accordingly. If the window size varies then the performances will obviously be varied, which are shown in the below tabulated readings. T X cost = (1 -N um of window T Xed messages T otal num of periodic transmissions ) × 100 (5) According to the T X cost metric of Eq.( 5), A-DAWF shows that it can suppress up to 90% of transmissions compared to the datasets sampling rate among the considered sensor nodes. On the other hand, CH node suppress 386 spatial redundancies or correlations of the nodes out of 393 data by a fixed optimal threshold η of 0.05 and at a window W Q of 50, which means CH only sent 7 times among the nodes spatial data. Furthermore, we also considered to evaluate the message costs (M cost ) over nodes. According to the authors of [START_REF] Haas | Energy evaluations in wireless sensor networks: a reality check[END_REF] have experimented with two payload sizes of a node, such as 1 and 90 bytes of data carries towards the sink or BS to demonstrate that increasing the payload size (bytes) to some extent does not vary the energy consumption cost over nodes. In our considerations, A-DAWF sends its total uncorrelated window data as a single packet during its window time intervals.

The further in-depth research work of the algorithm, measurements and system analysis among the proposed network are work-in-progress, which can be presented in the future work proceedings.

V. CONCLUSION AND FUTURE WORK

This paper is presented as work-in-progress implementations and the developed system models of spatio-temporal redundancy techniques for reducing TDRs in sensor nodes as well as both TDRs and SDRs in CH nodes. The preliminary results show that the proposed mechanism can suppress up to 90% of temporal redundant data among the considered sensor nodes by an optimal threshold of the window sizes as well as their spatial correlations are being suppressed effectively in a considered CH node compared to either periodic or a continuous transmission system. The ongoing measurements still need to be analyzed for measuring the message costs and overall network lifetime with the consideration of many CHs and their group nodes.

Further results and algorithm performances are being carried out with the consideration of our LE2P lab datasets from the recent network deployments. For comparative analysis, we consider to make a comparison between A-DAWF with our lab datsets and A-DAWF results from COOJA simulator. We are particularly interested to extend our propositions for monitoring different temporal physical properties at a time in nodes and their spatial correlations in CHs. Further experiments are also being examined in order to present comparative results with other well-known literature works to demonstrate the system performances especially in terms of quality of data, latency, message cost, and energy cost. Additionally, we also consider to design and develop A-DAWF mechanisms for multi-hop networks.

Fig. 1 :

 1 Fig. 1: Example of a cluster-star topology, show the nature of communication between sensor nodes and CH.

1 Fig. 2 :

 12 Fig. 2: Number of window transmissions (TX) through the various window sizes.

Figure 2 1 Fig. 3 :

 213 Figure2illustrates the total number of window data transmissions over the window w M sizes at various error thresholds η as shown in the table I. Without A-DAWF, 1316 sensor readings would be sent by every node towards the network. In this scenario, we consider three cluster nodes which they do the TDRs filtration, where A-DAWF transmit 131 readings only by individual node to the CH out of 1316

1 Fig. 4 :

 14 Fig. 4: Mean Absolute Error (MAE) cost over the window sizes.

Fig. 5 :

 5 Fig. 5: CH A-DAWF among nodes spatial data with different Thresholds.Figure5demonstrates the spatial correlations of data

  Algorithm 1 nodes sensed phenomena (p i (t))

	procedure : Initialize(n, t, r)
	Parameters: t T R count ← |pi+1-pi| pi r ← window round time interval of the sensor ← node time intervals, > η
	nodes
	if p i (t) then
	Store into the Window buffer w M if abs(pi+1-pi) > η then pi Count Temporal correlations
	if T R count mod 10 == 0 then
	Add mean µ K averaging data
	else
	Count non-correlated Data
	Send as single data packet
	end if
	end if
	end if
	end procedure
	instance in our case we consider Tmote-sky for simulations
	and TelosB devices for network deployment.

TABLE I :

 I A-DAWF performances at various thresholds and window sizes.

		window	Nodes
	Thresh-	size (w)	Temporal
	old		data TXs (out
	(η)		of 1136)
	0.001	10	383
	0.002	25	167
	0.005	40	47
	0.008	55	26
	0.01	70	21
	0.05	85	15
	0.1	100	13