Ricardo Silva Peres
email: ricardo.peres@uninova.pt

Andre Dionisio Rocha
email: andre.rocha@uninova.pt

Jose Barata

Dynamic Simulation for MAS-based Data Acquisition and Pre-processing in Manufacturing Using V-REP

Keywords: Simulation, V-REP, Manufacturing, MAS, CPS, Data Acquisition

With the advent of the Industry 4.0 movement, smart multiagentbased cyber-physical systems (CPS) are being more and more often proposed as a possible solution to tackle the requirements of intelligence, pluggability, scalability and connectivity of this paradigm. CPS have been suggested for a wide array of applications, including control, monitoring and optimization of manufacturing systems. However, there are several associated challenges in terms of validating and testing these systems due to their innate characteristics, emergent behavior, as well as the availability and cost of physical resources. Therefore, a dynamic simulation model constructed in V-REP is proposed as a way to test, validate and improve such systems, being applied to a data acquisition and pre-processing scenario as one of the key aspects of the interaction between a CPS and the shop-floor.

Introduction

In recent years, the manufacturing field has been undergoing profound changes, in great part due to the developments in the information technologies fields, in regards particularly to Cloud Computing and the Internet of Things.

An evidence of that is the emergence of the Industry 4.0 movement [START_REF] Lee | A Cyber-Physical Systems architecture for Industry 4.0based manufacturing systems[END_REF], advocating the existence of highly interconnected, intelligent devices and the growth in the amount of data shared between them. This led to the emergence of several new solutions based on smart cyber-physical systems (CPS), focused on translating these characteristics into added value for the manufacturers.

The work detailed in the following sections is motivated by such a case, more specifically whether or not a smart CPS-based predictive maintenance system can improve productivity and reduce operation expenditures in Industry 4.0 systems. Playing a pivotal role in such an approach, the simulation of the data acquisition and pre-processing stage of these systems can be seen as a first step towards validating and measuring the potential impact that these solutions may have in real environments, being thus the focus of the research documented in the subsequent sections.

As such, the work detailed in the remaining sections is organized as follows. Subsections 1.1 and 1.2 contextualize simulation in regards to Smart Systems, and provide an overview of the related work in the current literature. Afterwards, Section 2 describes the simulation model, while Section 3 provides the details on a specific application case. Finally, Section 4 presents the discussion of the results obtained from the simulation environment, followed by some brief conclusions and description of future work in Section 5.

Simulation in Smart Systems

In the last few years the Smart Industrial Cyber-Physical Systems emerged as an important trend in the new manufacturing ecosystem. In this sense, there is a demand to support customization and the capability to learn and adapt the production systems in real time. This stems from the need to deal with disturbances and unpredictability in production, which lead into the development and research of very dynamic systems.

Consequently, the traditional simulation environments need to evolve in the same direction, otherwise the demonstration and validation of the added value presented by these new Smart Systems cannot be done. Hence, the simulation of these systems will allow researchers and developers of such solutions to improve and keep improving their own solutions and will also allow the migration of these solutions to real production environments.

Related Work

Emergent Production Paradigms. That evolution has brought new production paradigms such as evolvable systems [START_REF] Frei | Evolvable Production Systems Context and Implications[END_REF], reconfigurable distributed processing, among others. In short, adaptive and reconfigurable systems, using optimization strategies to deal with unexpected events and adversities. The strongest branch of research lies on the multi-agent systems, MAS, with several implementations of solutions following these paradigms [START_REF] Rocha | PRIME as a Generic Agent Based Framework to Support Pluggability and Reconfigurability Using Different Technologies[END_REF]- [START_REF] Lepuschitz | Toward self-reconfiguration of manufacturing systems using automation agents[END_REF].

Simulation in Self-Adaptable Manufacturing Systems. In the current market and with the emergent needs faced by the manufacturing companies, such as mass customization, small sized batches, etc. simulation appears as a cost effective and valuable tool. Due to the current trends of distributed, intelligent control systems and/or reconfigurable manufacturing systems [START_REF] Rocha | An agent based framework to support plug and produce[END_REF], [START_REF] Ribeiro | Self-organization in automation -the IDEAS pre-demonstrator[END_REF] and all the associated distributed software or functionalities such as diagnosis [START_REF] Rocha | An Artificial Immune Systems Based Architecture to Support Diagnoses in Evolvable Production Systems Using Genetic Algorithms as an Evolution Enabler[END_REF], monitoring [START_REF] Rocha | An agent based monitoring architecture for plug and produce based manufacturing systems[END_REF], among others, the traditional rigid simulation environments are not up to par to simulate these emergent systems with their inherent requirements.

These new proposed production paradigms [START_REF] Leitão | ADACOR: A holonic architecture for agile and adaptive manufacturing control[END_REF]- [START_REF] Koren | Reconfigurable Manufacturing Systems[END_REF] can be simulated and tested in two different stages, during the design and after the development. During the design of the solution it is often important to test and tune the algorithms and the behavior of the logic system. At this stage a software like Netlogo [START_REF] Barbosa | Simulation of multi-agent manufacturing systems using Agent-Based Modelling platforms[END_REF], [START_REF] Barbosa | Modelling and simulating self-organizing agent-based manufacturing systems[END_REF] or AnyLogic [START_REF] Zheng | Comparative Study on Push and Pull Production System Based on Anylogic[END_REF] is capable to mimic the behaviors and results from the logic point of view, where the developers are more focused on the interactions and behaviors of each distributed entity in order to validate and evaluate the overall behavior of the system.

After the development of the logic system, for instance the software responsible for abstracting a production line, it is important to mimic a real industrial line before deploying the technology in a production ready state. This previous assessment using simulation is not only important to validate the solution but also to demonstrate the expected results regarding the usage of these new solutions due to their partially unpredictable and non-deterministic components, based on self-organized and selfadaptable behaviors. Henceforth a new kind of simulation model with intelligent entities in the loop, capable of meeting these new requirements of adaptability and flexibility in run-time is required. With this goal in mind, V-REP [START_REF] Rohmer | V-REP: A versatile and scalable robot simulation framework[END_REF] appears as an effective possibility to meet these new simulation needs, being based on a modular, distributed architecture and presenting high versatility and flexibility across a wide array of applications.

Simulation Model

Typically, one of the main goals when designing a simulation model is to maintain a balance between making it as close as possible to a real system and managing its complexity, so that it can be easily altered and experimented with. Despite not representing any concrete system, the model described in this section still abides by these principles, being however focused on providing useful data so that acquisition and processing systems can be quickly tested. Furthermore, another critical point in this modelling process was making sure that the model could be easily extendible to accommodate additional data sources (e.g. sensors and parameters), stations or functionalities, as well as dynamically changeable to cope with new conditions or parameters in run-time. The model itself consists in a simple production line, in which a conveyor belt transports parts from a source to a working station shared by two robots, as seen in Fig. 1.

Fig. 1. Overview of the Simulation Model

Once the part has arrived at the station sensor, robot1 performs a welding operation on it (see Fig. 3), followed by robot2 picking it up and moving it to a storage box (Pick and Place operation, or P&P).

Model design entails a series of key steps, including the clear definition of the system boundaries, its actors, input variables and quantitative criteria or performance measures on the basis of which the performance of different configurations can be compared and evaluated [START_REF] Maria | Introduction to modelling and simulation[END_REF]. For this model, the input parameters define the velocity for the conveyor belt, as well as for each of the robots. The meaningful data points to be extracted include each robot's state (e.g. picking, welding), part presence information from the source and station sensors, and finally force sensor information from the picking robot's gripper.

Therefore, the Key Performance Indicators (KPI) associated with measuring the performance of the system consist in the overall Production Time, and the Cycle Time of each robot. A summary of these inputs and outputs is provided in Fig. 2.

Fig. 2. Simulation Model -Inputs and Outputs

In this instance the Cycle Time indicates the time it takes each robot to carry out its respective task on the part, while the Production Time represents the interval between the moment the part arrives at the workstation and the instant it is put into storage.

To construct such a model, the Virtual Robot Experimentation Platform (V-REP) was chosen [START_REF] Rohmer | V-REP: A versatile and scalable robot simulation framework[END_REF], [START_REF] Freese | Virtual robot experimentation platform V-REP: A versatile 3D robot simulator[END_REF]. V-REP is a versatile 3D robot simulator based on a modular and distributed architecture, capable of concurrently simulating control, actuation, sensing and monitoring. Both the simulation environment and a close-up of the welding operation during simulation run-time can be seen in Fig. 3.

Fig. 3. Simulation Environment (a) and Process Close-up (b)

The scene was built using two UR5 flexible robots (one coupled with an RG2 gripper and the other with a welding gun) and a simple conveyor with two partpresence sensors, one at the source and one at the workstation.

The simulation model was programmed using a collection of Lua scripts, each associated to its own scene object. These scripts are responsible for controlling the system execution, with the resources communicating via signals using V-REP's own API. These communications can be observed in Fig. 4.

Fig. 4. Robot interactions within the simulation model

Furthermore, the simulator allows the connection of external clients with relative ease via V-REP's Remote API, which supports several programming languages including Java, C/C++ and Python.

In this context, Section 3 provides a detailed description of an application case in which this API was used in order to connect the simulation environment to an external MAS-based data acquisition and pre-processing CPS implemented in Java. This description is focused on the system modelling and communication aspects of the integration process.

Application Case

Industry 4.0 manufacturing systems typically comprise highly interconnected, modular and flexible intelligent components producing large amounts of data at often astounding rates. This makes data acquisition an essential stage in several applications, such as control, monitoring, preventive maintenance and energy optimization. Moreover, solutions proposed regarding these topics tend to rely on data mining and analysis techniques, which not only make it crucial for raw data to be extracted, but also preprocessed in order for it to be translated into a potential business advantage.

With this in mind, the MAS-based data acquisition and preprocessing CPS described in [START_REF] Rocha | An agent based monitoring architecture for plug and produce based manufacturing systems[END_REF], [START_REF] Rocha | A multiagent based knowledge extraction framework to support plug and produce capabilities in manufacturing monitoring systems[END_REF] was instantiated in order to collect data from the simulation model and compute more complex values from the raw data readily available from the simulated shop floor. Subsections 3.1 and 3.2 further specify the system modelling in the CPS and the integration aspects regarding the communication between both tools, respectively.

Modelling the System

The CPS proposed in [START_REF] Rocha | An agent based monitoring architecture for plug and produce based manufacturing systems[END_REF] differentiates between two main types of agents for data acquisition, namely the Component Monitoring Agent (CMA) and the Higher-Level Component Monitoring Agent (HLCMA). The CMA and HLCMA are similar in the sense that both perform data acquisition and handle the preprocessing of raw data to generate more complex knowledge. However, they both operate at different abstraction levels, with the CMA handling a component/resource and the HLCMA being responsible for a subsystem (aggregation of components and/or other subsystems cooperating towards a common goal). As described in Section 2, the simulation model encompasses 3 resources (UR5#1, UR#2 and the Conveyor Belt), hence resulting in the agent topology detailed in Fig. 5.

Fig. 5. Agent Topology

Agents are initially deployed in accordance to an XML blueprint file, which should be modelled in order to provide an accurate description of the system. This description is then interpreted by the MAS to understand not only the system topology in terms of available resources, but also regarding the relevant values to be extracted or calculated.

As an example, the Cycle Time relative to the welding operation can be represented in this XML file as: <Timespan id="welding_time" source="agent"> <TimespanMapping> <StartConditions> <StateValue id="welding" value="true"/> </StartConditions> <EndConditions> Dynamic Simulation for MAS-based Data Acquisition and Pre-processing 131 <StateValue id="welding" value="false"/> </EndConditions> </TimespanMapping> </Timespan>

Communication

As it can be seen in Fig. 5, each agent has a generic hardware communication interface that should be implemented for each application case, thus allowing actual CPS to be generic and easily integrated into different scenarios independently from the underlying technologies and protocols. For this case the interface was implemented using V-REP's remote Java API, and allows data to be acquired by two different means, more specifically either periodically polling of relevant data, or subscription to data values on value-change events. For this application case, data acquisition follows the latter.

On V-REP's side, an additional script was developed in order to act as a server for the simulation data. On each pass the script polls the simulation data and checks if it has changed compared to the last value. If so, it sets up a signal with the new value so that it can be read by the CPS, acting similarly to what one can find for instance in an OPC-UA client-server subscription model.

Discussion of Results

In order to validate the repeatability of the simulation results, as well as the capability of the MAS to acquire raw data from the simulator and preprocess it accurately, five different runs were executed. Each simulation run was executed until twenty samples of the cycle time for each robot (Welding and P&P) as well as the production time for the line were extracted, thus resulting in a total of one hundred samples across all runs for each KPI. The results can be observed in Fig. 6.

Fig. 6. Timespans calculated by the MAS

For each of the KPIs represented in Fig. 6, the standard deviation σ was calculated through:

.

(

) 1
The application of Equation 1 resulted in standard deviation values of 56.099ms, 49.639ms and 68.977ms for the Welding, P&P and Cycle Time variables, respectively. In order to measure the repeatability of the results, the coefficient of variation Cv of each KPI was calculated using: .

(

) 2
Through Equation 2 each coefficient of variation was obtained, resulting in 3.07% for Welding, 1.45% for the Production Time and 1.70% for P&P. The coefficient of variation has been shown to be a good indicator of repeatability in non-negative datasets [START_REF] Pryseley | Estimating precision, repeatability, and reproducibility from Gaussian and non-Gaussian data: a mixed models approach[END_REF], as such these percentages suggest that the repeatability of the simulation results is reliable, with the extracted data presenting relatively low levels of variability.

Conclusion and Future Work

A dynamic and extendable simulation model was proposed for the validation of MAS-based CPS data acquisition and pre-processing solutions to be applied in the context of Industry 4.0.

The model was implemented in V-REP, a versatile and modular 3D robot simulator capable of concurrently handling the simulation of control, actuation, sensing and monitoring. This implementation consisted in a conveyor belt continuously transporting parts from a source to a workstation, where two UR5 robots would weld the part and then move it to a storage box.

An example of an application case was also shown, consisting in the integration of a MAS-based data acquisition and pre-processing CPS with the simulation environment, from which it extracted raw data such as robot states and calculated relevant KPIs, namely cycle times and overall production time. Through the analysis of the results it can be concluded that the simulation model implemented in V-REP presents reliable levels of repeatability with low degrees of variance in the collected data across different runs, hence possibly being a viable solution to validate these kinds of systems in the context of Industry 4.0.

Looking at the application of dynamic simulation for data acquisition and preprocessing as a first step, future work will include the extension of the simulation model to accommodate the testing of plug and produce and reconfiguration functionalities, aiming at providing a flexible environment to validate not only systems tailored for data extraction, but also other contexts such as shop floor control and optimization.


Bruno S Miranda
email: miranda.bsm@live.com

Gian M Meira
email: gian.meira@hotmail.com

Sidney J Montebeller
email: sidney.montebeller@facens.br

Edinei P Legaspe
email: edineilegaspe@hotmail.com

Joel R Pinto

Diolino J Santos Filho
email: diolino@usp.br

Paulo E Miyagi
email: pemiyagi@usp.br

Efficient Fuzzy Controller to Increase Soybean Productivity

Keywords: Soybean, fuzzy, multiparameter sensor

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Brazilian soybean fields have gradually increased production over the last three decades. Consequently, Brazil has firmly established as the second leading worldwide soybean producer. Although agriculture technology has increased average yield per acre significantly, soybean frontier's expansion in Brazilian agriculture has been especially held out as the main responsible for high production levels [START_REF] Sutton | A Comparative Analysis of Soybean Production Between the United States, Brazil, and Argentina[END_REF] [START_REF] Meade | Corn and Soybean Production Costs and Export[END_REF].

Eventually, expanding soybean fields over a huge country such as Brazil ought to bring unprecedented challenges to the producers. In addition, the diversity of climates and soil types characteristics have led farmers to search for new technologies [START_REF] Salin | Soybean Transportation Guide: Brazil[END_REF] [START_REF] Formaggio | Object Based Image Analysis (OBIA) and Data Mining (DM) in Landsat time series for mapping soybean in intensive agricultural regions[END_REF].

Therefore, potential scenario begins to emerge as soybean farmers increase investments, and modern irrigation techniques appear to be suitable in soybean fields, but, in generally, irrigation systems are quite inefficient, spend water resources and electricity poorly and do not goal production results. Although Brazil has a privileged stand before many countries containing large part of available fresh water in the world, global trends defend the conscious consumption of resources, promoting sustainability in agriculture [START_REF] Guimaraes | Identification of erosion susceptible areas in Grande river basin (Brazil)[END_REF].

This paper presents the development of a control system that applies to irrigation devices in soybean fields. It was developed an embedded fuzzy controller, a supervisory control software and continuous monitoring sensor units. The control decision was settled according to soybean characteristics such as growth stages and water demand and also soil type, temperature and relative air moisture. The main objective of the system is to automate soybean irrigation mechanisms in order that production could increase based on fuzzy control and agricultural knowledges.

Relationship to Smart Systems

Smart system describes embedded and cyber-physical solutions consisting of diverse sub-systems and smart embedded applications are expected to deliver miscellaneous functionalities and services [START_REF] Crepaldi | A Top-Down Constraint-Driven Methodology for Smart System Design[END_REF]. Although the development of precision agriculture claims to achieve a high-quality management of cultivable land and environment resources, wireless sensor network and advanced control techniques have been studied apart on agricultural developments and so, combined solutions are hardly available among farm applications [START_REF] Xiao | The realization of precision agriculture monitoring system based on wireless sensor network[END_REF] [START_REF] Liao | Precision agriculture monitoring system based on wireless sensor networks[END_REF].

The proposed irrigation system control could increase efficiency in soybean fields crossing precision wireless monitoring and specialist control techniques, which base on environment characteristics and soybean water requirements. As shown in Fig. 1, the proposed system integrates in-field measurement and supervisory control software.

Design of Fuzzy Logic Controller

Fuzzy logic has been used in many segments such as industrial controllers, artificial intelligence applications, medical researches and robotics. Fuzzy logic approaches real world attributes/linguistics and computational capabilities. As a result, the amount of time spent by developers decreases as well as precision, optimization and efficiency increase slightly [START_REF] Legaspe | Open source fuzzy controller for Programmable Logic Controllers[END_REF]. A fuzzy control model was appropriate for the present work due to the complexity of the input variables and long cycle operation required, which has many variations throughout each stage, besides fuzzy logic easily approaches the specialist knowledge and could be comprehend and accepted by farmers.

The control model bases on two fuzzy logics: main control algorithm and equalizer algorithm. The selection of input variables based on soybean water requirements, which relate to soil water status and soybean growth stage, and environmental conditions, such as temperature and relative moisture. The following topics present input variables.

Soybean Growth Stage. Soybean growth stages divide in vegetative stages and reproductive stages. A slight shortage of water during the vegetative stage do not affect production drastically. However, water deficits over reproductive stages could decrease soybean yield severely. In addition, the required amount of water depends on stage of growth due to the requirement of maintaining adequate water supply till the bottom of the root system [START_REF] Sutton | A Comparative Analysis of Soybean Production Between the United States, Brazil, and Argentina[END_REF] [4] [START_REF] Guimaraes | Identification of erosion susceptible areas in Grande river basin (Brazil)[END_REF].

Soil Type. The identification of soil type is important due to the distinct physical and chemical properties which take action on the movement and storage of water and nutrients. The soil texture is based on the relative amount of three main components: sand, silt and clay. According to the soil texture, each type of soil has a limited capacity of water storage known as Field Capacity. This typical feature is fundamental to the irrigation process and it should be a limit on water distribution. As a consequence of exceeding field capacity, there will be infiltration and nutrient loss eventually. Then soil type must be identified and based on each kind, the proposed system can adjust the amount of water required [START_REF] Mutalib | Soil Classification: An Application of Self Organizing Map and k-means[END_REF] [START_REF] Yang | Effect of Different Preprocessing Methods on Principal Component Analysis for Soil Classification[END_REF].

Soil Moisture. Monitoring soil moisture over irrigation fields is fundamental on crop management practices applied to boost yields. Water availability is a control factor over soybean growth and development and water stress may reduce nutrient absorption because it increases the difficulty of nutrient uptake. However, the management of irrigation fields should be more reliable combining soil moisture data and climate monitoring information in irrigation fields.

Air Temperature and Relative Air Moisture. In order to attach environmental conditions to the control model, air temperature and relative air moisture balance the controller output. These input variables have strong action under harsh environmental conditions such as high temperature or poor air moisture levels.

The equalizer algorithm was planned according to the temperature and air moisture classification. The design of equalizer model rules bases on the statement that water demand slightly increases on a hot day and decreases on cold day. The same strategy embraces air moisture. The developed membership functions were shown in Fig. 2. The rule design in the main control algorithm considers the type of soil, soybean growth stages and soil moisture and the output was defined as the ideal amount of water to reach field capacity according to each soil, which increases production because then the fuzzy controller could assure that water supply have been provided to the entire soybean root system. Also, the main algorithm was developed to be in control of the whole plant cycle. The output variable was divided in many classes to achieve precision and a large number of rules were requested to approach the ideal amount of water over soybean stages. The fuzzy control output represents the amount of water which should be applied based on agricultural knowledge and ought to be compensated due to environmental conditions. The membership functions are presented in Fig. 3. According to Fig. 4, the control model was developed on two fuzzy algorithms due to computational resource limitations. The divided solution reduces computational time and simplifies code compilation. The amount of water (SHEET OF WATER) can be obtained through the main algorithm (CONTROLLER) without compensation factor. The environmental compensation relays on temperature and air moisture combined through compensation algorithm (EQUALIZER). The large number of divisions in the output membership function simplifies defuzzification process because the linguistic terms from amount of water corresponds directly to the control variable. This application encompasses 268 rules in main control algorithm and 25 rules in equalizer algorithm.

Design of Continuous Monitoring Sensor Unit

The developed sensor bases on a capacitive element and the calibration method provides a soil moisture relationship. The sensor unit has an electronic circuit that is able to measure capacitance precisely. Also, the sensor unit integrates wireless connection, air temperature and relative air moisture elements.

Procedure of Calibration

The developed capacitive probe was calibrated in the Laboratory of Chemical Engineering at Faculdade de Engenharia de Sorocaba, where it was possible to detect a variation of the capacitance according to the mass quantity of water applied in each sample.

Two soil samples were used: clay soil and medium loam soil. These samples were dried and sifted to accomplish low moisture level. Each dried sample was weighed and had capacitance measured, and so, an acknowledged mass of water was applied on the soil sample and capacitance measured again.

Through collected data on capacitive probe calibration, it was highlighted the relationship between the percentage of moisture and measured capacitance, therefore it was possible to reach the exponential equation of calibration for clay soil and medium loam soil that works on developed sensor.

Design of Controller Unit and Supervisory Control Software

As shown in Fig. 5, it presents the developed electronic controller device and the capacitive sensor element. The wireless communication network operates between sensor and controller. The supervisory control software was developed to Windows application as shown in Fig. 6. The controller and the supervisory communicate through USB protocol. The controller unit has a digital output assigned to the fuzzy control. The output value can be adjusted by the supervisory control software based on irrigation mechanism to correspond a period of time. The supervisory control software displays collected data and storages each measurement.

Results

A simulation was built in the software Matlab Simulink version 2015a. Adopted parameters have been used to better analyze the developed control logic. As shown in Fig. 6, it has demonstrated that the created system was able to process the input variables and the project met expectations for the proposed model. In addition, the surface view on the right of the picture presents the output response without equalizer action based on clay soil characteristics. Also, these surfaces present fixed soil type due to the number of available axes. The surface on the left shows the equalizer output, which adjusts system output based on air temperature (TEMPERATURE) and relative air moisture (AIR-MOISTURE). The designed system has been able to operate on the complete soybean cycle and the control output has reached the ideal amount of water, according to the proposed objective.

Conclusion

The proposed control system provides resources for continuous data acquisition of particular field variables and the developed control algorithm analyses these aspects successfully.

In order to evaluate the performance of the developed devices, it was monitored the system operation and it was realized that the control module and the sensor unit have been acting satisfactory, according to the design specifications.

The present solution can be applied to others cultivable plants. In addition, the proposed system might integrate a meteorological station to accomplish prediction capability. Furthermore, the fuzzy architecture could be easily modified to aggregate other unexplored features, which could improve overall performance.

Fig. 1 .

 1 Fig. 1. Architecture of the Irrigation Control System. It represents Continues Monitoring Units (right) which operate in-field and transmit data over wireless network and the Irrigation System Controller (left) that processes received data and communicates to supervisory control software.

Fig. 2 .

 2 Fig. 2. This figure shows the membership functions of air temperature (top and left) and relative air moisture (top and right). The membership function of compensation factor (bottom) presents the response of equalizer algorithm.

Fig. 3 .

 3 Fig. 3. As shown in figure, the membership functions are soybean growth stage (top and left), soil type (top and left), soil moisture (bottom and left) and amount of water (bottom and right). The last two membership functions are partial views because there is a large number of linguistic variables in soil moisture and amount of water.

Fig. 4 .

 4 Fig. 4. The architecture of the developed fuzzy control model has based on two individual fuzzy algorithms.

Fig. 5 .

 5 Fig. 5. Irrigation System Controller (left) and supervisory control software (right). The operator can adjust parameters from the irrigation mechanism on software.

Fig. 6 .

 6 Fig. 6. Surface view model of the equalizer block (left) and Surface view model of the controller block for the soil type clay (right).


Sérgio Onofre
email: onofre@holos.pt

João Gomes
email: jfn.gomes@campus.fct.unl.pt

João Paulo Pimentão

Pedro Alexandre Sousa

Data Fusion of Georeferenced Events for Detection of Hazardous Areas

Keywords: Data-fusing, Smart System, Industry 4.0, IoT, Event Geographic position, Data Correlation, Forecast

When dealing with events in moving vehicles, which can occur over widespread areas, it is difficult to identify sources that do not derive from material fatigue, but from situations that occur in specific spots. Considering a railway system, problems could occur in trains, not because of train's equipment failure, but because the train is crossing a specific location. This paper presents a new smart system being developed that is able to generate geolocated sensor-data; transmit it for smart processing and fusing to the inference engine being built to correlate the data, and drill-down the information. Using a statistical approach within the inference engine, it is possible to combine results collected over long periods of time in a "heat-map" of frequent fault areas, mapping faulty events to detect hazardous locations using georeferenced sensor data, collected from several trains that will be integrated in these maps to infer high probability risk areas.

Introduction

The association of faults to geographic locations can be used to alert and provide support to incident intervention crews to deal with current issues and, especially to prevent forecast events in a specific location in a nation-wide (or trans-national) railway network.

Deciding on whether a location is faulty or not depends on the quotient between the number of trains that crossed the railway and how much breakdowns occurred around that location. A statistical approach is used where data is be auto correlated based on past events. To get the information needed to feed the system's databases it uses a sensor fusing system, where it combines and study the data collected from the trains that populate the railway network. This system will be integrated in a multi-agent architecture [START_REF] Jennings | An agent-based approach for complex software systems[END_REF] used in a distributed surveillance system (DVA 1 ), where events, sensors and human resources are georeferenced. New features will be added to the system such as reasoning in terms of what should be considered a correlated event (geographically speaking) and forecast of events using the knowledge acquired from previous events and data.

This paper starts by presenting a state of the art in data fusion architectures, Geolocation sensing, data correlation, event forecasting and finally presenting the foreseen method to integrate this system in the DVA for providing data correlation and forecasting functionalities.

Contribution to Smart Systems

The work described in this paper will encompass the collection of real-time raw data from sensors, which are then sent to a central processing unit that will perform reasoning and prediction based on the knowledge acquired data along a large period of time. In this paper a third-generation smart system [START_REF] Gessner | Smart Systems Integration and Simulation[END_REF], is presented, that performs human-like perception and acts autonomously, i.e without any human decision. Moreover, it is equipped with the capability to predict and adapt according to this readings.

In practice, one could argue that the current system encompasses all the components that are the base for the concept of "Industry 4.0", although applied to mobility: the cyber-physical systems that is achieved by the monitoring of the trains and the availability of the data through Internet protocols; the "IoTization" of trains allowing them to be remotely sensed and monitored, and finally the application of Cloud Computing and Big Data technologies and methodologies to both gather and process the high volumes of information that each train produces every second.

State of the Art

Data Fusion Architectures

Nowadays sensor nodes integrate multiple capabilities that include sensing, global positioning system (GPS), computing and communication. Fusing multiple simple sensor nodes, one can deliver scalable, reliable and more complex sensor based systems.

Multiple Architectures have been proposed for sensor fusing. First example is the one proposed in Fig 1 (a) where static sensors nodes are wired to a central gateway that handles all the requests and processing. An implementation of this type is [START_REF] Suarez | A secure IoT management architecture based on Information-Centric Networking[END_REF] where all the equipment in an office (printers, thermometers, ventilation, etc..) are connected to a central gateway. All the clients that require information connect to the gateway that proceeds to service the requests. Fig 1 (b) is used when the data collected volume is large and therefore needs some local processing before sending the compressed/processed version to the server. Finally, when different layers of processing are responsible for different aspects of data manipulation there is a hierarchical processing as presented in Fig 1 (c), in which the collected data passes through several layers of data processing and manipulation before reaching the central server.

Fig. 1. Typical Sensor Fusing Architectures. [START_REF] Biswas | Mobile-agent-based collaborative sensor fusion[END_REF].

Recently there has been a more hybrid approach where mobile agents are used in combination with static nodes [START_REF] Xu | Distributed computing paradigms for collaborative signal and information processing in sensor networks[END_REF]. An example of this is the Interoperable-agent-mode for sensor network [START_REF] Biswas | Mobile-agent-based collaborative sensor fusion[END_REF] that is FIPA standards compliant [START_REF]FIPA97 Foundation for intel. physical agents spec. Part 1 Agent Management[END_REF], [7], which considers static wireless nodes distributed in an area and a mobile agent that goes physically to the sensor nodes to collect the data. The mobile agent is responsible for making his own itinerary around the sensor nodes and when the battery runs low he returns to the central station.

As another example, the DVA surveillance system [START_REF] Onofre | Multi-sensor geo-referenced surveillance system[END_REF] is a human-machine collaborative distributed system where static nodes are in place and in case of alert the mobile agents (be it policeman, security, firefighter or civil protection) are called in to deal with the occurrence.

Geolocation Relation

The next question that arises is how one should relate events, since the goal is to associate locations to breakdowns. It makes sense to define a radius around said events so that it is possible to search for previous nearby occurrences (since location is not 100% accurate). Speed (Velocity) of the train is an important aspect to consider in defining said radius since the faster the train goes the more difficult it is to track down the exact location of the breakdown, therefore, the radius should be wider.

Several approaches have been developed in terms of geolocation radius but in different contexts such as [START_REF] Li | Failure event prediction using the Cox proportional hazard model driven by frequent failure signatures[END_REF] where automatic safety envelopes (that can be both dynamic or static) are defined around different construction equipment based on their width, length and velocity, the goal here is to alert the workers that can, without their knowledge, be putting their safety in jeopardy by going too near dangerous equipment. These envelopes radius are created according to the equation [START_REF] Jennings | An agent-based approach for complex software systems[END_REF].

where [START_REF] Jennings | An agent-based approach for complex software systems[END_REF] Where L,W,v are the length, width and velocity (both in X and Y axis) respectively of the moving equipment.. The r represents the Safe distance of the equipment (which means the distance it will go before completely stopping), t represents the time it will take to break.

Event Processing

To make the system reliable and robust it is very important not to have false positives that can mislead the analysis. Therefore, there is a need to implement a layer between the sensors and the interface, so that it can analyze the raw sensor data and do the reasoning so to differentiate between false alarms and real events. Model-Based approaches have been proposed in [START_REF] Fernández | Agent-based monitoring service for management of disruptive events in supply chains[END_REF] applied to supply chains where the authors propose a set of resources, orders, specifications and a set of milestones throughout the supply chain. The milestones are then monitored and agents actively look for deviations of the expected models. Fuzzy logic has also been widely used in the literature to solve these kinds of problems.

In [START_REF] Albusac | Dynamic weighted aggregation for normality analysis in intelligent surveillance systems[END_REF] the authors define multiple arrays of input variables (Vi), their domains (DDV), and their constraints (Φ) the values that the system receives from each individual set of input variables are classified in terms of "normality" (Ncn) which is a scalar between [0,1] being 1 the most normal situation and the 0 the most abnormal. Several solutions have been proposed to make decisions towards this "normality factor". One simple solution as presented in [START_REF] Albusac | Dynamic weighted aggregation for normality analysis in intelligent surveillance systems[END_REF] is finding the minimum value of normality (Ncn) and consider only this value to classify the situation, however, this is limiting because it is only considered one set of input variables while the others are discarded, which means that there is the possibility that values with information important for the classification are being ignored. Choquet Integral, as defined in [START_REF] Albusac | Dynamic weighted aggregation for normality analysis in intelligent surveillance systems[END_REF] proposed, to define specific weights (μ(Ai)) to normality concepts and correlate them. Equation ( 2) it is shows how Choquet Integral correlates the normality factors.

(

2)

This means that once the normality concepts are defined (Nc1, Nc2…Ncn), it is needed to relate them by defining weights. The conclusions are made based on the final value of the equation. The OWA aggregation operators presented in [START_REF] Yager | On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decisionmaking[END_REF], use the normality factors times a weight this makes a junction between different criteria (normality factors) using weights. In (3) is presented how the OWA aggregators are calculated.

(3)

Where (a1,a2,…,an) represents the values that are collected to make the decision, wj represents the weights and bj is the ordered set of (a1,a2,..an).

Event Prediction (Forecasting)

In [START_REF] Yager | On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decisionmaking[END_REF] it is presented a way to predict events in real time streams of data. The authors look for event signatures (what happened before the event) and make sequences of readings that lead to the event. The readings are then compared with the sequences in the database that are known to cause breakdown events. Since there can be a lot of events signatures in the database it is not feasible to compare every sensor reading with the values in the database, the authors, propose to look for triggers. These triggers are then used in forward rules which means, for example, that If Trigger 1 and Trigger 2 are found THEN event C will happen. In the paper it is also proposed to once a new signature is found to generate readings (real or unreal) that lead to this event. The idea is to have a model of the event.

Architecture

The DVA system as explained in [START_REF] Dias | Architecture and message protocol proposal for robot's integration in multi-agent surveillance system[END_REF] is "is a Geo-referenced multi-agent surveillance system, composed by several agents: Sensor agent -provides sensor information; Processor agent -transforms sensor information into parameters; Inference agentuses parameters in rules for event detection; Action agent -executes predefined actions for each event; Backup agent -stores all the system information; Interface agent -shows (in maps) the values of the sensors, events, actions and system status; Mobile agent -Associated with a human, equipped with a mobile device who is responsible to perform events' actions, such as confirming the event or handling the event; Monitor agent -monitors all system's agents, ensuring correct system performance." In this paper, two new agents to be added to this architecture are presented: "correlate agent" (that makes links between events) and the "forecast agent" (that looks for current sensor readings and predicts problems that might arise in the near future).

Proposed Architecture

The Fig 2 represents the proposed changes (in red) to the DVA architecture. The "Correlate" Agent will be activated every time a new event is detected. It will look for previously events nearby (stored in the Backup Agent) and if it detects a similar event in the area, it will create a new type of event (Correlated Event) that will be shown on the interface. The Forecast Agent is receiving the data from the processor and previously readings stored in the Backup Agent to make predictions about potential problems. It will also generate a new event type (Forecasted Event) in the system so it can make decisions to prevent damage.

Proposed Changes to the Architecture

Correlated Event: The correlated event, links separate events to geographical areas. It is described by: location of the center of the event (since the goal is to map these events, it is need a geographical center given by the center of mass of the composing events), a geographical radius (distance between the center of mass and the further away event), a time of occurrence (when it was detected), number of occurrences (how many events it is linking) and a vector with the events correlated.

To link the geographical events an adaptation is made from the equation ( 1) considering that the width as well as the breaking time (time to full stop) of the train are irrelevant since the events will be detected while the train is still moving.

where (4)

Where L represents the length of the train, and the v (composed by its X and Y component) represents the velocity of the train at the time of the fault detection. Delta ( ) is a parameter, between 0 and 1 that allows the adjustment of the radius according to what is verified in practice. For the time being it is considered to be 0.25s which at a standard train speed of 100km/h will produce a length of roughly 110m which is half the usual length of a train. Events are therefore correlated if the circumferences centered at the location of each event with their respective radius (R) intersect within a timeframe (the timeframe being a parameter that will be adjusted as more and more information is collected).

Forecasted Event: The Forecast Agent is actively looking for readings that can lead to breakdown, for example, if the value of a variable has been steadily rising to a point that if its growing rate continues soon it will become a problem, the agent, generates an Forecasted event with a description of the of the predicted event as well as the readings relevant and finally the measures that train should do to avoid damage. These are the "triggers" that were previously presented in section 3.4.

Correlation Agent: In the next figure it is shown the correlation agent's flow chart (Fig. 3.) where it is explained in more detail its operation. The agent is activated once a new fault event is detected. It's assumed that it is not dealing with false positives since it is already filtered using the techniques described in section 3.3. Afterwards it's defined a geolocation radius around the event as presented in section 4.2. Once the area is defined the agent looks in the event database for previous occurrences in the vicinities. Subsequently two things can happen: either it finds no other events in the area therefore the agent shuts down until another fault event occurs or it finds other events (at least 2) and generates a new Correlation Event. If the new Correlation is defined it is then sent to the Interface Agent that flags this area in a form of heat map that has the warm colors to show the areas of found malfunctioning. Forecast Agent: The Forecast Agent looks for patterns in the readings before breakdown events (called event signatures), in brief, every time a fault is detected it will make a history of readings just before the event. These readings will then be used as comparison for the active trains and if a pattern that previously led to breakdown is a found in an active train this agent will generate Forecasted Events.

There are critical points in the railway grid where power sub-stations are changed, these points usually are associated with high voltage differences (more than 5kV). These variations tend to cause peeks of currents/voltage in the trains' electrical systems, this can eventually become a source of malfunctioning or breakdown.

The forecasting needs to be done on multiple active trains in the grid, this means that it needs threads to take care of these multiple tasks. Due to the high volume of data that it is collected and sent (at a rate of 3000bps), the Apache Hadoop's Map-Reduce Paradigm (from big data analysis) is used. This gives the ability to separate trains into different parallel tasks. The Hadoop then processes all the information sent by the trains and looks for patters that can be known to be troublesome. The results are shown in the interface with a delay associated with the receiving the information, the processing and the actual writing in the interface, however, this delay is not critical since the goal of this system its not to control the train in real time, but to give insight to the maintenance crews of potential problems within the electrical grid and/or with the trains. Every time there is a forecasted event of breakdown this agent produces a report of how many critical points did the train endure before. The idea is to give an average of the number of how many critical points the trains can endure before needing a maintenance team to check its electrical protections. More detailed information about Apache Hadoop can be found in [START_REF] Liroz-Gistau | FP-Hadoop: Efficient processing of skewed MapReduce jobs[END_REF]. To extract knowledge the data mining techniques presented in section 3.4 are used.

Conclusion

DVA is a developed and tested system that protects both people and goods from all sorts of dangers (both natural and human caused) using statically nodes spread out across vast areas. In the presented approach, some of its behavior had to be adapted because we are now considering moving trains, which makes the sensors' positions change over time. So, no static location relations to identify events are now available. And this was a cornerstone for the inference engine of DVA.

In this new adaptation, the DVA architecture had to be adjusted so that it could correspond to the specific needs of the current problem, one of these was to link multiple events to specific geographical areas. The other big need was to embed the DVA with the capability of predicting, this brings an advantage since the protection of the trains starts even before the incidents occur giving more time to warn the right mobile agents (Maintenance crews) and the right measures. The correlation brings an advantage in detection of specific dangerous locations, for example, areas that are problematic (i.e. areas where often events are triggered) that require more attention from the involved agents and be more often checked. The correlation also has an impact in how the maintenance crews are spread across their working, for example, after a careful study of the common problem areas of the agents (maintenance crews) should be closer to where the problems usually appear. This architecture is currently under implementation and results are expected to be presented soon.

Fig. 2 .

 2 Fig. 2. Architecture Proposed.

Fig. 3 .

 3 Fig. 3. Flow Chart Correlation Agent.

Advanced Surveillance System, for more information see: dva.holos.pt


Slavisa Tomic
email: s.tomic@campus.fc.unl.pt

Marko Beko
email: mbeko@uninova.pt

Rui Dinis
email: rdinis@fct.unl.pt

Milan Tuba
email: tuba@ieee.org

Nebojsa Bacanin
email: nbacanin@megatrend.edu.rs

MAP Estimator for Target Tracking in Wireless Sensor Networks for Unknown Transmit Power

Keywords: Target tracking, maximum a posteriori (MAP) estimator, received signal strength (RSS), angle of arrival (AoA)

This paper addresses the target tracking problem, by extracting received signal strength (RSS) and angle of arrival (AoA) information from the received radio signal, in the case where the target transmit power is considered unknown. By combining the radio observations with prior knowledge given by the target transition state model, we apply the maximum a posteriori (MAP) criterion to the marginal posterior distribution function (PDF). However, the derived MAP estimator cannot be solved directly, so we tightly approximate it for small noise power. The target state estimate is then easily obtained at any time step by employing a recursive approach, typical for Bayesian methods. Our simulations confirm the effectiveness of the proposed algorithm, offering good estimation accuracy in all considered scenarios.

Introduction

Motivation

Locating a moving object in real-time has become an attractive topic in the academic and research community worldwide recently, owing to its great applicability potential in both military and commercial fields [START_REF] Dardari | Indoor Tracking: Theory, Methods, and Technologies[END_REF]- [START_REF] Khan | A Comparative Study of Target Tracking With Kalman Filter, Extended Kalman Filter and Particle Filter Using Received Signal Strength Measurements[END_REF]. Taking advantage of the existing technologies, e.g., terrestrial radio frequency sources, to provide a solution to the realtime object localization problem is strongly encouraged, due to cost minimization. These include for example, extracting time of arrival, received signal strength (RSS), angle of arrival (AoA) information from the received radio signal, or a combination of them [START_REF] Khan | A Comparative Study of Target Tracking With Kalman Filter, Extended Kalman Filter and Particle Filter Using Received Signal Strength Measurements[END_REF]- [START_REF] Tomic | A Closed-form Solution for RSS/AoA Target Localization by Spherical Coordinates Conversion[END_REF]. Which approach to use depends mainly on the available hardware. In this work, we employ combined RSS and AoA measurements, because nowadays, practically all devices can measure the RSS information, and the AoA information can be extracted from RSS measurements by using rotating a directional antenna and choosing the direction from which the highest RSS measurement is obtained [START_REF] Niculescu | Vor Base Stations for Indoor 802.11 Positioning[END_REF], [START_REF] Elnahrawy | Adding Angle of Arrival Modality to Basic RSS Location Management Techniques[END_REF].

Research Question

Designing real-time localization algorithms is a very difficult task, because of numerous challenges that have to be taken into consideration, such as accuracy, execution time and limited energy resources to name a few. Therefore, the research question of this work can be formulated as:

How to design an efficient (highly accurate and computationally low complex) localization algorithm, robust to network topology and channel characteristics, applicable in real-time?

In order to address the research question, we have begun with the following hypothesis:

An efficient real-time localization algorithm can be developed by linearizing the considered non-linear measurement model and by following Bayesian approach, where prior knowledge is integrated with observations to enhance the estimation accuracy of an estimator.

Related Work

Works [START_REF] Tomic | Distributed RSS-Based Localization in Wireless Sensor Networks Based on Second-Order Cone Programming[END_REF]- [START_REF] Tomic | A Closed-form Solution for RSS/AoA Target Localization by Spherical Coordinates Conversion[END_REF] investigated the classical target localization problem, where the proposed estimators were based on radio measurements only, and prior knowledge was disregarded. The authors in [START_REF] Beaudeau | RSSI-based Multitarget Tracking by Cooperative Agents Using Fusion of Cross-target Information[END_REF], [START_REF] Masazade | Dynamic Bit Allocation for Object Tracking in Wireless Sensor Networks[END_REF] and [START_REF] Khan | A Comparative Study of Target Tracking With Kalman Filter, Extended Kalman Filter and Particle Filter Using Received Signal Strength Measurements[END_REF] considered a problem of tracking of a moving target, and they combined the available radio measurements with prior knowledge given from the target state model. However, in [START_REF] Beaudeau | RSSI-based Multitarget Tracking by Cooperative Agents Using Fusion of Cross-target Information[END_REF], [START_REF] Masazade | Dynamic Bit Allocation for Object Tracking in Wireless Sensor Networks[END_REF] and [START_REF] Khan | A Comparative Study of Target Tracking With Kalman Filter, Extended Kalman Filter and Particle Filter Using Received Signal Strength Measurements[END_REF] only pure RSS-based target tracking problem was investigated. In [START_REF] Khan | Tracking of Wireless Mobile Nodes in the Presence of Unknown Path-loss Characteristics[END_REF], the target tracking problem which employs hybrid, RSS and AoA, measurements was considered. The authors in [START_REF] Khan | Tracking of Wireless Mobile Nodes in the Presence of Unknown Path-loss Characteristics[END_REF] proposed a Kalman filter (KF) and a particle filter (PF) to solve the tracking problem. Furthermore, they proposed a generalized pattern search method for estimating the path loss exponent (PLE) for each link in every time step.

Contributions

In this paper, we investigate the RSS-AoA-based target tracking problem for unknown target transmit power. This setting is of practical interest for low-cost systems in which testing and calibration is not the priority. Also, due to sensor's battery drain during time, the true value transmit power becomes not perfectly known over time. By integrating prior knowledge given by the target transition model and by employing the maximum a posteriori (MAP) approach, we propose a tight approximation of the MAP estimator. Based on a well-known recursive approach, typical for Bayesian methods, we develop an iterative algorithm which updates the mean and covariance of the target state in each time step.

Relationship to Smart Systems

Smart systems embrace functions of sensing, actuation, and control with a goal of describing and analyzing an environment, and making decisions based on the available data in a predictive or adaptive manner. A good example of such a system is a wireless sensor network, composed of a number of scattered sensors that cooperate between themselves in order to respond in an attentive, adaptive and active way to the changes in the environment registered by sensors. However, in many applications, the information gathered by the sensors is meaningless if not correlated to accurate location of where the changes are occurring (e.g., a system might be set up to respond locally to changes in sensor data).

Therefore, determining accurate location of sensors is a very important task in forming a smart system. Moreover, accurate localization of objects and people in both indoor and outdoor environments enables new applications in emergency and commercial services that can improve safety and efficiency in everyday life (e.g., smart parking, monitoring of storage conditions and goods, assistance for elderly or people with disabilities) [START_REF] Yang | Smart Parking Service based on Wireless Sensor Networks[END_REF].

Another important example where target localization helps building a smart system is video surveillance. It has been used to monitor security sensitive areas such as banks, highways, borders, etc. Owing to rapid advances high-speed network infrastructure and computing power, as well as large capacity storage devices, multi sensor video surveillance systems have been developed recently. However, traditional video outputs that were controlled by human operators became overwhelming both for operators and storage devices, due to the increased number of cameras. Therefore, in order to filter out redundant information and increase the response time to forensic events motivated the development of smart video surveillance systems. Such systems require fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. [START_REF] Dedeoglu | Moving Object Detection, Tracking and Classification for Smart Video Surveillance[END_REF] 3 Problem Formulation Let and , for , denote the unknown location of a moving target at time instant andknown location of the -th static anchor, respectively1. For simplicity, we assume a constant velocity target state transition model [START_REF] Dardari | Indoor Tracking: Theory, Methods, and Technologies[END_REF]- [START_REF] Khan | A Comparative Study of Target Tracking With Kalman Filter, Extended Kalman Filter and Particle Filter Using Received Signal Strength Measurements[END_REF], [START_REF] Kay | Fundamentals of Statistical Signal Processing: Estimation Theory[END_REF], [START_REF] Wang | On MAP-based Target Tracking Using Range-only Measurements[END_REF], i.e., [START_REF] Dardari | Indoor Tracking: Theory, Methods, and Technologies[END_REF] where represents the target state at time t (described by its location and velocity, , in a 2-dimensional plane) and is the state process noise [1]- [START_REF] Khan | A Comparative Study of Target Tracking With Kalman Filter, Extended Kalman Filter and Particle Filter Using Received Signal Strength Measurements[END_REF]. This process noise is assumed to be zero-mean Gaussian with a covariance matrix , i.e.,

. Covariance and state transition matrix, , are defined as with and denoting the state process noise intensity and the sampling interval between two consecutive time steps [START_REF] Dardari | Indoor Tracking: Theory, Methods, and Technologies[END_REF], [START_REF] Masazade | Dynamic Bit Allocation for Object Tracking in Wireless Sensor Networks[END_REF], [START_REF] Kay | Fundamentals of Statistical Signal Processing: Estimation Theory[END_REF], [START_REF] Wang | On MAP-based Target Tracking Using Range-only Measurements[END_REF].

At each time step, , the target emits a signal to the anchors which then withdraw the RSS and AoA information from it. Thus, the observation model can be written as [START_REF] Beaudeau | RSSI-based Multitarget Tracking by Cooperative Agents Using Fusion of Cross-target Information[END_REF] where is the observation vector composed of RSS, , and AoA, measurements at time instant . The function in ( 2) is defined as for [START_REF] Rappaport | Wireless Communications: Principles and Practice[END_REF], and for , [START_REF] Yu | 3-D Localization Error Analysis in Wireless Networks[END_REF], where is the reference power at a distance ,and is the PLE. The measurement noise is modeled as , where the noise covariance is defined as , with (dB) and (rad) being the variances of the RSS and AoA measurement noise, respectively, representing the identity matrix of size and symbol denoting the Kronecker product. Often in practice, testing and calibration are not the priority in order to keep low network implementation costs; hence, the target transmit power, , is often not calibrated, i.e., not known. Not knowing corresponds to not knowing in (2) [START_REF] Rappaport | Wireless Communications: Principles and Practice[END_REF], [START_REF] Patwari | Locating the Nodes: Cooperative Localization in Wireless Sensor Networks[END_REF].

We employ (1) and ( 2) to build the marginal posterior probability distribution function (PDF),

, from which we can quantify the confidence we have in the values of the state given all the past measurements . From , we can obtain an estimate at any desired time step.

Below we show a recursive procedure, typical for Bayesian methods [1]- [START_REF] Khan | A Comparative Study of Target Tracking With Kalman Filter, Extended Kalman Filter and Particle Filter Using Received Signal Strength Measurements[END_REF], for the evaluation of at any time instant.  Initialization: The marginal posterior PDF at is set to the prior PDF of .  Prediction: According to the state transition model (3), the predictive PDF of the state at is found as

(3)
 Update: By following the Bayes' rule [START_REF] Dardari | Indoor Tracking: Theory, Methods, and Technologies[END_REF], [START_REF] Elnahrawy | Adding Angle of Arrival Modality to Basic RSS Location Management Techniques[END_REF], we have

It is worth noting that the denominator in ( 4) is just a normalizing constant. In general, the marginal PDF at cannot be calculated analytically, and the integral in (3) cannot be obtained analytically if the state model is non-linear. Therefore, some approximations are required in order to obtain .

The Proposed MAP Algorithm

A state estimate, , of can be obtained from by employing the MAP criteria [START_REF] Elnahrawy | Adding Angle of Arrival Modality to Basic RSS Location Management Techniques[END_REF], i.e., by maximizing the marginal posterior PDF [START_REF] Khan | A Comparative Study of Target Tracking With Kalman Filter, Extended Kalman Filter and Particle Filter Using Received Signal Strength Measurements[END_REF] The problem in ( 5) resembles the maximum likelihood estimator, apart from the existence of the prior PDF. This problem is highly non-convex and its analytical solution cannot be obtained in general. Therefore, we approximate (5) by another estimator whose solution is readily available.

First, for small noise power we can write from the RSS and AoA model respectively Hence, we can approximate the RSS and AoA measurement models by ( 8) and ( 7), respectively, which written in a vector form and applying the weighted least squares (WLS) criterion leads to [START_REF] Tomic | Distributed RSS-AoA Based Localization with Unknown Transmit Powers[END_REF] where , and

The solution of ( 9) is given by . Once we have an estimate of the target location, we can use it to find the maximum likelihood estimate of , , from the RSS measurement model as [START_REF] Tomic | Distributed Algorithm for Target Localization in Wireless Sensor Networks Using RSS and AoA Measurements[END_REF] At this point, we can benefit from this estimated value by using it to resume the estimation process as if is known, i.e., we can compute in order to get

As far as the prior knowledge part in ( 5) is concern, we can assume that has Gaussian distribution with mean and covariance [START_REF] Wang | On MAP-based Target Tracking Using Range-only Measurements[END_REF]. Then, according to (3), we obtain [START_REF] Tomic | A Closed-form Solution for RSS/AoA Target Localization by Spherical Coordinates Conversion[END_REF] where is a constant, and and are respectively the mean and covariance of the one-step predicted state, obtained through (1) as [START_REF] Niculescu | Vor Base Stations for Indoor 802.11 Positioning[END_REF] Therefore, (5) can be written as [START_REF] Elnahrawy | Adding Angle of Arrival Modality to Basic RSS Location Management Techniques[END_REF] Similarly as in ( 9), the problem in ( 13) can be rewritten as [START_REF] Yang | Smart Parking Service based on Wireless Sensor Networks[END_REF] where , , , . The solution to ( 14) is given by . The step by step proposed MAP algorithm for unknown , is outlined in Algorithm 1.

Algorithm 1.The Proposed MAP Algorithm Description

Require: ,

Simulation Results

This section validates the performance of the proposed algorithm through computer simulation. All simulations in this work were performed in MATLAB.

Simulation Set-up

Two scenarios, in which the target takes sharp maneuvers and a smoother one, are investigated. The state model ( 1) is used for the target state transition, and the radio measurements are acquired according to (2) at each time instant. Three anchors are fixed at , and a sample is taken every s during s trajectory duration in each Monte Carlo, , run. The reference distance is set to m, the reference power to dBm, and the PLE to , dB, rad, and . The performance metric used here is the root mean square error (RMSE), defined as , where represents the estimate of the true target location, , in the -th Mc run at time instant . The performance of the proposed MAP algorithm is compared with a classical approach described by [START_REF] Tomic | Distributed RSS-AoA Based Localization with Unknown Transmit Powers[END_REF], which makes use of the radio measurements only and disregards the prior knowledge, labeled here as WLS.

Results

Fig. 1 illustrates the true target trajectory in the two considered scenarios as well as the estimated trajectories given by the considered approaches. In the first scenario, the initial target's location was set to , whereas in the second scenario the starting point was set to . We can see that the proposed MAP estimator performs considerably better in both scenarios than the WLS one. However, it seems like there is still room for further improvement of the proposed approach, since the results are not particularly smooth. This might be done by better utilization of the prior knowledge, i.e., in the update step of the state covariance matrix, and is left for future work. Fig. 2 illustrates the RMSE (m) versus (s) performance comparison of the considered approaches. From it, one can see that the proposed algorithm performs well in both scenarios, obtaining an average RMSE below 3 m. Furthermore, the superiority of the Bayesian approach, which combines the observations with prior knowledge, over the classical one, which utilizes the observations exclusively, is clearly observed in every time step.

Conclusions

In this work, we have investigated the target tracking problem which makes use of the combined RSS and AoA measurements, in which the target transmit power is considered not known. By resorting to Bayesian approach and relying on the MAP criterion, we proposed a tracking algorithm, which efficiently solves the target tracking in all considered scenarios and significantly outperforms the considered classical approach in every time instant. Although the proposed algorithm offers excellent estimation accuracy, it seems that there is still room for further improvement, which will be the topic of our future work. This work is a part of our ongoing research, and we have validated the performance of our algorithms by means of simulations only. As a part of our future work, we plan to validate their performance using real measurements from [START_REF] Niculescu | Vor Base Stations for Indoor 802.11 Positioning[END_REF] and [START_REF] Elnahrawy | Adding Angle of Arrival Modality to Basic RSS Location Management Techniques[END_REF].

  use of the available azimuth angle observations to define , and rewrite (6) as By multiplying the left hand side of the above equation by , we arrive at (8)

Fig. 1 .Fig. 2 .

 12 Fig. 1. The true target trajectory in: a) the first and b) the second considered scenario and the estimated ones.

For simplicity and without loss of generality, here we focus on

2-dimensional scenario. The generalization to a 3-dimensional scenario is straightforward.

Acknowledgments

. This work was partially supported by Fundação para a Ciência e a Tecnologia under Project PEst-OE/EEI/UI0066/2014 (UNINOVA), and Project UID/EEA/50008/2013 (Instituto de Telecomunicações), Program Investigador FCT under Grant IF/00325/2015 and Grant SFRH/BD/91126/2012.


Hessamedin Bayanifar
email: hessamedin.bayanifar@ovgu.de

Hermann Kühnle
email: hermann.kuehnle@ovgu.de

Enhancing Dependability and Security of Cyber-Physical Production Systems

Keywords: Cyber-Physical Production Systems, Smart Manufacturing Unit, Dependability and Security, Multi-Agent Systems

Despite all its potentials, new industrial revolution enabled by cyberphysical systems (CPS), still has major concerns and obstacles to overcome with regards to dependability and security on its way to be fully appreciated. This study targets these concerns by proposing a generic model for intelligent distributed dependability and security supervision and control mechanisms to enable components to autonomously meet their own security and dependability objectives through real-time distributed improvement cycles, using multi-agent systems approach to enable full exploitation of the model's evolution capabilities.

Introduction

Smart distributed manufacturing systems consist of a large number of widely dispersed loosely-coupled yet collaborating heterogeneous components that are vastly connected to and communicate with cyber space. To enhance their capabilities, these systems try to exploit smart properties through enhancing their own intelligence and processing power, or via accessing the internet and its vast options to enhance these properties. On the one hand, using these properties and enhancing capabilities can offer manufacturing enterprises a plethora of opportunities and strategic advantages, on the other hand, however, such vast diversity and total exposure to cyber space, as well as versatility of processes and structures, raise major vulnerabilities as dependability and data security issues that may lower the motivation to rely on these enormous capabilities. In order to increase acceptance, three research hypotheses are posited:

H1: Additional properties and enhanced capabilities increase risks in playing

manufacturing on the base of smart manufacturing units. Multi-agent software module patterns are most appropriate for supporting and optimising smart manufacturing units' applications and risks may be lowered by continuously monitoring and analysing the readiness (maturity) of properties and the cleanness of data flows (failure probabilities multiply).

H2: A powerful tool for monitoring and analysing dependability as well as maturities of properties and units can be established on the base of dynamic modelling. The model architecture may be specified by expected contributions and resulting requirements. The desired features for tool implementations may be provided by Multi-agent design pattern. H3: A decision table for failure risks, disturbance cases, decisions for actions and inputs for learning may be established. Using agents, adequate architecture may be implemented into a learning experimentation environment for simulation or manufacturing scenarios and evaluations of risks.

Following the proposed three hypotheses the research question is formed as weather an agent-based distributed dependability and security supervision and control that has inherited smart system properties, can enhance overall dependability and security of the system. The corresponding factor to analyse the performance of the adopted approach is to see to what extent it promotes or enable flexibility, responsiveness, learning capability, scalability, level of autonomy, reconfigurability, and reusability of the model's modules. To assure maximum dependability throughout an enterprise, the adopted approach must be able to deal with all incorporated components, information flows among them as well as the cyber areas, networks, databases and servers. To this goal, a distributed Dependability and Security Model (Fig. 1) is introduced for covering the entire system, every units and components down to all levels of detail (LoD). The model includes a core model, a control loop, and a connection to the virtual world. The core model consists of two main parts: object description, and risk model, where the former focuses more on objects' context and self-awareness, and imports data about object's environment, collaborations, functions and modules, objectives, application and task description, etc. that are needed to develop risk model, and the latter covers accordingly all Dependability and Security parameters, vulnerabilities and risks, and the ways of measuring and dealing with them. The core model, in other words, feeds the improvement process to be done by the control loop. The relevant data for object description section are imported from the cloud or sensed as a part of the object's self-/context-awareness. The risk model contains a scalable feature for considering possible risks, assessing their criticality and their possible effects on the object or the system in total (e.g. Failure Mode, Effect and Criticality Analysis (FMECA)/Fault Tree Analysis (FTA)). Self-optimizing occur via sharing knowledge with all other smart units, and updating its own structure and database through continuous feedbacks (control loops).

The Control Loop invokes the process of Inspection (i.e. Monitoring, Detecting, and Identifying and Measuring), and Reaction (i.e. giving Alarms, taking Action, and doing the Reconfiguration afterwards) in real-time. All steps can be carried out fullyor semi-autonomously by smart objects through this attached core model, which is located in the cyber space and is in collaboration with all other models. This gives the components all abilities to collaborate with the common objective of raising and maintaining the dependability and security of the total system.

Relationship to Smart Systems

The suggested solution aims at improving the dependability and security of smart systems with its focus on (but not limited to) cyber physical production systems (CPPS). To this aim, we introduce a generic dependability model and architecture that tries to harness the capabilities and properties of smart systems, that are elaborated in [1] (i.e. interoperability, autonomy, scalability, modularity, heterogeneity, reconfigurability, and context-awareness), for maximising its performance and versatility. In other word, it aims to be mechanism equipped with smart systems' properties, to ensure the dependability and security of smart systems. In order to enable these properties, Intelligent Agents are to be summoned as the implementation toolset for our model.

Review of Literature

Smart Distributed Manufacturing systems, enabled by Cyber-Physical Systems, have major structural similarities, as they both address three main layers: physical layer, cyber layer, and data communication and integration layer. Each of these layers has its own concerns with regards to dependability and security. Accordingly, many studies tried to point out these issues or suggest countermeasures [START_REF] Huang | Cyber-physical system security for networked industrial processes[END_REF][START_REF] Wells | Cyber-physical security challenges in manufacturing systems[END_REF][START_REF] Wu | A survey on the security of cyber-physical systems[END_REF][START_REF] Zhang | Security threats and measures for the cyber-physical systems[END_REF][START_REF] Karnouskos | Chapter 6 -Industrial Agents Cybersecurity[END_REF], or to point out and to evaluate the cyber-physical vulnerabilities and their impacts on manufacturing systems [START_REF] Desmit | Cyber-physical Vulnerability Assessment in Manufacturing Systems[END_REF]. In [START_REF] Hu | Robust Cyber-Physical Systems: Concept, models, and implementation[END_REF], the approach tries to design and to implement a robust cyber physical system, and [START_REF] Sanislav | An approach to model dependability of cyber-physical systems[END_REF] attempts to model ontology-based dependability in CPSs using FMEA techniques are outlined. Mathematical approaches were used in [START_REF] Orojloo | A method for modeling and evaluation of the security of cyber-physical systems[END_REF] to model security risks of CPSs and to quantitatively evaluate their risks. Dependability of self-optimizing systems was studied and analysed in [11], where various methods covering conceptual design, and development phases were introduced. Authors in [12] used systems' context awareness to increase security in information access, by asking questions like: who wants what information, how, from where, and when? This study, on the other hand, tries to propose a generic model, and an agent-based structure for developing an intelligent and autonomous distributed dependability and security supervision, and control in CPPS. The model, given its structure, and enabled by intelligent agents, is expected to bring more flexibility and responsiveness. Also, higher autonomy and scalability is predicted through context-awareness and summoning the capabilities of intelligent agents. Moreover, due its decentralized realtime monitoring and control, higher coverage and improved stability is likely to be achieved.

Research Contribution and Innovation

This section describes the multi-agent based architecture of the dependability and security mechanism explained in the introduction, and elaborates on the way smart systems properties are manifested and practised in this model. In the end an example shows how the model can function in a given condition using its capabilities enabled by the properties it possesses.

Multi-Agent Systems (MAS) for Model Architecture

The dependability and security model as described, with the potential properties mentioned, requires a toolset to enable such properties. Accordingly, Multi-Agents Systems (MAS) can be a decent candidate, since intrinsically, intelligent agents (IA) demonstrate responsiveness, proactiveness, goal-orientation, social-ability, scalability, flexibility, robustness, self-configuration, adaptability/ re-configurability, along with their decentralized architecture and learning capabilities [13]. After determining the tool, the model is to be translated into an architecture composed of interacting agents. The first step would split the model task-wise onto single agents. Doing so, the following table 1 and figure 2 demonstrate how to introduce agents and their task descriptions, as well as their overall structure and collaborations' relations. Data captured by sensors are sent to the VO (virtual object), which is the cyber representative or digital twin of the component (e.g. industry 4.0 component). Then, these data are filtered (to omit unnecessary data), monitored, and at the same time this stage is being fed into a status/topology manager to gain the approved context for comparing the filtered sensed data to detect anomalies. When detected, info is sent to the level one analyser, where in collaboration with assessment agent and database, the risk will be identified, and its severity will be measured as the sum of possible losses it can cause to other components or parts of the system. Risk data will be sent to the decision-making agent (DM level one) for making decisions on the appropriate actions, e.g. alarms, and send the command to actuators. However, if the problem requires more advanced analysis, it will be sent to the analyser level two, where harder problems can be analysed, and negotiations with other agents might be necessary to make the right measurements and analyses to provide accurate data for DM level 2. In decision making level two, more complex actions, and if required, negotiations with agents of other components (e.g. for sharing resources in fixing an issue), take place. After the actions are carried out, and issues are confirmed to be solved, the result is fed into the core model to update the risk assessor.

Properties, as Seen in the Model, and an Agent-Based Example

Table 2 shows the expected contribution of the smart systems properties and accordingly the resulting requirements.

An example can consider a job-shop unit with several automated machines and conveyors, using multi-agent systems to control their production system. Simultaneously, along with data form sensors, machines and controllers' interactions will be checked via "monitoring agents", receiving all information being send and captured by controller units and machines. Two of the possible risks can be either one of the controller agents itself be compromised by an adversary, or something unintentionally occurs to one of the machines. Some cyber security risks associated with the former might be cloning, repudiation, MITM attack, DDoS, eavesdropping, and some risks concerning the machines (can be partial or full breakdowns, connectivity loss, etc.). Taking the breakdown of one of the machines as an example, the monitoring agent will notice the change in the system in real-time (e.g. the number of parts passing across a specific sensor), the analyser will identify the issue (i.e. in this case breakdown of a machine (machine B, in figure 3)) and will measure the impact on the system and its components it collaborates with, and will send the data to decision the maker agent: Alarms will be published to the right entities (e.g. controllers, maintenance centre, spare part inventory, etc.). The machine will be stopped and called unavailable. The request will be sent to other agents for availability of another machines to do the task instead of the broken-down machine and after locating the alternative machine, ways (e.g. conveyors, AVGs, etc.), will be found to send the parts to the new alternative machine. And finally, a feedback will be sent to the core model for updating the data base and the assessment model, and for generating reports.

Fig. 1 .

 1 Fig. 1. Smart Dependability and Security architecture

Fig. 2 .

 2 Fig. 2. Proposed Agent Architecture for components' intelligent dependability and security

Table 1 .

 1 Applied agents and their task descriptionFor more complicated problems, analyser level 1 sends the case to analyser level two with more abilities. If needed this analyser practises negotiations with other components agents to provide best global data of the risk to feed the DM level 2.

		Agents	Task Description
			Updates the status of components. The main part of context-
		Status manager	awareness. Knows the approved context, authorities, topography
			of the system, etc.
			Stores components models and risk data. Data are stored here in
			modules for each type of risk to be accessed by analysers and
	model	Database	assessment agent. Other components when authorized, can have access to some of the data during negotiation or when they are new to the system, to get updated with vulnerabilities and
	Core		measures, etc. Receives data from analyser and assesses the risk through
		Assessment agent	negotiating with other components' assessors, receiving context data from TS manager, and having access to the database and the
			object model
			For negotiations between agents of other components. Updating
		Interface	the topography and context information, more accurate and global risk assessment, providing access to databases of other
			components.
	/ detection Monitoring	Data filter Monitoring	Filtering out redundancies. Looking for useful data among loads of data Looking for anomalies and risks, by comparing the current-state sensed data with current-state approved context. Then sends the detected cases to level one analyser.
			For simpler problems/quicker responses. Data analyser does the
			identification and measurement of risks. Lvl 1 analyser does the
	Measurement	Data analyser lvl 1 Data analyser lvl 2	simple analysis and communicates with assessment agent to provide proper input for DM level one. It then provides feedback to the core model.
			For simpler reactions/ quicker responses. After supplied with
	Alarm/ Action/ Reconfiguration	Decision-Making lvl 1 Decision-Making lvl 2	proper risk information, releases alarm to right entities and send proper commands to actuators to apply right corrective actions, and reconfiguration when needed. Feedback is then being provided to the core model Provides higher lever reactions, and reconfigurations, and more advanced alarms for more complicated issues. If needed asks for collaboration of other agents and components resources to solve
			a problem. Feedback is then being provided to the core model.

Conclusion

Based on available technologies, a structure based on multi-agent systems was suggested as the dependability and security model. Moreover, intelligent agents are shown to be capable of equipping the process of dependability and security supervision and control with the smart systems' properties to improve it and make it more efficient. The next step of the study would be testing the model in various cases and extending its performance and capabilities (H3). One case is simulating the example described above, and the other will focus on the data security risks (e.g. intrusion attack, DDoS attack) on one component to test the models performance in detecting and blocking it, and after disinfection, reconfiguring the component to be used again by the system. The experiment is to be done by simulating DDoS attack i.e. by overloading and increasing data traffic, assessing the models' reactions in handling the risk and its learning progresses for feedback.