Salinee Santiteerakul

Aicha Sekhari

Yacine Ouzrout

Apichat Sopadang

Social Indicators for Sustainable Supply Chain Management

Keywords: social indicators, sustainable supply chain, performance measurement

Because of sustainable development has recognized for last 10 years. Most interpretations of sustainable development recognize that there are constraints on long-term human activities consisting of three dimensions: economic, environmental and social. From the literature review, we found that indicators of environmental and economic performance are relatively well established. They can be combined to indicate the sustainability of products, services and supply chains. Meanwhile indicators of social performance are more problematic, particularly indicators to describe the social value of products and services. This paper reviews a context of social indicators and proposes a metric to measure social performance for sustainable supply chain.

I. INTRODUCTION

The most often quoted definition of sustainability is that of the Brundtland Commission as "development that meets the needs of the present without compromising the ability of future generations to meet their needs." According to this commission, [START_REF] Elkington | Cannibals with Forks: The Triple Bottom Line of 21st Century[END_REF] developed the idea of the triple bottom line which simultaneously considers and balances economic, environmental and social goals from a micro economics standpoint. The triple bottom line suggests that at the intersection of social, environmental and economic performance, there are activities that organizations can engage in which not only positively affect the natural environment and society, but which also result in long-term economic benefits and competitive for the firm.

While the social and the environmental are clearly associated in the sustainable development context, there is very little research addressing the social dimension. The interconnectedness of social, economic, and environmental factors is the essence of sustainability. Therefore, environmental decision-making tools or practices, such as a life-cycle assessment (LCA), which focuses solely on environmental parameters, may be counterproductive to attaining overall sustainability goals (Matosand Hall, 2007). Consequently, sustainability derives considerably from social engagements such as cultural norms, individual and group behaviors, the role of government and community, the relationship with science, and the relationship with the natural environment [START_REF] Muller | The contribution of environmental and social standards towards ensuring legitimacy in supply chain governance[END_REF]. While there are a limited number of case studies in the literature that have introduced supplier evaluation schemes that integrate environmental and social criteria [START_REF] Koplin | Incorporating sustainability into supply management in the automotive Industry -the case of the Volkswagen AG[END_REF], [START_REF] Trowbridge | A case study of green supply-chain management at advanced micro devices[END_REF]), the practice and understanding of SSCM is still fragmented and heavily oriented to the environmental dimension. For example, a comprehensive literature review on SSCM identified that out of 191 papers, 140 addressed the environmental dimension while only 20 addressed the social dimension (Seuring, 2008).

Therefore, social indicators applicable to products and service are needed but are not generally available. This work reviewed the literature on corporate social performance measurement and proposed a metric of social indicators for sustainable supply chain performance measurement.

Section II introduces a supply chain management definition and a scope of "supply chain" in this work. Section III introduces a sustainable supply chain definition and a sustainable supply chain performance measurement. Section IV describes corporate social performance (CSP) concept and Section V is a cooperate social performance integrating with a sustainable supply chain perspective.

II. SUPPLY CHAIN MANAGEMENT

A. Defining supply chain scope Mentzer et al. (2001) defined a supply chain as a set of three or more entities (organizations or individual) directly involved in the upstream flows of products, services, finances, and/or information from a source to a customer. Moreover, they identified three degrees of supply chain complexity: a direct supply chain, and extended supply chain and an ultimate supply chain. As shown in Fig. 1, a direct supply chain consists of a company, a supplier and a customer involved in the upstream and/or downstream flows of products, services, finances, and/or information. (Figure 1a.). An extended supply chain includes suppliers of the immediate supplier and customers of the immediate customer, all involved in the upstream and/or downstream flows of products, services, finances, and/or information (Figure 1b.). An ultimate supply chain includes all the organizations involved in the upstream and downstream flows of products, services, finances, and information from the ultimate supplier to the ultimate customer (Figure 1c.). All of three degrees of supply chain complexity.

Wood (2010) mentioned the supply chain management appeared in the field of the corporate social performance (CSP) research since 2005 as a responsive stakeholder management practices. Merging CSP concept with supply chain management research is in a primary stage. Therefore we scoped a degree of supply chain in the less complexity as a "direct supply chain". That means we considered the social performance which involved in a company, a supplier and a customer. For the detail of CSP will describe in section IV. As a philosophy, SCM has three characteristics: (1) a system approach to viewing the supply chain as a whole, and to managing the total flow of goods inventory from the supplier to the ultimate customer, (2) a strategic orientation toward cooperative efforts to synchronize and converge intrafirm and interfirm operational and strategic capabilities into a unified whole; and (3) a customer focus to create unique and individualized sources of customer value, leading to customer satisfaction.

In adopting a supply chain management philosophy, Mentzer et al (2001) suggested various activities necessary to successfully implement a SCM philosophy i.e. integrated behavior, mutually sharing information, mutually sharing risks and rewards, cooperation, the same goal and the same focus on serving customers, integration of processes, and partner to build and maintain long-term relationships.

To successfully implement SCM, others authors have focused on management processes. All firms within a supply chain must overcome their own functional silos and adopt a process approach (Lambert et al, 1998). Thus, all the functions within a supply chain are recognized as key processes. A process is a specific ordering of work activities across time and place, with a beginning, and end, clearly identified inputs and outputs, and a structure for action. The key processes include customer relationship management, customer service management, demand management, order fulfillment, manufacturing flow management, procurement, and product development and commercialization According to three categories of supply chain definition, Mentzer et al. (2001) defined two concepts with term of supply chain management. The idea of viewing the coordination of a supply chain from an overall system perspective, with each of the tactical activities of distribution flows seen within a broader strategic context is called Supply Chain Orientation (SCO). The actual implementation of this orientation, across various companies in the supply chain is called Supply Chain Management (SCM). Thus, supply chain orientation (SCO) is defined as the recognition by an organization of the systemic, strategic implications of the tactical activities involved in managing the various flows in a supply chain. That means a company processes a supply chain orientation (SCO) if its management can see the implications of managing the upstream and downstream flows of products, services, finances, and information across their suppliers and their customers. In the other words, a supply chain orientation is a management of philosophy, and supply chain management is the sum total of all the over management actions undertaken to realize that philosophy.

Supply chain relationships are typically long-term and require considerable strategic coordination. SCO is a willingness by one company to address the importance issues involved relationship management across company (i.e. trust, commitment, interdependence, organizational compatibility, vision, key processes, leader, and top management support). When contiguous companies in a supply chain each achieve a SCO, they can begin the implementation process to realize SCM. Management of the supply chain is only accomplished when several companies in line in the supply chain have that orientation and move toward implementing the management philosophy of SCO.

Mentzer et al (2001) defined a supply chain management as the systemic, strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain, for the purposes of improving the long-term performance of the individual companies and the supply chain as a whole.

This research work, we scoped a supply chain boarder in SCO perspective to determine corporate social performance indicators for contiguous companies which willing to address their long-term relationships with an upstream and downstream orientation.

III. SUSTAINABLE SUPPLY CHAIN MANAGEMENT

As the traditional supply chain management concept focusing on economic issue, but the only economic dimension is not sufficient condition for the overall sustainability of a corporation. A single-minded focus on economic sustainability can succeed in the short run. But in the long run sustainability also requires environment and social view [START_REF] Elkington | Cannibals with Forks: The Triple Bottom Line of 21st Century[END_REF]. Seuring et al. point out that the triggers for sustainable supply chain management are external pressure and incentives from government, customer and stakeholders (Seuring, et al., 2008). The focal company is pressured by legal demands/regulation, responses to the stakeholders, the competitive advantage, the customer demands, the reputation loss, and the environmental and social pressure groups. The legal demands are the most frequently mentioned, closely followed by customer demands and response to stakeholders. When the focal company is pressured, it usually passes this pressure on to supplier. Here, one distinctive feature of sustainable supply chain management emerges.

A. Scope and definition

Sustainable Supply Chain Management (sSCM) has defined by Carter and Rogers [START_REF] Carter | A Framework of sustainable supply chain management: Moving toward new theory[END_REF] as the strategic achievement and integration of an organization's social, environmental and economic goals through the systematic coordination of key inter-organization business processes to improve the longterm economic performance of the individual company and its value network. This definition of sSCM based on the triple bottom line and the four supporting facets of sustainabilityrisk management, transparency, strategy, and culture.

While the publications in term of sustainable (either environment or social) and supply chain context were found in 1990s but the first published papers found were from the year 1994 [START_REF] Seuring | Core issues in sustainable supply chain management -a delphi study[END_REF]. Indeed, while the social and the environmental issues are clearly associated in the sustainable supply chain definition, a recent comprehensive literature review [START_REF] Seuring | Core issues in sustainable supply chain management -a delphi study[END_REF]) revealed a deficit on the social dimension in sustainable supply chain research papers. Specifically, only 10 percent of the 191 studied papers are addressing social issues while over 73 percent are focused on environmental issues and 16 percent on both social and environmental issues.

Therefore, the social issue is also needed to be developed as a framework and indicators. As mentioned above that the environment issue is well established. Corporate social responsibility is an issue that has been receiving greater attention in discussions on business and sustainability. Thus, our research focused only the social issue and proposed a metric of social indicators in the framework of corporate social performance which will be explained in section IV.

B. Sustainable performance measurement

There are various approaches to measuring, monitoring, and assessing a company's progress towards sustainability e.g. using standards and codes, sustainability indicators, metrics for sustainability performance.

For using standards and codes, companies adopt international standard, codes or guideline to meet legal compliance requirements, to build trust and credibility, to gain certification, to gain or restore stakeholder confidence, and to improve management systems through the use of standards and processes. The most adopted standards or guideline involved with sustainability context as ISO 14000, ISO 26000, AA1000, SA8000.

Numerous organizations are presently trying to develop a set of indicators to state the progress of a company towards sustainability. Veleva Whereas economic performance can be measured easily by internationally accepted standard measures, and environmental performance can be evaluated through input-output measurements, it is difficult to measure social performance and the intangible assets of a company (Székely and Knirsch, 2005)

IV. CORPORATE SOCIAL PERFORMANCE (CSP)

A. CSP's concept and definition

The concept of corporate social performance (CSP) is similar with corporate social responsibility (CSR), corporate social responsiveness, and socially responsible behavior (Fauzi et al (2010), Wood (2010). Generally, the terms social and environment are covered in the concept of CSP (Carroll, 1979, Wartick and Cocharn, 1985, Wood, 1991, Clarkson, 1995).

Carroll (1979) defined CSR as the intersection of three dimensions (1) corporate social responsibility -principles to be perceived in four dimensions (i.e. economic, legal, ethical, and discretionary), (2) the social issues that a firm should be concern (consumerism, environment, discrimination, product safety, occupational safety, and shareholders), and (3) the philosophies of responsiveness.

Wartick and Cochran (1985) updated Carroll's model and folded in some additional concepts that made the CSP model more robust and logical (Wood, 2010, Fauzi et al, 2010). They presented what they saw as three challenges to CSR: economic responsibility, public responsibility, and social responsiveness. For the economic responsibility, the principles of CSR were taken from Carroll (1979): economic, legal, ethical and discretionary. The processes of responsiveness were, also following Carroll, reactive, defensive, accommodative and proactive. Finally, were proposed to manage social issues, including policies of issue identification, issue analysis and response development.

Wood (1991) proposed a CSP-model which became a one of the most influential and comprehensive conceptualization of CSP (Orlitzky et al., 2003, Fauzi et al., 2010). Wood (1991) defined CSP as a business organization's configuration of principles of social responsibility, processes of social responsiveness, and policies, programs, and observable outcomes as the relate to the firm's societal relationship.

Calrkson (1995) suggested applying stakeholder theory as a framework to model CSP, which would then be defined as a company's ability to manage its stakeholders in a way that is satisfactory to them. Corporate social performance is a set of descriptive categorization of business activity, focusing on the impacts and outcomes for society, stakeholders and the firm. Types of outcome are determined by the linkage, both general and specific, defined by the structural of CSR. The processes by which these outcomes are produced, monitored evaluated compensated and rectified are defined by the processes of corporate social responsiveness.

B. CSP's model

The CSP model which proposed by Wood (1991) is one of the most influential, helpful, parsimonious, and comprehensive conceptualizations of CSP (Orlitzky et al., 2003). Therefore, our research used Wood's CSP model as a framework to determine a metric of social indicators for sustainable supply chain. Note that in our research we only focused on a social dimension, an environmental dimension is not in our scope.

FIG 2 Wood's CSP Model

V. CORPORATE SOCIAL PERFORMANCE IN SUSTAINABLE SUPPLY CHAIN PERSPECTIVE

A. Sustainable supply chain framework and social metric

As described in section II, this research work scoped a supply chain boarder in supply chain oriented perspective to determine corporate social performance indicators for contiguous companies which willing to address their longterm relationships with an upstream and downstream orientation.

Székely and Knirsch (2005) proposed the 5 areas of social indicators for measuring and managing companies' social performance as follow:

• Human rights: with the rapid globalization of business, human rights performance in several countries is under scrutiny.

B. Social indicators for sustainable supply chain

According to the supply chain oriented definition and the CSP model, we define our scope in this research work as follow:

• The social indicators which we have proposed focused in one company as a foal company.

Measuring the social performance of focal company activities involved a relationship management in both suppliers and customers side. • A focal company has a willingness to address the importance issues involved relationship management across its supplier and customer i.e. trust, commitment, interdependence, organizational compatibility, vision, key processes, leader, and top management support.

We categorized the social indicators into 3 categories following CSP's model: Principle of social responsibility, processes of social responsiveness and outcome and impacts of performance.

1) Principle of social responsibility Wood (1991) structured the principles of CSR include a 'legitimacy principle', which addresses business as a whole, a 'public responsibility principle', which applies to particular organizations, and a 'discretionary principle', which refers specifically to the duties of individual employees as a moral agent. The social indicators related to social responsibility as shown in Tab1.

According to the principle of legitimacy, society has the right to establish and enforce a balance of power among its institutions and to define their legitimate functions. Society has available refusal that can be used when business's obligations as a social institution are not met. One way to test the principle of legitimacy is to analyze what happens to companies that violate social expectations. Thus, we can measure how the company applies a legal on its duty or we can measure how often and how much is a monetary value of its sanctions with laws and regulations.

The principle of social responsibility is the organization's duty to act affirmatively for social well-being (Wood, 1991). This principle establishes that content of CSR will move from company to company because every firm is responsible for fixing what it has broken, for avoiding future breakage, and for helping to solve those social problems that effect it. This is a relationship principle, emphasizing each firm's relationship to its own specific environment. This principle operates at the organizational level of analysis, connecting the individual firm to a nexus of stakeholders set within a larger sociopolitical environment. For example, an automobile maker is rightly held responsible for helping to solve problems of vehicle safety and air pollution, and such a company might reasonably become involved with driver's education program and public transportation policy. Therefore, we can measure this principle by the level of management effort to involve a local community or the impact of product or service.

The principle of managerial discretion is based on human choice and will, focusing on the options and opportunities available to individual actors within their organizational and institutional contexts. The domain of discretionary responsibility typically has been operationalized as corporate philanthropy, or occasionally as corporate involved in publicprivate partnerships or collaborative social problem-solving ventures. Wood (1991) phrasing this principle: 'Managers are moral actors. Within every domain of corporate social responsibility, they are obliged to exercise such discretion as is available to them, toward socially responsible outcomes'. Thus, the managerial discretion is not only philanthropy or community involvement programs but also a moral choice decision in organizations. The individual's right and responsibility to decide and to act are affirmed within the bounds of economic, legal and ethical constraints. Therefore, we can measure how the company involves with a local community or the others activity which is not directly affected from company's product or service, and how the company encourages an ethical issue in its operation. Percentage and total number of business units analyzed for risks related to corruption.

2) Processes of social responsiveness

Responsiveness contributes an action dimension that is needed to complement the normative and motivational concept of corporate social responsibility. Fredrick (1978) defined corporate social responsiveness as 'the capacity of a corporation to respond to social pressure'. As the second facet of the CSP model, responsiveness provides an action counterpoint to the principle reflection of social responsibility. Ackerman (1975) suggested three characteristic behaviors of a responsive company: (1) it monitors and assesses environmental conditions, (2) it attends to the many stakeholder demands placed on it, and (3) it designs plans and policies to respond to changing conditions. Thus, the processes of social responsiveness is a "how to" component, how managers and companies act regarding environmental and stakeholder conditions and expectations. Social responsiveness consists of three main areas: environmental assessment, stakeholder management, and issues management. The indicators of processes of social responsiveness are shown in Tab 2.

According to environmental assessment, it is a process which gathers the information needed to understand and analyze the firm's social, political, legal and ethical environment. As we mentioned on the scope of this research, we are focusing on only social dimension thus the environmental or ecological issue will not address in this work. Our point of view for environmental scanning is the general data which reflects how the company manages and provides the benefits to its employees.

Stakeholder management facet is active and constructive engagement in relationships with stakeholders. We divided stakeholders into two levels: internal stakeholder and external stakeholder. Internal stakeholder is a company's employees who directly affected from a company's policy. The indicators for internal stakeholder are involved how a company manages the relationship with its employees and how it improves the employee's skill and knowledge. External stakeholder includes both supplier and customer side. Our research concentrates on how company manages stakeholder relationships in the context of social and public policy and how does the company manage both of supplier and customer demands which involved a social issue. Therefore the indicators for external stakeholder management are about, for example, level of commitment with supplier on social and public policy in supply chain, level of commitment with customer on social and public policy in supply chain, practices related to customer satisfaction, including results of surveys measuring customer satisfaction

The third facet of social responsiveness in the CSP model, issues/public affair management, is a set of processes that allow a company to identify, analyze and act on the social or political issues that may affect it significantly (Wood, 1991). Issues management involves devising and monitoring internal and external processes for managing a company's responses to social issues (Brown, 1979), with the purpose of 'minimizing surprise' (Wartick and Cocharan, 1985). In their conceptualization, issues management is further classified as issues identification, issues analysis, and response development. Corporate behavior relevant to issues management that has been studied is crisis management (Srivastava, 1987), in the other word, issues management can consider as a risk management in the social issues of a company. The indicators for issues management are assessment/risk management costs (the cost of risk assessment and management activities for a specific area, supplier, product, etc.) and nature, scope, and effectiveness of any programs and practices that asses and manage the impacts of operations on communities, including entering, operating, and exiting.

Tab 2 Social responsiveness indicators

Sub-Category Indicators

Environmental Scanning

3) Outcome and impact of performance

The outcomes of corporate behavior are direct and obvious interest in the assessment of corporate social performance. Corporate social performance is a set of descriptive categorizations of business activity, focusing on the impacts and outcomes for society, stakeholders and the firm (Wood, 2010). Wood divided the outcomes and impacts into three categories, effects on people and organizations, effects on the natural and physical environments, and effects on social systems and institutions. The effects on natural and physical environments are generally measured by a corporate environmental performance such as pollution emission (CO 2 emission, green house gas emission, BOD, COD, etc.), energy utilization, material use, etc. Because of the environmental performance is out of our research scope thus we have not proposed the indicators for the effects on natural and physical environments in this work. Therefore we have proposed the indicators for the effects on people and organizations, and the effects on social systems and institutions which shown in Tab 3.

According to the effects on people and organization, we considered the 'people' of organization as the employees of the company. Measures of employee satisfaction have occasionally been used as substituted of CSP. Further, Longo et al. (2005) extended to measure effects on employee by examining a number of employee-related issues as indicators of CSP, including employees health and safety at work, development of worker's skills, and wellbeing and satisfaction of workers (cited in Wood, 2010). The employee health and safety can be measured by rate of injury, occupational diseases, lost days, and absenteeism. The development of worker's skills and satisfaction of workers can be measured by employee training's satisfaction and employee job's satisfaction respectively.

According to the effects on social systems and institutions, measurement this type of these categories is especially important for understanding how business activity benefits and harms stakeholders and the larger society. The corporate behavior impacts of social systems can be measured by the result of life cycle assessment in which health and safety impacts of products and services. For stakeholder's impacts, we considered direct stakeholders in both customers and suppliers side. Wood (2010) found from her literature reviewed that measurement efforts on the outcomes and impacts related to customers have taken two paths -one employing various kinds of perceptual or attitudinal data and one using objective indicators from the regulatory requirements such as safety recall. So the effects on customer side can be measured by customer satisfaction and the number of compliance related with safety recalls, false advertising, and product-related regulatory. Corporate social effects on supplier side recently appeared on supply chain management research field as examples of responsive stakeholder management practices (cited in Wood, 2010). The phenomenon of supplier capture, for example, is well-known in strategy, but rarely as having CSP consequences. Wood (2010) suggested that global supply chain issues such as product safety and information transparency, child labor, and other practices considered exploitative deserve to be considered in CSP research. Thus, we can be measured the effects of supplier side in term of total number of compliance related with product safety, child labor and human rights which caused by supplier's operation. Further, we can measure the outcomes from supplier management by the supplier satisfaction and percentage of suppliers that meet labor and human right screening condition.

VI. CONCLUSION

This work has proposed the metrics of social indicators for sustainable supply chain. In the sustainable supply chain perspective a company should recognizes not only economic benefit of organization but also the social and environment impacts of managing the upstream and downstream flows of products, services, finances, and information across their suppliers and their customers. From the literature review, we found that the social issue still in an embryonic stage and involve with many domains and disciplines e.g. stakeholder management, business relations, business ethics, strategy, etc. Social performance need to make a concerted effort to accommodate the suitable indicators in such related investigation.

Our research work used Wood's CSP model as a framework to determine a metric of social indicators for sustainable supply chain. This model consists of three facets: principle of social responsibility processes of responsiveness, and outcomes and impacts of performance. The first facet, principle of social responsibility, implies the motivation level of implementing social responsibility into organization. Such companies motivated by the principle of legitimacy, they response to society's expectation and obey the law. Such companies motivated by the principle of public responsibility they more focus on an organizational level and try to improve their impacts and effects involved with their stakeholders. But such companies motivated by the principle of managerial direction, they focus not only society and organizational level but also an individual level of employees such as an ethical issue of manager and employee.The social responsiveness indicators measure how managers and companies act regarding stakeholder conditions and expectations. This matrix related with stakeholder management in both supplier and customer sides. Finally, the outcome or impacts indicators measure understanding how business activity benefits and harms stakeholders and the larger society.

The social indicators in this work considered by motivation principles, processes of company's behavior, and impacts of company's behavior. And we scoped the framework of supply chain as the supply chain oriented (SCO) which recognize the implementation of company's supplier and customer. In the future work, we will extend these indicators to cover the implementation across various companies in the supply chain.

1 B

 1 Source: Mentzer et al. (2001) Fig. . Defining supply chain management framework Firstly, we have to determine a clear understanding of supply chain management (SCM) framework in this research work because without a clear understanding of SCM, we cannot expect wide application of SCM in practice or research. Mentzer et al. (2001) reviewed 64 articles for defining supply chain management. They categorized SCM into three categories: a management of philosophy, implementation of a management philosophy, and a set of management processes.

  From Carroll's (1979)'s article laid out the first conceptual model of CSP. Wood (1991) modified a CSP-model from Carroll's. She organized the literature into structural principles of responsibility as inputs, and finally outputs and outcomes. Her elements, as revised in 1994, are shown in Fig 2.

  • Labor/employment issues: standard issues such as health and safety, education, training, industrial relations, wages, benefits, conditions of work/employment, accountability, image/reputation and harassment.

	• Supplier relationships: contractual agreements with
	suppliers, supplier diversity and company policies
	on the screening of suppliers.
	• Community initiatives: involvement in local
	communities, contribution to the local economy,
	ensuring local wealth and skills.
	• Corporate philanthropy: donations, pre-tax profits
	and grant programs.
	The most popular guideline for measure sustainability
	performance is using the Global Reporting Initiative (GRI) as
	a guideline. It is useful to precede from board categories
	though definite aspects to specific indicators, interpreted as:
	• Categories: board areas or groupings of economic,
	environmental or social issues of concern to
	stakeholders.
	• Aspects: general types of information related to a
	specific category (e.g. labor management,
	customer health and safety or donations to host
	communities)
	• Indicators: specific measurements of an individual
	aspect that can be used to track and demonstrate
	performance.
	In term of social category in GRI, divided into 4 categories:
	labor practices and decent work, human rights, society, and
	product responsibilities.
	• Labor practice and decent work category consists of
	4	aspects	i.e.	employment,	labor
	management/relation, occupational health and
	safety, and education and training.
	• Human rights category consists of 7 aspects i.e.
	investment and procurement practices, non-
	discrimination, freedom of association and
	collective bargaining, child labor, forced
	compulsory labor, security practices, and
	indigenous rights
	• Society category consists of 5 aspects i.e. community,
	corruption, public policy, anti-competitive
	behavior, and compliance.
	• Product responsibility consist of 5 aspects i.e.
	customer health and safety, product and service
	labeling, marketing communications, customer
	privacy, and compliance.
	Our research work used GRI and other literatures (Veleva
	and Ellnbecker, 2001, Clift, 2003, Kranjac and Glavic, 2003,
	Olugu et al, 2010) as a guideline of social indicators.


Yasmine Harbi
email: harbiyas@yahoo.com

Zibouda Aliouat

Sarra Hammoudi
email: hammoudisarah@univ-setif.dz

Enhancement of IoT Applications Dependability using Bayesian Networks

Keywords: Internet of Things, Wireless Sensor Networks, Smart sensors, Fault tolerance, Bayesian Networks, Reliability

Sensors play a vital role in Internet of Things (IoT) monitoring applications. However, the harsh nature of the environment influences negatively on the sensors reliability. They may occasionally generate inaccurate measurements when they are exposed to high level of humidity, temperature, etc. Forwarding incorrect data to the base station may cause disastrous decision-making in critical applications. To address this problem, we proposed a new fault-tolerant mechanism based on Bayesian Networks that carefully solve such a problem. The mechanism is called Reliability of Captured Data (RCD); it enables sensors to both detect and recover faulty data. To show the performance of our algorithm, we compared this latter with another recent algorithm called Fault Detection Scheme (FDS) using Network Simulator 2 (NS2). The results showed that our strategy gives remarkable enhancements in terms of fault detection accuracy and fault alarm rate.

Introduction

The IoT refers to the next generation of the Internet [START_REF] Li | The internet of things: a survey[END_REF]. It cuts across many critical applications. Among of these applications are: process automation applications, infrastructure monitoring networks, process control networks, smart building, patient monitoring, oil and gas industry [START_REF] Li | The internet of things: a survey[END_REF][2][3] [START_REF] Cauchi | Efficient Probabilistic Model Checking of Smart Building Maintenance using Fault Maintenance Trees[END_REF]. The IoT is empowered by the proliferation of a myriad number of miniature sensors and actuators. Sensors can efficiently monitor critical environments in diverse domains. Fault tolerance is one of the critical problems in IoT applications. The problem of missing sensor node and data are inevitable in Wireless Sensor Networks. Failure problems are due to various factors such as power depletion, environmental impact and dislocation of sensor nodes [START_REF] Kakamanshadi | A Survey on Fault Tolerance Techniques in Wireless Sensor Networks[END_REF]. In such a case, sensors may generate incorrect measurements, cause false alarms and degrade the reliability of collected data [START_REF] Samson Arun Raj | A Survey on Classification of Fault Tolerance Techniques Available in Wireless Sensor Network[END_REF]. Therefore, it is extremely important to ensure the accuracy of sensed data before decision-making process. A wide variety of classificationbased approaches including Bayesian Networks (BNs), Neural Networks (NNs), and Hidden Markov Models (HMMs) can be used to detect inaccurate data. To address such a problem, this paper presents a new fault-tolerant mechanism, called Reliability of Captured Data (RCD) that is able to achieve higher data reliability and accuracy. The RCD makes sensors more intelligent, it enables them to efficiently detect the faulty nodes by analyzing the measured data. The algorithm is also able to recover the unreliable data before forwarding it to the base station. The accuracy of the faulty node detection reflects in the diagnosis of the sensed data using Bayesian Networks. The Bayesian Network is a probabilistic model which enables the sensors to locally predict their current readings based on their own past readings and the current readings of their neighbors [START_REF] Ben-Gal | Bayesian Networks. Encyclopedia of Statistics in Quality and Reliability[END_REF] [START_REF] Zhang | Outlier Detection Techniques for Wireless Sensor Networks: A Survey[END_REF]. The choice of the Bayesian Networks helps to accurately detect the faulty nodes since its rate of misclassification is very low compared to NN and HMM [START_REF] Ripley | Pattern recognition and neural networks[END_REF]. In addition, the BN requires fewer parameters than the HMM to represent the same information, and provides the spatial-temporal correlation between the sensors [START_REF] Ghahramani | An introduction to Hidden Markov Models and Bayesian Networks[END_REF] [START_REF] Ayadi | Outlier detection approaches for wireless sensor networks: A survey[END_REF].

To show the performance of our proposal, we have conducted a several series using Network Simulator 2 (NS2). We compared our strategy to a recent one which is called Fault Detection Scheme (FDS) [START_REF] Titouna | FDS: Fault Detection Scheme for Wireless Sensor Networks[END_REF]. The results show that our mechanism achieves high amelioration in terms of fault detection accuracy and false alarm rate. The rest of this paper is organized as follows. Section 2 presents the related works in the context of fault tolerance. Section 3 describes the concept of Bayesian classifier. Section 4 discusses the proposal and the simulation results are presented in Section 5. Section 6 concludes our work.

Related works

Failure detection and recovery is a crucial task in IoT since sensors are deployed in harsh and unattended environments. M.Yuvaraja and M.Sabrigiriraj in [START_REF] Yuvaraja | Fault detection and recovery scheme for routing and lifetime enhancement in WSN[END_REF] designed a fault detection and recovery scheme for routing protocols in WSN. The sink periodically broadcasts agent packets to all its neighbors. The non faulty nodes reply with an acknowledgment. The agents form a query path towards the dead or faulty node. To decrease the broadcasting cost overhead, the receiving nodes apply the Random Decision Function (RDF) to be able to take a decision to whether they forward the agent packet or not in order to detect the dead or faulty nodes. Once the faulty nodes is detected, the connectivity is restored by replacing faulty node with block movement. The technique is called Least-Disruptive Topology Repair (LeDiR). The shortcoming of this paper is when applying the RDF function risks the agent packets to not arrive at the final nodes of the network. Therefore, sink may consider it as faulty nodes. Titouna et al. in [START_REF] Titouna | FDS: Fault Detection Scheme for Wireless Sensor Networks[END_REF] proposed Fault Detection Scheme (FDS) which consider both battery power and sensed data to identify faulty nodes. The FDS is performed in two-levels where sensor nodes detect outlier (faulty data) using a Bayesian model, then transmit the decision to Cluster Head (CH) which verifies only sensor nodes that have a primary decision as faulty. This second verification is based on similarity between primary decisions of cluster's members. The drawback of this approach is with a high number of faulty nodes, the process of detection is very slow and false alarm rate reaches a higher value. The authors in [START_REF] Titouna | Distributed Fault-Tolerant Algorithm for Wireless Sensor Network[END_REF] presented an extension of previous work [START_REF] Titouna | FDS: Fault Detection Scheme for Wireless Sensor Networks[END_REF] named Distributed Fault-Tolerant Algorithm (DFTA) in order to recover faulty nodes in WSN. The main idea is to eliminate the faulty node from the network and replace it with a switch-off node which has a higher degree of connectivity and belongs to the same cluster. Sleeping nodes are selected by CH after deployment of the network. The sleeping nodes neighbors' number must be less than the average number of cluster's members. After detecting faulty node, the CH sends a wakeup message to all sleeping nodes. These latter update their routing table and send the number of hops to reach the faulty node to CH. Finally, the CH chooses the appropriate recovery node that has fewer hops to reach the faulty one. The shortcoming of this strategy is that during the recovery phase, the CH drains too much energy since it sends three types of messages. Which overtime may decrease the network's lifetime. H.Yuan et al. in [START_REF] Yuan | A Distributed Bayesian Algorithm for Data Fault Detection in Wireless Sensor Networks[END_REF] proposed a Distributed Bayesian Algorithm (DBA) which can efficiently detect faulty readings in WSN using Bayesian networks. The sensor nodes exchange their reading with neighbors and calculate the probability of fault. This latter can be incorrect if the most of the neighbors are faulty. Then, the faulty node will consider itself as good node, or the good node may consider itself as faulty one. To avoid such a case, the probability of fault must be adjusted by border nodes. If a node has two neighbors that have a low probability of fault, but have different status (i.e. one is faulty and other is good), then it will be considered as border node. The drawback of this algorithm is that, in dense networks exchanging with all neighbors will increase the energy consumption and decrease the network's lifetime. Also, the algorithm does not take into account the overhead cost. A.V.Sutagundar et al. in [START_REF] Sutagundar | Agent Based Fault Tolerance in Wireless Sensor Networks[END_REF] proposed a fault-tolerant approach based on static and mobile agents which reside in all nodes of the network. This approach is developed at three levels: node level, CH level and Sink level. At node level, the Fault Tolerant Agent (FTA) checks whether the reading is in the reference range. Then, it discards the faulty readings. The Data Transfer Agent (DTA) transmits correct readings to CH. This latter compares received data and computes the average of all corrected data, then sends it to sink through DTA. The Sink Manager Agent (SMA) broadcasts periodically "alive messages" to all nodes and duplicate sink. If the principal sink fails, the duplicate one replaces it. The shortcoming of this mechanism is that in agent based approaches the network experiences a significant amount of delay to retrieve the state of the node, because it needs to wait for an agent to visit the node. Furthermore, agent-based approaches do not perform well in large scale WSNs. Since the number of sensor nodes increases, so do the number of agents. The authors in [START_REF] Siddique | Formal specification and dependability analysis of optical communication networks[END_REF] proposed a fault-tolerant mechanism for link failure in optical networks since optical fiber cuts is the most frequent failure in metropolitan areas. The proposed approach is based on pre-configured protection cycles (pcycles) and Double Rings topology with Dual Attachment (DRDA). The network consists of two rings with the same number of nodes. The nodes of the inner ring and the outer one are connected through bidirectional links. The authors formed two p-cycles that cover all nodes with disjoint links. They defined three recovery strategies in order to recover single and multiple link failure. Also, they provided a formal model of the network using Continuous Time Markov Chains, and they verified dependability properties using probabilistic model checking.

The proposed work gives useful information for designing dependable optical networks in metropolitan areas. However, this redundant topology may provide poor availability in a case of multiple link failure by putting an excessive load on the other links. Moreover, it may be very expensive and hard to manage or maintain.

Bayesian Classifier

In order to detect sensors that are prone to failure, their captured data should be analyzed and classified to know whether it is correct or not. This classification is based on Bayesian classifier.

A Bayesian classifier is a simple probabilistic classifier based on the Bayes theorem, it allows to classify the samples measured on observations [START_REF] Zhang | The optimality of naive Bayes[END_REF] [START_REF] Murphy | Machine learning : a probabilistic perspective[END_REF].

Bayes theorem estimates the probability of an event, based on prior knowledge of conditions that might be related to this events [START_REF] Zhang | The optimality of naive Bayes[END_REF] Where A and B are events. P(A) and P(B) are the probabilities of observing A and B independently. P (B|A) a conditional probability, is the probability of B given that A is true. P (A|B) a conditional probability, is the posterior probability of A given that B is true.

To classify the measured values in the predefined classes using the Bayesian classifier, we will have to define a Bayesian network.

A Bayesian network is an acyclic oriented graph in which nodes present random variables and arcs indicate the dependencies between these variables [START_REF] Ben-Gal | Bayesian Networks. Encyclopedia of Statistics in Quality and Reliability[END_REF].

Figure 1 represents the structure of a Naive Bayesian Classifier. Node C represents the class c i and nodes X = x 1 , x 2 , ..., x n are the features of the Naive Bayesian Classifier which are independent (i.e. there are strong or naive independence assumptions between the features) [START_REF] Murphy | Machine learning : a probabilistic perspective[END_REF]. The Bayesian network allows us to calculate the posterior probability P (C = c i |X) for each possible class c i by using the Bayes theorem as stated in Equation 2.

P (C = c i |X) = P (X|C = c i )P (C = c i ) P (X) (2)
The probability P (X|C = c i ) is often impractical to compute without imposing independence assumptions between features X. The Naive Bayesian Classifier assumes that each features x j is conditionally independent of every other feature [START_REF] Murphy | Machine learning : a probabilistic perspective[END_REF], this yields Equation 3.

P (X|C = c i ) = n j=1 P (x j |C = c i ) (3)
Now, we obtain the Equation 4.

P (C = c i |X) = n j=1 P (x j |C = c i )P (C = c i ) P (X) (4)
The target class c i , output by the Bayesian classifer, can be inferred using the concept of Maximum A Posteriori (MAP) [START_REF] Murphy | Machine learning : a probabilistic perspective[END_REF] as indicated in Equation 5.

c M AP = argmax ci∈C P (C = c i |X) = argmax ci∈C n j=1 P (x j |C = c i )P (C = c i ) P (X) (5
) Since the denominator is constant and identical for all classes, it does not affect the maximization. After deleting it, we got the Equation 6.

c M AP = argmax ci∈C P (C = c i |X) = argmax ci∈C n j=1 P (x j |C = c i )P (C = c i ) (6)
According to Equation 6, we can classify the measured data given various observations.

Reliability of Captured Data (RCD)

Our contribution is based on Bayesian classifier where every sensor node checks its own sensed data based on his last reading and current readings of their two nearest neighbors. Then, it detects if the captured data is correct or not. Finally, it recovers faulty data by adjusting this latter to avoid transmission of erroneous values. Thus, it ensures the reliability of data before decision-making process.

System assumptions

Our network consists of a large number of stationary and homogeneous sensor nodes organized in clusters. Each cluster has a Cluster Head (CH) that aggregates data transmitted by its cluster's members and forwards the collected data to the Base Station (BS). Detection and recovery of inaccurate sensed data is distributed since every sensor nodes in the cluster checks its current data using the Bayesian Classifier. We also assume that we do not have malicious attacks in the network.

Mechanism Design

In our system model, the sensed data can belong to the interval values I = [a, b]. We divide this range into many classes that are mutually exclusive and exhaustive. According to Section 3, the Bayesian Network of our mechanism is represented in Figure 2. The RCD's algorihtm is composed of three phases:

Learning phase This phase is executed after the deployment of the network. We assume that it is free from faults. The learning phase aims to calculate the parameters of the classifier which are: the probability of each class P (CD ∈ c i ) and conditional probabilities P (LD|CD), P (N 1|CD) and P (N 2|CD). Algorithm 1 explains the steps of the learning phase executed by sensor nodes.

Algorithm 1 Learning phase

Input : Predefined classes Output : The parameters of the classifier BEGIN Defines the two nearest neighbors. Gets current data of the two neighbors. Calculates the classes' probabilities and the conditionnal probabilities. END Every sensor node sense 'P' samples and gets the samples (readings) of its two neighbors over a period of time. Then, it calculates the classes' probabilities and the conditional probabilities. The complexity of the algorithm 1 is quadratic since the running time of the first instruction is proportional to the number of the cluster's members while the second one has constant running time. The last instruction has two running times; one is proportional to the number of classes for calculating the classes' probabilities, and the other is proportional to the square of the number of classes for calculating the conditionnal probabilities.

Inference phase In this phase, sensor node detects faulty data by calculating the posterior probability of c i using Bayes theorem. Then, it deduces the most probable class that its sensed value should belong to. The most probable class is calculated using the MAP (See Equation 5). Each sensor node calculates the posterior probability of c i according to Equation 6. We replace x j by three observations; the last sensed data (LD), the current data of the first neighbor (N1), and the current data of the first neighbor (N2). Hence, we obtain the Equation 7.

c M AP = argmax ci∈I P (LD|c i )P (N 1|c i )P (N 2|c i )P (c i ) [START_REF] Ben-Gal | Bayesian Networks. Encyclopedia of Statistics in Quality and Reliability[END_REF] After calculating the posterior probability of each predefined or learned class c i , the sensor node compares the class that has the highest probability with the class of the captured value. If they are identical, the sensor considers that the captured value is correct. Otherwise, it identifies it as a faulty one. The steps of the inference phase are detailed in Algorithm 2. The time complexity of the inference algorithm is linear since the first instruction has constant running time. The second one has a running time proportional to the number of classes, and the running time of if-else statements is constant in the two posibilities (i.e. if sequence and else sequence). Fault Detection Accuracy (FDA) is the ratio of the number of faulty sensor nodes detected to the total number of faulty sensor nodes. Ideally, this ratio should be equal to 1.

False alarm rate (FAR) is defined as the ratio of the number of non-faulty nodes diagnosed as faulty to the total number of non-faulty nodes. The FAR should be very small to achieve a reliable detection.

Results and Discussions

As we mentionned above, to evaluate the performance of our proposal, we injected 15 faulty nodes in the network. Figure 3 shows the fault detection accuracy in both of FDS and RCD. According to Figure 3, we notice that the curve of our protocol evolves very quickly to reach 1 during 1.5s. This is due to the distribution of the detection which is performed by nodes before data transmission. The data transmission is performed in rounds. Sensor nodes transmit data during its time slot of the round. Unlike the RCD protocol, the FDS reaches the value 1 after 100s because the detection of faulty sensed data is performed in two-levels: sensor node's level and CH's level. We conclude that both of RCD and FDS reach the ratio 1 which is the ideal case. However the fault detection accuracy using the RCD algorithm is faster than when using the FDS one. According to Figure 4, we notice that the maximum rate of false alarm in the FDS is 0,18 at 60s, then, it decreased to 0 at 200s. While in the RCD protocol, it reaches 1 at 80s. This proves that our proposal can achieve an accurate and reliable detection.

Conclusion

In IoT, the application reliability is strongly correlated with the accuracy of data captured by sensors. When a sensor captures wrong data, this may lead to wrong decision-making, which is highly undesirable in critical applications. To overcome such a problem, we proposed in this paper a new fault-tolerant mechanism called RCD (Reliability of Captured Data). The RCD enables sensors to detect faulty data using the Bayesian Network. This mechanism is also able to recover these innacurate and unreliable data before forwarding them to the BS. To show the efficiency of our work, we compared the RCD strategy to the FDS one using NS2. The results show a remarkable improvement in terms of false alarm rate and fault detection accuracy. As a future perspective of our current work, we aim to integrate safety into data communication of IoT applications.

  [START_REF] Murphy | Machine learning : a probabilistic perspective[END_REF]. Equation 1 represents the Bayes theorem.

Fig. 1 .

 1 Fig. 1. A Bayesian Network corresponding to a Naive Bayesian Classifier

Fig. 2 .

 2 Fig. 2. RCD Bayesian Network

Fig. 3 .

 3 Fig. 3. Fault Detection Accuracy in FDS and RCD with 15 faulty nodes

Figure 4

 4 Figure 4 represents the false alarm rate for both FDS and RCD.

Fig. 4 .

 4 Fig. 4. False Alarm Rate in FDS and RCD with 15 faulty nodes

Table 1 .

 1 Simulation parameters

	Parameter	Value
	Network size	100m*100m
	Number of sensor nodes 100
	Initial energy	2J
	Number of clusters	4
	Round duration	20s
	Slot duration	0,02s

Algorithm 2 Inference phase

Input : CD, LD, N1 and N2 Output : The most probable class BEGIN Exchange with the two neighbors. Calculate the posterior probability of each class using Equation 7. if (CD ∈ the most probable class) then Captured data is correct. else Captured data is incorrect.

Recovery phase. end if END

Recovery phase In this phase, sensor nodes adjust faulty data by approximate the data value to the average of the most probable class (the result of the classifer)(See Algorithm 3). The recovery phase aims to connect the regions isolated by faulty nodes. The algorithm of the recovery phase has constant complexity.

Algorithm 3 Recovery phase

Input : Posterior probability of all classes Output : Adjusted data BEGIN CD = the average of the most probable class (the class with the highest posterior probability) END We can conclude that the time complexity of our RCD's algorithm is quadratic.

Simulation Results

In order to evaluate our contribution, we have conducted several series of simulation using Network Simulator 2 (NS2). The simulation results are compared to FDS detailed in Section 2. To illustrate the performance of our proposal, we injected some faulty nodes in the network.

Simulation Parameters

The simulation parameters are summarized in Table 1.

Performance Metrics

For comparaison purposes, we have considered the following performance metrics.


Mahdi Bounouni
email: bounouni@gmail.comlouizamedjkoune@yahoo.fr

Louiza Bouallouche-Medjkoune

Hybrid Acknowledgment Punishment Scheme based on Dempster-Shafer Theory for MANET

Keywords: Mobile ad hoc network, security, cooperation, Dempster Shafer theory, uncertainty

In this paper, we cope with malicious nodes dropping packets to disrupt the well-functioning of mobiles ad hoc networks tasks. We propose a new hybrid acknowledgment punishment scheme based on Dempster Shafer theory, called HAPS. The proposed scheme incorporates three interactive modules. The monitor module monitors the behaviour of one-hop nodes in the data forwarding process. The reputation module assesses the direct and the indirect reputation of nodes using Dempster Shafer theory, which is a mathematical method, that can aggregate multiple recommendations shared by independent sources, while some of these recommendations might be unreliable. Since recommendations exchange between nodes consumes resources, a novel recommendation algorithm has been incorporated to deal with false dissemination attack and to minimize the recommendation traffic. The exclusion module punishes nodes regarded as malicious. The simulation results show that HAPS improves the throughput and reduces the malicious dropping ratio in comparison to existing acknowledgment scheme.

Introduction

Mobile ad hoc network (MANET) is a collection of wireless mobile nodes that are able to perform the network tasks without requiring a fixed infrastructure or centralized administration. The communication between nodes follows a multihop approach. This approach depends on the assumption that all mobile nodes cooperate. Nevertheless, this assumption cannot be ensured due to the MANET features including the distributed nature, resource constraint of nodes [START_REF] Peng | Survivability modeling and analysis on 3D mobile ad-hoc networks[END_REF]. These features make MANET vulnerable to selfish and malicious nodes. Selfish nodes may refuse to relay packets for other nodes to preserve their resources. On the other hand, malicious nodes may drop all packets passing through them in order to disrupt the functioning of the networks activities. Therefore, to improve the network performance, it is critical to cope against the selfish and malicious behavior.

In the literature, one can categorize two types of related works dealing with selfish and malicious nodes dropping packets: credit-based schemes [START_REF] Buttyan | Stimulating cooperation in self-organizing mobile ad hoc networks[END_REF][START_REF] Zong | Sprite: A simple, cheat-proof, credit-based system for mobile ad-hoc networks[END_REF][START_REF] Bansal | Observation-based cooperation enforcement in ad hoc networks[END_REF][START_REF] Zhu | Smart: A secure multilayer creditbased incentive scheme for delay-tolerant networks[END_REF][START_REF] Mahmoud | FESCIM: fair, efficient, and secure cooperation incentive mechanism for multihop cellular networks[END_REF] and reputation-based schemes [2, 5, 8-10, 12, 13, 18-21, 24]. The goal of incentivebased schemes consists of encouraging nodes to relay packets for the benefits of other nodes by using credit. Node earns credits by relaying packets for other nodes and loses credits to send their packets. In the reputation-based schemes, each node monitors its one-hop nodes and computes their reputation values according to their behaviour. Almost of the reputation-based schemes use the watchdog technique [START_REF] Marti | Mitigating routing misbehavior in mobile ad hoc networks[END_REF] for the monitoring. However, this technique presents several feebleness as reported in [START_REF] Marti | Mitigating routing misbehavior in mobile ad hoc networks[END_REF][START_REF] Yau | Reputation methods for routing security for mobile ad hoc networks[END_REF]. To deal with this feebleness, the acknowledgment technique is proposed in [START_REF] Balakrishnan | TWOACK: preventing selfishness in mobile ad hoc networks[END_REF]. This technique permits to expand the range of neighbours monitoring to the two-hop by introducing a new kind of packet called TWOACK packet.

One of the recent scheme employing the acknowledgment technique is EAACK scheme [START_REF] Shakshuki | EAACK-A Secure Intrusion-Detection System for MANETs[END_REF]. EAACK can detect and punish malicious links. EAACK can effectively resolve some feebleness of the watchdog technique. However, EAACK is still vulnerable to other threats. (1) When nodes move faster, their neighbourhoods change often and therefore, malicious nodes have a several chances to drop more packets. Because, each new neighbour for malicious node forms a potential malicious link. This threat is inherited from TWOACK scheme [START_REF] Balakrishnan | TWOACK: preventing selfishness in mobile ad hoc networks[END_REF], since TWOACK scheme can detect only malicious links and EAACK is based on TWOACK. (2) All requests initiated by malicious nodes are still relayed because the purpose is to relive malicious nodes from relaying data packets instead of punishing them.

To address the above threats, we propose a hybrid acknowledgment punishment scheme based on Dempster Shafer theory [START_REF] Shafer | Mathematical Theory of Evidence[END_REF][START_REF] Sudkamp | The consistency of Dempster-Shafer updating[END_REF][START_REF] Bloch | Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images considering partial volume effect[END_REF], called HAPS. HAPS scheme aims to enhance the performance of EAACK [START_REF] Shakshuki | EAACK-A Secure Intrusion-Detection System for MANETs[END_REF] by punishing malicious nodes more severely. HAPS is structured around three interactive modules: monitor, reputation, exclusion. The monitor module monitors the behavior of one-hop nodes in the data forwarding process. The reputation module computes the direct and indirect reputation values of neighbour nodes based on the information provided by the monitor module and the recommendations shared between nodes. We propose a new combination algorithm based on Demspter Shafer theory [START_REF] Shafer | Mathematical Theory of Evidence[END_REF][START_REF] Sudkamp | The consistency of Dempster-Shafer updating[END_REF] to compute the direct reputation value of the node. Thus, HAPS enables nodes to share their recommendations about other nodes, but only when it is necessary, and the combination of different recommendations is done based on Dempster Shafer Theory. The exclusion module punishes all nodes having reputation values smaller than the reputation threshold.

The remainder of this paper is organized as follows. In the section 2, We present some preliminaries on Dempster Shafer Theory. Section 3 is devoted to the adversarial model. In section 4, we present our proposed scheme (HAPS). In section 5, we examine the performance of HAPS via simulation and finally conclude the paper.

Preliminaries on Dempster-Shafer Theory

Dempster Shafer theory of evidence [START_REF] Shafer | Mathematical Theory of Evidence[END_REF][START_REF] Sudkamp | The consistency of Dempster-Shafer updating[END_REF] is a mathematical method, handling the uncertainty and the subjective judgment. This method is especially efficient in situation when there is a need to aggregate multiple evidences shared by independent sources while some shared evidences might be unreliable, imprecise or incomplete/ambiguous. Let ϕ = {A 1 , .., A n } be a finite set of mutually exclusive and exhaustive hypotheses denoted as the frame of discernment, where A i are the individual hypotheses [START_REF] Campos | An extended approach for Dempster-Shafer theory[END_REF]. 2 ϕ denotes the possible subsets (or power set) of ϕ. In this section, we outline some basic concepts of Dempster Shafer theory.

Definition 1 [START_REF] Sudkamp | The consistency of Dempster-Shafer updating[END_REF] : A basic probability assignment function (BPA) or a mass function m is a function that assigns to each subset of φ a quantity of belief which is a number between 0 and 1. m is defined from 2 ϕ → [0, 1] and satisfying the following two constraints:

m (∅) = 0 et A∈ϕ m(A) = 1 (1)
Definition 2 [START_REF] Sudkamp | The consistency of Dempster-Shafer updating[END_REF] : let m : 2 ϕ → [0, 1] be a mass function. The belief function bel : 2 ϕ → [0, 1] related to the mass function m over ϕ is defined as follows

bel (A) = B∈A m(B) (2)
bel(A) corresponds to the total of belief given to the hypotheses A.

Definition 3 [START_REF] Sudkamp | The consistency of Dempster-Shafer updating[END_REF] : Dempsters rule of combination permits to combine independent evidences issued from independent sources by applying the orthogonal sum . Given two mass functions m 1 and m 2 over the same frame of discernment ϕ. According to the Dempsters rule of combination, m 1 and m 2 can be combined into a new mass function m : 2 ϕ → [0, 1] as follows:

m (C) = m 1 (A) m 2 (B) = Ai∩Bj =C m 1 (A i )m 2 (B j ) 1 -K 12 (3)
Where

K 12 = Ai∩Bj = ∅ m 1 (A i ) m 2 (B j )
. m (c) represents the mass function of the combined evidence and K 12 reflects the amount of conflicts between m 1 and m 2 .

According to the Dempsters rule of combination, we can combine n evidences as follows:

m 1 m 2 . . . m n (C) = C1∩...∩Cn=C m 1 (C 1 )m 2 (C 2 ) . . . m n (C n ) 1 -K 1...n (4)
Where

K = C1∩...∩Cn=∅ m 1 (C 1 ) m 2 (C 2 ) . . . m n (C n ) < 1.

Adversarial Model

According to their purposes, nodes may behave maliciously in order to degrade the network performance. In our paper, we suppose that malicious nodes may launch: (1) Black hole attack by dropping all data packets passing through them.

(2) False dissemination attack by sharing fake recommendation to falsely improve or degrade the reputation value of the malicious or honest node, respectively.

4

The Proposed HAPS Scheme HAPS scheme is structured around three interactive modules: monitor, reputation, exclusion.

Monitor Module

This module monitors the behaviour of one-hop nodes in the data forwarding process. HAPS employ the monitoring technique proposed in the EAACK scheme [START_REF] Shakshuki | EAACK-A Secure Intrusion-Detection System for MANETs[END_REF]. EAACK scheme is the result of the combination of three modes: ACK, S-ACK and MRA. In this paper, we implement only ACK and S-ACK modes. In the ACK mode, the destination node should send back an ACK packet to the source node for every data packet received. The S-ACK mode is similar to the TWOACK scheme. In the S-ACK mode, a new kind of packet called S-ACK is used. Each node forwarding data packets should send an S-ACK packet to the two-hop node in the opposite direction of the forwarding path. To illustrate the functioning of this technique, let p = {N s , ..., N i , N j , N k , ..., N d } the selected forwarding path, < N i , N j , N k >∈ p a triplet of nodes taken as an example (see Fig. 1). List ID denotes the list of ID of data packets sent or forwarded waiting to be acknowledged. The source N s sends data packets to the destination N d through the path p. In the startup, the ACK mode is employed. In this mode, N s adds the ID of each data packet sent D to List ID . Each ID is maintained for θ second. Upon reception of D at N d , it should send back an ACK packet to N s . For each ACK packet relayed by all nodes N i ∈ p, the monitor module of N i registers a good action through the link (N j , N k ). If N s receives an ACK packet before θ expires, which means that there are no malicious actions along the path p, it removes the ID of D from List ID . Otherwise, N s switches to the S-ACK mode. N i adds the ID of each data packet forwarded D to List ID . Each ID is maintained for ϑ second. N j Will forward D to N k if it behaves cooperatively. Once the packet D reaches N k , it should send back an S-ACK packet N i if it does not behave maliciously. If N i receives S-ACK packet before ϑ expire, it deletes the ID of D from List ID and registers a good action against the link (N j , N k ). Otherwise, if N i does not receive S-ACK packet after ϑ expires, N i removes the ID of D from List ID and registers a bad action against the link (N j , N k ). The same process is repeated for each triplet of nodes along p. This process is repeated until N s receives a switch packet from N d , which means that p is a safer path. Therefore, N s switches to the ACK mode.

Reputation Module

This module assesses and manages the reputation values of one-hop nodes. The reputation is classified into three types: direct, indirect and final. This module maintains four parameters ranging from 0 and 1: the reputation value of each monitored link (N j , N k ) ∈ F L j i denoted by R i (j, k) where F L j i denotes the set of forwarding links in which N j is involved, The direct, indirect and final reputation values denoted byDR j i (t), IR j i (t) and F R j i (t), respectively, where denotes the time of the computation of the reputation value.

Direct Reputation

A reputation is considered type direct, if it is computed based only on the recommendation of the monitor module. The reputation module of N i evaluates the trustworthiness of N j in all forwarding links (N j , N k ) ∈ F L j i in which is involved (see Fig. 1). If the monitor module detects a good action, R i (j, k) is increased. Otherwise, R i (j, k) is decreased. To compute the direct reputation value DR j i (t) of N j at time slot t, we propose a combination algorithm based on Demspter Shafer theory [START_REF] Shafer | Mathematical Theory of Evidence[END_REF][START_REF] Sudkamp | The consistency of Dempster-Shafer updating[END_REF][START_REF] Bloch | Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images considering partial volume effect[END_REF]. This algorithm combines and aggregates the reputation values of all links R i (j, k) , (N j , N k ) ∈ F L j i to come up to a single reputation value of N j . The proposed algorithm functions as follows. In the startup, R i (j, k) of each link (N j , N k ) ∈ F L j i is initialized to neutral v and it is updated according to the action detected by the monitor module. We consider two exclusive and exhaustive hypothesis that construct the frame of discernment ϕ = {C, C} where C means that the node N j is cooperative and C means that the node N j is uncooperative. The power set 2 ϕ consists of four elements:∅,C = cooperative, C = uncooperative and hypothesis U = ϕ ( N j is either cooperative or uncooperative which represents the uncertainty). In this scenario, the reputation module of N i perceives the reputation of each link (N j , N k ) ∈ F L j i as recommendation provided by N k . The reputation module determines the state of the node N j in the link (j, k) according to R i (j, k). If reputation module states that N j is cooperative through the link(j, k) , which means that R i (j, k) ≥ neutral v , The BPA of N k is:

m k (C) = R i (j, k) m k C = 0 m k (U ) = 1 -R i (j, k) (5)
If reputation module states that N j is uncooperative through the link(j, l), which means that R i (j, k) < neutral v , the BPA of N l is

m l (C) = R i (j, l) m l C = neutral v -R i (j, l) m l (U ) = 1 -neutral v (6)
The direct reputation value DR j i (t) is computed by combining all recommendations collected from all links (N j , N k ) ∈ F L j i by applying the Dempster rule of combination. neutral v -R i (j, l) value reflects the degree of maliciousness of the link (j, l).The rational of this algorithm is that: a malicious node should compromise multiple forwarding links (multiple forwarding paths) to achieve its purpose that consists on disrupting the data forwarding process. Therefore, it is involved in multiple bad actions that cause the degradation of its direct reputation value. On the other hand, an honest node is involved in more good actions than bad actions; therefore, it can improve its direct reputation value.

Indirect Reputation

A reputation is considered indirect, if it is computed based only on the recommendations shared between neighbours. In HAPS, this reputation is calculated and used only when there is need. The exchange method is done only when it is necessary, especially when a particular neighbour needs to send its packets. The goal is to improve the accuracy of the computation of nodes reputation and to minimize the recommendations traffics. When a node termed requestor needs to relay its packets through neighbours, all neighbours exchange their computed direct reputation values about this requestor. After that, they compute its indirect reputation value by aggregating all received recommendations using Dempster Shafer theory. Note that recommendations from nodes regarded as malicious are ignored.

When N i receives a RREQ packet, it checks whether the requestor N j is a neighbour (N j ∈ N G i ). If the requestor N j is not neighbour, N i simply forwards the RREQ packet. Else, N i shares its recommendation about N j in the neighbourhood and set the timer T r . To prevent malicious nodes from colluding with other nodes or from manipulating the reputation values of some nodes, node N i accepts only recommendation received before T r expire. In order to compute the indirect reputation value of N j , N i aggregates all received recommendations using Dempster Shafer Theory. The recommendation of N i about N j is one among the set {cooperative-uncooperative}. Therefore, the frame of discernment is ϕ = {cooperative, uncooperative}. For instance, the reputation value of N k at N i is DR k i (t). If N k states that N j is cooperative, the BPA of N k is [START_REF] Chen | Dempster-Shafer theory for intrusion detection in ad hoc networks[END_REF]:

m k (C) = DR k i (t) m k C = 0 m k (U ) = 1 -DR k i (t) (7)
If N k claims that N j is uncooperative, the BPA of N k is:

m k (C) = 0 m k C = DR k i (t) m k (U ) = 1 -DR k i (t) (8)
The indirect reputation value of N j is obtained after combining all recommendations using the Dempster's rule of combination. According to Dempster Shafer Theory features, the relevance of a recommendation depends on the reputation value of the recommender, which permit to get a reliable reputation value. This feature make our approach resilient to false recommendation dissemination. Thus, our approach can cope with collusion attack that occurs when a group of malicious nodes provides fake recommendations about an honest node. Because, the time T r ec between the diffusion and the combination of recommendations is very low. Then, these nodes have not sufficient times to collude.

Illustration example:

Let assume three nodes N k , N l and N f with reputation values 0.4, 0.45 and 0.9 at N i , respectively. They share their recommendations about N j . N k claims that N j is cooperative, N l and N f claims that N j is uncooperative. Hence, the mass function are:

m k (C) = 0.9, m k C = 0, m k (U ) = 0.1 m l (C) = 0 , m l C = 0.3, m l (U ) = 0.7 m f (C) = 0, m f C = 0.3, m f (U ) = 0.7
The reputation module of N i combines m k and m l as follows:

K kl = m k (C) m l C + m k C m l (C) = 0.27 m kl (C) = m k m l (C) = m k (C) m l (C) + m k (C) m l (U ) + m k (U )m l (C) 1 -K kl = 0.863 m kl C = m k m l (C) = m k C m l C + m k C m l (U ) + m k (U )m l (C) 1 -K kl = 0.041 m kl (U ) == m k m l (U ) = m k (U ) m l (U ) 1 -K klf = 0.0958
The obtained m kl is combined with m f as:

K klf = m kl (C) m f C + m kl C m f (C) = 0.258 m klf (C) = m kl m f (C) = m kl (C) m f (C) + m kl (C) m f (U ) + m kl (U )m f (C) 1 -K klf = 0.815 m klf C = m kl m f (C) = m kl C m f C + m kl C m f (U ) + m kl (U )m f (C) 1 -K kl = 0.094 m klf (U ) == m kl m f (U ) = m kl (U ) m f (U ) 1 -K klf = 0.0905
From this result, the indirect reputation value IR j i (t) of node N j is 0.81.

Final reputation

After obtaining DR j i (t) and IR j i (t), the final reputation value F R j i (t) is computed by combining DR j i (t) and IR j i (t) with the following equation:

F R j i (t) = δ * DR j i (t) + (1 -δ) * IR j i (t). (9)
Where δ( 0 < δ < 1) determines the relevance of direct reputation compared to the indirect reputation.

Exclusion Module

The exclusion module is responsible for punishing malicious nodes. It considers a node having final reputation value smaller than the threshold as malicious. This module puts the detected node in its black list Black l ist, and it sends a misbehaving report to the source node of data packets to proceed to its punishment. All nodes forwarding, receiving or overhearing the misbehaving report put the detected node in their Black l ist and proceed to its punishment. The punishment consists on: (1) invalidating all forwarding paths involving this node and evicting to route their data packets through this node. (2) Refusing to forward packets initiated from this node by discarding all its RREQ packets generated.

Performance Evaluation

In this section, we conduct a series of simulation experiments to examine the performance efficiency of the HAPS scheme using the network simulator NS-2.34. We evaluate the effectiveness of the HAPS scheme on the exclusion of malicious nodes dropping data packets in comparison to EAACK [START_REF] Shakshuki | EAACK-A Secure Intrusion-Detection System for MANETs[END_REF]. We simulate 40 mobile nodes deployed within an area of 700 * 700 m. The number of malicious nodes varies from 2 to 12. The rest of simulation parameters are shown in Table 1. The following two metrics are used to examine the efficiency of HAPS: -Average throughput (Kbps) represents the total size of data packets that successfully reached their destination over the simulation times. -Malicious dropping ratio refers to the ratio between the total numbers of data packets dropped by malicious nodes to the total numbers of data packets sent.

All plotted results are obtained after averaging the result of 20 simulation runs.

Average Throughput (Kbps)

Fig. 2 shows the average throughput of HAPS and EAACK under varying the number of malicious nodes. We can observe that as the number of malicious nodes increases, the average throughput of two schemes decreases. However, the obtained results indicate that HAPS improves the average throughput much more than EAACK. This improvement is due to the fact that HAPS can identify and isolate malicious nodes instead of malicious links. Therefore, using HAPS scheme, the established paths between each pair of nodes is more reliable.

Number of malicious nodes

Malicious Dropping Ratio

Fig. 3 depicts the malicious dropping ratio of HAPS and EAACK as a function of the number malicious nodes. The results show that the malicious dropping ratio increases as the number of malicious nodes increases. But, the malicious dropping ratio with HAPS increases more gently than with EAACK. This is because AASC penalizes malicious nodes more effectively and severely compared EAACK that is able to isolate only malicious links. Fig. 4 plots the malicious dropping ratio across varying the node speed. In this scenario, the network contains 4 malicious nodes. From this figure, we can observe that HAPS has a lower malicious dropping ratio in all cases compared to EAACK. This gap is more apparent when the nodes move faster. Because, when the nodes move faster, their neighbourhoods change frequently. As EAACK isolates only malicious links, each new neighbour for malicious nodes forms a new opportunity (malicious link) to drop more packets. Therefore, HAPS is more resilient to topology changes.

Conclusion and Future Work

In this paper, we have proposed HAPS, which is a novel acknowledgment Punishment Scheme aiming to detect and punish malicious nodes dropping data packets. In HAPS scheme, the reputation values of all links in which node is involved, are perceived as recommendations. Using Dempster Shafer Theory, these recommendations are combined to compute the reputation value of the node. HAPS incorporates a novel manner to exchange recommendations between nodes following the nature of on-demand routing protocol. The recommendations exchange is performed only when it is necessary and the aggregation is done based on Dempster Shafer Theory. HAPS punishes malicious nodes, whose the reputation values are smaller than the threshold by refusing to forward their packets, and isolating them from all network activities. The simulation results demonstrate that HAPS improves the throughput and reduces the malicious dropping ratio. As future work, We plan to evaluate mathematically the complexity of HAPS approach, and by simulation the effect of other network parameters in the effectiveness of HAPS approach (such Node density). We also plan to extend the HAPS scheme with an incentive mechanism aiming to cope against the selective packet dropping attack, while to motivate the cooperation of selfish nodes.

Fig. 1 .

 1 Fig. 1. Monitoring scenario

Fig. 2 .Fig. 3 .Fig. 4 .

 234 Fig. 2. Average throughput vs Number of malicious nodes

Table 1 .

 1 Malicious scenario parameters

	Parameter	Value
	Number of node	40
	Routing protocol	DSR
	Simulation area	700 m × 700 m
	Transmission range	250 m
	Node speed	2m/s, 4m/s, 6m/s,8m/s and 10m/s
	Pause time	0 s
	Number of malicious nodes 2, 4, 6, 8, 10, 12
	Number of CBR	20 connections
	Simulation time	600 s


Reda Alhajj
email: alhajj@ucalgary.ca

Advanced Technology and Social Media Influence on Research, Industry and Community

Keywords: social media, social networks, data analysis, big data, frequent pattern mining, clustering, bioinformatics

The rapid development in technology and social media has gradually shifted the focus in research, industry and community from traditional into dynamic environments where creativity and innovation dominate various aspects of the daily life. This facilitated the automated collection and storage of huge amount of data which is necessary for effective decision making. Indeed, the value of data is increasingly realized and there is a tremendous need for effective techniques to maintain and handle the collected data starting from storage to processing and analysis leading to knowledge discovery. This chapter will cite our accomplished works which focus on techniques and structures which could maximize the benefit from data beyond what is traditionally supported. In the listed published work, we emphasized data intensive domains which require developing and utilizing advance computational techniques for informative discoveries. We described some of our accomplishments, ongoing research and future research plans. The notion of big data has been addressed to show how it is possible to process incrementally available big data using limited computing resources. The benefit of various data mining and network modeling mechanisms for data analysis and prediction has been addressed with emphasize on some practical applications ranging from forums and reviews to social media as effective means for communication, sharing and discussion leading to collaborative decision making and shaping of future plans.

Introduction

Data is a major resource for decision making. Its value and importance has never been ignored since the existence of mankind on earth. It has been collected, stored and maintained using a wide variety of affordable means ranging from primitive to advanced. Indeed, collecting, storing and maintaining data was a cumbersome task in the past, mainly prior to the development of various technologies that gradually helped humans in handling data. However, the recent development in technology rapidly influenced data collection, storage and maintenance. For instance, sensors are becoming popular in all aspects of the daily life; they have been installed in almost every indoor and outdoor equipment. They are widely available and equipped with wireless communication skills which allow them to feed huge amounts of data that should be captured, stored, cleaned, and processed for knowledge discovery as main ingredient of effective decision making.

In the past, humans used computing devices in a limited way. Database management systems were developed to facilitate flexibility in storing and retrieving data. Making sense of data was left to domain experts who are expected to retrieve and study data related to a specific problem in a way to draw some conclusions which may guide the decision-making process. Automating the knowledge discovery process was better realized towards the end of the 20 th century when various machine learning and data mining techniques were developed and put in practice to serve a variety of application domains including business, health, security, etc.

To cope with the new era, researchers, developers and practitioners realized the need to develop new techniques and technologies capable handling growing volumes of data captured incrementally from heterogeneous sources. In other words, growth in volume and types of data expected to be processed suddenly witnessed a boom. Social networks and social media platforms are gaining increased popularity and are generating tremendous amounts of data. Surveillance devices are available almost everywhere. Even traditional archives are digitized. Consequently, storage media and techniques which were previously accepted as sufficient are no more capable of handling new needs. For instance, hard drives of personal computers were only couple megabytes in capacity when they were initially manufactured with less than one megabyte of main memory. People were happily competing to get the honor of owning and using such devices. It may be impossible to image using same computing platform in the current era where gegabytes of storage are no more sufficient. In fact, computing resources have improved rapidly to partially meet human needs but will never be satisfactory. Therefore, researchers and developers are always seeking new technologies and techniques, and hence conducting and advancing research will continue to attract more attention and investment.

Explicitly speaking, data volume, characteristics and associated expectations may be described as a moving target. This necessitates the availability of enough room for storage and sophisticated techniques for processing. People will continue to collect more data as time passes, but they will never afford to increase their computing power to handle their data effectively. Thus, the need for algorithms and techniques that can depend only on limited computing resources to deal with various aspects of data from dynamicity to volume, among others. Along this direction, we contributed various techniques and algorithms that could successfully satisfy a variety of applications which require handling large volumes of dynamic and stream data. These techniques are described in our published papers listed in the references at the end of this paper. Scalability is the main aspect considered by our techniques, including frequent pattern mining, clustering, network analysis, finding repeating patterns in long sequences, etc.

Our completed and ongoing research addresses various aspects of data from definition to construction to manipulation and analysis leading to knowledge discovery for decision making. Our initial contributions focused on traditional aspects related to handling and manipulation of data which were popular during the last two decades of the 20th century. We then gradually moved onto more advanced techniques which we realized as necessities since 1990s. These techniques, include, network analysis, data mining and machine learning techniques which have tremendously and visibly served various applications. We also realized scalability as a serious need especially in the current era of big data. We developed advanced techniques and adapt them to various domains, including: § Bioinformatics and Health informatics § Data partitioning and allocation § Homeland security and terror/criminal network analysis § Financial data analysis: from stock market to FOREX to fraud detection § Web/network data analysis: from structure to content to usage § Social media analysis and opinion mining including spam detection § Recommendation and customer behavior analysis § Network representation is a powerful mechanism for modeling many-tomany relationships.

A network consists of a set of nodes corresponding to the entities in the application domain and a set of links representing certain types of relationships between the entities. On the other hand, data mining includes a set of powerful techniques for studying the relationships/connections between various objects. Further, data mining may be used in the network construction phase. To construct a network data may be first analyzed using techniques like frequent pattern mining or clustering. Once a network is constructed, it can be analyzed for knowledge discovery.

Most of the traditional approaches for frequent pattern mining assume unlimited main memory which is not realistic. Therefore, scalability is a major concern when it comes to practical applications where data streams dynamically and available in large volume. To tackle these problems, we developed a novel approach which satisfies the following: § The ability to mine in a bounded amount of memory space that may vary based on task priority. Thus, it is possible to mine using common PC. § Improve external data access and make the mining process more I/O conscious. § Introduce a specialized mining task aware memory manager for both RAM and the external memory

We build a tree structure namely, Frequent-Pattern (FP) tree, which summarizes the given data and allows for effective discovery of frequent patterns. Each branch represents at least one transaction. We build the tree from left to right and from top to bottom as shown in Figure 1. This way, we can store on the disk left side of the tree as it grows to the right. Therefore, our upper bound is the size of free disk space rather than the available memory. To facilitate effective data investigation and analysis, we build our own tool, namely NetDriller which is capable of analyzing raw data to derive a network. Then various network analysis techniques could be applied on the network to identify actors which may reveal some important aspects related to the analyzed network, like most knowledgeable employee, most dangerous criminal, least performing student, best team to undertake next project, etc. The basics of NetDriller are summarized in Figure 2. We utilized NetDriller to analyze September 11 terror network. It is surprising to realize that those who planned for the attacks considered all difficulties they could face. In other words, the network continues to be connected after removing terrorists who were identified as leaders down to level 6. The same is not true when the network of Madrid attacks was analyzed. The latter network became disperse only after removing second level leaders as shown in Figure 4. NetDriller was also employed using gene expression data related to prostate cancer to identify proteins attributed to the disease. The main result reported by Net-Driller is shown in Figure 5.

Finally, next are some of our ongoing and planned research activities based on the promising results reported in our already published papers. First, in bioinformatics we are tracking disease evolution: spatial and temporal aspects, drug repositioning, etc. Second, in health Informatics we are working on patient monitoring, referral optimization and prediction, etc. Third, we demonstrated the applicability and effectiveness of sequence analysis and prediction for various domains, including financial (e.g., stock, forex), weather, traffic, energy, etc. Finally, other domains and applications considered by our research recommendation, sentiment analysis, opinion mining, spam detection, homeland security, close monitoring and analysis for early warning, etc.

To sum up, our research efforts described in our papers published in the literature and listed in the bibliography illustrate how data mining and network analysis are powerful techniques for data analysis. Further, it is possible to analyze huge amounts of data using limited computing resources, and to develop integrated solutions by combining various aspects leading to robust framework. We have succeeded in developing some techniques from scratch and we also expanded some existing techniques to produce working solutions for our industrial and academic partners. We could help in sophisticated data analysis to maximize knowledge discovery for informative decision making.

Fig. 1

 1 Fig. 1 Construction of FP-tree top-down and left-to-right

Fig 2 .

 2 Fig 2. Basic Characteristics of NetDriller.

Fig 3 .

 3 Fig 3. Sep. 11 network changes after excluding each level nodes for eigenvector centrality measure.

Fig 4 .

 4 Fig 4. Madrid network changes after excluding each level nodes for eigenvector centrality measure

Fig 5 .

 5 Fig 5. A list of genes and part of the gene-gene network related to prostate cancer.