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Wireless Sensor Network (WSN) technology has gained importance in recent years due to its various benefits, practicability and extensive utilization in diverse applications. The innovation helps to make real-time automation, monitoring, detecting and tracking much easier and more effective than previous technologies. However, as well as their benefits and enormous potential, WSNs are vulnerable to cyber-attacks. This paper is a systematic literature review of the security-related threats and vulnerabilities in WSNs. We review the safety of and threats to each WSN communication layer and then highlight the importance of trust and reputation, and the features related to these, to address the safety vulnerabilities. Finally, we highlight the open research areas which need to be addressed in WSNs to increase their flexibility against security threats. [ABSTRACT FROM AUTHOR]

The accuracy and reliability of XAI methods are important to establish their credibility and use in complex decision-making tasks. Existing XAI methods provide little information about the correctness and reliability of their outputs. Furthermore, post-hoc explanation approaches explain the outcomes after producing them, not in a step-by-step glass-box manner to explain how an output is reached. Our proposed approach addresses these drawbacks by designing a Belief-Rule-Based (BRB) framework that interprets in a glass-box manner why a particular decision has been reached. It does that by determining the chance of different output classes occurring for a specific time period by considering the different possible permutations of the inputs along with their influence. This also assists the user to determine if the given input dataset is incomplete, vague, imprecise or inconsistent before trusting the analysis emanating from it. We compare the performance of the proposed BRB approach against the different eXplainable artificial intelligence (XAI) methods, such as SHAP, LIME and LINDA-BN to ensure the users of the trustworthiness of its analysis. This also enables users to determine the extent to which each of the XAI techniques meets the requirements of XAI and the gaps that need to be addressed. [ABSTRACT FROM AUTHOR]

Blockchain technology has the potential to be applied widely in supply chain operations. One such area is proactive supply chain risk management (SCRM). In this area, existing researchers have highlighted the fraudulent behaviour of supply chain partners who do not disclose information on the risks that impact their operations. Blockchain can address this problem by encoding each partner's commitment to SCRM and achieve consensus. However, before this can be achieved, a key challenge to address is the inability of existing consensus mechanisms such as Proof of Work (PoW), Proof of Authority (PoA) and Proof of Stake (PoS) to deal with information that does not have a digital footprint. In this paper, we address this gap by proposing the Proof by Earnestness (PoE) consensus mechanism which accounts for the authenticity, legitimacy and trustworthiness of information that does not have a digital footprint. We also propose the Subjective Information Authenticity Earnestness Framework (SIAEF) as the overarching framework that assists PoE in achieving its aim. We test the applicability of SIAEF and PoE in a real-world blockchain environment by deploying it as a decentralized application (Dapp) and applying it in BscScan Testnet which is an official test blockchain network. [ABSTRACT FROM AUTHOR]

In today's highly dynamic economy and society, performance assessment of decision making units (DMUs) is a problem of great significance. In order to make efficient decisions and beneficial improvements, decision makers and top management need to have a comprehensive view over capabilities and performance of DMUs which could be defined as organizational units. This study presents an efficient model for analyzing the outputs of performance measurement methodologies, by means of trust that provides explicit qualitative scales rather than pure numerical data. To the best of our knowledge, this is the first attempt for implementing the concept of trust in terms of performance measurement. In order to develop the structure of our proposed framework, fifteen scenarios are established based on number of DMUs and timeslots. These scenarios form the basis of the proposed structure. The efficiency rate of current, previous and upcoming years, as well as the average efficiency and standard deviation, are five inputs for this model. The approach incorporates time series forecasting to predict the future efficiency rate. Furthermore, auto correlation function (ACF) is used as the input selection in time series context. The model utilizes t-norms and t-conorms as the final modeling tools. To show the applicability and superiority of the proposed model, it is applied to a data set which is provided by running a simulation structured by a unique logic. The results provided by this model are more user-centric and have been represented in a potentially more transparent and intelligible way for decision makers. [ABSTRACT FROM AUTHOR]

Abstract: Cognitive Radio Networks (CRNs) deal with opportunistic spectrum access in order to fully utilize the scarce of spectrum resources, with the development of cognitive radio technologies to greater utilization of the spectrum. Nowa-days Cognitive Radio (CR) is a promising concept for improving the utilization of limited radio spectrum resources for future wireless communications and mobile computing. In this paper, we propose two approaches. At first we propose a trust aware model to authenticate the secondary users (SUs) in CRNs which provides a reliable technique to establish trust for CRNs. Secondly, we propose trust throughput mechanism to measure throughput in CRNs. [Copyright &y& Elsevier]

Cognitive Radio Networks (CRNs) is a promising technology which deals with shared spectrum access and usage in order to improve the utilization of limited radio spectrum resources for future wireless communications and mobile computing. Security becomes a very challenging issue in CRNs as different types of attacks are very common to cognitive radio technology compared to general wireless networks. The proper working of cognitive radio and the functionality of CRNs relies on the compliant behaviour of the secondary user. In order to address this issue, we propose two approaches in this paper. Firstly, we propose a trust aware model to authenticate the secondary users of CRNs which offers a reliable technique to provide a security-conscious decision by using trust evaluation for CRNs. Secondly, we propose an analytical model for analyzing the availability of spectrum in CRNs using a stochastic approach. We have modeled and analyzed the availability of free spectrum for the usage of secondary users by adopting different activities in a spectrum management scheme to improve the spectrum availability in CRNs. [ABSTRACT FROM AUTHOR]

Abstract: With the rapid development of wireless communication, the growth of Cognitive Radio (CR) is increasing day by day. Because CR is flexible and operates on the wireless network, there are more security threats to CR technology than to the traditional radio environment. In addition, there is no comprehensive framework for achieving security in Cognitive Radio Networks (CRNs), and the role of trust for achieving security in CRNs has not been explored previously. Security vulnerability in cognitive radio technology is unavoidable due to the intrinsic nature of the technology, so it is critical to ensure system security in CRNs. The issue of secure communication in CRNs thus becomes more important than it is in conventional wireless networks. In this paper, we propose a conjoint trust assessment approach (combining trust assessment from the Primary User Network and the Secondary User Network) in a CRN to solve the security threats brought about by untrustworthy entities, such as selfish, malicious, and faultless nodes, and to ensure secure spectrum sharing in CRNs. A numerical analysis shows the feasibility of our proposed approach. [Copyright &y& Elsevier]

Abstract: The credit card industry has been growing rapidly and thus huge numbers of consumers' credit data are collected by the credit department of the banks. The credit scoring managers often evaluate the consumer's credit with intuitive experience. However, with the support of the credit classification models, the managers can accurately evaluate the applicants' credit score. In this study, a neurocomputing-based granular approach is proposed to model credit scoring. Granular computing is used to compute the size of training and testing groups. Artificial neural networks (ANN) and data envelopment analysis (DEA) are used to model credit lending decisions in the online and offline manner, respectively. Proposed method is composed of three distinct stages based on trust and credibility concept. Trust is introduced and modeled via ANN in online module. Also credibility is modeled via DEA in offline module in present study. This paper is a pioneer in examining the concept of granularity for selecting the optimum size of testing and training group in machine learning area. In addition, proposing flexible trust ranges comparing to the current constant ones will support the importance of customers with higher credit scores to financial markets. To show the applicability and superiority of the proposed algorithm, it is applied to a credit-card data set obtained from the UCI repository. [Copyright &y& Elsevier]