The Application of Fuzzy Logic Controller to Compute a Trust Level for Mobile Agents in a Smart Home

Agents that travel through many hosts may cause a threat on the security of the visited hosts. Assets, system resources, and the reputation of the host are few possible targets for such an attack. The possibility for multi-hop agents to be malicious is higher compared to the one-hop or two-hop boomerang agents. The travel history is one of the factors that may allow a server to evaluate the trustworthiness of an agent. This paper proposes a technique to define levels of trust for multi-hop agents that are roaming in a smart home environment. These levels of trust are used later to determine actions taken by a host at the arrival of an agent. This technique uses fuzzy logic as a method to calculate levels of trust and to define protective actions in regard to those levels

Fuzzy-based forest fire prevention and detection by wireless sensor networks

Forest fires may cause considerable damages both in ecosystems and lives. This proposal describes the application of Internet of Things and wireless sensor networks jointly with multi-hop routing through a real time and dynamic monitoring system for forest fire prevention. It is based on gathering and analyzing information related to meteorological conditions, concentrations of polluting gases and oxygen level around particular interesting forest areas. Unusual measurements of these environmental variables may help to prevent wildfire incidents and make their detection more efficient. A forest fire risk controller based on fuzzy logic has been implemented in order to activate environmental risk alerts through a Web service and a mobile application. For this purpose, security mechanisms have been proposed for ensuring integrity and confidentiality in the transmission of measured environmental information. Lamport's signature and a block cipher algorithm are used to achieve this objective

Fuzzy TOPSIS-based Secure Neighbor Discovery Mechanism for Improving Reliable Data Dissemination in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) being an indispensable entity of the Internet of Things (IoT) are found to be more and more widely utilized for the rapid advent of IoT environment. The reliability of data dissemination in the IoT environment completely depends on the secure neighbor discovery mechanism that are utilized for effective and efficient communication among the sensor nodes. Secure neighbor discovery mechanisms that significantly determine trustworthy sensor nodes are essential for maintaining potential connectivity and sustaining reliable data delivery in the energy-constrained self organizing WSN. In this paper, Fuzzy Technique of Order Preference Similarity to the Ideal Solution (TOPSIS)-based Secure Neighbor Discovery Mechanism (FTOPSIS-SNDM) is proposed for estimating the trust of each sensor node in the established routing path for the objective of enhancing reliable data delivery in WSNs. This proposed FTOPSIS-SNDM is proposed as an attempt to integrate the merits of Fuzzy Set Theory (FST) and TOPSIS-based Multi-criteria Decision Making (MCDM) approach, since the discovery of secure neighbors involves the exchange of imprecise data and uncertain behavior of sensor nodes. This secure neighbor is also influenced by the factors of packet forwarding potential, delay, distance from the Base Station (BS) and residual energy, which in turn depends on multiple constraints that could be possibly included into the process of secure neighbor discovery. The simulation investigations of the proposed FTOPSIS-SNDM confirmed its predominance over the benchmarked approaches in terms of throughput, energy consumption, network latency, communication overhead for varying number of genuine and malicious neighboring sensor nodes in network

Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

The evaluation of E-business related technologies in the Railway Industry

For the purposes of this paper, e-business is defined as: "the performance, automisation and organisation of transactions, or chains of them, and the gathering and publishing of data, electronically over a communication protocol" Little research has been conducted either into how e-business technology can be successfully evaluated, or into the associated costs and benefits specifically related to the transportation and railway industries. Based upon a review of the current literature and a series of interviews held with railway operators, track managers and transportation customers from the Australian Fortune 100, the paper puts forward a framework for the evaluation of e-business investments within the railway industry. The research reported here is aimed at developing a flexible interface that enables the decision maker to assess and evaluate a wide variety of complex interacting variables. The proposed approach uses a variety of evaluation methods, as opposed to searching for a single "best" approach. Additionally, an attempt is being made to include the complex interaction between the implementation of the new technology and the changing organisational setting. A model is proposed using fuzzy logic to handle incomplete and uncertain knowledge; as well as to combine criteria within a conceptual model from which "real-worth" evaluations can be performed. This model provides a systematic approach to guide the decision maker in the deployment of e-business and emerging technologies in the industry. After discussing the main findings from a literature review on the use of evaluation frameworks in IT related projects, the paper deals with the proposed framework in detail. The use of empirical data, which was obtained transportation customers to help define the main framework factors, is also discussed. Finally, the paper summarises the main implications for rail freight of customers’s perceptions and stated needs in the e-business domain

Critical Controlling for the Network Security and Privacy Based on Blockchain Technology: A Fuzzy DEMATEL Approach

The Internet of Things (IoT) has been considered in various fields in the last decade. With the increasing number of IoT devices in the community, secure, accessible, and reliable infrastructure for processing and storing computed data has become necessary. Since traditional security protocols are unsuitable for IoT devices, IoT implementation is fraught with privacy and security challenges. Thus, blockchain technology has become an effective solution to the problems of IoT security. Blockchain is an empirical data distribution and storage model involving point-to-point transmission, consensus mechanism, asymmetric encryption, smart contract, and other computer technologies. Security and privacy are becoming increasingly important in using the IoT. Therefore, this study provides a comprehensive framework for classifying security criteria based on blockchain technology. Another goal of the present study is to identify causal relationship factors for the security issue using the Fuzzy Decision-Making Trial-and-Evaluation Laboratory (FDEMATEL) approach. In order to deal with uncertainty in human judgment, fuzzy logic is considered an effective tool. The present study’s results show the proposed approach’s efficiency. Authentication (CR6), intrusion detection (CR4), and availability (CR5) were also introduced as the most effective and essential criteria, respectively