Context-Aware Gossip-Based Protocol for Internet of Things Applications

This paper proposes a gossip-based protocol that utilises a multi-factor weighting function (MFWF) that takes several parameters into account: residual energy, Chebyshev distances to neighbouring nodes and the sink node, node density, and message priority. The effects of these parameters were examined to guide the customization of the weight function to effectively disseminate data to three types of IoT applications: critical, bandwidth-intensive, and energy-efficient applications. The performances of the three resulting MFWFs were assessed in comparison with the performances of the traditional gossiping protocol and the Fair Efficient Location-based Gossiping (FELGossiping) protocol in terms of end-to-end delay, network lifetime, rebroadcast nodes, and saved rebroadcasts. The experimental results demonstrated the proposed protocol’s ability to achieve a much shorter delay for critical IoT applications. For bandwidth-intensive IoT application, the proposed protocol was able to achieve a smaller percentage of rebroadcast nodes and an increased percentage of saved rebroadcasts, i.e., better bandwidth utilisation. The adapted MFWF for energy-efficient IoT application was able to improve the network lifetime compared to that of gossiping and FELGossiping. These results demonstrate the high level of flexibility of the proposed protocol with respect to network context and message priority. Keywords: Internet of Things (IoT); wireless sensor network (WSN); gossiping protocol; context-aware; content-aware; routing protocolKing Saud University (RG-1438-002

An Adjusted Free-Market-Inspired Approach to Mitigate Free-Riding Behavior in Peer-to-Peer Fog Computing

Peer-to-peer (P2P) architecture is increasingly gaining attention as a potential solution for the scalability problem facing the Internet of Things (IoT). It can be adopted for the fog computing layer to sustain the massive flow of data from constrained IoT nodes to the cloud. The success of a P2P-based system is entirely dependent on the continuity of resource sharing among individual nodes. Free riding is a severe problem that contradicts this main principle of P2P systems. It is understood that peers tend to consume resources from other peers without offering any in return. This free riding behavior can decrease system scalability and content availability, resulting in a decline in performance. Significant efforts have been made to hinder this behavior and to encourage cooperation amongst peers. To this end, we propose AFMIA, an Adjusted Free-Market-Inspired Approach that considers resources as goods that have dynamic prices based on the amount of supply and demand. Peers have wealth that can be increased by providing resources and spent by consuming them. The experimental results indicate that the proposed algorithm can successfully improve fairness without compromising on success rates.King Abdullah University of Science and Technology. Researchers Supporting Unit (Project RSP-2020/204

HonestPeer: An enhanced EigenTrust algorithm for reputation management in P2P systems

The visible success of the Peer to Peer (P2P) paradigm is associated with many challenges in finding trustworthy peers as reliable communication partners. Reputation management systems are emerging in the face of these challenges. The EigenTrust reputation management system is among the most known and successful reputation systems. On the other hand, a main drawback of this system is its reliance on a set of pre-trusted peers which causes nodes to center around them. As a consequence, other peers are ranked low despite being honest, marginalizing their effect in the system. To tackle this problem, this paper proposed enhancing the EigenTrust algorithm by giving peers with high reputation values (honest peers) a role in calculating the global reputation of other peers. Rather than solely depending on the static group of pre-trusted peers, the proposed algorithm, HonestPeer, selects the most reputable nodes, honest peers, dynamically based on the quality of the provided files. This makes HonestPeer more robust to the increase in the number of files and nodes in the system. Through simulation, it has been shown that HonestPeer has successfully maintained higher success rate and lower percentage of inauthentic downloads when compared to the original algorithm

Lightweight Building of an Electroencephalogram-Based Emotion Detection System

Brain–computer interface (BCI) technology provides a direct interface between the brain and an external device. BCIs have facilitated the monitoring of conscious brain electrical activity via electroencephalogram (EEG) signals and the detection of human emotion. Recently, great progress has been made in the development of novel paradigms for EEG-based emotion detection. These studies have also attempted to apply BCI research findings in varied contexts. Interestingly, advances in BCI technologies have increased the interest of scientists because such technologies’ practical applications in human–machine relationships seem promising. This emphasizes the need for a building process for an EEG-based emotion detection system that is lightweight, in terms of a smaller EEG dataset size and no involvement of feature extraction methods. In this study, we investigated the feasibility of using a spiking neural network to build an emotion detection system from a smaller version of the DEAP dataset with no involvement of feature extraction methods while maintaining decent accuracy. The results showed that by using a NeuCube-based spiking neural network, we could detect the valence emotion level using only 60 EEG samples with 84.62% accuracy, which is a comparable accuracy to that of previous studies

Managing Trust and Detecting Malicious Groups in Peer-to-Peer IoT Networks

Peer-to-peer (P2P) networking is becoming prevalent in Internet of Thing (IoT) platforms due to its low-cost low-latency advantages over cloud-based solutions. However, P2P networking suffers from several critical security flaws that expose devices to remote attacks, eavesdropping and credential theft due to malicious peers who actively work to compromise networks. Therefore, trust and reputation management systems are emerging to address this problem. However, most systems struggle to identify new smart models of malicious peers, especially those who cooperate together to harm other peers. This paper proposes an intelligent trust management system, namely, Trutect, to tackle this issue. Trutect exploits the power of neural networks to provide recommendations on the trustworthiness of each peer. The system identifies the specific model of an individual peer, whether good or malicious. The system also detects malicious collectives and their suspicious group members. The experimental results show that compared to rival trust management systems, Trutect raises the success rates of good peers at a significantly lower running time. It is also capable of accurately identifying the peer model

Fish-Inspired Heuristics: A Survey of the State-of-the-Art Methods

The collective behaviour of fish schools, shoals and other swarms in nature has long inspired researchers to develop solutions for optimization problems. Instinct influences the behaviour of fish to group into schools to increase safety, enhance foraging success, and promote breeding. According to these instinctive behaviours, several fish-inspired algorithms have been introduced to solve hard problems. This paper presents a comprehensive survey of fish-inspired heuristics, exploring their evolution within the context of general optimization problems. To our knowledge, this survey is the first to cover both main fish-inspired heuristics in the literature, namely, the artificial fish swarm algorithm (AFSA) and Fish school search (FSS), in addition to other algorithms inspired by specific fish species. The review covers more than 50 papers published in the Web of Science and IEEE databases since 2000. We first review the basic fish heuristics, highlighting their advantages and drawbacks, and then detail attempts in the literature to improve their behaviour to solve complex, multi-objective and high-dimensional problems in several domains. Our work is intended to provide guidance for researchers and practitioners for the purpose of further advancing research in the area of fish-inspired heuristics. We aspire to encourage their utilization in various fields for global optimization and in real-life applications. The survey findings indicate that fish-inspired heuristics are very alive in recent literature and still have great potential. Several challenges and future research directions are also identified among the findings of this survey, which can help to enhance this vibrant line of research

WhatsTrust: A Trust Management System for WhatsApp

Online communication platforms face security and privacy challenges, especially in broad ecosystems, such as online social networks, where users are unfamiliar with each other. Consequently, employing trust management systems is crucial to ensuring the trustworthiness of participants, and thus, the content they share in the network. WhatsApp is one of the most popular message-based online social networks with over one billion users worldwide. Therefore, it is considered an attractive platform for cybercriminals who spread malware to gain unauthorized access to users’ accounts to steal their data or corrupt the system. None of the few trust management systems proposed in the online social network literature have considered WhatsApp as a use case. To this end, this paper introduces WhatsTrust, a trust management system for WhatsApp that evaluates the trustworthiness of users. A trust value accompanies each message to help the receiver make an informed decision regarding how to deal with the message. WhatsTrust is extensively evaluated through a strictly controlled empirical evaluation framework with two well-established trust management systems, namely EigenTrust and trust network analysis with subjective logic (TNA-SL) algorithms, as benchmarks. The experimental results demonstrate WhatsTrust’s dominance with respect to the success rate and execution time

TrustyFeer: A Subjective Logic Trust Model for Smart City Peer-to-Peer Federated Clouds

Cloud computing plays a major role in smart cities development by facilitating the delivery of various services in an efficient and effective manner. In a Peer-to-Peer (P2P) federated clouds ecosystem, multiple Cloud Service Providers (CSPs) collaborate and share services among them when experiencing a shortage in certain resources. Hence, incoming service requests to this specific resource can be delegated to other members. Nevertheless, the lack of preexisting trust relationship among CSPs in this distributed environment can affect the quality of service (QoS). Therefore, a trust management system is required to assist trustworthy peers in seeking reliable communication partners. We address this challenge by proposing TrustyFeer, a trust management system that allows peers to evaluate the trustworthiness of other peers based on subjective logic opinions, formulated using peers’ reputations and Service Level Agreements (SLAs). To demonstrate the utility of TrustyFeer, we evaluate the performance of our method against two long-standing trust management systems. The simulation results show that TrustyFeer is more robust in decreasing the percentage of services that do not conform to SLAs and increasing the success rate of exchanged services by good CSPs conforming to SLAs. This should provide a trustworthy federated clouds ecosystem for a better, more sustainable future