Virtual reality (VR) is an emerging technology with a broad range of applications in training, entertainment, and business. To maximize the potentials of virtual reality as a medium, the unwelcome feeling of cybersickness needs to be minimized. Cybersickness is a type of simulation sickness that is experienced in virtual reality. It is a significant challenge for the usability of virtual reality systems. Even with advancements in virtual reality, the usability concerns are barriers for a wide-spread acceptance. Several factors (hardware, software, human) play a part towards a pleasant virtual reality experience. In this paper, we review the potential factors which cause sickness and minimize the usability of virtual reality systems. The reviewed scientific articles are mostly part of documents indexed in digital libraries. We review the best practices from a developer’s perspective and some of the safety measures a user must follow while using the virtual reality systems from existing research. Even after following some of the guidelines and best practices virtual reality environments do not guarantee a pleasant experience for users. Limited research in virtual reality environments towards requirements specification, design, and development for maximum usability and adaptability was the main motive for this work.

Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use KGs in downstream tasks, it is desirable to predict missing links in KGs. Different approaches have been recently proposed for representation learning of KGs by embedding both entities and relations into a low-dimensional vector space aiming to predict unknown triples based on previously visited triples. According to how the triples will be treated independently or dependently, we divided the task of knowledge graph completion into conventional and graph neural network representation learning and we discuss them in more detail. In conventional approaches, each triple will be processed independently and in GNN-based approaches, triples also consider their local neighborhood.

The fish industry experiences substantial illegal, unreported, and unregulated (IUU) activities within traditional supply chain systems. Blockchain technology and the Internet of Things (IoT) are expected to transform the fish supply chain (SC) by incorporating distributed ledger technology (DLT) to build trustworthy, transparent, decentralized traceability systems that promote secure data sharing and employ IUU prevention and detection methods. We have reviewed current research efforts directed toward incorporating Blockchain in fish SC systems. We have discussed traceability in both traditional and smart SC systems that make use of Blockchain and IoT technologies. We demonstrated the key design considerations in terms of traceability in addition to a quality model to consider when designing smart Blockchain-based SC systems. In addition, we proposed an Intelligent Blockchain IoT-enabled fish SC framework that uses DLT for the trackability and traceability of fish products throughout harvesting, processing, packaging, shipping, and distribution to final delivery. More precisely, the proposed framework should be able to provide valuable and timely information that can be used to track and trace the fish product and verify its authenticity throughout the chain. Unlike other work, we have investigated the benefits of integrating machine learning (ML) into Blockchain IoT-enabled SC systems, focusing the discussion on the role of ML in fish quality, freshness assessment and fraud detection.

As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution.

Wireless Sensor Networks (WSNs) are the key underlying technology of the Internet of Things (IoT); however, these networks are energy constrained. Security has become a major challenge with the significant increase in deployed sensors, necessitating effective detection and mitigation approaches. Machine learning (ML) is one of the most effective methods for building cyber-attack detection systems. This paper presents a lightweight ensemble-based ML approach, Weighted Score Selector (WSS), for detecting cyber-attacks in WSNs. The proposed approach is implemented using a blend of supervised ML classifiers, in which the most effective classifier is promoted dynamically for the detection process to gain higher detection performance quickly. We compared the performance of the proposed approach to three classical ensemble techniques: Boosting-based, Bagging-based, and Stacking-based. The performance comparison was conducted in terms of accuracy, probability of false alarm, probability of detection, probability of misdetection, model size, processing time, and average prediction time per sample. We applied two independent feature selection techniques. We utilized the simulation-based labeled dataset, WSN-DS, that comprises samples of four internal network-layer Denial of Service attack types: Grayhole, Blackhole, Flooding, and TDMA scheduling, in addition to normal traffic. The simulation revealed promising results for our proposed approach.