The Industrial Revolution 4.0 (IR 4.0) has drastically impacted how the world operates. The Internet of Things (IoT), encompassed significantly by the Wireless Sensor Networks (WSNs), is an important subsection component of the IR 4.0. WSNs are a good demonstration of an ambient intelligence vision, in which the environment becomes intelligent and aware of its surroundings. WSN has unique features which create its own distinct network attributes and is deployed widely for critical real-time applications that require stringent prerequisites when dealing with faults to ensure the avoidance and tolerance management of catastrophic outcomes. Thus, the respective underlying Fault Tolerance (FT) structure is a critical requirement that needs to be considered when designing any algorithm in WSNs. Moreover, with the exponential evolution of IoT systems, substantial enhancements of current FT mechanisms will ensure that the system constantly provides high network reliability and integrity. Fault tolerance structures contain three fundamental stages: error detection, error diagnosis, and error recovery. The emergence of analytics and the depth of harnessing it has led to the development of new fault-tolerant structures and strategies based on artificial intelligence and cloud-based. This survey provides an elaborate classification and analysis of fault tolerance structures and their essential components and categorizes errors from several perspectives. Subsequently, an extensive analysis of existing fault tolerance techniques based on eight constraints is presented. Many prior studies have provided classifications for fault tolerance systems. However, this research has enhanced these reviews by proposing an extensively enhanced categorization that depends on the new and additional metrics which include the number of sensor nodes engaged, the overall fault-tolerant approach performance, and the placement of the principal algorithm responsible for eliminating network errors. A new taxonomy of comparison that also extensively reviews previous surveys and state-of-the-art scientific articles based on different factors is discussed and provides the basis for the proposed open issues.

Food security, as defined by the United Nations' Committee on World Food Security, means that “all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food that meets their food preferences and dietary needs for an active and healthy life”. Food security is not just ensuring food sufficiency, but also ensuring the quality and the reliability of supply chains which have become more complex with diversified sources of farm productions. Some common issues in food supply chains are lacking efficient traceability of product origins and distributions. Blockchain is an emerging technology for the agricultural supply chain which could monitor food quality and safety by digitally tracking data and processes of material resources. Meanwhile, the Internet of Things (IoT) system is used for diagnosis and control in smart agriculture such as crop growth observation that can produce good quality and sustainable farm production. This work proposes a framework of the integration of blockchain with IoT to improve the reliability of food supply chain (FSC) by focusing on optimizing the consensus algorithm of blockchain. The expected outcome is to produce an efficient traceability and higher quality transactions which can integrate all procedures and dealings in real-time processes across the FSC.

Software-defined networks (SDN) is an evolution in networking field where the data plane is separated from the control plane and all the controlling and management tasks are deployed in a centralized controller. Due to its features regarding ease management, it is emerged in other fields such as cloud and fog computing in order to manage asymmetric communication across nodes, thus improving the performance and reducing the power consumption. This study focused on research that were conducted in SDN-based clouds and SDN-based fogs. It overviewed the important contributions in SDN clouds in terms of improving network performances and energy optimization. Moreover, state-of-the-art studies in SDN fogs are presented. The features, methods, environment, dataset, simulation tool and main contributions are highlighted. Finally, the open issues related to both SDN clouds and SDN fogs are defined and discussed.

The Internet of Things (IoT) and cloud computing are rapidly gaining momentum as decentralized internet-based technologies and have led to an increase in information in nearly every technical and commercial industry. However, ensuring the security of IoT systems is a pressing issue due to the complexities involved in connected and shared environments. Networks are guarded by Intrusion Detection Systems (IDS) against various cyber threats such as malware, viruses, and unauthorized access. IDS have recently adopted Machine Learning (ML) and Deep Learning (DL) techniques to identify and classify security risks. However, the effective utilization of these technologies depends on the availability, quality, and characteristics of the data used to train models. Moreover, data lack, data leak, and dimensionality (DLLD) are common problems in data science and ML. This paper surveys existing research and suggests solutions for overcoming DLLD-related issues to improve IDS model.