Industry 4.0 or Industrial Internet of Things (IIoT), has become one of the most talked-about industrial business concepts in recent years. Thus, to efficiently integrate Internet of Things (IoT) technology into industry, the collected and sensed data from IIoT need to be scheduled in real time constraints; especially for big factories. To this end, we propose in this paper a hierarchical fog servers’ deployment at the network service layer across different tiers. Using probabilistic analysis models, we prove the efficiency of the proposed hierarchical fog computing compared with the flat architecture. In this work, IIoT data and requests are divided into both high priority and low priority requests; the high priority requests are urgent/emergency demands that need to be scheduled rapidly. Therefore, we use two-priority queuing model in order to schedule and analyze IIoT data. Finally, we further introduce a workload assignment algorithm to offload peak loads over higher tiers of the fog hierarchy. Using realistic industrial data from Bosh group, the benefits of the proposed architecture compared to the conventional flat design are proved using various performance metrics and through extensive simulations.

Despite its technological benefits, the Internet of Things (IoT) has cyber weaknesses due to vulnerabilities in the wireless medium. Machine Larning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. An Advanced Persistent Threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys that fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth bridging the state of the art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.

High electric vehicles (EVs) penetration is expected to increase smart grid solicitation especially with various EV charging demands. As result, the EV charging process at the supply station has to be managed in the way to promote the EV satisfaction level while preserving smart grid stability. In this paper, the bidirectional power flow between EV and grid; Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G), is exploited. We make a profit from the unused electric power of EVs and we present an EV load management technique based on EV charging and EV discharging coordination. We propose a peak load management model (PLM) used to schedule EVs for charging or discharging service according to the power demand with the timing and location where each EV need to be served. Also, we propose an Electric Vehicle Supply Equipment (EVSE) selection model to guide EVs to the supply station. We develop a mathematical formalism for handling requests for EV charging/discharging at EVSE based on queuing theory. Those models are evaluated while considering the mobility of vehicles in an urban scenario and time-of-use-pricing (TOUP). Finally, extensive matlab simulations are conducted to validate the proposed approach and demonstrate its effectiveness.

Through the digitization of essential functional processes, Industry 4.0 aims to build knowledgeable, networked, and stable value chains. Network trustworthiness is a critical component of network security that is built on positive interactions, guarantees, transparency, and accountability. Blockchain technology has drawn the attention of researchers in various fields of data science as a safe and low-cost platform to track a large number of eventual transactions. Such a technique is adaptable to the renewable energy-trade sector, which suffers from security and trustworthy issues. Having a decentralized energy infrastructure, that is supported by blockchain and artificial intelligence, enables smart and secure microgrid energy trading. The new age of industrial production will be highly versatile in terms of production volume and customization. As such a robust collaboration solution between consumers, businesses, and suppliers must be both secure and sustainable. In this article, we introduce a cooperative and distributed framework that relies on computing, communication, and intelligence capabilities of edge and end devices to enable secure energy trading, remote monitoring, and network trustworthiness. The blockchain and federated learning-enabled solution provide secure energy trading between different critical entities. Such a technique, coupled with 5G and beyond networks, would enable mass surveillance, monitoring, and analysis to occur at the edge. Performance evaluations are conducted to test the effectiveness of the proposed solution in terms of reliability and responsiveness in a vehicular network energy-trade scenario.

With significant advancements in information and communication technologies, connected and autonomous vehicles (CAVs) can provide improved transportation services. At present, a variety of technologies, such as vehicular networks, communication interfaces, and modern hardware devices enable CAVs to support reliable, safe, and quality transportation system options with improved performance and increased effectiveness. In this paper, we carefully explore a set of distinguished state-of-the-art CAV systems with a focus on On-board Computational Unit (OBCU) hardware architectures, communication technologies, deployment challenges, and performance aspects. The exploration critically identifies important area transformations and anticipates future trends influencing CAV communications and processing requirements. To that end, we propose the design of a future generic OBCU architecture that can be customized with appealing features and used in CAVs.