This paper surveys the conceptual aspects, as well as recent developments in fault detection, isolation, and service restoration (FDIR) following an outage in an electric distribution system. This paper starts with a discussion of the rationale for FDIR, and then investigates different areas of the FDIR problem. Recently reported approaches are compared and related to discussions on current practices. This paper then addresses some of the often-cited associated technical, environmental, and economic challenges of implementing self-healing for the distribution grid. The review concludes by pointing toward the need and directions for future research.

This paper studies the multihop packet delivery delay in a low density vehicular ad hoc network (VANET). We address a disrupted vehicle-to-infrastructure communication scenario, where an end-to-end path is unlikely to exist between a vehicle and the nearest road side unit (RSU). We present an analytical framework, which takes into account the randomness of vehicle data traffic and the statistical variation of the disrupted communication channel. Our framework employs the effective bandwidth theory and its dual, the effective capacity concept, in order to obtain the maximum distance between RSUs that stochastically limits the worst case packet delivery delay to a certain bound (i.e., allows only an arbitrarily small fraction of the packets received by the farthest vehicle from the RSU to exceed a required delay bound). Our study also investigates the effect of the vehicle density, transmission range, and speed difference between vehicles on the end-to-end packet delivery delay. Extensive simulation results validate our analytical framework.

As essential building blocks of the future smart grid, microgrids can efficiently integrate various types of distributed generation (DG) units to supply the electric loads at the minimum cost based on the economic dispatch. In this paper, we introduce a decentralized economic dispatch approach such that the optimal decision on power generation is made by each DG unit locally without a central controller. The prerequisite power generation and load information for decision making is discovered by each DG unit via a multiagent coordination with guaranteed convergence. To avoid a slow convergence speed which potentially increases the generation cost because of the time-varying nature of DG output, we present a heterogeneous wireless network architecture for microgrids. Low-cost short-range wireless communication devices are used to establish an ad hoc network as a basic information exchange infrastructure, while auxiliary dual-mode devices with cellular communication capabilities are optionally activated to improve the convergence speed. Two multiagent coordination schemes are proposed for the single-stage and hierarchical operation modes, respectively. The optimal number of activated cellular communication devices is obtained based on the tradeoff between communication and generation costs. The performance of the proposed schemes is analyzed and evaluated based on real power generation and load data collected from the Waterloo Region in Canada. Numerical results indicate that our proposed schemes can better utilize the cellular communication links and achieve a desired tradeoff between the communication and generation costs as compared with the existing schemes.

The upcoming 6G networks are sixth-sense next-generation communication networks with an ever-increasing demand for enhanced end-to-end (E2E) connectivity towards a connected, sustainable world. Recent developments in artificial intelligence (AI) have enabled a wide range of novel technologies through the availability of advanced machine learning (ML) models, large datasets, and high computational power. In addition, intelligent resource management is a key feature of 6G networks that enables self-configuration and self-healing by leveraging the parallel computing and autonomous decision-making ability of ML techniques to enhance energy efficiency and computational capacity in 6G networks. Consequently, ML techniques will play a significant role in addressing resource management and mobility management challenges in 6G wireless networks. This article provides a comprehensive review of state-of-the-art ML algorithms applied in 6G wireless networks, categorized into learning types, including supervised and unsupervised machine learning, Deep Learning (DL), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL) and Federated Learning (FL). In particular, we review the ML algorithms applied in the emerging networks paradigm, such as device-to-device (D2D) networks, vehicular networks (Vnet), and Fog-Radio Access Networks (F-RANs). We highlight the ML-based solutions to address technical challenges in terms of resource allocation, task offloading, and handover management. We also provide a detailed review of the ML techniques to improve energy efficiency and reduce latency in 6G wireless networks. To this end, we identify the open research issues and future trends concerning ML-based intelligent resource management applications in 6G networks.

Vehicular ad hoc networking (VANET) is an emerging paradigm that is expected to increase the public safety standards and enhance the safety level of drivers/passengers and pedestrians on roads through a variety of applications. We have recently proposed VeMAC, a medium access control protocol that supports a reliable one-hop broadcast service necessary for high priority safety applications in VANETs. This paper explains how the VeMAC protocol can deliver both periodic and event-driven safety messages in vehicular networks and presents a detailed delivery delay analysis, including queueing and service delays, for both types of safety messages. The probability mass function of the service delay is first derived; then, the D/G/1 and M/G/1 queueing systems are used to calculate the average queueing delay of the periodic and event-driven safety messages, respectively. In addition, a comparison between the VeMAC protocol and IEEE 802.11p standard is presented via extensive simulations using the network simulator ns-2 and the microscopic vehicle traffic simulator VISSIM. A real city scenario is considered and different performance metrics are evaluated, including the network goodput, protocol overhead, channel utilization, protocol fairness, probability of a transmission collision, and message delivery delay.

Task offloading from smartphones to the cloud is a promising strategy to enhance the computing capability of smartphones and prolong their battery life. However, task offloading introduces a communication cost for those devices. Therefore, the consideration of the communication cost is crucial for the effectiveness of task offloading. To make task offloading beneficial, one of the challenges is to estimate the energy consumed in communication activities of task offloading. Accurate energy estimation models will enable these devices to make the right decisions as to whether or not to perform task offloading, based on the energy cost of the communication activities. Simply put, if the offloading process consumes less energy than processing the task on the device itself, then the task is offloaded to the cloud. To design an energy-aware offloading strategy, we develop energy models of the WLAN, third-generation, and fourth-generation interfaces of smartphones. These models make smartphones capable of accurately estimating the energy cost of task offloading. We validate the models by conducting an extensive set of experiments on five smartphones from different vendors. The experimental results show that our estimation models accurately estimate the energy required to offload tasks.