Emerging Edge Computing Technologies for Distributed Internet of Things (IoT) Systems

The ever-increasing growth in the number of connected smart devices and various Internet of Things (IoT) verticals is leading to a crucial challenge of handling massive amount of raw data generated from distributed IoT systems and providing real-time feedback to the end-users. Although existing cloud-computing paradigm has an enormous amount of virtual computing power and storage capacity, it is not suitable for latency-sensitive applications and distributed systems due to the involved latency and its centralized mode of operation. To this end, edge/fog computing has recently emerged as the next generation of computing systems for extending cloud-computing functions to the edges of the network. Despite several benefits of edge computing such as geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed in realizing edge computing technologies for future IoT systems. In this regard, this paper provides a holistic view on the current issues and effective solutions by classifying the emerging technologies in regard to the joint coordination of radio and computing resources, system optimization and intelligent resource management. Furthermore, an optimization framework for edge-IoT systems is proposed to enhance various performance metrics such as throughput, delay, resource utilization and energy consumption. Finally, a Machine Learning (ML) based case study is presented along with some numerical results to illustrate the significance of edge computing.Comment: 16 pages, 4 figures, 2 tables, submitted to IEEE Wireless Communications Magazin

Energy Efficiency Enhancement of Photovoltaics by Phase Change Materials through Thermal Energy Recovery

Photovoltaic (PV) panels convert a certain amount of incident solar radiation into electricity, while the rest is converted to heat, leading to a temperature rise in the PV. This elevated temperature deteriorates the power output and induces structural degradation, resulting in reduced PV lifespan. One potential solution entails PV thermal management employing active and passive means. The traditional passive means are found to be largely ineffective, while active means are considered to be energy intensive. A passive thermal management system using phase change materials (PCMs) can effectively limit PV temperature rises. The PCM-based approach however is cost inefficient unless the stored thermal energy is recovered effectively. The current article investigates a way to utilize the thermal energy stored in the PCM behind the PV for domestic water heating applications. The system is evaluated in the winter conditions of UAE to deliver heat during water heating demand periods. The proposed system achieved a ~1.3% increase in PV electrical conversion efficiency, along with the recovery of ~41% of the thermal energy compared to the incident solar radiation

Emerging Edge Computing Technologies for Distributed IoT Systems

The ever-increasing growth of connected smart devices and Internet of Things (IoT) verticals is leading to the crucial challenges of handling the massive amount of raw data generated by distributed IoT systems and providing timely feedback to the end-users. Although existing cloud computing paradigm has an enormous amount of virtual computing power and storage capacity, it might not be able to satisfy delaysensitive applications since computing tasks are usually processed at the distant cloud-servers. To this end, edge/fog computing has recently emerged as a new computing paradigm that helps to extend cloud functionalities to the network edge. Despite several benefits of edge computing including geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed for future IoT systems. In this regard, this paper provides a comprehensive view on the current issues encountered in distributed IoT systems and effective solutions by classifying them into three main categories, namely, radio and computing resource management, intelligent edge-IoT systems, and flexible infrastructure management. Furthermore, an optimization framework for edge-IoT systems is proposed by considering the key performance metrics including throughput, delay, resource utilization and energy consumption. Finally, a Machine Learning (ML) based case study is presented along with some numerical results to illustrate the significance of ML in edge-IoT computing