Edge computing aims to make internet-based services and remote computing power close to the user by placing information technology (IT) infrastructure at the network edges. This proximity provides data centers with low-latency and context-aware services. Edge computing power consumption is mainly caused by data centers, network equipment, and user equipment. With edge computing (EC), energy management platforms for residential, industrial, and commercial sectors are built. Energy efficiency is considered to be one of the key aspects of edge power constraints. This paper provides the state of the art of power consumption and energy management for edge computing, the computation offloading methods, and more important highlights the power efficiency of edge computing systems. Furthermore, renewable energy and related concepts will also be explored and presented since no human participation is required in replacing or recharging batteries when using such energy sources. Based on such study, a recommendation is to develop a dynamic system for energy management in real-time with the assessment of local renewable energy so that the system be reliable with minimum power consumption. Also, regarding energy management, we recommend providing backup energy sources (or using more than one energy source) or (a hybrid technique).

Chest diseases are among the most common diseases today. More than one million people with pneumonia enter the hospital, and about 50,000 people die annually in the U.S. alone. Also, Coronavirus disease (COVID-19) is a risky disease that threatens the health by affecting the lungs of many people around the world. Chest X-ray and CT-scan images are the radiological imaging that can be helpful to detect COVID-19. A radiologist would need to compare a patient's image with the most similar images. Content-based image retrieval in terms of medical images offers such a facility based on visual feature descriptor and similarity measurements. In this paper, a retrieval algorithm was developed to tackle such challenges based on deep convolutional neural networks (e.g., ResNet-50, AlexNet, and GoogleNet) to produce an effective feature descriptor. Also, similarity measures such as City block and Cosine were employed to compare two images. Chest X-ray and CT-scan datasets used to evaluate the proposed algorithms with a highest performance applying ResNet -50 (99% COVID-19 (+) and 98% COVID-19 (–)) and GoogleNet (84% COVID-19 (+) and 81% COVID-19 (–)) for X-ray and CT-scan respectively. The percentage increased about 1-4% when voting was used by a k-nearest neighbor classifier