Due to their potential to accomplish complicated missions more effectively, UAV swarms have attracted a lot of attention lately. UAV swarm offers enhanced intelligence, improved coordination, increased flexibility, survivability, and reconfigurability. It is a multidisciplinary system that necessitates a tight integration of several subsystems, including optimal trajectory planning, localization, task coordination, etc. This review covers the important aspects of UAV swarms, including swarm formation control, communication, swarm path planning, autonomy, coordination, and security. It additionally explores recent technical advancements in UAV swarm algorithms that have made the development of complex UAV swarm systems possible. This article also provides insight into ethical aspects and the use cases of UAV swarms in various military, civilian, and entertainment applications. This article is concluded by highlighting the potential future directions and challenges of UAV swarm technology and the need for more research and development to exploit their potential fully. Overall, this article presents a comprehensive review of UAV swarm technology, addressing its potential for revolutionizing many fields and supporting advancements in the future.

Unmanned Aerial Vehicles (UAV) equipped with onboard embedded platforms and camera sensors provide access to difficult-to-reach areas and facilitate in remote sensing and autonomous decision-making capabilities in disaster recovery and management applications. Onboard computations are preferred due to connectivity, privacy, and latency problems. However, edge implementation becomes challenging because of limited onboard hardware resources (in terms of area, power, and storage). In this paper, we propose a UAV assisted edge computation framework that compresses the Convolutional Neural Networks (CNN) models to be run on an onboard embedded Graphics Processing Unit (GPU) for real-time disaster scenario classification. We use an imbalanced dataset named, Aerial Image Database for Emergency Response (AIDER), to replicate real-world disaster scenarios. Our experimental results show that optimized compressed model’s throughput is increased by about 99% which is up to 92x faster than the native model. Furthermore, the model size reduction enabled through proposed framework is about 84% without compromising accuracy and thus makes it suitable for edge GPUs.

The fabrication of lightweight, ultra-thin, low power and intelligent body-borne sensors leads to novel advances in wireless body area networks (WBANs). Depending on the placement of the nodes, it is characterized as in/on body WBAN; thus, the channel is largely affected by body posture, clothing, muscle movement, body temperature and climatic conditions. The energy resources are limited and it is not feasible to replace the sensor’s battery frequently. In order to keep the sensor in working condition, the channel resources should be reserved. The lifetime of the sensor is very crucial and it highly depends on transmission among sensor nodes and energy consumption. The reliability and energy efficiency in WBAN applications play a vital role. In this paper, the analytical expressions for energy efficiency (EE) and packet error rate (PER) are formulated for two-way relay cooperative communication. The results depict better reliability and efficiency compared to direct and one-way relay communication. The effective performance range of direct vs. cooperative communication is separated by a threshold distance. Based on EE calculations, an optimal packet size is observed that provides maximum efficiency over a certain link length. A smart and energy efficient system is articulated that utilizes all three communication modes, namely direct, one-way relay and two-way relay, as the direct link performs better for a certain range, but the cooperative communication gives better results for increased distance in terms of EE. The efficacy of the proposed hybrid scheme is also demonstrated over a practical quasi-static channel. Furthermore, link length extension and diversity is achieved by joint network-channel (JNC) coding the cooperative link.