Resource Constraints and Energy Efficient Optimization Algorithms for Aerial Aided Edge Device

The Aerial-Aided Edge Computing (AAEC) is a novel paradigm in computing that incorporates the aerial environments as the mobile edge nodes (MEs) and the Unmanned Aerial Vehicles (UAVs) are used as the least amount of deployment resources in scenarios. However, the quantity of resources in UAVs including energy, computation and communication is limited and these are cited as the core challenges towards the use of UAVs in edge computing. To address these challenges, it is mandatory to employ optimization algorithms that consume low energy to boost the functionality of UAV assisted edge devices. This chapter focuses on the limited resources of UAVs in AAEC systems that include battery lifetime, processing power and bandwidth. Such limitations necessitate the development of energy-efficient algorithms in processing the information as well as the exchange of information between nodes on aerial and ground platforms. Several paradigms are considered which involves task offloading, dynamic resource allocation, path planning and load balancing so that aerial edge devices can utilize less power but obtain maximum results. The heuristic, metaheuristic, and the machine learning based approaches mentioned herein about are intended to solve the problems in the domain of the AAEC. In addition, the chapter covers the trade-off between EE and QoS, predictive algorithms applied in response to EM circumstances. Based on the discussed survey on energy-efficient optimization algorithms and use cases, this chapter offers insight into how resource-limited UAVs can be effectively employed in edge computing. The main features of the solutions shown indicate the potential for implementing successive high-performance AAEC systems capable of supporting actual-time applications in context including smart city, emergency response and industrial automation.