Energy Efficient Resource Allocation and Task Offloading in Satellite Assisted Internet of Things Networks

Abstract

Satellite-assisted IoT networks have emerged as a promising solution to provide global coverage and seamless connectivity. However, resource allocation and task offloading in such networks pose significant challenges due to the unique characteristics of satellite communication systems. The findings of this research contribute to the development of energy-efficient and reliable satellite-assisted IoT networks. The work investigates the impact of different Quality of Service (QoS) requirements on resource allocation and task offloading strategies. It explores the trade-offs between energy efficiency, network throughput, and fairness in the distribution of resources among IoT devices. The proposed techniques OAA can enable seamless connectivity and efficient utilization of resources.The problem under consideration is a concave fractional programming problem that is transformed into a concave optimization problem using the Charnes-Cooper transformation. To solve this concave optimization problem, an innovative outer approximation algorithm is employed.The performance of the epsilonoptimal solution is evaluated by executing the algorithm with different system parameters, such as the number of IoT devices, their association, fairness among devices, and resource block fairness.