Energy efficient task offloading and resource allocation for NOMA-Enabled IoT in HAP-Assisted MEC

Abstract

Purpose The authors study the problem of task offloading and resource allocation in a multi-access edge computing (MEC) system consisting of high altitude platform (HAP) and multiple internet of things devices (IoTDs) to minimize the total energy consumption of IoTDs, while ensuring the stability of system performance and battery energy level, so that IoTDs can provide users with more durable services. Design/methodology/approach This paper explores how to effectively use HAP-assisted MEC technology with energy harvesting (EH) and non-orthogonal multiple access (NOMA) in infrastructure-limited IoT environments while satisfying system stability and energy saving requirements. Findings This paper proposes a NOMA-enabled Energy Efficient Task Offloading (NEETO) algorithm. The algorithm can dynamically formulate EH, task offloading and resource allocation strategies. Through theoretical analysis and experimental verification, it is proved that the NEETO algorithm effectively reduces the energy consumption of IoTD while ensuring system performance, achieving a trade-off between total energy consumption and system performance. Originality/value This study provides a new communication and computing idea for remote areas that lack stable network infrastructure and reliable cellular networks. At the same time, it takes into account the battery capacity of IoTD, allowing it to continuously process various computing tasks and reduce the energy consumption of IoTD during long-term service.