A Distributed Task Allocation Methodology for Edge Computing in a LEO Satellite IoT Context

The Internet of Things (IoT) is one of the most promising applications in the field of computer networking. Edge computing is a computationally efficient method for processing user data in a terrestrial-satellite hybrid environment, where each device is connected exclusively through a low-elevation (LEO) satellite. This paper focuses on an IoT context, introducing methodologies to effectively manage the computation-communication trade-off by strategically distributing processing tasks across various satellites. In particular, an adaptive load balancing approach is considered for efficient utilization of satellite resources. The proposed method can be implemented in a distributed manner, enabling each satellite to evaluate its task handling capacity and forward tasks if it is beyond its capability. The numerical results demonstrate the effectiveness of the proposed method compared to conventional fixed allocation and cloud processing methodologies.