Inter-server computation offloading and resource allocation in multi-drone aided space-air-ground integrated IoT networks

Abstract

Combining mobile edge computing (MEC), the multi-drone aided space-air-ground integrated Internet of things (SAG-IoT) networks can provide ground IoT devices (GIDs) highquality wireless access and computing services. However, the diverse tasks, moving drones, and limited network resources reveal great challenges for the task offloading and resource allocation scheme exploitation. Especially, given the restricted computation resources, how to make full use of available applications deployed on MEC servers (MECSs) to compute various types of tasks, is even an important issue. To the best of our knowledge, it is an entirely new problem since most existing works in this line assume that all types of applications can be deployed on one MECS so as to process various offloaded tasks. Toward this end, we present this paper to investigate inter-server computation offloading, resource allocation, and drone deployment to minimize the overall computation overhead of all GIDs. An iteratively optimization algorithm is proposed which alternately utilizes heuristic greedy and successive convex approximation methods. Simulation results verify that, for different GID numbers, optimization schemes, and computing models, our devised schemes can not only significantly reduce the overall computation overhead but also achieve optimal decisions of computation offloading, spectrum allocation, and drone deployment.

Joint Resource Allocation Optimization in Space–Air–Ground Integrated Networks

“…Algorithm 1 Optimization algorithm for the problem of Equation ( 31) In conclusion, we propose a comprehensive alternating optimization algorithm using the BCD method to solve Equation (22). Specifically, we divide the variables in Equation ( 22) into different blocks, which are a,c,b, D, and u U .…”

“…By jointly optimizing task assignment, transmission power, bandwidth allocation, and UAV computing resources, it achieves the minimization of the maximum computation latency among GTs. MEC services are frequently deployed in SAGIN networks; the authors in [22] presented an iterative optimization approach and utilized a greedy algorithm and the SCA method to minimize the total computing cost of all GTs. Due to the limited battery capacity, the authors in [23] decreased energy consumption by enhancing the offloading ratios and allocation of processing resources for terrestrial users, UAVs, and satellites.…”