Abstract
Satellite‐assisted internet of things (S‐IoT), especially the S‐IoT based on low earth orbit (LEO) satellite, plays an important role in future wireless systems. However, the limited on‐board communication and computing resource and high mobility of LEO satellites make it hard to provide satisfied service for IoT users. To maximize the task completion rate under latency constraints, collaborative computing and resource allocation among LEO networks are jointly investigated in this paper, and the joint task offloading, scheduling, and resource allocation is formulated as a dynamic mixed‐integer problem. To tack the complex problem, we decouple it into two subproblems with low complexity. First, the max‐min fairness is adopted to minimize the maximum latency via optimal resource allocation with fixed task assignment. Then, the joint task offloading and scheduling is formulated as a Markov decision process with optimal communication and computing resource allocation, and deep reinforcement learning is utilized to obtain long‐term benefits. Simulation results show that the proposed scheme has superior performance compared with other referred schemes.
Highlights
Internet of things (IoT) plays an important role in future intelligent society, and many techniques have been evaluated and implemented to provide better service for the data transmission in IoT network
We focus on the edge computing-enhanced low earth orbit (LEO) satellite networks, and the advantages of LEO satellite-assisted IoT over the other satellite systems can be summarized as follows: (1) The propagation delay introduced by the LEO satellite is low
(i) A framework for collaborative computing among multiple LEO satellites with varying topology is provided, and the effects of satellite-to-ground and satellite-to-satellite links on the processing latency are jointly considered (ii) The collaborative computing and resource allocation for user tasks are formulated as a joint task offloading, scheduling, and multidimensional resource allocation problem to maximize the completion rate of tasks, and the complex problem is divided into two subproblems with low complexity (iii) Deep reinforcement learning (DRL) and max-min fairness optimization are adopted to achieve longterm benefits in terms of task completion rate, and simulation results verify the performance of the proposed algorithms
Summary
Internet of things (IoT) plays an important role in future intelligent society, and many techniques have been evaluated and implemented to provide better service for the data transmission in IoT network. (i) A framework for collaborative computing among multiple LEO satellites with varying topology is provided, and the effects of satellite-to-ground and satellite-to-satellite links on the processing latency are jointly considered (ii) The collaborative computing and resource allocation for user tasks are formulated as a joint task offloading, scheduling, and multidimensional resource allocation problem to maximize the completion rate of tasks, and the complex problem is divided into two subproblems with low complexity (iii) Deep reinforcement learning (DRL) and max-min fairness optimization are adopted to achieve longterm benefits in terms of task completion rate, and simulation results verify the performance of the proposed algorithms.
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