Collaborative Computing and Resource Allocation for LEO Satellite‐Assisted Internet of Things

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.

Optimal Traffic Load Allocation for Aloha-Based IoT LEO Constellations

“…Different from [23]- [28], [32], in this paper we consider an Aloha-based network, which is in line with some practical LEO satellite networks for IoT. Using the model in [31], where the satellites are considered to have different erasure probabilities, in this work we focus on the design of a traffic load strategy that maximizes the throughput of a direct-to-satellite 1 IoT system; guaranteeing ultra-scalability and efficient performance, without very high complexity processing that increases energy consumption in the satellite, as in [37]- [39].…”

“…Therefore, it is necessary to consider different traffic load balancing strategies to reduce packet losses and to increase the system throughput. Indeed, several works introduced load balancing strategies in the last few years, as for instance [23]- [28], [32].…”

“…Moreover, a dense LEO based integrated terrestrial-satellite network is considered in [25], while a scheduling strategy subject to backhaul capacity constraints to jointly maximize the sum rate and the number of served users is proposed. In addition, in [28], the authors study collaborative computing and resource allocation among multiple LEO satellites considering deep reinforcement learning and a max-min fairness optimization strategy to maximize the tasks completion rate. In [32], a LEO satellite based narrowband IoT (NB-IoT) system is investigated and a novel scheduling strategy to select the set of transmitting users is proposed to maximize a sum profit.…”

“…However, the above works assume medium access control (MAC) based on time division multiple access (TDMA) [23], [24], [26], [27] or code division multiple access (CDMA) [25], [28], [32], which require tight synchronization and/or power control, and therefore demand a considerable amount of signalling that may be not practical for some IoT satellite networks [33]. In a different direction, a two-phase communication system is proposed in [30].…”

Research on task scheduling in satellite edge computing

“…1. In this manner, satellite acquisition data can be processed in real-time at satellite edge nodes [4] [7][8] [12], conserving bandwidth and allowing for quick mission reaction. The TIANSUAN Constellation test star [13], co-chaired by Beijing University of Posts and Telecommunications and other institutions, for example, has validated remote sensing image inference and computing services, laying the groundwork for its proposed future multi-star edge collaborative computing plan.…”