IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture

TL;DR

This paper presents a risk-sensitive reinforcement learning method for optimizing task offloading to aerial base stations in smart agriculture, ensuring timely processing while considering energy constraints.

Contribution

It introduces a novel multi-actor risk-sensitive reinforcement learning approach for ABS task scheduling in smart farms, addressing deadline and energy limitations.

Findings

Outperforms heuristics and classic Q-Learning in simulations

Provides a lower bound using mixed integer linear programming

Enhances ABS hovering time and task processing guarantees

Abstract

Aerial base stations (ABSs) allow smart farms to offload processing responsibility of complex tasks from internet of things (IoT) devices to ABSs. IoT devices have limited energy and computing resources, thus it is required to provide an advanced solution for a system that requires the support of ABSs. This paper introduces a novel multi-actor-based risk-sensitive reinforcement learning approach for ABS task scheduling for smart agriculture. The problem is defined as task offloading with a strict condition on completing the IoT tasks before their deadlines. Moreover, the algorithm must also consider the limited energy capacity of the ABSs. The results show that our proposed approach outperforms several heuristics and the classic Q-Learning approach. Furthermore, we provide a mixed integer linear programming solution to determine a lower bound on the performance, and clarify the gap between our risk-sensitive solution and the optimal solution, as well. The comparison proves our extensive simulation results demonstrate that our method is a promising approach for providing a guaranteed task processing services for the IoT tasks in a smart farm, while increasing the hovering time of the ABSs in this farm.