Heuristically Modified LSTM-Based Reinforcement Learning for Task offloading in Industrial IoT Edge Computing

Abstract

An intensive computation source has become increasingly important in recent years to meet the intensive resource and low-latency needs of industrial internet of things (IIoT) systems. Existing IIoT devices are built with limited computational resource, delivering results in a limited fashion when used in highly resource-intensive and delay-sensitive applications. It is difficult to process time-critical IIoT task due to varying demand like low latency, intensive computation and high data transmission. Offloading computing tasks to mobile edge computing (MEC) servers in the network's perimeter can effectively reduce delay. However, MEC server collected fewer resources than the resource cloud. To improve the resource utilization and minimize cost, this research develops an adaptive task offloading decision model through multi-constraint objective function. The goal is to minimize service delay, energy consumption, and maximize resource utilization through prediction based decision model. This study examines a non-orthogonal multiple access (NOMA) -based MEC for IIoT system, where edge nodes offload their tasks to nearby edge servers for execution. Heuristically modified long short-term memory (H-LSTM) employing hybrid cat and mouse dingo optimization (HCMDO)-based reinforcement learning is suggested to distribute tasks optimally. We formulate joint optimization by considering multiple parameters using HCMDO. Further, these optimal parameters are used in training H-LSTM along with benchmark dataset. The outcome of the H-LSTM network utilized in deep reinforcement learning (DRL) to improve convergence speed, accuracy and stability by predicting task and best server. Average energy consumption analysis performed in the developed model attained 19.8%, 15.1%, 16.9%, and 15.6% than conventional approaches. In addition, the experimental results shows developed model attain better outcome in terms of delay and resource utilization.