A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical Wireless Sensor Networks Using Long Short-Term Memory

Abstract

The multiple objective optimisation (MOO) challenges encountered in the context of wireless sensor networks (WSNs) present a formidable NP-hard problem. These issues primarily arise from the constraints imposed by critical factors such as connectivity, coverage, and, most notably, energy consumption. Simultaneously fulfilling these three requirements is no longer considered the standard approach for enhancing system dependability. To illustrate, a prospective solution may optimise one or two of these requirements while bolstering overall network energy efficiency. Nonetheless, prior endeavours documented in the extant literature reveal unexplored avenues for enhancement. Hence, this paper introduces a new methodology aimed at alleviating MOO concerns and thereby enhancing the quality of service (QoS) in WSNs. A long short-term memory (LSTM) model is proposed as an analytical tool to deliver an energy-efficient scheduling solution that aligns and optimises WSN parameters, striving to attain the most favourable system performance. The LSTM algorithm’s effectiveness is assessed through the iterative application of periods, confirming the desired QoS levels. The unique feature of LSTM lies in its capability to observe specific event sequences and subsequently establish them as the system’s default configuration for its entire operational lifespan. Once these favourable parameters are identified, LSTM automatically ensures consistent service availability and reliability throughout the network’s lifespan. The results obtained demonstrate the superiority of the proposed LSTM-based scheduling algorithm in comparison to the self-organising map (SOFM)-based node scheduling algorithm. The LSTM-based approach outperforms the SOFM-based alternative by a remarkable 75% in terms of coverage and exhibits a 20% enhancement in network lifetime, all while maintaining equivalent levels of connectivity (i.e., 99%) in both algorithms.

A Single-Stack Output Power Prediction Method for High-Power, Multi-Stack SOFC System Requirements

“…In the existing field of fuel cell power prediction, the focus often deviates from the system environment. Due to the effective application of backpropagation (BP) and long short-term memory (LSTM) in various fields, their predictive capabilities have been widely recognized [13,14]. Therefore, techniques such as BP and LSTM have been introduced for predicting the operating conditions of fuel cell stacks [15,16], with data sourced from the impedance and temperature of the SOFC stack.…”

A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical WSN Using Spatial Self-Organizing Feature Map

“…Thus, we propose a novel SOFM node-scheduling algorithm to maintain network dependability, viability, availability, and reliability. The novelty of our approach is in its application in the MOO problem domain which is an NP-hard problem [9,10]. Furthermore, the proposed solution provides an optimal network configuration based on the SOFM spatial representation model in the context of safety-critical WSNs.…”