Genetic Algorithm-Based Grouping Strategy for IEEE 802.11ah Networks

Abstract

The IEEE 802.11ah standard is intended to adapt the specifications of IEEE 802.11 to the Internet of Things (IoT) scenario. One of the main features of IEEE 802.11ah consists of the Restricted Access Window (RAW) mechanism, designed for scheduling transmissions of groups of stations within certain periods of time or windows. With an appropriate configuration, the RAW feature reduces contention and improves energy efficiency. However, the standard specification does not provide mechanisms for the optimal setting of RAW parameters. In this way, this paper presents a grouping strategy based on a genetic algorithm (GA) for IEEE 802.11ah networks operating under the RAW mechanism and considering heterogeneous stations, that is, stations using different modulation and coding schemes (MCS). We define a fitness function from the combination of the predicted system throughput and fairness, and provide the tuning of the GA parameters to obtain the best result in a short time. The paper also includes a comparison of different alternatives with regard to the stages of the GA, i.e., parent selection, crossover, and mutation methods. As a proof of concept, the proposed GA-based RAW grouping is tested on a more constrained device, a Raspberry Pi 3B+, where the grouping method converges in around 5 s. The evaluation concludes with a comparison of the GA-based grouping strategy with other grouping approaches, thus showing that the proposed mechanism provides a good trade-off between throughput and fairness performance.

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

“…To achieve fairness in inter-group throughput and channel utilization the authors in [9,26] proposed heuristic grouping algorithms. Furthermore, in [27,28], the authors introduced grouping strategies based on greedy algorithms and on genetic algorithms, respectively.…”

Deep Reinforcement Learning for Optimizing Restricted Access Window in IEEE 802.11ah MAC Layer

“…To achieve fairness in inter-group throughput and channel utilization, authors in [9] and [26] proposed heuristic grouping algorithms. Furthermore, in [27] and [28], authors introduced grouping strategies based on greedy algorithms and genetic algorithms, respectively.…”

Perspectives on recent advancements in energy harvesting, sensing and bio-medical applications of piezoelectric gels

“…190 Based on these advantages, the exploration of piezoelectric hydrogels is of growing interest since they are biocompatible, stretchable and can stimulate endogenous This journal is © The Royal Society of Chemistry 2023 ''bioelectricity'' to promote tissue regeneration and repair. 191 Due to the physical features of hydrogel-based piezoelectrics that mirror biological functions, these characteristics represent a major potential for integrating biological organs and electronics. It has been reported that piezoelectric hydrogels are mechanically robust and adequately stable in various environments allowing to monitor external stimuli such as pressure, strain, temperature, pH, humidity and human-motion sensing via electrical signals.…”