eprintid: 19357
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/93/57
datestamp: 2024-06-04 14:11:49
lastmod: 2024-06-04 14:11:49
status_changed: 2024-06-04 14:05:32
type: article
metadata_visibility: show
creators_name: Sarang, S.
creators_name: Stojanovic, G.M.
creators_name: Drieberg, M.
creators_name: Stankovski, S.
creators_name: Bingi, K.
creators_name: Jeoti, V.
title: Machine Learning Prediction Based Adaptive Duty Cycle MAC Protocol for Solar Energy Harvesting Wireless Sensor Networks
ispublished: pub
keywords: Digital storage; Energy utilization; Forecasting; Internet protocols; Machine learning; Medium access control; Power management (telecommunication); Solar energy; Wireless sensor networks, Adaptive duty cycle; Duty-cycle; Energy harvesting aware communication; Energy harvesting based wireless sensor network; Energy prediction; Energy-consumption; Machine-learning; Medium access control protocols; Prediction-based; Solar energy prediction, Energy harvesting
note: cited By 9
abstract: The dynamic nature of energy harvesting rate, arising because of ever changing weather conditions, raises new concerns in energy harvesting based wireless sensor networks (EH-WSNs). Therefore, this drives the development of energy aware EH solutions. Formerly, many Medium Access Control (MAC) protocols have been developed for EH-WSNs. However, optimizing MAC protocol performance by incorporating predicted future energy intake is relatively new in EH-WSNs. Furthermore, existing MAC protocols do not fully harness the high harvested energy to perform aggressively despite the availability of sufficient energy resources. Therefore, a prediction-based adaptive duty cycle (PADC) MAC protocol has been proposed, called PADC-MAC, that incorporates current and future harvested energy information using the mathematical formulation to improve network performance. Furthermore, a machine learning model, namely nonlinear autoregressive (NAR) neural network, is employed that achieves good prediction accuracy under dynamic harvesting scenarios. As a result, it enables the receiver node to perform aggressively better when there is sufficient inflow of incoming harvesting energy. In addition, PADC-MAC uses a self-adaptation technique that reduces energy consumption. The performance of PADC-MAC is evaluated using GreenCastalia in terms of packet delay, network throughput, packet delivery ratio, energy consumption per bit, receiver energy consumption, and total network energy consumption using realistic harvesting data for 96 consecutive hours under dynamic solar harvesting conditions. The simulation results show that PADC-MAC provides lower average packet delay of the highest priority packets and all packets, energy consumption per bit, and total energy consumption by more than 10.7, 7.8, 81, and 76.4, respectively when compared to three state-of-the-art protocols for EH-WSNs. © 2013 IEEE.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149168531&doi=10.1109%2fACCESS.2023.3246108&partnerID=40&md5=4564a7ae1b7f537434e1eadbd73c5c4a
id_number: 10.1109/ACCESS.2023.3246108
full_text_status: none
publication: IEEE Access
volume: 11
pagerange: 17536-17554
refereed: TRUE
citation:   Sarang, S. and Stojanovic, G.M. and Drieberg, M. and Stankovski, S. and Bingi, K. and Jeoti, V.  (2023) Machine Learning Prediction Based Adaptive Duty Cycle MAC Protocol for Solar Energy Harvesting Wireless Sensor Networks.  IEEE Access, 11.  pp. 17536-17554.