Open AccessZigBee wireless smart plug network with RSSI multi‐lateration‐based proximity estimation and parallelised machine learning capabilities for demand response
Author(s) -
Deese Anthony S.,
Daum Julian
Publication year - 2020
Publication title -
iet wireless sensor systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.433
H-Index - 27
ISSN - 2043-6394
DOI - 10.1049/iet-wss.2018.5047
Subject(s) - automation , wireless , plug in , embedded system , control reconfiguration , wireless network , transmission (telecommunications) , computer science , real time computing , home automation , cluster analysis , engineering , telecommunications , artificial intelligence , mechanical engineering , programming language
This study explores how wireless ZigBee technology may be applied to automation of electric loads in residential and commercial spaces, allowing to participate in demand response initiatives. The authors discuss development of a custom smart plug with sensing, wireless communication, and electric load actuation capabilities along with several innovative upgrades. There are many commercially available smart plugs that contain multiple sensors and relays. However, very few provide the ability to effectively estimate the proximity between modules or the ability to perform robust system‐wide optimisation. The authors propose two innovative smart plug eco‐system improvements. One is the use of a received signal strength indicator (RSSI) multi‐lateration‐based method to estimate the relative proximities of modules. The RSSI values for almost all transmission paths within the ZigBee network are acquired via the authors' forced network reconfiguration algorithm, addressing the limitations of RSSI observation within a star structure. A second innovation is the development of a parallelised neural network training method for application to load automation. The authors use a k ‐means clustering algorithm to divide training data into subsets such that training may be parallelised.