ZigBee无线智能插头网络,具有RSSI基于多方的接近估计和并行机器学习功能,用于需求响应

IF 1.5 Q3 TELECOMMUNICATIONS
Anthony S. Deese, Julian Daum
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引用次数: 5

摘要

本研究探讨无线ZigBee技术如何应用于住宅和商业空间的电力负荷自动化,从而参与需求响应计划。作者讨论了具有传感、无线通信和电力负载驱动能力的定制智能插头的开发以及一些创新升级。市面上有许多包含多个传感器和继电器的智能插头。然而,很少有软件能够有效地估计模块之间的接近程度,或者执行健壮的系统范围优化。作者提出了两个创新的智能插头生态系统的改进。一种是使用基于接收信号强度指标(RSSI)的多方位方法来估计模块的相对接近度。ZigBee网络中几乎所有传输路径的RSSI值都是通过作者的强制网络重构算法获得的,解决了星型结构中RSSI观测的局限性。第二个创新是开发了一种并行神经网络训练方法,用于应用程序负载自动化。作者使用k-means聚类算法将训练数据划分为子集,以便训练可以并行化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

ZigBee wireless smart plug network with RSSI multi-lateration-based proximity estimation and parallelised machine learning capabilities for demand response

ZigBee wireless smart plug network with RSSI multi-lateration-based proximity estimation and parallelised machine learning capabilities for demand response

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.

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来源期刊
IET Wireless Sensor Systems
IET Wireless Sensor Systems TELECOMMUNICATIONS-
CiteScore
4.90
自引率
5.30%
发文量
13
审稿时长
33 weeks
期刊介绍: IET Wireless Sensor Systems is aimed at the growing field of wireless sensor networks and distributed systems, which has been expanding rapidly in recent years and is evolving into a multi-billion dollar industry. The Journal has been launched to give a platform to researchers and academics in the field and is intended to cover the research, engineering, technological developments, innovative deployment of distributed sensor and actuator systems. Topics covered include, but are not limited to theoretical developments of: Innovative Architectures for Smart Sensors;Nano Sensors and Actuators Unstructured Networking; Cooperative and Clustering Distributed Sensors; Data Fusion for Distributed Sensors; Distributed Intelligence in Distributed Sensors; Energy Harvesting for and Lifetime of Smart Sensors and Actuators; Cross-Layer Design and Layer Optimisation in Distributed Sensors; Security, Trust and Dependability of Distributed Sensors. The Journal also covers; Innovative Services and Applications for: Monitoring: Health, Traffic, Weather and Toxins; Surveillance: Target Tracking and Localization; Observation: Global Resources and Geological Activities (Earth, Forest, Mines, Underwater); Industrial Applications of Distributed Sensors in Green and Agile Manufacturing; Sensor and RFID Applications of the Internet-of-Things ("IoT"); Smart Metering; Machine-to-Machine Communications.
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