Development of an adaptive device-free human detection system for residential lighting load control. (July 2021)

• Record Type: Journal Article
• Authors: Santiprapan, Phonsit; Sengchuai, Kiattisak; Jindapetch, Nattha; Saito, Hiroshi; Booranawong, Apidet
• Is Part Of: Computers & electrical engineering. Volume 93(2021)
• Publication Date: 2021-07
• Subjects: RSSI -- Human movement -- Device-free detection -- Wireless sensor network -- Lighting load control; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107233
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A device-free human detection system using RSSI for residential lighting load control was developed. A device-free human detection algorithm as the adaptive algorithm was proposed. A hardware set for lighting load control and a lighting load control method were developed. The system could detect the moving persons with the accuracy of 100%. The system could control the residential lighting load in real-time with electricity energy saving. Abstract: A real-time device-free human detection system using received signal strength indicator (RSSI) for residential lighting load control is developed in this work. The proposed system consists of four parts: a designed wireless network based on ZigBee 2.4 GHz, RSSI filters using moving average (MA) and exponentially weighted moving average (EWMA) techniques, an adaptive device-free human detection algorithm, and a hardware set for lighting load control and a lighting control method. Experiments are carried out in a laboratory with two scenarios: a walking female (a home scenario) and a walking female and a man with his motorcycle (a parking scenario). Results show that our system can detect the moving persons with 100% accuracy and can control the residential lighting in real-time. By the proposed system, the cumulative electricity energy consumption of the lighting load reduces by (72.12% and 29.31%) for the first scenario and (86.19% and 54.84%) for the second scenario. Graphical abstract: Image, graphical abstract