A distributed scheme for energy-efficient event-based target recognition using Internet of Multimedia Things. (May 2022)

• Record Type: Journal Article
• Authors: Alsabhan, Manal; Soudani, Adel; Almusallam, Manan
• Is Part Of: International journal of distributed sensor networks. Volume 18:Number 5(2022)
• Publication Date: 2022-05
• Subjects: Multimedia Internet of Things -- wireless multimedia sensor network -- multimedia sensing -- features extraction -- object recognition -- low-energy processing -- Fourier descriptors -- distributed processing; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/15501329221100326
• Languages: English
• ISSNs: 1550-1329

Abstract

The availability of low-cost embedded devices for multimedia sensing has encouraged their integration with low-power wireless sensors to create systems that enable advanced services and applications referred to as the Internet of Multimedia Things. Image-based sensing applications are challenged by energy efficiency and resource availability. Mainly, image sensing and transmission in Internet of Multimedia Things severely deplete the sensor energy and overflow the network bandwidth with redundant data. Some solutions presented in the literature, such as image compression, do not efficiently solve this problem because of the algorithms' computational complexities. Thus, detecting the event of interest locally before the communication using shape-based descriptors would avoid useless data transmission and would extend the network lifetime. In this article, we propose a new approach of distributed event-based sensing scheme over a set of nodes forming a processing cluster to balance the processing load. This approach is intended to reduce per-node energy consumption in one sensing cycle. The conducted experiments show that our novel method based on the general Fourier descriptor decreases the energy consumption in the camera node to only 2.4 mJ, which corresponds to 75.32% of energy-saving compared to the centralized approach, promising to prolong the network lifetime significantly. In addition, the scheme achieved more than 95% accuracy in target recognition.