Design and Implementation of Home Video Surveillance Systems Based on IoT Location Service

Design and Implementation of Home Video Surveillance Systems Based on IoT Location Service

Wei Xu, Yujin Zhai
DOI: 10.4018/IJITSA.318658
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Abstract

Due to its simplicity, directness, and rich content, home video surveillance has gradually become the core content of the smart home security system. The rapid development of computer technology, the internet, video image processing, and transmission technology, as well as the emergence of “wireless cities,” have continuously expanded the coverage of wireless internet such as 5G and WiFi and greatly changed data processing methods and transmission speeds. Video surveillance has also gradually developed from traditional wired networking to mobile video surveillance of wireless networks. Therefore, this paper will focus on the design of the home video monitoring system based on the IoT location service. The system combines traditional network video surveillance with intelligent mobile terminals, which can meet the needs of users for video surveillance of home conditions at any location and at any time. And it compared the sensitivity, accuracy, capture time interval, and energy consumption of the two by comparing with traditional internet video surveillance.
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To achieve intelligent identification, positioning, tracking, supervision, and other functions, any object can be connected to the network using information-sensing hardware and in accordance with a predetermined protocol. The connected objects then exchange information through an information dissemination medium. Experts and academics at home and abroad have addressed the potential uses of IoT location services in numerous industries and conducted pertinent research on their technical and application aspects. Zhou et al. proposed a Wi-Fi-enabled, non-intrusive device–user association technique called WinDUA, through a new unsupervised association learning algorithm to verify user identity and achieve mobile localization. This technique uses a Wi-Fi-based indoor positioning system to obtain the historical location data of each mobile device. Through hierarchical clustering and the location similarity matching between its location data and the user’s personal space, the specific location of the user is associated (Zhou et al., 2019).

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