Abstract
Wireless sensor networks have been proposed for many location-dependent monitoring applications. Existing localization methods often suffer low accuracy and high energy consumption. In response to ...
Highlights
Wireless sensor networks (WSNs) integrated with sensor technology, embedded computing technology, wireless communications technologies, and distributed information processing technology can monitor, perceive, and acquire various environmental or object information
As an important branch of wireless measurement systems, WSNs have been widely adopted in many locationsensitive applications,[1] such as environment surveillance,[2] industry data collection,[3] and object tracking.[4]
We propose a novel method to evaluate the parameters of Log-normal Shadowing model for received signal strength (RSS) sensing, which fully utilizes the location information of known nodes and can improve the localization accuracy of the received signal strength indicator (RSSI)-based localization algorithm
Summary
Wireless sensor networks (WSNs) integrated with sensor technology, embedded computing technology, wireless communications technologies, and distributed information processing technology can monitor, perceive, and acquire various environmental or object information. Range-free localization schemes utilize simple sensing, such as wireless connectivity,[7] anchor proximity,[8] or localization event detection,[9] to localize nodes These approaches are cost-efficient but exhibit low accuracy. A unmanned aerial vehicle– assisted node localization (UNL) approach is proposed, in which a miniature UAV carrying the Global Positioning System (GPS) and a image sensor is employed to assist localization. A novel localization scheme is proposed based on image recognition and processing techniques. We propose a novel method to evaluate the parameters of Log-normal Shadowing model for received signal strength (RSS) sensing, which fully utilizes the location information of known nodes and can improve the localization accuracy of the RSSI-based localization algorithm. Section ‘‘Conclusion’’ concludes the article and provides future work direction
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