GLT: Grouping Based Location Tracking for Object Tracking Sensor Networks
| Autor: | Xingfu Wang, Hassan Kuhlani, Adili Abudukelimu, Rafia Ghoul, Saleem Karmoshi, Ammar Hawbani, Aiman Ghannami |
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| Rok vydání: | 2017 |
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Article Subject
Computer Networks and Communications Computer science Distributed computing Real-time computing 02 engineering and technology lcsh:Technology 01 natural sciences lcsh:Telecommunication Reduction (complexity) lcsh:TK5101-6720 Node (computer science) 0202 electrical engineering electronic engineering information engineering Overhead (computing) Electrical and Electronic Engineering Spanning tree lcsh:T 010401 analytical chemistry 020206 networking & telecommunications Energy consumption Object (computer science) 0104 chemical sciences Tree (data structure) Video tracking Wireless sensor network Information Systems |
| Zdroj: | Wireless Communications and Mobile Computing, Vol 2017 (2017) |
| ISSN: | 1530-8677 1530-8669 |
| Popis: | The use of wireless sensor networks (WSN) in tracking applications is growing rapidly. In these applications, the nodes detect, monitor, and track a target, object, or event. In this paper, we consider the problem of tracking mobile objects in wireless sensor networks (WSN). We present a novel tracking model, named Grouping based Location Tracking (GLT), scaling well with the number of nodes and the number of mobile objects. GLT is based on the Grouping Hierarchy Structure, GHS. In GHS, nodes are partitioned into groups (not clusters) according to their maximum covered region (MCR) such that each group contains a number of nodes and a number of leaders. GLT consists of two tiers. The first tier, which is called the Notification Tree (NT), enhances the activation mechanism, the data cleaning mechanism, and the energy balancing mechanism. On the other hand, the second tier, which is called the Hierarchical Spanning Tree (HST), supports the data reporting mechanism and the lifetime prolonging mechanism. Simulations results show that GLT reduces the communication node selections overhead without diminishing object tracking accuracy and achieves a significant energy consumption reduction and network lifetime extension compared with the state-of-the-art approaches. |
| Databáze: | OpenAIRE |
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