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Wireless sensor networks have been deployed widely. Sensor networks involve sensor nodes which are very small in size, low in cost and have a short battery-life. One of the critical wireless sensor network applications is localisation and tracking mobile sensor nodes. ZigBee is a new emerging technology for low rate, low power and low range communication networks, which aims to provide long battery life for network devices. In this paper, we discuss various localisation and tracking techniques and categorise these techniques based on the communication between nodes in centralised and decentralised localisation systems. We propose a decentralised ZigBee-based tracking system to detect and track the location of mobile nodes indoors based on the received signal strength (RSS). The proposed tracking system is a range-free system, which does not require additional hardware, depends on a new weight function, and can be deployed wherever the node density is low. The tracking system is implemented by ZigBee sensor devices, and experiments are done to evaluate the proposed tracking system based on accuracy and communication cost. Keywords: localisation; tracking.Reference to this paper should be made as follows: Alhmiedat, T.A. and Yang, S-H. (2008) 'A ZigBee-based mobile tracking system through wireless sensor networks', Int.

In Wireless Sensor Network (WSN) applications it is critical to accurately determine the location of the distributed sensor nodes in order to report the data that is geographically meaningful. Since localization and tracking algorithms have been attracting research and development attention recently, a wide range of existing approaches regarding this topic have emerged. Tracking and localization algorithms have been proposed for different WSN applications including civilian, industrial and safety applications. A few research studies focused on tracking Threats through military applications, such as detecting and tracking Threats through border security area. Therefore this paper studies and investigates the existing WSN based tracking and localization algorithms and summarizes the potential requirements for localizing and tracking Threats through military applications. The existing systems are categorized and discussed. A critical analysis is found in this paper, in order to guide the developer to design and implement a WSN-based tracking system for military applications.

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The area of localization in wireless sensor networks (WSNs) has received considerable attention recently, driven by the need to develop an accurate localization system with the minimum cost and energy consumption possible. On the other hand, machine learning (ML) algorithms have been employed widely in several WSN-based applications (data gathering, clustering, energy-harvesting, and node localization) and showed an enhancement in the obtained results. In this paper, an efficient WSN-based fingerprinting localization system for indoor environments based on a low-cost sensor architecture, through establishing an indoor fingerprinting dataset and adopting four tailored ML models, is presented. The proposed system was validated by real experiments conducted in complex indoor environments with several obstacles and walls and achieves an efficient localization accuracy with an average of 1.4 m. In addition, through real experiments, we analyze and discuss the impact of reference point density on localization accuracy.