Low-Complexity Localization and Tracking in Hybrid Wireless Sensor Networks

Localization in Wireless Sensor Networks (WSNs) is an important research topic: readings come from sensors scattered in the environment, and most of applications assume that the exact position of the sensors is known. Due to power restrictions, WSN nodes are not usually equipped with a global positioning system—hence, many techniques have been developed in order to estimate the position of nodes according to some measurements over the radio channel. In this paper, we propose a new technique to track a moving target by combining distance measurements obtained from both narrowband IEEE 802.15.4 and Ultrawideband (UWB) radios, and then exploiting a novel speed-based algorithm for bounding the error. This process is applied to a real dataset collected during a measurement campaign, and its performance is compared against a Kalman filter. Results show that our algorithm is able to track target path with good accuracy and low computational impact.

[1]  Andrea Zanella,et al.  Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks , 2008, REALWSN '08.

[2]  Reza Olfati-Saber,et al.  Distributed Kalman filtering for sensor networks , 2007, 2007 46th IEEE Conference on Decision and Control.

[3]  Friedrich Jondral,et al.  Software-Defined Radio—Basics and Evolution to Cognitive Radio , 2005, EURASIP J. Wirel. Commun. Netw..

[4]  Andrea Conti,et al.  Wireless Sensor and Actuator Networks: Technologies, Analysis and Design , 2008 .

[5]  Emanuele Goldoni,et al.  Impact of channel access on localization in cooperative UWB sensor network: A case study , 2012, 2012 9th Workshop on Positioning, Navigation and Communication.

[6]  Luca Reggiani,et al.  Hybrid active and passive localization for small targets , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[7]  Stergios I. Roumeliotis,et al.  SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using the Sign of Innovations , 2006, IEEE Trans. Signal Process..

[8]  Hojung Cha,et al.  An Empirical Study of Antenna Characteristics Toward RF-Based Localization for IEEE 802.15.4 Sensor Nodes , 2007, EWSN.

[9]  Alexander Wessels,et al.  Dynamic indoor localization using multilateration with RSSI in wireless sensor networks for transport logistics , 2010 .

[10]  YangYang,et al.  Wireless sensor and actuator networks , 2010 .

[11]  Mohamed Essayed Bouzouraa,et al.  Robust method for outdoor localization of a mobile robot using received signal strength in low power wireless networks , 2008, 2008 IEEE International Conference on Robotics and Automation.

[12]  F. Sottile,et al.  Design, deployment and performance of a complete real-time ZigBee localization system , 2008, 2008 1st IFIP Wireless Days.

[13]  S. Challa,et al.  Simultaneous Localization and Mapping in Wireless Sensor Networks , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[14]  Emanuele Goldoni,et al.  Experimental analysis of RSSI-based indoor localization with IEEE 802.15.4 , 2010, 2010 European Wireless Conference (EW).

[15]  Daniele Trinchero,et al.  Localization, tracking, and imaging of targets in wireless sensor networks: An invited review , 2011 .

[16]  Amitangshu Pal,et al.  Localization Algorithms in Wireless Sensor Networks: Current Approaches and Future Challenges , 2010, Netw. Protoc. Algorithms.

[17]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[18]  Moe Z. Win,et al.  Network localization and navigation via cooperation , 2011, IEEE Communications Magazine.