An Energy Consumption Aware Solution for the 3D Localization and Synchronization Problems in WSNs

Localization and synchronization are fundamental services for many applications in Wireless Sensor Networks (WSNs), since it is often required to know the sensor nodes' position and global time to relate a given event detection to a specific location and time. However, the localization and synchronization tasks are often performed after the sensor nodes' deployment in the sensor field. Since manual configuration of sensor nodes is usually an impractical activity, it is necessary to rely on specific algorithms to solve both the problems of localizing and synchronizing the clock of sensor nodes. With this in mind, in this work we propose a joint solution for the problem of 3D localization and time synchronization in WSNs using an unmanned aerial vehicle (UAV). A UAV equipped with a GPS flies over the sensor field area broadcasting its geographical position. Therefore, sensor nodes are able to estimate their geographical position and global time without the need to equip them with a GPS device. By means of simulations we show that our proposed join solution leads to smaller time synchronization and localization errors as well as a lower energy consumption when compared to solutions found in the literature.

[1]  Ajay D. Kshemkalyani,et al.  Clock synchronization for wireless sensor networks: a survey , 2005, Ad Hoc Networks.

[2]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[3]  Christoph Lenzen,et al.  Optimal clock synchronization in networks , 2009, SenSys '09.

[4]  Azzedine Boukerche,et al.  Localization in time and space for wireless sensor networks: An efficient and lightweight algorithm , 2009, Perform. Evaluation.

[5]  Azzedine Boukerche,et al.  Time-space correlation for real-time, accurate, and energy-aware data reporting in wireless sensor networks , 2011, MSWiM '11.

[6]  Qun Li,et al.  Global clock synchronization in sensor networks , 2006, IEEE Transactions on Computers.

[7]  Azzedine Boukerche,et al.  DRINA: A Lightweight and Reliable Routing Approach for In-Network Aggregation in Wireless Sensor Networks , 2013, IEEE Transactions on Computers.

[8]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[9]  B. R. Badrinath,et al.  Ad hoc positioning system (APS) using AOA , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[10]  Gyula Simon,et al.  The flooding time synchronization protocol , 2004, SenSys '04.

[11]  Azzedine Boukerche,et al.  A small world approach for scalable and resilient position estimation algorithms for wireless sensor networks , 2012, MobiWac '12.

[12]  Jo Ueyama,et al.  3D Localization in Wireless Sensor Networks Using Unmanned Aerial Vehicle , 2013, 2013 IEEE 12th International Symposium on Network Computing and Applications.

[13]  S. Pattem,et al.  Distributed online localization in sensor networks using a moving target , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[14]  Deborah Estrin,et al.  Directed diffusion: a scalable and robust communication paradigm for sensor networks , 2000, MobiCom '00.

[15]  Lixia Zhang,et al.  Recursive position estimation in sensor networks , 2001, Proceedings Ninth International Conference on Network Protocols. ICNP 2001.