Lightweight clustering in wireless sensor-actuator networks on obstructed environments

Wireless sensor-actuator networks (WSANs) can be useful to cope with the connectivity limitations of sparse networks by allowing powerful and mobile actuators periodically collect data from sensors. We propose a low-overhead algorithm that takes advantage of any potential connectivity present in sensors to form clusters that can expose single collection points, therefore, optimizing actuator data collection rates. No prior knowledge assumptions on the location of sensors, localization algorithms, or environment conditions are made in the design of the algorithm. Environment exploration is introduced as well as self-correcting tour mechanisms. Detailed simulations of high level statistical accuracy support our clustering approach and demonstrate the critical design issues of the algorithm.

[1]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[2]  Emanuel Melachrinoudis,et al.  Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[3]  Sanjeev Arora,et al.  Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems , 1998, JACM.

[4]  Jun Luo,et al.  Joint mobility and routing for lifetime elongation in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[5]  Ian F. Akyildiz,et al.  Wireless sensor and actor networks: research challenges , 2004, Ad Hoc Networks.

[6]  Ashutosh Sabharwal,et al.  Using Predictable Observer Mobility for Power Efficient Design of Sensor Networks , 2003, IPSN.

[7]  Eiji Nakano,et al.  On robot self-navigation in outdoor environments by color image processing , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[8]  Ricardo Lent,et al.  INES: network simulations on virtual environments , 2008, SimuTools.

[9]  Michael R. Lyu,et al.  Reliable Reporting of Delay-Sensitive Events in Wireless Sensor-Actuator Networks , 2006, 2006 IEEE International Conference on Mobile Ad Hoc and Sensor Systems.

[10]  Dario Pompili,et al.  Handling Mobility in Wireless Sensor and Actor Networks , 2010, IEEE Transactions on Mobile Computing.

[11]  Ellen W. Zegura,et al.  A message ferrying approach for data delivery in sparse mobile ad hoc networks , 2004, MobiHoc '04.

[12]  Jon Jouis Bentley,et al.  Fast Algorithms for Geometric Traveling Salesman Problems , 1992, INFORMS J. Comput..

[13]  Deborah Estrin,et al.  Intelligent fluid infrastructure for embedded networks , 2004, MobiSys '04.

[14]  Alhussein A. Abouzeid,et al.  Stochastic Event Capture Using Mobile Sensors Subject to a Quality Metric , 2006, IEEE Transactions on Robotics.

[15]  Zhen Zhang,et al.  Route Design for Multiple Ferries in Delay Tolerant Networks , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[16]  Chang-Gun Lee,et al.  Partitioning based mobile element scheduling in wireless sensor networks , 2005, 2005 Second Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2005. IEEE SECON 2005..

[17]  Satish Kumar,et al.  Next century challenges: scalable coordination in sensor networks , 1999, MobiCom.

[18]  U. Qidwai,et al.  Intelligent sensor network for obstacle avoidance strategy , 2005, IEEE Sensors, 2005..

[19]  Waylon Brunette,et al.  Data MULEs: modeling a three-tier architecture for sparse sensor networks , 2003, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003..