Clustering intelligent transportation sensors using public transportation

Advanced transportation sensors use a wireless medium to communicate and use data fusion techniques to provide complete information. Large-scale use of intelligent transportation sensors can lead to data bottlenecks in an ad-hoc wireless sensor network, which needs to be reliable and should provide a framework to sensors that constantly join and leave the network. A possible solution is to use public transportation vehicles as data fusion nodes or cluster heads. This paper presents a mathematical programming approach to use public transportation vehicles as cluster heads. The mathematical programming solution seeks to maximize benefit achieved by covering both mobile and stationary sensors, while considering cost/penalty associated with changing cluster head locations. A simulation is developed to capture realistic considerations of a transportation network. This simulation is used to validate the solution provided by the mathematical model.

[1]  Nicolas Monmarché,et al.  A new clustering algorithm based on the chemical recognition system of ants , 2002 .

[2]  Adrian Perrig,et al.  ACE: An Emergent Algorithm for Highly Uniform Cluster Formation , 2004, EWSN.

[3]  Atsuo Suzuki,et al.  A NEW CLUSTERING MODEL OF WIRELESS SENSOR NETWORKS USING FACILITY LOCATION THEORY , 2009 .

[4]  M. Mehdi Afsar,et al.  Clustering in sensor networks: A literature survey , 2014, J. Netw. Comput. Appl..

[5]  Makoto Takizawa,et al.  A Survey on Clustering Algorithms for Wireless Sensor Networks , 2010, NBiS.

[6]  Rakesh Nagi,et al.  Clustering Sensors in Wireless Ad Hoc Networks Operating in a Threat Environment , 2005, Oper. Res..

[7]  James Llinas,et al.  An introduction to multisensor data fusion , 1997, Proc. IEEE.

[8]  Rajan Batta,et al.  Optimal placement of omnidirectional sensors in a transportation network for effective emergency response and crash characterization , 2014 .

[9]  Rajan Batta,et al.  A simulation approach to study emergency response , 2014, J. Simulation.

[10]  Pascal Vasseur,et al.  Introduction to multi-sensor data fusion , 2004 .

[11]  Ameer Ahmed Abbasi,et al.  A survey on clustering algorithms for wireless sensor networks , 2007, Comput. Commun..

[12]  Moslem Afrashteh Mehr.  Cluster Head Election using Imperialist Competitive Algorithm (CHEI) for Wireless Sensor Networks , 2014 .

[13]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[14]  Peter A. Rogerson,et al.  Location coverage models with demand originating from nodes and paths: Application to cellular network design , 2008, European Journal of Operational Research.

[15]  Prithwish Basu,et al.  A mobility based metric for clustering in mobile ad hoc networks , 2001, Proceedings 21st International Conference on Distributed Computing Systems Workshops.

[16]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[17]  James Llinas,et al.  An introduction to multi-sensor data fusion , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[18]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[19]  Panos M. Pardalos,et al.  Mathematical Programming Techniques for Sensor Networks , 2009, Algorithms.