Strategies for Searching Targets Using Mobile Sensors in Defense Scenarios

Full Text (PDF, 715KB), PP.61-70

Views: 0 Downloads: 0


Tanmoy Hazra 1,* CRS Kumar 1 Manisha J. Nene 1

1. Department of Computer Science and Engineering, Defence Institute of Advanced Technology, Pune 411025, India

* Corresponding author.


Received: 12 Aug. 2016 / Revised: 20 Nov. 2016 / Accepted: 15 Feb. 2017 / Published: 8 May 2017

Index Terms

Mobile Sensors, Stable Marriage Problem, College Admission Problem, Schulze Method, Target Searching


Target searching is one of the challenging research areas in defense. Different types of sensor networks are deployed for searching targets in critical zones. The selection of optimal strategies for the sensor nodes under certain constraints is the key issue in target searching problem. This paper addresses a number of target searching problems related to various defense scenarios and introduces new strategic approaches to facilitate the search operation for the mobile sensors in a two-dimensional bounded space. The paper classifies the target searching problems into two categories: preference-based and traversal distance based. In the preference based problems, the strategies for the mobile sensors are determined by Stable Marriage Problem, College Admission Problem, and voting system; they are analyzed with suitable examples. Alternatively, traversal distance based problems are solved by our proposed graph searching approaches and analyzed with randomly chosen examples. Results obtained from the examples signify that our proposed models can be applied in defense-related target searching problems.

Cite This Paper

Tanmoy Hazra, CRS Kumar, Manisha J. Nene, "Strategies for Searching Targets Using Mobile Sensors in Defense Scenarios", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.5, pp.61-70, 2017. DOI:10.5815/ijitcs.2017.05.08


[1]S. Shen, G. Yue, and Q. Cao, “A survey of game theory in wireless sensor networks security,” J. Networks, 6 (3), pp. 521-532, 2011. 

[2]H. Y. Shi, W. L. Wang, N.M. Kwok, and S. Y. Chen, “Game theory for wireless sensor networks: A survey,” Sensors 12 (7), pp. 9055-9097, 2012.

[3]R. Machado, and S. Tekinay, “A survey of game-theoretic approaches in wireless sensor networks,” Comput. Networks (Elsevier), 52, pp. 3047-3061, 2008.

[4]J. S. Cox, and E. H. Durfee, “An efficient algorithm for multiagent plan coordination,” 4th International joint conference on Autonomous agents and multiagent systems, pp. 828-835, 2005.

[5]J. Berger, N. Lo, and M. Noel, “A new multi-target, multi-agent search-and-rescue path planning approach,” Int. J. Comput., Elec., Auto., Contr. and Info. Engineering 8(6), pp. 935-944, 2014.

[6]Y. Meng, “Multi-robot searching using game-theory based approach,” Int. J. Adv. Robotic Systems 5(4), pp. 341-350, 2008.

[7]T. Das, and S. Roy, “Game theory inspired mobile object trapping system in mobile wireless sensor network,” IEEE International Conference Electronic Systems, Signal Processing and Computing Technologies (ICESC), pp. 245-250, 2014.

[8]T. Hadzic, K. N. Brown, and C. J. Sreenan, “Real-time pedestrian evacuation planning during emergency,” 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 597-604, 2011.

[9]Prinzie, H. Oppewal, “A sequence alignment distance measure for preference rank aggregation,” ANZMAC Conference: Marketing Research and Research Methodologies (quantitative), pp. 100-107, 2005.

[10]Antoniades, H. J. Kim, and S. Sastry, “Pursuit-evasion strategies for teams of multiple agents with incomplete information,” 42nd IEEE International Conference Decision and Control (ICDC), pp. 756-761, 2003.

[11]T. H. Chung, and J. W. Burdick, “Multi-agent probabilistic search in a sequential decision-theoretic framework,” IEEE International Conference of Robotics and Automation (ICRA), pp. 146-151, 2008.

[12]S. Waharte, A. Symington, and N. Trigoni, “Probabilistic search with agile UAVs,” IEEE International Conference of Robotics and Automation (ICRA), pp. 2840-2845, 2010.

[13]Khan, E. Yanmaz, and B. Rinner, “Information merging in multi-UAV cooperative search,” IEEE International Conference of Robotics and Automation (ICRA), pp. 3122-3129, 2014.

[14]Strode, “Optimising multistatic sensor locations using path planning and game theory,” Computational Intelligence for Security and Defense Applications (CISDA), IEEE Symposium on, pp. 9-16, 2011.

[15]Gale, and L. S. Shapley, “College admissions and the stability of marriage,” The American Math. Monthly 69(1), pp. 9-15, 1962.

[16]E. Roth, “The college admissions problem is not equivalent to the marriage problem,” J. Econ. Theory, vol. 36, pp. 277-288, 1985.

[17]P. Teo, J. Sethuraman, and W. P. Tan, “Gale-Shapley stable marriage problem revisited: strategic issues and applications,” Management Sci. 47 (9), pp. 1252-1267, 2001.

[18]M. Schulze, “A new monotonic, clone-independent, reversal symmetric, and condorcet-consistent single-winner election method,” Soc. Choice Welf. 36, pp. 267-303, 2011.

[19]S. M. Nieberg, E. Kropat, S. Pickl, and A. Bordetsky, “Intercepting a target with sensor swarms,” 46th IEEE Hawaii International Conference on System Sciences (HICSS), pp. 1222-1230, 2013.

[20]Nussbaum, and A. Y¨or¨ukc¨u, “Moving target search with subgoal graphs,” Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling, pp. 179-187, 2015.

[21]V. Isler, S. Kannan, and S. Khanna, “Randomized pursuit-evasion in a polygonal environment,” IEEE Trans. on Robototics, 21 (5), pp. 875-884, 2005.

[22]Renzaglia, N. Noori, and V. Isler, “The role of target modeling in designing search strategies,” IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 4260-4265, 2014.

[23]P. Maxwell, A. A. Maciejewski, H. J. Siegel, J. Potter, and J. Smith, “A mathematical model of robust military village searches for decision making purposes,” International Conference on Information and Knowledge Engineering (IKE 09), pp. 311-316, 2009.