Lecture Notes in Computer Science 4837

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References Efficient Sensor Network Design 31 1. Alon, N., Moshkovitz, D., Safra, M.: Algorithmic construction of sets for krestrictions. ACM Transactions on Algorithms (TALG) 2(2), 153–177 (2006) 2. Aspnes, J., Goldberg, D., Yang, Y.: On the computational complexity of sensor network localization. In: Nikoletseas, S.E., Rolim, J.D.P. (eds.) ALGOSENSORS 2004. LNCS, vol. 3121, pp. 32–44. Springer, Heidelberg (2004) 3. Chakrabarty, K., Iyengar, S.S., Qi, H., Cho, E.: Grid coverage for surveillance and target location in distributed sensor networks. IEEE Trans. Comput. 51(12) (2002) 4. Dhilon, S.S., Chakrabarty, K., Iyengar, S.S.: Sensor placement for grid coverage under imprecise detections. In: FUSION 2002. Proc. 5th Int. Conf. Information Fusion, Annapolis, MD, pp. 1–10 (July 2002) 5. Duh, R.-c., Fuerer, M.: Approximation of k -Set Cover by Semi-Local Optimization. In: ACM STOC (1997) 6. Feige, U.: A Threshold of ln n for Approximating Set Cover. Journal of the ACM (JACM) 45(4), 634–652 (1998) 7. Gui, C., Mohapatra, P.: Power conservation and quality of surveillance in target tracking sensor networks. In: Proc. ACM MobiCom Conference (2004) 8. Gupta, R., Das, S.R.: Tracking moving targets in a smart sensor network. In: Proc. VTC Symp. (2003) 9. Halldorsson, M.M.: Approximating Discrete Collections via Local Improvements. In: ACM SODA (1995) 10. Halldorsson, M.M.: Approximating k-Set Cover and Complementary Graph Coloring. In: Cunningham, W.H., Queyranne, M., McCormick, S.T. (eds.) Integer Programming and Combinatorial Optimization. LNCS, vol. 1084, Springer, Heidelberg (1996) 11. Jain, S., Demmer, M., Patra, R., Fall, K.: Using Redundancy to Cope with Failures in a Delay Tolerant Network. In: Proc. SIGCOMM 2005 (2005) 12. Liu, J., Liu, J., Reich, J., Cheung, P., Zhao, F.: Distributed group management for track initiation and maintenance in target localization applications. In: Zhao, F., Guibas, L.J. (eds.) IPSN 2003. LNCS, vol. 2634, Springer, Heidelberg (2003) 13. Lund, C., Yannakakis, M.: On the hardness of approximating minimization problems. Journal of the ACM (JACM) 41(5), 960–981 (1994) 14. Poduri, S., Sukhatme, G.S.: Constrained coverage in mobile sensor networks. In: ICRA 2004. Proc. IEEE Int. Conf. Robotics and Automation, New Orleans, LA, April-May 2004, pp. 40–50 (2004) 15. Rao, N.S.V.: Computational complexity issues in operative diagnosis of graph based systems. IEEE Trans. Comput. 42(4) (April 1993) 16. Shorey, R., Ananda, A., Chan, M.C., Ooi, W.T.: Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions. Wiley, Chichester (2006) 17. Zhang, W., Cao, G.: Optimizing tree reconfiguration for mobile target tracking in sensor networks. In: Proc. IEEE InfoCom (2004) 18. Zhao, F., Guibas, L.: Wireless Sensor Networks: An Information Processing Approach. Morgan Kaufmann, San Francisco (2004) 19. Zou, Y., Chakrabarty, K.: Sensor deployment and target localization based on virtual forces. In: Proc. IEEE InfoCom, San Francisco, CA, pp. 1293–1303 (April 2003)
Counting Targets with Mobile Sensors in an Unknown Environment Beat Gfeller 1 ,Matúˇs Mihalák 1 , Subhash Suri 2,⋆ , Elias Vicari 1,⋆⋆ ,andPeterWidmayer 1,⋆⋆ 1 Department of Computer Science, ETH Zurich, Zurich, Switzerland {gfeller,mmihalak,vicariel,widmayer}@inf.ethz.ch 2 Department of Computer Science, University of California, Santa Barbara, USA suri@cs.ucsb.edu Abstract. We consider the problem of counting the number of indistinguishable targets using a simple binary sensing model. Our setting includes an unknown number of point targets in a (simply- or multiplyconnected) polygonal workspace, and a moving point-robot whose sensory input at any location is a binary vector representing the cyclic order of the polygon vertices and targets visible to the robot. In particular, the sensing model provides no coordinates, distance or angle measurements. We investigate this problem under two natural models of environment, friendly and hostile, which differ only in whether the robot can visit the targets or not, and under three different models of motion capability. In the friendly scenario we show that the robots can count the targets, whereas in the hostile scenario no (2 − ε)-approximation is possible, for any ε > 0. Next we consider two, possibly minimally more powerful robots that can count the targets exactly. 1 The Problem and the Model Simple, small and inexpensive computational and sensing devices are currently at the forefront of several research areas in computer science. These devices promise to bring computational capabilities into areas where previous approaches (usually consisting of complex and bulky hardware) are not feasible or cost-effective. Such devices are being successfully used in various monitoring systems, military tasks, and other information processing scenarios. Their main advantages are quick and easy deployment, scalability, and cost-effectiveness. However, in order to realize the full potential of these technologies, many new and challenging ⋆ Work done while the author was a visiting professor at the Institute of Theoretical Computer Science, ETH, Zurich. The author wishes to acknowledge the support provided by the National Science Foundation under grants CNS-0626954 and CCF-0514738. ⋆⋆ Work partially supported by the National Competence Center in Research on Mobile Information and Communication Systems NCCR-MICS, a center supported by the Swiss National Science Foundation under grant number 5005 – 67322. M. Kuty̷lowski et al. (Eds.): ALGOSENSORS 2007, LNCS 4837, pp. 32–45, 2008. c○ Springer-Verlag Berlin Heidelberg 2008
Page 1 and 2: Lecture Notes in Computer Science 4
Page 3 and 4: Volume Editors Mirosław Kutyłowsk
Page 5 and 6: Organization Conference and Program
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Page 26 and 27: Acknowledgements Codes for Sensors:
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Analysis of the Bounded-Hops Conver
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Correlation, Coding, and Cooperatio
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2 System Model Correlation, Coding,
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3.1 Static Scheduling Correlation,
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5 Conclusions Correlation, Coding,
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Local Approximation Algorithms for
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2.2 Shifting Strategy Local Approxi
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Assigning Sensors to Missions with
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Algorithm 2. Shifting PTAS (error
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Maximal Breach in Wireless Sensor N
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a’ a i Maximal Breach in Wireless
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6 Conclusions and Future Work Maxim
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Counting-Sort and Routing in a Sing
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Intrusion Detection of Sinkhole Att
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% neighbors with successfull detect
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References Intrusion Detection of S
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