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Convergecast and Broadcast by Power-Aware Mobile Agents
- 作者:Julian Anaya ; Jérémie Chalopin ; Jurek Czyzowicz ; Arnaud Labourel…
- 关键词:Convergecast ; Broadcast ; Mobile agent ; Power ; aware ; Centralized algorithm ; Distributed algorithm ; Competitive ratio ; Graph
- 刊名:Algorithmica
- 出版年:2016
- 出版时间:January 2016
- 年:2016
- 卷:74
- 期:1
- 页码:117-155
- 全文大小:725 KB
- 参考文献:1.Albers, S.: Energy-efficient algorithms. Commun. ACM 53(5), 86–96 (2010)MathSciNet CrossRef
2.Albers, S., Henzinger, M.R.: Exploring unknown environments. In: Proceedings of the 29th Annual ACM Symposium on Theory of Computing (STOC), pp. 416–425 (1997) 3.Alpern, S., Gal, S.: The Theory of Search Games and Rendezvous, vol. 55. Springer, Berlin (2003)MATH 4.Ambühl, C.: An optimal bound for the mst algorithm to compute energy efficient broadcast trees in wireless networks. In: Proceedings of the International Colloquium on Automata, Languages, and Programming (ICALP), Lecture Notes in Computer Science, vol. 3580, pp. 1139–1150 (2005) 5.Ando, H., Oasa, Y., Suzuki, I., Yamashita, M.: Distributed memoryless point convergence algorithm for mobile robots with limited visibility. IEEE Trans. Robot. Autom. 15(5), 818–828 (1999)CrossRef 6.Angluin, D., Aspnes, J., Diamadi, Z., Fischer, M., Peralta, R.: Computation in networks of passively mobile finite-state sensors. Distrib. Comput. 18(4), 235–253 (2006)MATH CrossRef 7.Annamalai, V., Gupta, S., Schwiebert, L.: On tree-based convergecasting in wireless sensor networks. IEEE Wirel. Commun. Netw. 3, 1942–1947 (2003) 8.Augustine, J., Irani, S., Swamy, C.: Optimal power-down strategies. In: Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science (FOCS), pp. 530–539 (2004) 9.Averbakh, I., Berman, O.: A heuristic with worst-case analysis for minimax routing of two travelling salesmen on a tree. Discrete Appl. Math. 68(1–2), 17–32 (1996)MATH MathSciNet CrossRef 10.Awerbuch, B., Betke, M., Rivest, R.L., Singh, M.: Piecemeal graph exploration by a mobile robot. Inf. Comput. 152(2), 155–172 (1999)MATH MathSciNet CrossRef 11.Awerbuch, B., Goldreich, O., Vainish, R., Peleg, D.: A trade-off between information and communication in broadcast protocols. J. ACM 37(2), 238–256 (1990)MATH MathSciNet CrossRef 12.Azar, Y.: On-line load balancing. In: Fiat A., Woeginger G. (eds.) Online Algorithms Lecture notes in Computer Science, vol. 1442, pp. 178–195 (1998) 13.Baezayates, R., Culberson, J., Rawlins, G.: Searching in the plane. Inf. Comput. 106(2), 234–252 (1993)MathSciNet CrossRef 14.Bar-Yehuda, R., Goldreich, O., Itai, A.: On the time-complexity of broadcast in multi-hop radio networks: an exponential gap between determinism and randomization. J. Comput. Syst. Sci. 45(1), 104–126 (1992)MATH MathSciNet CrossRef 15.Bender, M., Slonim, D.: The power of team exploration: two robots can learn unlabeled directed graphs. In: Proceedings of the 35th Annual Symposium on Foundations of Computer Science (FOCS), pp. 75–85 (1994) 16.Bender, M.A., Fernández, A., Ron, D., Sahai, A., Vadhan, S.: The power of a pebble: exploring and mapping directed graphs. Inf. Comput. 176(1), 1–21 (2002)MATH CrossRef 17.Betke, M., Rivest, R., Singh, M.: Piecemeal learning of an unknown environment. Mach. Learn. 18(2–3), 231–254 (1995) 18.Blum, A., Raghavan, P., Schieber, B.: Navigating in unfamiliar geometric terrain. SIAM J. Comput. 26(1), 110–137 (1997)MATH MathSciNet CrossRef 19.Bunde, D.: Power-aware scheduling for makespan and flow. J. Sched. 12(5), 489–500 (2009)MATH MathSciNet CrossRef 20.Chen, F., Johnson, M., Alayev, Y., Bar-Noy, A., La Porta, T.: Who, when, where: timeslot assignment to mobile clients. IEEE Trans. Mob. Comput. 11(1), 73–85 (2012)CrossRef 21.Cieliebak, M., Flocchini, P., Prencipe, G., Santoro, N.: Solving the robots gathering problem. In: Proceedings of the International Colloquium of Automata. Languages and Programming (ICALP), Lecture Notes in Computer Science, vol. 2719 pp. 1181–1196. Springer, Berlin (2003) 22.Cohen, R., Peleg, D.: Convergence properties of the gravitational algorithm in asynchronous robot systems. SIAM J. Comput. 34(6), 1516–1528 (2005)MATH MathSciNet CrossRef 23.Cord-Landwehr, A., Degener, B., Fischer, M., Hüllmann, M., Kempkes, B., Klaas, A., Kling, P., Kurras, S., Märtens, M., Meyer auf der Heide, F., Raupach, C., Swierkot, K., Warner, D., Weddemann, C., Wonisch, D.: A new approach for analyzing convergence algorithms for mobile robots. In: Aceto L., Henzinger M., Sgall J. (eds.) Proceedings of the International Colloquium of Automata, Languages and Programming (ICALP), Lecture Notes in Computer Science, vol. 6756 pp. 650–661 (2011) 24.Das, S., Flocchini, P., Santoro, N., Yamashita, M.: On the computational power of oblivious robots: forming a series of geometric patterns. In: Proceedings of the 29th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing (PODC), pp. 267–276 (2010) 25.Deng, X., Papadimitriou, C.H.: Exploring an unknown graph. J. Graph Theory 32(3), 265–297 (1999)MATH MathSciNet CrossRef 26.Dynia, M., Korzeniowski, M., Schindelhauer, C.: Power-aware collective tree exploration. In: Architecture of Computing Systems (ARCS), Lecture Notes in Computer Science, vol. 3894, pp. 341–351 (2006) 27.Flocchini, P., Prencipe, G., Santoro, N., Widmayer, P.: Gathering of asynchronous robots with limited visibility. Theor. Comput. Sci. 337(1–3), 147–168 (2005)MATH MathSciNet CrossRef 28.Fraigniaud, P., Gasieniec, L., Kowalski, D.R., Pelc, A.: Collective tree exploration. Networks 48(3), 166–177 (2006)MATH MathSciNet CrossRef 29.Frederickson, G., Hecht, M., Kim, C.: Approximation algorithms for some routing problems. SIAM J. Comput. 7(2), 178–193 (1978)MathSciNet CrossRef 30.Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York (1979)MATH 31.Irani, S., Shukla, S., Gupta, R.: Algorithms for power savings. ACM Trans. Algorithms 3(4), 41 (2007)MathSciNet CrossRef 32.Kesselman, A., Kowalski, D.R.: Fast distributed algorithm for convergecast in ad hoc geometric radio networks. J. Parallel Distrib. Comput. 66(4):578–585, 2006. Algorithms for Wireless and Ad-Hoc Networks 33.Krishnamachari, B., Estrin, D., Wicker, S.: The impact of data aggregation in wireless sensor networks. In: Proceedings of the 22nd International Conference on Distributed Computing Systems Workshops, pp. 575–578 (2002) 34.Megow, N., Mehlhorn, K., Schweitzer, P.: Online graph exploration: new results on old and new algorithms. Theor. Comput. Sci. 463, 62–72 (2012)MATH MathSciNet CrossRef 35.Rajagopalan, R., Varshney, P.: Data-aggregation techniques in sensor networks: a survey. IEEE Commun. Surv. Tutor. 8(4), 48–63 (2006)CrossRef 36.Santoro, N.: Design and Analysis of Distributed Algorithms, vol. 56. Wiley, New York (2006)CrossRef 37.Stojmenovic, I., Lin, X.: Power-aware localized routing in wireless networks. IEEE Trans. Parallel Distrib. Syst. 12(11), 1122–1133 (2001)CrossRef 38.Suzuki, I., Yamashita, M.: Distributed anonymous mobile robots: formation of geometric patterns. SIAM J. Comput. 28(4), 1347–1363 (1999)MATH MathSciNet CrossRef 39.Yamashita, M., Suzuki, I.: Characterizing geometric patterns formable by oblivious anonymous mobile robots. Theor. Comput. Sci. 411(26–28), 2433–2453 (2010)MATH MathSciNet CrossRef 40.Yao, F., Demers, A., Shenker, S.: A scheduling model for reduced cpu energy. In: Proceedings of the 36th Annual Symposium on Foundations of Computer Science, pp. 374–382 (1995)
- 作者单位:Julian Anaya (1)
Jérémie Chalopin (2) Jurek Czyzowicz (1) Arnaud Labourel (2) Andrzej Pelc (1) Yann Vaxès (2)
1. Université du Québec en Outaouais, C.P. 1250, succ. Hull, Gatineau, J8X 3X7, QC, Canada 2. LIF, CNRS & Aix-Marseille University, 13288, Marseille, France
- 刊物类别:Computer Science
- 刊物主题:Algorithm Analysis and Problem Complexity
Theory of Computation Mathematics of Computing Algorithms Computer Systems Organization and Communication Networks Data Structures, Cryptology and Information Theory
- 出版者:Springer New York
- ISSN:1432-0541
文摘
A set of identical, mobile agents is deployed in a weighted network. Each agent has a battery—a power source allowing it to move along network edges. An agent uses its battery proportionally to the distance traveled. We consider two tasks: convergecast, in which at the beginning, each agent has some initial piece of information, and information of all agents has to be collected by some agent; and broadcast in which information of one specified agent has to be made available to all other agents. In both tasks, the agents exchange the currently possessed information when they meet. The objective of this paper is to investigate what is the minimal value of power, initially available to all agents, so that convergecast or broadcast can be achieved. We study this question in the centralized and the distributed settings. In the centralized setting, there is a central monitor that schedules the moves of all agents. In the distributed setting every agent has to perform an algorithm being unaware of the network. In the centralized setting, we give a linear-time algorithm to compute the optimal battery power and the strategy using it, both for convergecast and for broadcast, when agents are on the line. We also show that finding the optimal battery power for convergecast or for broadcast is NP-hard for the class of trees. On the other hand, we give a polynomial algorithm that finds a 2-approximation for convergecast and a 4-approximation for broadcast, for arbitrary graphs.In the distributed setting, we give a 2-competitive algorithm for convergecast in trees and a 4-competitive algorithm for broadcast in trees. The competitive ratio of 2 is proved to be the best for the problem of convergecast, even if we only consider line networks. Indeed, we show that there is no (\(2-\epsilon \))-competitive algorithm for convergecast or for broadcast in the class of lines, for any \(\epsilon >0\).
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