Improved Approximation Bounds for Maximum Lifetime Problems in Wireless Ad-Hoc Network
A wireless ad-hoc network consists of a number of wireless devices (nodes), that communicate with each other within the network using their built-in radio transceivers. The nodes are in general battery-powered, thus their lifetime is limited. Therefore, algorithms for maximizing the network lifetime are of great interest. In this paper we consider the Rooted Maximum Network Lifetime (RMNL) problems: given a network N and a node r, the objective is to find a maximum-size collection of routing trees rooted at the node r for a specified communication pattern. The number of such trees represents the total number of communication rounds executed before the first node in the network dies due to battery depletion. We consider two communication patterns, broadcast and convergecast.
We follow the approach used by Nutov and Segal in , who developed polynomial time approximation algorithms with constant approximation ratios for the broadcast and convergecast RMNL problems. Our analysis of their algorithms leads to better approximation ratios than the ratios derived in . In particular, we show a 1/7 approximation ratio for the multiple topology convergecast RMNL problem, improving the previous ratio of 1/31.
KeywordsNetwork Lifetime Broadcast Convergecast Approximation algorithm Wireless ad-hoc network
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- 1.Bhalgat, A., Hariharan, R., Kavitha, T., Panigrahi, D.: Fast edge splitting and edmonds’ arborescence construction for unweighted graphs. In: SODA, pp. 455–464 (2008)Google Scholar
- 2.Deng, G., Gupta, S.K.S.: Maximizing broadcast tree lifetime in wireless ad hoc networks. In: GLOBECOM (2006)Google Scholar
- 3.Edmonds, J.: Edge-disjoint branchings. In: Rustin, B. (ed.) Combinatorial Algorithms, pp. 91–96. Academic Press (1973)Google Scholar
- 8.Kang, I., Poovendran, R.: Maximizing static network lifetime of wireless broadcast adhoc networks. In: IEEE International Conference on Communications, ICC 2003, pp. 2256–2261 (2003)Google Scholar
- 9.Kang, I., Poovendran, R.: Maximizing network lifetime of broadcasting over wireless stationary ad hoc networks. MONET 10(6), 879–896 (2005)Google Scholar
- 11.Lin, H.C., Li, F.J., Wang, K.Y.: Constructing maximum-lifetime data gathering trees in sensor networks with data aggregation. In: ICC, pp. 1–6 (2010)Google Scholar
- 13.Nutov, Z.: Approximating directed weighted-degree constrained networks. In: APPROX-RANDOM, pp. 219–232 (2008)Google Scholar
- 14.Nutov, Z.: Approximating maximum integral flows in wireless sensor networks via weighted-degree constrained k-flows. In: DIALM-POMC, pp. 29–34 (2008)Google Scholar
- 16.Orda, A., Yassour, B.A.: Maximum-lifetime routing algorithms for networks with omnidirectional and directional antennas. In: MobiHoc, pp. 426–437 (2005)Google Scholar
- 17.Park, J., Sahni, S.: Maximum lifetime broadcasting in wireless networks. In: AICCSA, p. 8. IEEE Computer Society (2005)Google Scholar
- 19.Stanford, J., Tongngam, S.: Approximation algorithm for maximum lifetime in wireless sensor networks with data aggregation. In: SNPD, pp. 273–277 (2006)Google Scholar
- 20.Wu, Y., Fahmy, S., Shroff, N.B.: On the construction of a maximum-lifetime data gathering tree in sensor networks: Np-completeness and approximation algorithm. In: INFOCOM, pp. 356–360 (2008)Google Scholar