A Distributed Algorithm to Construct Multicast Trees in WSNs: An Approximate Steiner Tree Approach

Multicast tree is a key structure for data dissemination from one source to multiple receivers in wireless networks. Minimum length multicast tree can be modeled as the Steiner Tree Problem, and is proven to be NP-hard. In this paper, we explore how to efficiently generate minimum length multicast trees in wireless sensor networks (WSNs), where only limited knowledge of network topology is available at each node. We design and analyze a simple and distributed algorithm, which we call Toward Source Tree (TST), to build multicast trees in WSNs. We show three metrics of TST algorithm, i.e., running time, tree length and energy efficiency. We prove that its running time is O(√nlog n), the best among all existing solutions to our best knowledge. We prove that TST tree length is in the same order as Steiner tree, give a theoretical upper bound and use simulations to show the ratio between them is only 1.114 when nodes are uniformly distributed. We evaluate energy efficiency in terms of the number of forwarding nodes in multicast trees, and prove that it is order-optimal. We give an efficient way to construct multicast tree in support of transmission of voluminous data.

[1]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[2]  J. Steele Growth Rates of Euclidean Minimal Spanning Trees With Power Weighted Edges , 1988 .

[3]  Anujan Varma,et al.  Distributed algorithms for multicast path setup in data networks , 1996, TNET.

[4]  Fabrizio Grandoni,et al.  Steiner Tree Approximation via Iterative Randomized Rounding , 2013, JACM.

[5]  M. Penrose A Strong Law for the Longest Edge of the Minimal Spanning Tree , 1999 .

[6]  Ding-Zhu Du,et al.  A proof of the Gilbert-Pollak conjecture on the Steiner ratio , 1992, Algorithmica.

[7]  Giuseppe Lo Re,et al.  An efficient distributed algorithm for generating multicast distribution trees , 2005, 2005 International Conference on Parallel Processing Workshops (ICPPW'05).

[8]  Hamid R. Sadjadpour,et al.  A Unifying Perspective on the Capacity of Wireless Ad Hoc Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[9]  Michael Segal,et al.  Improved structures for data collection in wireless sensor networks , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[10]  Eduardo Uchoa,et al.  Distributed Dual Ascent Algorithm for Steiner Problems in Networks , 2007 .

[11]  Sang-Ha Kim,et al.  Distributed multicast protocol based on beaconless routing for wireless sensor networks , 2013, 2013 IEEE International Conference on Consumer Electronics (ICCE).

[12]  R. Srikant,et al.  The Multicast Capacity of Large Multihop Wireless Networks , 2007, IEEE/ACM Transactions on Networking.

[13]  Pedro M. Ruiz,et al.  LEMA: Localized Energy-Efficient Multicast Algorithm based on Geographic Routing , 2006, Proceedings. 2006 31st IEEE Conference on Local Computer Networks.

[14]  Richard M. Karp,et al.  Reducibility Among Combinatorial Problems , 1972, 50 Years of Integer Programming.

[15]  Upkar Varshney,et al.  Multicast over wireless networks , 2002, CACM.

[16]  Laura Galluccio,et al.  GEographic Multicast (GEM) for Dense Wireless Networks: Protocol Design and Performance Analysis , 2013, IEEE/ACM Transactions on Networking.

[17]  F. Roberts Random minimal trees , 1968 .

[18]  Ivan Stojmenovic,et al.  GMR: Geographic Multicast Routing for Wireless Sensor Networks , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[19]  Xiang-Yang Li Multicast Capacity of Wireless Ad Hoc Networks , 2009, IEEE/ACM Transactions on Networking.

[20]  Piotr Zwierzykowski,et al.  Dijkstra-based localized multicast routing in Wireless Sensor Networks , 2012, 2012 8th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP).