Self-organizing fault-tolerant topology control in large-scale three-dimensional wireless networks

  • Authors:

  • Yu Wang;Lijuan Cao;Teresa A. Dahlberg;Fan Li;Xinghua Shi

  • Affiliations:

  • University of North Carolina at Charlotte, Charlotte, NC;Johnson C. Smith University, Charlotte, NC;University of North Carolina at Charlotte, Charlotte, NC;Beijing Institute of Technology, Beijing, P. R. China;University of Chicago, Chicago, IL

  • Venue:
  • ACM Transactions on Autonomous and Adaptive Systems (TAAS)
  • Year:
  • 2009

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Abstract

Topology control protocol aims to efficiently adjust the network topology of wireless networks in a self-adaptive fashion to improve the performance and scalability of networks. This is especially essential to large-scale multihop wireless networks (e.g., wireless sensor networks). Fault-tolerant topology control has been studied recently. In order to achieve both sparseness (i.e., the number of links is linear with the number of nodes) and fault tolerance (i.e., can survive certain level of node/link failures), different geometric topologies were proposed and used as the underlying network topologies for wireless networks. However, most of the existing topology control algorithms can only be applied to two-dimensional (2D) networks where all nodes are distributed in a 2D plane. In practice, wireless networks may be deployed in three-dimensional (3D) space, such as under water wireless sensor networks in ocean or mobile ad hoc networks among space shuttles in space. This article seeks to investigate self-organizing fault-tolerant topology control protocols for large-scale 3D wireless networks. Our new protocols not only guarantee k-connectivity of the network, but also ensure the bounded node degree and constant power stretch factor even under k−1 node failures. All of our proposed protocols are localized algorithms, which only use one-hop neighbor information and constant messages with small time complexity. Thus, it is easy to update the topology efficiently and self-adaptively for large-scale dynamic networks. Our simulation confirms our theoretical proofs for all proposed 3D topologies.