Fast distributed construction of small k-dominating sets and applications
Journal of Algorithms
On Constructing k-Connected k-Dominating Set in Wireless Networks
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Papers - Volume 01
Distributed routing algorithms for multi-hop ad hoc networks using d-hop connected d-dominating sets
Computer Networks and ISDN Systems
VLSI implementation of greedy-based distributed routing schemes for ad hoc networks
Soft Computing - A Fusion of Foundations, Methodologies and Applications
An energy-efficient heterogeneous dual routing scheme for mobile ad hoc and sensor networks
International Journal of Mobile Network Design and Innovation
Hierarchical routing in sensor networks using k-dominating sets
IWDC'05 Proceedings of the 7th international conference on Distributed Computing
FEED: fault tolerant, energy efficient, distributed clustering for WSN
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
Efficient transformation of distance-2 self-stabilizing algorithms
Journal of Parallel and Distributed Computing
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This paper focuses on the efficient selection of a special type of subset of network nodes, which we call a k-SPR set, for the purpose of coordinating the routing of messages through a network. Such a set is a special k-hop-connected k-dominating set that has an additional property that promotes the regular occurrence of routers in all directions. The distributed algorithms introduced here for obtaining a k-SPR set require that each node broadcast at most three messages to its k-hop neighbors. These transmissions can be made asynchronously. The time required to send these messages and the sizes of the resulting sets are compared by means of data collected from simulations. The main contribution is the adaptation of some variations of the distributed greedy algorithms to the problem of generating a small k-SPR set. These variations are much faster than the standard distributed greedy algorithm. Yet, when used with a sensible choice for a certain parameter, our empirical evidence strongly suggests that the resulting set size will generally be very close to the set size for the standard greedy algorithms.