Paper Info
Title
Resource optimization algorithm for sparse time-driven sensor networks
Abstract
Time-driven sensor networks are devoted to the continuous reporting of data to the user. Typically, their topology is that of a data-gathering tree rooted at the sink, whose vertexes correspond to nodes located at sampling locations that have been selected according to user or application requirements. Thus, generally these locations are not close to each other and the resulting node deployment is rather sparse. In a previous paper, we developed a heuristic algorithm based on simulated annealing capable of finding an optimal or suboptimal data-gathering tree in terms of lifetime expectancy. However, despite the enhanced lifetime, the overall link distance is not optimized, fact that increases the need for additional resources (relay nodes). Therefore, in this paper we propose the Link Distance Reduction algorithm, with the goal of reducing link distances as long as network lifetime is preserved. The benefits of this new algorithm are evaluated in detail.
Year
DOI
Venue
2010
10.1007/978-3-642-12963-6_22
Networking
Keywords
Field
DocType
lifetime expectancy,link distance,data-gathering tree,enhanced lifetime,sparse time-driven sensor network,resource optimization algorithm,new algorithm,previous paper,network lifetime,heuristic algorithm,overall link distance,link distance reduction algorithm,minimum spanning tree,spanning tree,sensor network,network planning,data gathering,simulated annealing
Simulated annealing,Mathematical optimization,Distributed minimum spanning tree,Network planning and design,Computer science,Heuristic (computer science),Brooks–Iyengar algorithm,Spanning tree,Wireless sensor network,Minimum spanning tree,Distributed computing
Conference
Volume
ISSN
ISBN
6091
0302-9743
3-642-12962-5
Citations 
PageRank 
References 
1
0.38
11
Authors
3
Name
Order
Citations
PageRank
María Luisa Santamaría150.86
Sebastià Galmés2216.70
Ramon Puigjaner322928.79