The Relative Neighborhood Graph, with an Application to Minimum Spanning Trees
Journal of the ACM (JACM)
Connectivity and Topology Control in Wireless Ad Hoc Networks with Realistic Physical Layer
ICWMC '07 Proceedings of the Third International Conference on Wireless and Mobile Communications
From theory to practice: topology control in wireless sensor networks
Proceedings of the tenth ACM international symposium on Mobile ad hoc networking and computing
Battery allocation for wireless sensor network lifetime maximization under cost constraints
Proceedings of the 2009 International Conference on Computer-Aided Design
Greedy Geographic Routing Algorithms in Real Environment
MSN '09 Proceedings of the 2009 Fifth International Conference on Mobile Ad-hoc and Sensor Networks
Benefits of multiple battery levels for the lifetime of large wireless sensor networks
NETWORKING'05 Proceedings of the 4th IFIP-TC6 international conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communication Systems
Energy concerns in wireless networks
IEEE Wireless Communications
Transmission range adaptation based energy efficient neighborhood discovery
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
An energy efficient adaptive HELLO algorithm for mobile ad hoc networks
Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
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Topology control in wireless sensor networks is an important issue for scalability and energy efficiency. It is often based on graph reduction performed through the use of Gabriel Graph or Relative Neighborhood Graph. This graph reduction is usually based on geometric values. In this paper we tackle the problem of possible connectivity loss in the reduced graph by applying a battery level based reduction graph. Experiments are conducted to evaluate our proposition. Results are compared with RNG reduction which takes into account only the strength of the received signal (RSSI). Results show that our algorithm maintains network connectivity longer than solutions from the literature and balances the energy consumption over nodes.