Using Battery Level as Metric for Graph Planarization

International audienceTopology 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

Transmission Power Adaptation Based Energy Efficient Neighborhood Discovery

International audienceAbstract--Neighborhood discovery - detection of the devices within communication range using HELLO messages - is a fundamental mechanism in wireless sensor networks (WSN) which enables usage of many di erent topology control and routing algorithms. Even though it is very important, most of the algorithms does not take into account parameters of the neighborhood discovery. We present two algorithms that adapt power of transmission of the sensors in a mobile WSN by still adapting frequency of HELLO messages in order to save energy and get accurate neighborhood tables. First solution is based on the knowledge of turnover - change in the number of neighbors in consecutive iterations of the neighborhood discovery - used in conjunction with adaptation of frequency and transmission range, minimizing general cost of transmission of the HELLO messages. Second solution is based on computing of optimal range. Both algorithms are based on theoretical analysis. Results show that they are energy e cient and outperform solutions of the literature