Abstract
This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selection scheme based on the information utility measure is used. To update the estimation in each selected node, a neighborhood radius equal to the communication range of the sensor nodes is defined and all sensors located in the neighborhood circle, whose radius is equal to the neighborhood radius and the selected node is its centre, collaborate their information. To decrease the energy consumption, the neighborhood radius is reduced gradually based on the error covariance value of the estimation. In addition, this paper includes a new method for the initial point calculation which is important in the recursive methods used for distributed algorithms in wireless sensor networks. Numerical examples are used to study the performance of the algorithms. Simulation results show the accuracy of the new algorithm becomes better while its energy consumption is low enough.
Highlights
Scattered low-cost sensor nodes provide a rich and complex information source about the sensed world
The energy consumption increases because of large amount of transmission. To mitigate this energy consumption we propose Energy Efficient Modified information-driven collaborative processing (IDCP) (MIDCP) (EFMIDCP) in which the neighborhood radius is decreased gradually based on the estimation covariance value
We addressed the problem of developing an accurate and energy efficient distributed algorithm for a diffusive source location estimation in wireless sensor networks
Summary
Scattered low-cost sensor nodes provide a rich and complex information source about the sensed world. Sensor nodes transmit their row observations to a fusion centre for processing [3,4,5] Some of their innate properties such as high-energy consumption limit their use in a wireless sensor network [6]. The distributed estimation is yielded by implementing the common centralized estimation methods like Maximum Likelihood (ML) estimation in a distributed manner [10] As in this type the communication burden is high, the most important challenge is to develop the accuracy of algorithms while considering the total energy consumption. We propose a new method to improve the accuracy of diffusive source estimation while maintaining the energy consumption in a reasonable level.
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