Abstract
Long‐term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long‐term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point‐source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption.
Highlights
Wireless sensor networks (WSNs) are continuing to attract significant interest from the research community, with the promise of revolutionizing a wide range of application domains including environmental, building, and industrial process monitoring [1]
8 Conclusions The estimation error and energy conservation are of major importance to data acquisition in a WSN
In this paper, based on the concept of virtual cluster (VC), we present a novel framework to accurately estimate the event signal while maintaining the energy-efficient operation being balanced across the network
Summary
Wireless sensor networks (WSNs) are continuing to attract significant interest from the research community, with the promise of revolutionizing a wide range of application domains including environmental, building, and industrial process monitoring [1]. Signal estimation error can be reduced by only fusing data from a subset of the sensor nodes, but the estimation will be affected by spatial and temporal correlation between nodes Through these two factors, at any one time, the selection of cooperation nodes will affect both the energy conservation and the estimation accuracy. Using an attenuated and delayed sensing model and incorporating signal propagation delays, their work closer resembled the properties of real environments and applications Both of these papers conclude that a finite number of sensor nodes can cooperate to reduce the estimation error. Karjee and Jamadagni [15] analyzed the estimation accuracy of clustered WSNs using the same correlation model and method presented by Vuran et al [9] They showed through simulations that a subset of sensor nodes could satisfy a given requirement of estimation accuracy.
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