Abstract
Wireless sensor networks (WSNs) have emerged as a promising solution for various applications due to their low cost and easy deployment. Typically, their limited power capability, i.e., battery powered, make WSNs encounter the challenge of extension of network lifetime. Many hierarchical protocols show better ability of energy efficiency in the literature. Besides, data reduction based on the correlation of sensed readings can efficiently reduce the amount of required transmissions. Therefore, we use a sub-clustering procedure based on spatial data correlation to further separate the hierarchical (clustered) architecture of a WSN. The proposed algorithm (2TC-cor) is composed of two procedures: the prediction model construction procedure and the sub-clustering procedure. The energy conservation benefits by the reduced transmissions, which are dependent on the prediction model. Also, the energy can be further conserved because of the representative mechanism of sub-clustering. As presented by simulation results, it shows that 2TC-cor can effectively conserve energy and monitor accurately the environment within an acceptable level.
Highlights
As the rapid advances recently in wireless communications and sensor technologies, wireless sensor networks (WSNs) have emerged as the promising solution for various applications due to their low cost and easy deployment
Their limited power capability, i.e., battery powered, make WSNs encounter the challenge of extension of network lifetime
In order to efficiently reduce the energy consumption and further prolong the network lifetime, we focus on the literatures of energy conservation, especially those hierarchical protocols with concern on energy efficiency and data reduction schemes using data correlation and prediction
Summary
As the rapid advances recently in wireless communications and sensor technologies, wireless sensor networks (WSNs) have emerged as the promising solution for various applications due to their low cost and easy deployment. The first category exploits the power management (including mechanisms on MAC layer, network layer and cross layers) for energy saving by considering duty cycling [6,7,8,9] To this end, various criterions can be used to decide which/when sensor nodes should be active. The second category focuses on sensor readings and can be further be subdivided into two main classifications: data reduction and energy-efficient data acquisition The former adopts in-network processing, such as aggregation [13,14], compression [15] or prediction [16,17], in order to reduce the amount of data readings that need to be transmitted. Under this kind of architecture, the energy conservation benefits by the reduced transmissions, which are dependent on the prediction model. Conclusion is given in the last section of the paper
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