SC-EEDC: Similarity Based Clustering for Energy Efficient Data Collection in WSN
- PDF / 4,612,581 Bytes
- 33 Pages / 439.37 x 666.142 pts Page_size
- 31 Downloads / 160 Views
SC‑EEDC: Similarity Based Clustering for Energy Efficient Data Collection in WSN Zhang Licui1 · Wang Pengcheng1 · Zhang Chunxia1 Accepted: 29 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Wireless sensor networks (WSN) are widely used in various situations. Energy saving is one of the most important issues because of the limited power. Communication is the mainly energy consuming part of sensor nodes. Reducing the size of transmitted data can conserve the energy of nodes. Spatial and temporal correlation is ubiquitous in wireless sensor networks. By exploiting spatial and temporal correlation, only a subset of data need to be transmitted and the rest of the data can be estimated. Data aware clustering is an effective way to exploit spatial correlation among sensor nodes. In our SC-EEDC framework, fuzzy ART artificial neural network is used to measure the shape similarity among data sequences and the magnitude similarity is estimated by a magnitude similarity model. And two corresponding estimation algorithm are proposed. We propose Weighting based K-means clustering algorithm (WK-means), which considers multiple clustering factors in addition to the data similarity. The K-means algorithm structure is introduced in clustering algorithm to search the more energy-saving clustering topology. Anchor node based data collection strategy is proposed to measure the spatial correlation in real time and suppress the transmission of spatial redundant data at source node. Sleeping scheduling is introduced to dynamically adjust the spatial sampling rate and the temporal redundancy is reduced using Length Encoding. The cluster maintenance scheme keeps the cluster structure’s efficiency over the network lifetime. Simulation results show that our SC-EEDC achieves significant data reduction without affecting the accuracy of collected data, reduces the energy consumption in each round of data collection and effectively prolongs the network lifetime. Keywords Wireless sensor network · Data similarity · Clustering · Data gathering
* Zhang Licui [email protected] Wang Pengcheng [email protected] Zhang Chunxia [email protected] 1
Department of Communication Engineering, Communication and Information System, Jilin University, Changchun 130012, China
13
Vol.:(0123456789)
Z. Licui et al.
1 Introduction With the rapid development of Micro-Electro-Mechanical System (MEMS), wireless communication technology and the highly integrated low-power digital electronics technology, the low power consumption and multifunctional sensors can be manufactured at a low cost [1, 2]. A sensor node acquires physical input and transform them into the electrical output, and then they are fed onto the processor to realize data processing. A sensor node can send and receive the processed data by an RF transceiver. WSN consists of a lot of this kind of sensor nodes distributed randomly. WSN uses sensors to monitor their surroundings for local data and these data is transmitted to sink through the network,
Data Loading...
