Compressive sensing and random walk based data collection in wireless sensor networks

2018 ◽  
Vol 129 ◽  
pp. 43-53 ◽  
Author(s):  
Ping Zhang ◽  
Jianxin Wang, ◽  
Kehua Guo
Keyword(s):  
Wireless Sensor Networks ◽  
Random Walk ◽  
Sensor Networks ◽  
Data Collection ◽  
Compressive Sensing ◽  
Wireless Sensor

Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach

2015 ◽  
Vol 26 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Haifeng Zheng ◽  
Feng Yang ◽  
Xiaohua Tian ◽  
Xiaoying Gan ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Random Walk ◽  
Sensor Networks ◽  
Compressive Sensing ◽  
Data Gathering ◽  
Wireless Sensor

scholarly journals A Random Walk-Based Energy-Aware Compressive Data Collection for Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Keming Dong ◽  
Chao Chen ◽  
Xiaohan Yu
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Markov Chain ◽  
Random Walk ◽  
Sensor Networks ◽  
Data Collection ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Wireless Sensor ◽  
Expected Cost

The energy efficiency for data collection is one of the most important research topics in wireless sensor networks (WSNs). As a popular data collection scheme, the compressive sensing- (CS-) based data collection schemes own many advantages from the perspectives of energy efficiency and load balance. Compared to the dense sensing matrices, applications of the sparse random matrices are able to further improve the performance of CS-based data collection schemes. In this paper, we proposed a compressive data collection scheme based on random walks, which exploits the compressibility of data vectors in the network. Each measurement was collected along a random walk that is modeled as a Markov chain. The Minimum Expected Cost Data Collection (MECDC) scheme was proposed to iteratively find the optimal transition probability of the Markov chain such that the expected cost of a random walk could be minimized. In the MECDC scheme, a nonuniform sparse random matrix, which is equivalent to the optimal transition probability matrix, was adopted to accurately recover the original data vector by using the nonuniform sparse random projection (NSRP) estimator. Simulation results showed that the proposed scheme was able to reduce the energy consumption and balance the network load.

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