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An Adaptive Data Gathering Scheme for Multi-Hop Wireless Sensor Networks Based on Compressed Sensing and Network

Jun Yin¹, Yuwang Yang², Lei Wang³

¹School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. junyin.nust@gmail.com.

Sensors (Basel, Switzerland)

|April 5, 2016

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This study introduces adaptive feedback for compressed data gathering in wireless sensor networks (WSNs). It efficiently collects data by adjusting measurements based on real-world variations, outperforming previous methods.

Area of Science:

Wireless Sensor Networks
Data Compression
Network Coding

Background:

Compressed Sensing (CS) and Network Coding (NC) offer a novel data gathering approach for multi-hop WSNs.
Existing Compressed Data Gathering (CDG) schemes assume constant data sparsity, which is unrealistic for practical WSN deployments.

Purpose of the Study:

To design a feedback-based CDG scheme that adaptively acquires measurements.
To address the limitations of static sparsity assumptions in WSN data gathering.

Main Methods:

Developed an adaptive measurement-formation procedure with detailed termination rules.
Formulated a NP-complete Maximum Leaf Nodes Minimum Steiner Nodes (MLMS) problem to minimize transmissions.
Implemented a scalable greedy algorithm to solve the MLMS problem.

Keywords:

compressed sensing data gathering network coding sensor networks

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Main Results:

The proposed adaptive measurement-formation method demonstrates superior performance compared to existing schemes.
Experimental validation using ocean temperature and practical network deployment datasets confirms the scheme's effectiveness.

Conclusions:

The feedback CDG scheme effectively handles data variations in WSNs.
The adaptive approach optimizes data gathering by dynamically adjusting measurements, improving efficiency and accuracy.