IDEG: Integrated Data and Energy Gathering Framework for Practical Wireless Sensor Networks Using Compressive Sensing

Abstract

Minimizing the energy consumption through efficient data gathering as well as energy harvesting both are open research problems in the context of practical wireless sensor networks (WSNs). Although the number of approaches has been proposed to cater the above, they are limited in terms of computational complexity, energy efficiency, and the ease of implementation. In this paper, we propose an integrated data and energy gathering framework, named hereby as “iDEG,” for practical WSNs. The proposed work is based on compressive sensing and utilizes partial canonical identity (PCI) matrix that samples/picks fewer sensor nodes randomly at any time point instead of picking up a linear combination of data from all sensor nodes at all time points as used in the conventionally popular Gaussian or Bernoulli matrices. This reduces the computation cost, implementation complexity, and energy losses while improving the data recovery. Furthermore, the fewer sensors at any time point selected by PCI contribute for recovering data, while the rest of the nodes harvest energy during those time points. Performance of iDEG has been tested on a real WSN dataset from Intel Lab. Comparative results of iDEG with the conventional approaches highlight its efficacy.