Abstract
Joint design of compressed sensing (CS) and network coding (NC) has been demonstrated to provide a new data gathering paradigm for multi-hop wireless sensor networks (WSNs). By exploiting the correlation of the network sensed data, a variety of data gathering schemes based on NC and CS (Compressed Data Gathering—CDG) have been proposed. However, these schemes assume that the sparsity of the network sensed data is constant and the value of the sparsity is known before starting each data gathering epoch, thus they ignore the variation of the data observed by the WSNs which are deployed in practical circumstances. In this paper, we present a complete design of the feedback CDG scheme where the sink node adaptively queries those interested nodes to acquire an appropriate number of measurements. The adaptive measurement-formation procedure and its termination rules are proposed and analyzed in detail. Moreover, in order to minimize the number of overall transmissions in the formation procedure of each measurement, we have developed a NP-complete model (Maximum Leaf Nodes Minimum Steiner Nodes—MLMS) and realized a scalable greedy algorithm to solve the problem. Experimental results show that the proposed measurement-formation method outperforms previous schemes, and experiments on both datasets from ocean temperature and practical network deployment also prove the effectiveness of our proposed feedback CDG scheme.
Highlights
Wireless sensor networks (WSNs) are currently deployed for many applications, such as environmental monitoring, civil structure maintenance, military surveillance, and so on
The first three approaches focus on the energy efficiency of data gathering protocols or strategies, while the last one aims at reducing the required number of data packets to be sent to the sink node by eliminating data redundancy [8], it complements the others
Notice that all interested nodes Iv within the communication range of node v can be considered considered to come from the same group, and based on this circumstance, we can perform a greedy to come from the same group, and based on this circumstance, we can perform a greedy iterative iterative algorithm to reduce the complexity of MLMS construction problem
Summary
Wireless sensor networks (WSNs) are currently deployed for many applications, such as environmental monitoring, civil structure maintenance, military surveillance, and so on In most of these kinds of applications, sensor nodes in the network are set to periodically report their sensed data (i.e., readings) to a sink node (or remote base station) through intermediate nodes’ relay. Under such circumstances, energy efficiency becomes one of the dominating issues of this data gathering process. Been proposed as a promising “recoverable” scheme, it enables the sink node acquire the complete network sensed in some an energy-efficient manner, as well as the energy consumption. 1a) and 1c), 1c), respectively [13]
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