Abstract
Data transmission consumes significant amount of energy in large scale wireless sensor networks (WSNs). In such an environment, reducing the in-network communication and distributing the load evenly over the network can reduce the overall energy consumption and maximize the network lifetime significantly. In this work, the aforementioned problem of network lifetime and uneven energy consumption in large scale wireless sensor networks is addressed. This work proposes a hierarchical compressed sensing (HCS) scheme to reduce the in-network communication during the data gathering process. Co-related sensor readings are collected via a hierarchical clustering scheme. A compressed sensing (CS) based data processing scheme is devised to transmit the data from the source to the sink. The proposed HCS is able to identify the optimal position for the application of CS to achieve reduced and similar number of transmissions on all the nodes in the network. An activity map is generated to validate the reduced and uniformly distributed communication load of the WSN. Based on the number of transmissions per data gathering round, the bit-hop metric model is used to analyse the overall energy consumption. Simulation results validate the efficiency of the proposed method over the existing CS based approaches.
Highlights
Wireless sensor networks (WSNs) have revolutionised today's practice of numerous scientific and engineering endeavours, including ecosystems, environmental sciences, military applications, scientific research etc
One of the First level cluster head (FCH) is designated as the Second level cluster head (SCH) and receives the data forwarded by all the FCHs
This paper addressed the problem of network lifetime and uneven energy consumption in large scale wireless sensor networks (WSNs)
Summary
Wireless sensor networks (WSNs) have revolutionised today's practice of numerous scientific and engineering endeavours, including ecosystems, environmental sciences, military applications, scientific research etc. Significant energy conservation in such networks can be achieved by: a) minimizing the cost of interaction between the nodes and b) achieving traffic load balancing during in-network communications [3]. Load balancing and optimized energy consumption are much sought after parameters for multi hop data transmission in WSNs. This work addresses the problem of uneven energy consumption and network lifetime maximization through a novel in-network data processing scheme which incorporates CS in a novel way over a clustered routing structure. The nodes are randomly deployed in a sensing area which is divided into homogenous sub-regions Such a division is done to model the real world scenario of an area such as a thermal power plant. The proposed scheme incorporates CS in a way to efficiently distribute the communication load evenly over the network.
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