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Abstract
There have been many studies performed about increasing network lifetime in wireless sensor networks that involve reducing data size, since the data transmission process takes up a large part of energy consumption. However, reducing data size results in increased delay time due to not only the compression computation time but also the waiting time to gather a sufficient amount of data for compression. Meanwhile, in solar-powered wireless sensor networks, the harvested energy may be surplus to the basic operations of sensor nodes. In this study, such surplus energy is utilized to reduce the delay time between nodes. Nodes with residual energy less than a certain threshold transfer data with compression in order to reduce energy consumption, and nodes with residual energy over the threshold (which means there is surplus energy) transfer data without compression to reduce the delay time between nodes by using the surplus energy. Simulation-based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay times compared with traditional methods.
Highlights
A wireless sensor network (WSN) can be defined as a network of devices, denoted as nodes, which can sense the environment and communicate information gathered from the monitored field through wireless links
In the energy-adaptive selective compression scheme (EASCS), the node operates in either CompressionMode or LatencyMode depending on the energy status of itself and the next-hop node on a route to the sink
If the amount of residual energy of both nodes exceeds a certain threshold, the node runs in LatencyMode where it transfers data without compression in order to decrease the delay
Summary
A wireless sensor network (WSN) can be defined as a network of devices, denoted as nodes, which can sense the environment and communicate information gathered from the monitored field through wireless links. There have been many studies performed about increasing network lifetime in WSNs involving reducing data size, since the data transmission process takes up a large proportion of the energy required by a sensor node. If the residual energy of a node is below this energy threshold (which means there is no surplus energy), a node compresses data before transmission in order to reduce its energy consumption; otherwise, the node transfers data without compression to reduce delay. This technique helps reduce end-to-end delay without causing blackout time, during which sensor nodes stop working without warning. We draw conclusions and discuss the scope for future work
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