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. 2017 May 27;17(6):1226.
doi: 10.3390/s17061226.

Adaptive Data Aggregation and Compression to Improve Energy Utilization in Solar-Powered Wireless Sensor Networks

Ikjune Yoon  1 , Hyeok Kim  2 , Dong Kun Noh  3
Affiliations

Adaptive Data Aggregation and Compression to Improve Energy Utilization in Solar-Powered Wireless Sensor Networks

Ikjune Yoon et al. Sensors (Basel). .

Abstract

A node in a solar-powered wireless sensor network (WSN) collects energy when the sun shines and stores it in a battery or capacitor for use when no solar power is available, in particular at night. In our scheme, each tiny node in a WSN periodically determines its energy budget, which takes into account its residual energy, and its likely acquisition and consumption. If it expects to acquire more energy than it can store, the data which has it has sensed is aggregated with data from other nodes, compressed, and transmitted. Otherwise, the node continues to sense data, but turns off its wireless communication to reduce energy consumption. We compared several schemes by simulation. Our scheme reduced the number of nodes forced to black out due to lack of energy so that more data arrives at the sink node.

Keywords: aggregation; compression; energy-harvesting; solar-powered; wireless sensor network.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overview of data transmissions and node operations.
Figure 2
Figure 2
Process of the proposed scheme.
Figure 3
Figure 3
State transition diagram of node operations.
Figure 4
Figure 4
Change in the average amount of residual energy.
Figure 5
Figure 5
Change in the number of black-out nodes.
Figure 6
Figure 6
Number of cumulative black-out nodes.
Figure 7
Figure 7
Comparison of the total amount of sensed data.
Figure 8
Figure 8
Comparison of the amount of data obtained according to the sensing data size.
Figure 9
Figure 9
Change in the amount of data arriving at the sink node with node density.
Figure 10
Figure 10
Comparison of the amount of data obtained according to the solar energy.
Figure 11
Figure 11
Comparison of the amount of data obtained according to the maximum aggregation size.
Figure 12
Figure 12
Comparison of the amount of data obtained according to the sensing period.
Figure 13
Figure 13
Comparison of the amount of data obtained according to the maximum transmission size.
Figure 14
Figure 14
Change in the amount of data arriving at the sink node with node density (greedy perimeter stateless routing (GPSR)).
See this image and copyright information in PMC

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