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. 2016 Jun 21;16(6):923.
doi: 10.3390/s16060923.

A Type of Low-Latency Data Gathering Method With Multi-Sink for Sensor Networks

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Free PMC article

A Type of Low-Latency Data Gathering Method With Multi-Sink for Sensor Networks

Chao Sha et al. Sensors (Basel). .
Free PMC article

Abstract

To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs), a type of low-latency data gathering method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods.

Keywords: Sensor Networks; balance of energy consumption; low-latency; mobile Sinks; redundancy on network coverage.

Figures

Figure 1
Figure 1
Data gathering unit in the network.
Figure 2
Figure 2
Distribution of DGAs (Case 1).
Figure 3
Figure 3
Distribution of DGAs (Case 2).
Figure 4
Figure 4
Distribution of DGAs (Case 3).
Figure 5
Figure 5
Distribution of DGAs (Case 4).
Figure 6
Figure 6
Leader nodes selection in each DGA.
Figure 7
Figure 7
Distributed sleep scheduling strategy in each DGA.
Figure 8
Figure 8
Overlapping sensing area judgment based on grids.
Figure 9
Figure 9
Sensing radius adjustment.
Figure 10
Figure 10
Next-hop node selection in DGA.
Figure 11
Figure 11
Weighted directed graph for data uploading.
Figure 12
Figure 12
Location of the traverse point.
Figure 13
Figure 13
The next available TPs for selection.
Figure 14
Figure 14
Redundancy on coverage (100 m × 100 m).
Figure 15
Figure 15
Number of active nodes in LDGM (100 m × 100 m).
Figure 16
Figure 16
Redundancy on coverage (200 m × 200 m).
Figure 17
Figure 17
Number of active nodes in LDGM (200 m × 200 m).
Figure 18
Figure 18
Percentage about the coverage degree (100 m × 100 m, 200 nodes).
Figure 19
Figure 19
Average residual energy of nodes (100 m × 100 m, 500 nodes).
Figure 20
Figure 20
Maximum time delay on data gathering (100 m × 100 m, 200 nodes).
Figure 21
Figure 21
Average time delay on data gathering (100 m × 100 m, 200 nodes).
Figure 22
Figure 22
Maximum time delay on data gathering (200 m × 200 m, 500 nodes).
Figure 23
Figure 23
Average time delay on data gathering (200 m × 200 m, 500 nodes).
Figure 24
Figure 24
Maximum time delay with different moving speeds of Sinks.
Figure 25
Figure 25
Average time delay with different moving speeds of Sinks.
Figure 26
Figure 26
Time interval of two consecutive times of data upload (100 m × 100 m, 200 nodes).
Figure 27
Figure 27
Time interval of two consecutive times of data upload (200 m × 200 m, 500 nodes).
Figure 28
Figure 28
Percentage of valid data collected by Sinks (100 m × 100 m, 200 nodes).
Figure 29
Figure 29
Total length of path for data transmission (100 m × 100 m, 200 nodes).
Figure 30
Figure 30
Data collection path of MWST.
Figure 31
Figure 31
Average energy consumption of nodes (100 m × 100 m, 200 nodes).
Figure 32
Figure 32
Average residual energy of nodes (100 m × 100 m, 200 nodes).

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References

    1. Francesco M.D., Das S.K., Anastasi G. Data Collection in Wireless Sensor Networks with Mobile Elements: A Survey. ACM Trans. Sens. Netw. 2011;8:72–102. doi: 10.1145/1993042.1993049. - DOI
    1. Mammu A.S.K., Hernandez-Jayo U., Sainz N., de la Iglesia I. Cross-Layer Cluster-Based Energy-Efficient Protocol for Wireless Sensor Networks. Sensors. 2015;15:8314–8336. doi: 10.3390/s150408314. - DOI - PMC - PubMed
    1. Salarian H., Chin K., Naghdy F. An Energy-Efficient Mobile-Sink Path Selection Strategy for Wireless Sensor Networks. IEEE Trans. Veh. Technol. 2014;63:2407–2419. doi: 10.1109/TVT.2013.2291811. - DOI
    1. De Freitas E.P., Heimfarth T., Vinel A., Wagner F.R., Pereira C.E., Larsson T. Cooperation among Wirelessly Connected Static and Mobile Sensor Nodes for Surveillance Applications. Sensors. 2013;13:12903–12928. doi: 10.3390/s131012903. - DOI - PMC - PubMed
    1. Shen J., Tan H., Wang J., Wang J., Lee S. A Novel Routing Protocol Providing Good Transmission Reliability in Underwater Sensor Networks. J. Internet Technol. 2015;16:171–178.
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