Name
Abstract | ||
|---|---|---|
Topology formation in wireless sensor networks is usually done assuming just either the geographical proximity between nodes or the signal strength of communication. In this paper, a heuristic called DbCTF is proposed to guide the formation of cluster-tree networks, which also considers data clustering techniques. The use of DbCTF allows the setup of a data-based topology in the cluster-tree, and also the prioritisation of monitored regions in which relevant events may be occurring. The performance of DbCTF has been compared with a state-of-the-art algorithm, for the specific case of a classical WSN laboratory experiment. The simulation assessment revealed that the cluster-tree formed by DbCTF was able to reduce by more than 20% the average communication delay of message streams conveying critical data, and was also able to increase by more than 35% the average lifetime of the network. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/INDIN.2018.8471938 | 2018 IEEE 16th International Conference on Industrial Informatics (INDIN) |
Keywords | Field | DocType |
Wireless sensor networks,big data,cluster-tree topology,k-means technique,large-scale systems | Cluster tree,Heuristic,Laboratory experiment,Real-time computing,Network topology,Signal strength,Engineering,Cluster analysis,Wireless sensor network | Conference |
ISSN | ISBN | Citations |
1935-4576 | 978-1-5386-4830-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. T. C. Andrade | 1 | 0 | 0.34 |
| D. Siedersberger | 2 | 0 | 0.34 |
| Carlos Montez | 3 | 156 | 25.48 |
| Ricardo Moraes | 4 | 125 | 17.58 |
| Erico Leao | 5 | 7 | 3.21 |
| F. Vasques | 6 | 476 | 58.57 |