A Betweenness Calibration Topology Optimal Control Algorithm for Wireless Sensor Networks
In self-organized wireless sensor networks (WSNs), any two sensor nodes can connect if they are placed in each other's communication range. Therefore, the physical topology of WSNs is usually a strongly connected topology. Sensor nodes should frequently receive and process data from their large...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-09-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/212074 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849415312269639680 |
|---|---|
| author | Ting Yang Zhixian Lin Bo Yuan |
| author_facet | Ting Yang Zhixian Lin Bo Yuan |
| author_sort | Ting Yang |
| collection | DOAJ |
| description | In self-organized wireless sensor networks (WSNs), any two sensor nodes can connect if they are placed in each other's communication range. Therefore, the physical topology of WSNs is usually a strongly connected topology. Sensor nodes should frequently receive and process data from their large number of neighbors, which will consume great amounts of energy. Shocking wireless channel collision also causes low throughput and high loss packets ratio during data transmission. To improve the transmission performance and save scarce energy, a logical topology generating from the physical one is necessary for the self-organized WSNs. Based on the complex network theory, this paper proposed a novel betweenness addition edges expansion algorithm (BAEE). With betweenness calibration, BAEE algorithm expanded the minimum-cost edges to optimize the network topology. Two performance metrics-connectivity functions, robustness function R ( G ) and efficiency function E ( G ) , were utilized to evaluate the network capability of the robustness and invulnerability. R ( G ) is the parameter to measure the topology connectivity, and E ( G ) is the parameter to evaluate the network exchanging information capability. Based on the simulation under various random failures and intentional attack scenarios, BAEE can effectively optimize WSNs' topology and improve the network's robust connectivity and extremely efficient exchanging information capability. |
| format | Article |
| id | doaj-art-57227c3dd2df4045b20aacbca358fcfd |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-57227c3dd2df4045b20aacbca358fcfd2025-08-20T03:33:34ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-09-01910.1155/2013/212074A Betweenness Calibration Topology Optimal Control Algorithm for Wireless Sensor NetworksTing YangZhixian LinBo YuanIn self-organized wireless sensor networks (WSNs), any two sensor nodes can connect if they are placed in each other's communication range. Therefore, the physical topology of WSNs is usually a strongly connected topology. Sensor nodes should frequently receive and process data from their large number of neighbors, which will consume great amounts of energy. Shocking wireless channel collision also causes low throughput and high loss packets ratio during data transmission. To improve the transmission performance and save scarce energy, a logical topology generating from the physical one is necessary for the self-organized WSNs. Based on the complex network theory, this paper proposed a novel betweenness addition edges expansion algorithm (BAEE). With betweenness calibration, BAEE algorithm expanded the minimum-cost edges to optimize the network topology. Two performance metrics-connectivity functions, robustness function R ( G ) and efficiency function E ( G ) , were utilized to evaluate the network capability of the robustness and invulnerability. R ( G ) is the parameter to measure the topology connectivity, and E ( G ) is the parameter to evaluate the network exchanging information capability. Based on the simulation under various random failures and intentional attack scenarios, BAEE can effectively optimize WSNs' topology and improve the network's robust connectivity and extremely efficient exchanging information capability.https://doi.org/10.1155/2013/212074 |
| spellingShingle | Ting Yang Zhixian Lin Bo Yuan A Betweenness Calibration Topology Optimal Control Algorithm for Wireless Sensor Networks International Journal of Distributed Sensor Networks |
| title | A Betweenness Calibration Topology Optimal Control Algorithm for Wireless Sensor Networks |
| title_full | A Betweenness Calibration Topology Optimal Control Algorithm for Wireless Sensor Networks |
| title_fullStr | A Betweenness Calibration Topology Optimal Control Algorithm for Wireless Sensor Networks |
| title_full_unstemmed | A Betweenness Calibration Topology Optimal Control Algorithm for Wireless Sensor Networks |
| title_short | A Betweenness Calibration Topology Optimal Control Algorithm for Wireless Sensor Networks |
| title_sort | betweenness calibration topology optimal control algorithm for wireless sensor networks |
| url | https://doi.org/10.1155/2013/212074 |
| work_keys_str_mv | AT tingyang abetweennesscalibrationtopologyoptimalcontrolalgorithmforwirelesssensornetworks AT zhixianlin abetweennesscalibrationtopologyoptimalcontrolalgorithmforwirelesssensornetworks AT boyuan abetweennesscalibrationtopologyoptimalcontrolalgorithmforwirelesssensornetworks AT tingyang betweennesscalibrationtopologyoptimalcontrolalgorithmforwirelesssensornetworks AT zhixianlin betweennesscalibrationtopologyoptimalcontrolalgorithmforwirelesssensornetworks AT boyuan betweennesscalibrationtopologyoptimalcontrolalgorithmforwirelesssensornetworks |