Well-Suited Similarity Functions for Data Aggregation in Cluster-Based Underwater Wireless Sensor Networks
This paper presents an efficient data aggregation approach for cluster-based underwater wireless sensor networks in order to prolong network lifetime. In data aggregation, an aggregator collects sensed data from surrounding nodes and transmits the aggregated data to a base station. The major goal of...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-08-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/645243 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832553108314521600 |
|---|---|
| author | Khoa Thi-Minh Tran Seung-Hyun Oh Jeong-Yong Byun |
| author_facet | Khoa Thi-Minh Tran Seung-Hyun Oh Jeong-Yong Byun |
| author_sort | Khoa Thi-Minh Tran |
| collection | DOAJ |
| description | This paper presents an efficient data aggregation approach for cluster-based underwater wireless sensor networks in order to prolong network lifetime. In data aggregation, an aggregator collects sensed data from surrounding nodes and transmits the aggregated data to a base station. The major goal of data aggregation is to minimize data redundancy, ensuring high data accuracy and reducing the aggregator's energy consumption. Hence, similarity functions could be useful as a part of the data aggregation process for resolving inconsistencies in collected data. Our approach is to determine and apply well-suited similarity functions for cluster-based underwater wireless sensor networks. In this paper, we show the effectiveness of similarity functions, especially the Euclidean distance and cosine distance, in reducing the packet size and minimizing the data redundancy of cluster-based underwater wireless sensor networks. Our results show that the Euclidean distance and cosine distance increase the efficiency of the network both in theory and simulation. |
| format | Article |
| id | doaj-art-2bfbb7369f4a420c9f1c6cc6af2f35ac |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-2bfbb7369f4a420c9f1c6cc6af2f35ac2025-02-03T05:55:24ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-08-01910.1155/2013/645243Well-Suited Similarity Functions for Data Aggregation in Cluster-Based Underwater Wireless Sensor NetworksKhoa Thi-Minh TranSeung-Hyun OhJeong-Yong ByunThis paper presents an efficient data aggregation approach for cluster-based underwater wireless sensor networks in order to prolong network lifetime. In data aggregation, an aggregator collects sensed data from surrounding nodes and transmits the aggregated data to a base station. The major goal of data aggregation is to minimize data redundancy, ensuring high data accuracy and reducing the aggregator's energy consumption. Hence, similarity functions could be useful as a part of the data aggregation process for resolving inconsistencies in collected data. Our approach is to determine and apply well-suited similarity functions for cluster-based underwater wireless sensor networks. In this paper, we show the effectiveness of similarity functions, especially the Euclidean distance and cosine distance, in reducing the packet size and minimizing the data redundancy of cluster-based underwater wireless sensor networks. Our results show that the Euclidean distance and cosine distance increase the efficiency of the network both in theory and simulation.https://doi.org/10.1155/2013/645243 |
| spellingShingle | Khoa Thi-Minh Tran Seung-Hyun Oh Jeong-Yong Byun Well-Suited Similarity Functions for Data Aggregation in Cluster-Based Underwater Wireless Sensor Networks International Journal of Distributed Sensor Networks |
| title | Well-Suited Similarity Functions for Data Aggregation in Cluster-Based Underwater Wireless Sensor Networks |
| title_full | Well-Suited Similarity Functions for Data Aggregation in Cluster-Based Underwater Wireless Sensor Networks |
| title_fullStr | Well-Suited Similarity Functions for Data Aggregation in Cluster-Based Underwater Wireless Sensor Networks |
| title_full_unstemmed | Well-Suited Similarity Functions for Data Aggregation in Cluster-Based Underwater Wireless Sensor Networks |
| title_short | Well-Suited Similarity Functions for Data Aggregation in Cluster-Based Underwater Wireless Sensor Networks |
| title_sort | well suited similarity functions for data aggregation in cluster based underwater wireless sensor networks |
| url | https://doi.org/10.1155/2013/645243 |
| work_keys_str_mv | AT khoathiminhtran wellsuitedsimilarityfunctionsfordataaggregationinclusterbasedunderwaterwirelesssensornetworks AT seunghyunoh wellsuitedsimilarityfunctionsfordataaggregationinclusterbasedunderwaterwirelesssensornetworks AT jeongyongbyun wellsuitedsimilarityfunctionsfordataaggregationinclusterbasedunderwaterwirelesssensornetworks |