Next Article in Journal
Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
Previous Article in Journal
Graphene-Based Materials for Biosensors: A Review
Previous Article in Special Issue
PSDAAP: Provably Secure Data Authenticated Aggregation Protocols Using Identity-Based Multi-Signature in Marine WSNs
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(10), 2168; https://doi.org/10.3390/s17102168

A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation

College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
Received: 12 July 2017 / Revised: 25 August 2017 / Accepted: 15 September 2017 / Published: 21 September 2017
(This article belongs to the Special Issue Marine Sensing)
View Full-Text   |   Download PDF [1737 KB, uploaded 22 September 2017]   |  

Abstract

Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events. View Full-Text
Keywords: marine sensor network; graph partitioning; multi-objective partition; genetic algorithm; incremental optimization strategy marine sensor network; graph partitioning; multi-objective partition; genetic algorithm; incremental optimization strategy
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Huang, D.; Xu, C.; Zhao, D.; Song, W.; He, Q. A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation. Sensors 2017, 17, 2168.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top