Next Article in Journal
Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care
Next Article in Special Issue
Static Memory Deduplication for Performance Optimization in Cloud Computing
Previous Article in Journal
Multispectral LiDAR Data for Land Cover Classification of Urban Areas
Previous Article in Special Issue
Dynamic Construction Scheme for Virtualization Security Service in Software-Defined Networks
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(5), 964; doi:10.3390/s17050964

An Adaptive Clustering Approach Based on Minimum Travel Route Planning for Wireless Sensor Networks with a Mobile Sink

College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China
*
Author to whom correspondence should be addressed.
Academic Editor: Yuh-Shyan Chen
Received: 6 February 2017 / Revised: 7 April 2017 / Accepted: 19 April 2017 / Published: 26 April 2017
View Full-Text   |   Download PDF [497 KB, uploaded 26 April 2017]   |  

Abstract

In recent years, Wireless Sensor Networks with a Mobile Sink (WSN-MS) have been an active research topic due to the widespread use of mobile devices. However, how to get the balance between data delivery latency and energy consumption becomes a key issue of WSN-MS. In this paper, we study the clustering approach by jointly considering the Route planning for mobile sink and Clustering Problem (RCP) for static sensor nodes. We solve the RCP problem by using the minimum travel route clustering approach, which applies the minimum travel route of the mobile sink to guide the clustering process. We formulate the RCP problem as an Integer Non-Linear Programming (INLP) problem to shorten the travel route of the mobile sink under three constraints: the communication hops constraint, the travel route constraint and the loop avoidance constraint. We then propose an Imprecise Induction Algorithm (IIA) based on the property that the solution with a small hop count is more feasible than that with a large hop count. The IIA algorithm includes three processes: initializing travel route planning with a Traveling Salesman Problem (TSP) algorithm, transforming the cluster head to a cluster member and transforming the cluster member to a cluster head. Extensive experimental results show that the IIA algorithm could automatically adjust cluster heads according to the maximum hops parameter and plan a shorter travel route for the mobile sink. Compared with the Shortest Path Tree-based Data-Gathering Algorithm (SPT-DGA), the IIA algorithm has the characteristics of shorter route length, smaller cluster head count and faster convergence rate. View Full-Text
Keywords: hierarchy wireless sensor network with a mobile sink; travel route planning; cluster; multi-hop communication; integer non-linear programming hierarchy wireless sensor network with a mobile sink; travel route planning; cluster; multi-hop communication; integer non-linear programming
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Tang, J.; Yang, W.; Zhu, L.; Wang, D.; Feng, X. An Adaptive Clustering Approach Based on Minimum Travel Route Planning for Wireless Sensor Networks with a Mobile Sink. Sensors 2017, 17, 964.

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