Accessibility navigation


Mobility based energy efficient and multi-sink algorithms for consumer home networks

Wang, J., Yin, Y., Zhang, J., Lee, S. and Sherratt, R. S. ORCID: https://orcid.org/0000-0001-7899-4445 (2013) Mobility based energy efficient and multi-sink algorithms for consumer home networks. IEEE Transactions on Consumer Electronics, 59 (1). pp. 77-84. ISSN 0098-3063

[img]
Preview
Text - Accepted Version
· Please see our End User Agreement before downloading.

795kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1109/TCE.2013.6490244

Abstract/Summary

With the fast development of the Internet, wireless communications and semiconductor devices, home networking has received significant attention. Consumer products can collect and transmit various types of data in the home environment. Typical consumer sensors are often equipped with tiny, irreplaceable batteries and it therefore of the utmost importance to design energy efficient algorithms to prolong the home network lifetime and reduce devices going to landfill. Sink mobility is an important technique to improve home network performance including energy consumption, lifetime and end-to-end delay. Also, it can largely mitigate the hot spots near the sink node. The selection of optimal moving trajectory for sink node(s) is an NP-hard problem jointly optimizing routing algorithms with the mobile sink moving strategy is a significant and challenging research issue. The influence of multiple static sink nodes on energy consumption under different scale networks is first studied and an Energy-efficient Multi-sink Clustering Algorithm (EMCA) is proposed and tested. Then, the influence of mobile sink velocity, position and number on network performance is studied and a Mobile-sink based Energy-efficient Clustering Algorithm (MECA) is proposed. Simulation results validate the performance of the proposed two algorithms which can be deployed in a consumer home network environment.

Item Type:Article
Refereed:Yes
Divisions:Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:32188
Publisher:IEEE

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation