Lifetime balanced data aggregation for the internet of things. (February 2017)

• Record Type: Journal Article
• Authors: Li, Zi; Zhang, Wensheng; Qiao, Daji; Peng, Yang
• Is Part Of: Computers & electrical engineering. Volume 58(2017)
• Page Start: 244
• Page End: 264
• Publication Date: 2017-02
• Subjects: Internet of things -- Data aggregation -- Lifetime-balancing -- End-to-end delay requirement; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.09.025
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A lifetime balanced data aggregation scheme for the Internet of Things, called LBA, is proposed. LBA adjusts the aggregation delays of neighboring devices in a collaborative manner. The lifetime between neighboring devices may be balanced without increasing the end-to-end delay. LBA is distributed, scalable, and able to dynamically adapt to the network changes in practice. Graphical abstract: Abstract: This paper proposes LBA, a lifetime balanced data aggregation scheme for the Internet of Things (IoT) under an application-specified end-to-end delay requirement. In contrast to existing aggregation schemes, LBA aims to prolong the IoT network lifetime under network heterogeneity and dynamics, while ensuring the required data delivery delay. To achieve this goal in a distributed manner, LBA adaptively adjusts the aggregation delays of neighboring devices to balance the lifetime between them. As such balancing takes place in all neighborhoods, the minimal device lifetime in the network is increased gradually, thus prolonging the lifetime of the entire network. The effectiveness of LBA is demonstrated via extensive experiments on a testbed. Generally, when the network presents a higher degree of heterogeneity and dynamics, LBA's performance gain over a state-of-the-art non-adaptive data aggregation scheme becomes more significant, and the gap between LBA's performance and its theoretical upper bound gets smaller.