Distributed sampling rate allocation for data quality maximization in rechargeable sensor networks. (15th February 2017)

• Record Type: Journal Article
• Authors: Lu, Ting; Liu, Guohua; Li, Wei; Chang, Shan; Guo, Wenjing
• Is Part Of: Journal of network and computer applications. Volume 80(2017)
• Page Start: 1
• Page End: 9
• Publication Date: 2017-02-15
• Subjects: Data quality -- Data sampling rate -- Energy -- Battery capacity -- Rechargeable sensor network; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2016.12.021
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Rechargeable sensor networks which are powered by reusable energy (e.g., solar energy, wind energy or thermal energy) are promising for perpetual data service. Due to the time-varying characteristics of harvested energy, the problem of data sampling rate allocation to maximize the network performance is a new challenging issue. In this paper, we are concerned with how to adaptively decide the data sampling rate to maximize the data quality of all sensor nodes. To solve this problem, we divide the data sampling rate allocation into two steps to decouple the energy and data sampling rate. First, we propose an energy allocation algorithm (EAA) to allocate the amount of energy that is allowed to be used by each sensor node in each interval, so that all sensor nodes cannot run out of their battery energy and can store more energy into the battery during the recharging period. Then, we present a data sampling rate allocation algorithm (RAA) to allocate the optimal data sampling rate for each node. Finally, we conduct extensive experiments using real collected data to evaluate the performance of the proposed algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed algorithms.