On the performance of sink placement in WSNs considering energy-balanced compressive sensing-based data aggregation. (1st April 2018)

• Record Type: Journal Article
• Authors: Pakdaman Tirani, Shima; Avokh, Avid
• Is Part Of: Journal of network and computer applications. Volume 107(2018)
• Page Start: 38
• Page End: 55
• Publication Date: 2018-04-01
• Subjects: Wireless sensor networks -- Compressive sensing -- Clustering -- Sink placement -- Load-balancing -- Network lifetime; 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.2018.01.012
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: This paper aims to address the problem of network lifetime in wireless sensor networks. Our analysis jointly considers the cluster-based routing, Compressive Sensing (CS) theory, load-balancing, and sink placement. We first provide a formulation to theoretically study the impact of the sink placement on the number of transmissions and the network lifetime. Then, we propose two data collection methods named "Energy-aware CS-based Data Aggregation (ECDA)" and "Energy-balanced High level Data aggregation Tree (EHDT)". The proposed algorithms not only balance the energy consumption among different nodes, but also increase the network lifetime. As an advantage, both schemes are compatible with constant and mobile sinks. In this regard, considering the CS theory, we discuss the impact of the sink location and show the role of the sink mobility on the network performance. Although both algorithms improve the network lifetime, EHDT further considers the load-balancing problem in the network. It applies the weighted routing trees for transmitting data projections from the cluster heads to the sink and fairly distributes the energy consumption among different nodes. Numerical results confirm the efficiency of the proposed algorithms in terms of the number of transmissions, the energy consumption, the energy consumption variance, and the network lifetime.