Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 57; no. 14; pp. 2689 - 2704
• Main Authors: Zhang, Pengfei, Xiao, Gaoxi, Tan, Hwee-Pink
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 04.10.2013; Elsevier; Elsevier Sequoia S.A
• Subjects: Algorithms; Applied sciences; Benchmarks; Clustering; Clustering algorithm; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Data processing. List processing. Character string processing; Energy harvesting wireless sensor; Exact sciences and technology; Memory organisation. Data processing; Network lifetime; Optimization algorithms; Radiocommunications; Sensors; Simulation; Software; Studies; Telecommunications; Telecommunications and information theory; Transmitters. Receivers; Wireless networks; Wireless sensor network; Wireless sensor network; Energy harvesting wireless sensor; Network lifetime; Clustering algorithm; Cluster analysis; Data analysis; Optimal algorithm; Liveness; Cluster; Durability; Calculator cluster; Energy recovery; Duration; Distributed system; Optimization; Transceiver; Leader election; Optimal solution; Wireless network; Sensor array
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2013.06.003

Motivated by recent developments in wireless sensor networks (WSNs), we present several efficient clustering algorithms for maximizing the lifetime of WSNs, i.e., the duration till a certain percentage of the nodes die. Specifically, an optimization algorithm is proposed for maximizing the lifetime of a single-cluster network, followed by an extension to handle multi-cluster networks. Then we study the joint problem of prolonging network lifetime by introducing energy-harvesting (EH) nodes. An algorithm is proposed for maximizing the network lifetime where EH nodes serve as dedicated relay nodes for cluster heads (CHs). Theoretical analysis and extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently, and therefore help provide useful benchmarks for various centralized and distributed clustering scheme designs.