Time‐pattern design for transmission energy allocation in wireless sensor networks. Issue 7 (31st March 2017)

• Record Type: Journal Article
• Authors: Li, Feng; Wang, Li; Meng, Limin; Zhang, Yu; Pan, Qing
• Is Part Of: IET communications. Volume 11:Issue 7(2017)
• Page Start: 1028
• Page End: 1035
• Publication Date: 2017-03-31
• Subjects: wireless sensor networks -- cooperative communication -- Rayleigh channels -- multipath channels -- relay networks (telecommunication) -- resource allocation -- probability -- channel capacity -- iterative methods -- game theory; time‐pattern design -- transmission energy allocation -- wireless sensor network -- cooperative transmission -- WSN -- power consumption -- wireless multipath channel -- fast fading inherent channel -- resource allocation -- Rayleigh fading channel -- relevant system model -- outage probability -- transmit‐time allocation -- iterative algorithm -- Nash equilibrium; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2016.0474
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : Cooperative transmission is an efficient method for wireless sensor networks (WSNs) to decrease power consumption of sensor nodes and combat the fast fading inherent to wireless multipath channels. During the course, a key issue is how a sensor relay handles the balance between assisting other nodes' communication and accomplishing its own transmission tasks. To address this problem, this study investigates the potential bargaining for resource allocation in WSNs over Rayleigh fading environments by considering energy fairness in cooperative transmission. Specifically, based on the characteristics of Rayleigh fading channel, a relevant system model is built. Then, after analysing the system capacity with a given outage probability over the fading channels, a system utility function for transmit‐time allocation is proposed to address the participants' energy concern. An iterative algorithm is subsequently achieved by solving the Nash equilibrium. Furthermore, essential analysis and discussion on the pure equilibrium and convergence conditions of the iterative algorithm are provided. Finally, numerical results are presented to evaluate the feasibility and system performance of the proposed framework.