Research challenges in wireless networks of biomedical sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Fine-grained network time synchronization using reference broadcasts
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Timing-sync protocol for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
The flooding time synchronization protocol
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Sensor Networks
Wireless Sensor Networks: Technology, Protocols, and Applications
Wireless Sensor Networks: Technology, Protocols, and Applications
Health monitoring of civil infrastructures using wireless sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Information Technologies and International Development
Underground coal mine monitoring with wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Learning to play games using a PSO-based competitive learning approach
IEEE Transactions on Evolutionary Computation
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Time Synchronization is a common requirement for most network applications. It is particularly essential in Wireless sensor networks (WSN) to allow collective signal processing, proper correlation of diverse measurements taken from a set of distributed sensor elements and for an efficient sharing of the communication channel. The Flooding Time Synchronization Protocol (FTSP) was developed explicitly for time synchronization of wireless sensor networks. In this paper, FTSP is optimized for clock drift management using Particle Swarm Optimization (PSO). The paper estimates the clock offset, clock skew and generates linear line and optimizes the value of average time synchronization error using PSO. This paper presents implementation and experimental results that produce reduced average time synchronization error optimized using PSO compared to that of linear regression used in FTSP.