Wireless sensor networks: a new regime for time synchronization
ACM SIGCOMM Computer Communication Review
Lightweight time synchronization for sensor networks
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Timing-sync protocol for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
TSync: a lightweight bidirectional time synchronization service for wireless sensor networks
ACM SIGMOBILE Mobile Computing and Communications Review - Special issue on wireless pan & sensor networks
Improved interval-based clock synchronization in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Fine-grained network time synchronization using reference broadcasts
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Passive Cluster Based Clock Synchronization in Sensor Network
AICT-SAPIR-ELETE '05 Proceedings of the Advanced Industrial Conference on Telecommunications/Service Assurance with Partial and Intermittent Resources Conference/E-Learning on Telecommunications Workshop
Cluster-based Hierarchical Time Synchronization for Multi-hop Wireless Sensor Networks
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 02
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In wireless sensor networks, time synchronization is a critical problem. In this paper, we propose SLTP, a Scalable Lightweight Time-synchronization Protocol for wireless sensor networks. By using passive clustering and linear regression SLTP can reduce the energy consumption of network nodes and also decrease the overhead of creating and maintaining the clusters. Moreover SLTP uses linear regression to compute the time. Therefore, it can calculate the clock skew and offset between each node and its cluster head in order to estimate the local time of remote nodes in the future or the past. Simulation results show that by this we can gain considerable improvements in power consumption, accuracy and scalability in comparison to similar algorithms.