Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Introduction to Algorithms
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
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
Proceedings of the 3rd international conference on Embedded networked sensor systems
Trio: enabling sustainable and scalable outdoor wireless sensor network deployments
Proceedings of the 5th international conference on Information processing in sensor networks
Dozer: ultra-low power data gathering in sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
An analysis of unreliability and asymmetry in low-power wireless links
ACM Transactions on Sensor Networks (TOSN)
Fidelity and yield in a volcano monitoring sensor network
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
Design, Modeling, and Capacity Planning for Micro-solar Power Sensor Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Koala: Ultra-Low Power Data Retrieval in Wireless Sensor Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Sundial: Using Sunlight to Reconstruct Global Timestamps
EWSN '09 Proceedings of the 6th European Conference on Wireless Sensor Networks
SolarStore: enhancing data reliability in solar-powered storage-centric sensor networks
Proceedings of the 7th international conference on Mobile systems, applications, and services
Recovering temporal integrity with Data Driven Time Synchronization
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Surviving sensor network software faults
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
On the scalability of routing integrated time synchronization
EWSN'06 Proceedings of the Third European conference on Wireless Sensor Networks
K2: a system for campaign deployments of wireless sensor networks
REALWSN'10 Proceedings of the 4th international conference on Real-world wireless sensor networks
A green wireless sensor network for environmental monitoring and risk identification
International Journal of Sensor Networks
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Harsh deployment environments and uncertain run-time conditions create numerous challenges for postmortem time reconstruction methods. For example, motes often reboot and thus lose their clock state, considering that the majority of mote platforms lack a real-time clock. While existing time reconstruction methods for long-term data gathering networks rely on a persistent basestation for assigning global timestamps to measurements, the basestation may be unavailable due to hardware and software faults. We present Phoenix, a novel offline algorithm for reconstructing global timestamps that is robust to frequent mote reboots and does not require a persistent global time source. This independence sets Phoenix apart from the majority of time reconstruction algorithms which assume that such a source is always available. Motes in Phoenix exchange their time-related state with their neighbors, establishing a chain of transitive temporal relationships to one or more motes with references to the global time. These relationships allow Phoenix to reconstruct the measurement timeline for each mote. Results from simulations and a deployment indicate that Phoenix can achieve timing accuracy up to 6 ppm for 99% of the collected measurements. Phoenix is able to maintain this performance for periods that last for months without a persistent global time source. To achieve this level of performance for the targeted environmental monitoring application, Phoenix requires an additional space overhead of 4% and an additional duty cycle of 0.2%.