Unreliable failure detectors for asynchronous systems (preliminary version)
PODC '91 Proceedings of the tenth annual ACM symposium on Principles of distributed computing
The weakest failure detector for solving consensus
PODC '92 Proceedings of the eleventh annual ACM symposium on Principles of distributed computing
Impossibility of distributed consensus with one faulty process
Journal of the ACM (JACM)
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Journal of the ACM (JACM)
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SIGCOMM '97 Proceedings of the ACM SIGCOMM '97 conference on Applications, technologies, architectures, and protocols for computer communication
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Journal of the ACM (JACM)
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ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
Chord: A scalable peer-to-peer lookup service for internet applications
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
On scalable and efficient distributed failure detectors
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
Perfect Failure Detection in Timed Asynchronous Systems
IEEE Transactions on Computers
The Consensus Problem in Unreliable Distributed Systems (A Brief Survey)
Proceedings of the 1983 International FCT-Conference on Fundamentals of Computation Theory
Failure Detectors for Large-Scale Distributed Systems
SRDS '02 Proceedings of the 21st IEEE Symposium on Reliable Distributed Systems
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ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
IEEE Transactions on Computers
Cluster-Based Failure Detection Service for Large-Scale Ad Hoc Wireless Network Applications
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies
IEEE Transactions on Mobile Computing
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
Event-to-sink reliable transport in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Detecting cuts in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Smart Home Sensor Networks Pose Goal-Driven Solutions to Wireless Vacuum Systems
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02
Distributed Systems: Concepts and Design (4th Edition) (International Computer Science)
Distributed Systems: Concepts and Design (4th Edition) (International Computer Science)
Distributed Online Simultaneous Fault Detection for Multiple Sensors
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
FIND: faulty node detection for wireless sensor networks
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
A gossip-style failure detection service
Middleware '98 Proceedings of the IFIP International Conference on Distributed Systems Platforms and Open Distributed Processing
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IEEE Transactions on Consumer Electronics
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Detecting failed nodes is important in a sensor network, because a failed node can cause coverage and connectivity holes. Since sensors are resource-limited devices, lack of a clocking mechanism imposes difficulties in implementing synchronous failure detection techniques. To counter this, asynchronous methods are needed for detecting failed sensor nodes. For example, in traditional distributed systems sent data packets and received acknowledgements are matched to identify any malfunctions. A similar approach can be adopted in sensor networks. An asynchronous failed sensor node detection (AFSD) method is proposed, where a numeric counter variable is used to track the received and sent data packets between active nodes. This variable acts as bounded for an active sensor node and unbounded for an out-of-communication or failed node. By observing the value of the counter, a decision can be made on a deployed sensor node. Whenever the counter value of a neighbour crosses a predefined threshold, that neighbour is suspected. However, measuring a counter value can always lead to a false positive. To avoid false suspicion, a consensus protocol is needed. Such a technique is described, which is proved as accurate and complete. The complexity analysis shows that the control, energy, and time overheads of the proposed method are linear and in the order of number of neighbours per node. Copyright © 2011 John Wiley & Sons, Ltd.