Probabilistic counting algorithms for data base applications
Journal of Computer and System Sciences
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Synopsis diffusion for robust aggregation in sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
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
Gossip-based aggregation in large dynamic networks
ACM Transactions on Computer Systems (TOCS)
Computing separable functions via gossip
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Network imprecision: a new consistency metric for scalable monitoring
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
Fault-Tolerant Aggregation for Dynamic Networks
SRDS '10 Proceedings of the 2010 29th IEEE Symposium on Reliable Distributed Systems
DISC'06 Proceedings of the 20th international conference on Distributed Computing
Fault-Tolerant aggregation: flow-updating meets mass-distribution
OPODIS'11 Proceedings of the 15th international conference on Principles of Distributed Systems
Dynamic computations in ever-changing networks
Proceedings of the 3rd International Workshop on Theoretical Aspects of Dynamic Distributed Systems
Continuous monitoring in the dynamic sensor field model
ALGOSENSORS'11 Proceedings of the 7th international conference on Algorithms for Sensor Systems, Wireless Ad Hoc Networks and Autonomous Mobile Entities
The cost of fault tolerance in multi-party communication complexity
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
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We present LiMoSense, a fault-tolerant live monitoring algorithm for dynamic sensor networks. This is the first asynchronous robust average aggregation algorithm that performs live monitoring, i.e., it constantly obtains a timely and accurate picture of dynamically changing data. LiMoSense uses gossip to dynamically track and aggregate a large collection of ever-changing sensor reads. It overcomes message loss, node failures and recoveries, and dynamic network topology changes. We formally prove the correctness of LiMoSense; we use simulations to illustrate its ability to quickly react to changes of both the network topology and the sensor reads, and to provide accurate information.