Approximate counting, uniform generation and rapidly mixing Markov chains
Information and Computation
Building efficient wireless sensor networks with low-level naming
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Directed diffusion for wireless sensor networking
IEEE/ACM Transactions on Networking (TON)
The Impact of Data Aggregation in Wireless Sensor Networks
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
Scalable Fault-Tolerant Aggregation in Large Process Groups
DSN '01 Proceedings of the 2001 International Conference on Dependable Systems and Networks (formerly: FTCS)
Supporting Aggregate Queries Over Ad-Hoc Wireless Sensor Networks
WMCSA '02 Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications
Impact of Network Density on Data Aggregation in Wireless Sensor Networks
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Locally constructed algorithms for distributed computations in ad-hoc networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
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)
A space-time diffusion scheme for peer-to-peer least-squares estimation
Proceedings of the 5th international conference on Information processing in sensor networks
A scheme for robust distributed sensor fusion based on average consensus
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
IEEE Transactions on Parallel and Distributed Systems
Theoretical Computer Science - Foundations of software science and computation structures
Communication in dynamic radio networks
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
IEEE Transactions on Parallel and Distributed Systems
Fault-Tolerant Aggregation by Flow Updating
DAIS '09 Proceedings of the 9th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems
Brief announcement: locality-based aggregate computation in wireless sensor networks
Proceedings of the 28th ACM symposium on Principles of distributed computing
An Early-Stopping Protocol for Computing Aggregate Functions in Sensor Networks
PRDC '09 Proceedings of the 2009 15th IEEE Pacific Rim International Symposium on Dependable Computing
Fault-Tolerant Aggregation for Dynamic Networks
SRDS '10 Proceedings of the 2010 29th IEEE Symposium on Reliable Distributed Systems
Geographic Gossip: Efficient Averaging for Sensor Networks
IEEE Transactions on Signal Processing
LiMoSense --- live monitoring in dynamic sensor networks
ALGOSENSORS'11 Proceedings of the 7th international conference on Algorithms for Sensor Systems, Wireless Ad Hoc Networks and Autonomous Mobile Entities
Spectra: robust estimation of distribution functions in networks
DAIS'12 Proceedings of the 12th IFIP WG 6.1 international conference on Distributed Applications and Interoperable Systems
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Flow-Updating (FU) is a fault-tolerant technique that has proved to be efficient in practice for the distributed computation of aggregate functions in communication networks where individual processors do not have access to global information. Previous distributed aggregation protocols, based on repeated sharing of input values (or mass) among processors, sometimes called Mass-Distribution (MD) protocols, are not resilient to communication failures (or message loss) because such failures yield a loss of mass. In this paper, we present a protocol which we call Mass-Distribution with Flow-Updating (MDFU). We obtain MDFU by applying FU techniques to classic MD. We analyze the convergence time of MDFU showing that stochastic message loss produces low overhead. This is the first convergence proof of an FU-based algorithm. We evaluate MDFU experimentally, comparing it with previous MD and FU protocols, and verifying the behavior predicted by the analysis. Finally, given that MDFU incurs a fixed deviation proportional to the message-loss rate, we adjust the accuracy of MDFU heuristically in a new protocol called MDFU with Linear Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave very well in practice, even under high rates of message loss and even changing the input values dynamically.