Matrix analysis
Byzantine Agreement in the Presence of Mixed Faults on Processors and Links
IEEE Transactions on Parallel and Distributed Systems
Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Reaching Agreement in the Presence of Faults
Journal of the ACM (JACM)
Exploiting Omissive Faults in Synchronous Approximate Agreement
IEEE Transactions on Computers
The Byzantine Generals Problem
ACM Transactions on Programming Languages and Systems (TOPLAS)
Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
Reaching Approximate Agreement with Mixed-Mode Faults
IEEE Transactions on Parallel and Distributed Systems
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Fault Tolerance in Collaborative Sensor Networks for Target Detection
IEEE Transactions on Computers
A survey of peer-to-peer content distribution technologies
ACM Computing Surveys (CSUR)
Gossip-based aggregation in large dynamic networks
ACM Transactions on Computer Systems (TOCS)
Time synchronization attacks in sensor networks
Proceedings of the 3rd ACM workshop on Security of ad hoc and sensor networks
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With the diverse new capabilities that sensor and ad hoc networks can provide, applicability of data aggregation is growing. Data aggregation is useful in dealing with multi-value domain information, which often requires approximate agreement decisions among nodes. In contrast to fully connected networks, the research on data aggregation for partially connected networks is very limited. This is due to the complexity of formal proofs and the fact that a node may not have a global view of the entire network, which makes it difficult to attain the convergence properties. The complexity of the problem is compounded in the presence of message dropouts, faults, and orchestrated attacks. By exploiting the properties of Discrete Markov Chains, this study investigates the data aggregation problem for partially connected networks to obtain: the number of rounds of message exchanges needed to reach a network-convergence, the average convergence rate in a round of message exchange, and the number of rounds required to reach a stationary-convergence.