Optimal gossip-based aggregate computation
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Fault-Tolerant aggregation: flow-updating meets mass-distribution
OPODIS'11 Proceedings of the 15th international conference on Principles of Distributed Systems
An early-stopping protocol for computing aggregate functions in Sensor Networks
Journal of Parallel and Distributed Computing
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Dynamical connection graph changes are inherent in networks such as peer-to-peer networks, wireless ad hoc networks, and wireless sensor networks. Considering the influence of the frequent graph changes is thus essential for precisely assessing the performance of applications and algorithms on such networks. In this paper, using Stochastic Hybrid Systems (SHSs), we model the dynamics and analyze the performance of an epidemic-like algorithm, Distributed Random Grouping (DRG), for average aggregate computation on a wireless sensor network with dynamical graph changes. Particularly, we derive the convergence criteria and the upper bounds on the running time of the DRG algorithm for a set of graphs that are individually disconnected but jointly connected in time. An effective technique for the computation of a key parameter in the derived bounds is also developed. Numerical results and an application extended from our analytical results to control the graph sequences are presented to exemplify our analysis.