Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Distributed Algorithms
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Epidemic-Style Proactive Aggregation in Large Overlay Networks
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Robust Aggregation Protocols for Large-Scale Overlay Networks
DSN '04 Proceedings of the 2004 International Conference on Dependable Systems and Networks
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
IEEE Transactions on Parallel and Distributed Systems
Mobile Ad Hoc Networks: Energy-Efficient Real-Time Data Communications
Mobile Ad Hoc Networks: Energy-Efficient Real-Time Data Communications
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
Asynchronous distributed averaging on communication networks
IEEE/ACM Transactions on Networking (TON)
ACC'09 Proceedings of the 2009 conference on American Control Conference
Finite-time convergent gradient flows with applications to network consensus
Automatica (Journal of IFAC)
IEEE Transactions on Information Theory
Distributed computation of averages over ad hoc networks
IEEE Journal on Selected Areas in Communications
ACC'09 Proceedings of the 2009 conference on American Control Conference
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This paper addresses the problem of averaging numbers across a wireless network from an important, but largely neglected, viewpoint: bandwidth/energy efficiency. We show that existing distributed averaging schemes are inefficient, producing networked dynamical systems that evolve with wasteful communications. To improve efficiency, we develop Controlled Hopwise Averaging (CHA), a distributed asynchronous algorithm that attempts to "make the most" out of each transmission. Unlike the existing schemes, CHA fully exploits the broadcast nature of wireless medium and enables greedy, decentralized, feedback control of when to initiate an iteration. We show that CHA admits a common quadratic Lyapunov function for analysis and control, establish its exponential convergence, and characterize its worst-case convergence rate. Finally, through extensive simulation on random geometric graphs, we show that CHA is substantially more efficient than several existing schemes, requiring far fewer transmissions to complete an averaging task.