Complexity in information theory
A coding theorem for distributed computation
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Communication complexity
Computation in noisy radio networks
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Finding OR in a noisy broadcast network
Information Processing Letters
Distributed Algorithms
Introduction to Coding Theory
The number of neighbors needed for connectivity of wireless networks
Wireless Networks
Distributed Computing: Fundamentals, Simulations and Advanced Topics
Distributed Computing: Fundamentals, Simulations and Advanced Topics
Computing in Fault Tolerance Broadcast Networks
CCC '04 Proceedings of the 19th IEEE Annual Conference on Computational Complexity
Lower Bounds for the Noisy Broadcast Problem
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Computing separable functions via gossip
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Distributed Symmetric Function Computation in Noisy Wireless Sensor Networks
IEEE Transactions on Information Theory
Computing and communicating functions over sensor networks
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
In this paper we study distributed function computation in a noisy multi-hop wireless network, in which n nodes are uniformly and independently distributed in a unit square. We adopt the adversarial noise model, for which independent binary symmetric channels are assumed for any point-to-point transmissions, with (not necessarily identical) crossover probabilities bounded above by some constant ε. Each node holds an m-bit integer per instance and the computation is started after each node collects N readings. The goal is to compute a global function with a certain fault tolerance, in this distributed setting; we mainly deal with divisible functions, which essentially covers the main body of interest for wireless applications. We focus on protocol designs that are efficient in terms of communication complexity. We first devise a general protocol for evaluating any divisible functions, addressing both one-shot (N = O(1)) and block computation, and both constant and large m scenarios; its bottleneck in different scenarios is also analyzed. Based on this analysis, we then endeavor to improve the design for two special cases: identity function, and some restricted type-threshold functions, both focusing on the constant m and N scenario.