EZ-AG: Structure-free data aggregation in MANETs using push-assisted self-repelling random walks

TL;DR

EZ-AG is a novel structure-free data aggregation protocol for MANETs that uses push-assisted self-repelling random walks to achieve fast, scalable, and low-overhead aggregation.

Contribution

It introduces a push-assisted self-repelling random walk method that significantly improves data aggregation speed and reduces message overhead in MANETs.

Findings

Achieves O(N) time and message complexity for data aggregation.

Outperforms existing structure-free methods by at least a log(N) factor.

Supports scalable, robust, and multi-resolution aggregation in large networks.

Abstract

This paper describes EZ-AG, a structure-free protocol for duplicate insensitive data aggregation in MANETs. The key idea in EZ-AG is to introduce a token that performs a self-repelling random walk in the network and aggregates information from nodes when they are visited for the first time. A self-repelling random walk of a token on a graph is one in which at each step, the token moves to a neighbor that has been visited least often. While self-repelling random walks visit all nodes in the network much faster than plain random walks, they tend to slow down when most of the nodes are already visited. In this paper, we show that a single step push phase at each node can significantly speed up the aggregation and eliminate this slow down. By doing so, EZ-AG achieves aggregation in only O(N) time and messages. In terms of overhead, EZ-AG outperforms existing structure-free data aggregation by a factor of at least log(N) and achieves the lower bound for aggregation message overhead. We demonstrate the scalability and robustness of EZ-AG using ns-3 simulations in networks ranging from 100 to 4000 nodes under different mobility models and node speeds. We also describe a hierarchical extension for EZ-AG that can produce multi-resolution aggregates at each node using only O(NlogN) messages, which is a poly-logarithmic factor improvement over existing techniques.