Document Type
Article
Publication Date
2018
College/Unit
Statler College of Engineering and Mining Resources
Department/Program/Center
Not Listed
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
Digital Commons Citation
Kulathumani, V; Nakagawa, M; and Arona, A, "EZ-AG: structure-free data aggregation in MANETs using push-assisted self-repelling random walks" (2018). Faculty & Staff Scholarship. 1733.
https://researchrepository.wvu.edu/faculty_publications/1733
Source Citation
Kulathumani, V., Nakagawa, M., & Arora, A. (2018). EZ-AG: structure-free data aggregation in MANETs using push-assisted self-repelling random walks. Journal of Internet Services and Applications, 9(1). https://doi.org/10.1186/s13174-018-0077-4
Comments
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.