John Heidemann

Impact of Network Density on Data Aggregation in Wireless Sensor Networks

TitleImpact of Network Density on Data Aggregation in Wireless Sensor Networks
Publication TypeTechnical Report
Year of Publication2001
AuthorsC. Intanagonwiwat, D. Estrin, R. Govindan, and J. Heidemann
Date Publishednov
Institutionusc-csd
Abstract

In-network data aggregation is essential for wireless sensor networks where resources (e.g., bandwidth, energy) are limited. In a previously proposed data dissemination scheme, data is opportunistically aggregated at the intermediate nodes on a low-latency tree which may not necessarily be energy efficient. A more energy-efficient tree is a greedy tree which can be incrementally constructed by connecting each source to the closest point of the existing tree. In this paper, we propose a greedy approach for constructing a greedy aggregation tree to improve path sharing. We evaluated the performance of this greedy approach by comparing it to the prior opportunistic approach. Our preliminary result suggests that although the greedy aggregation and the opportunistic aggregation are roughly equivalent at low-density networks, the greedy aggregation can achieve signficant energy savings at higher densities. In one experiment we found that the greedy aggregation can achieve up to 45% energy savings over the opportunistic aggregation without an adverse impact on latency or robustness.

URLhttp://www.isi.edu/%7ejohnh/PAPERS/Intanagonwiwat01b.html
Groups: