Journal Article10.1109/cis.2007.44
MPIDA: A Sensor Network Topology Inference Algorithm
Tao Zhao,Wandong Cai,Yongjun Li +2 more
pp 451-455
TL;DR: Sensor network topology inference algorithm based on end-to-end measurement for data aggregation.
read more
Abstract: Knowledge of sensor network topology is useful for understanding the structure of the sensor network, and it also plays an important role in resource management and deployment. Additionally, it is a major component of sensor network tomography techniques. Considering sensor network characteristics, we propose a topology inference algorithm based on end-to-end measurement in this paper. Especifically, we consider the case of inferring sensor network topology during the aggregation of the data from a collection of sensor nodes to a sink node. The simulation shows that the proposed approach can discover the sensor network topology accurately and quickly, and scale to the large networks. Keywords: Sensor network, network tomography, topology inference, data aggregation, sensor network tomography
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
References
A survey on sensor networks
TL;DR: The current state of the art of sensor networks is captured in this article, where solutions are discussed under their related protocol stack layer sections.
Directed diffusion for wireless sensor networking
TL;DR: In this article, the authors explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network analytically and experimentally and demonstrate that directed diffusion can achieve significant energy savings and can outperform idealized traditional schemes under the investigated scenarios.
A factor graph approach to link loss monitoring in wireless sensor networks
TL;DR: This paper presents a low complexity algorithmic framework for link loss monitoring based on the recent modeling and computational methodology of factor graphs, and suggests that this algorithm is particularly suitable as a long-term monitoring facility.
114
•Journal Article
Internet tomography
TL;DR: This article introduces the new field of network tomography, a field which it is believed will benefit greatly from the wealth of signal processing theory and algorithms.
Loss inference in wireless sensor networks based on data aggregation
Gregory Hartl,Baochun Li +1 more
- 26 Apr 2004
TL;DR: The problem of inferring per node loss rates from passive end-to-end measurements in wireless sensor networks is considered as a maximum-likelihood estimation (MLE) problem and shown how it can be efficiently solved using the expectation-maximization (EM) algorithm.