.png){kind=icon}
.png){kind=icon}
Return
Wireless-Sensor Network Topology Optimization in Complex Terrain: A Bayesian Approach
.png){kind=icon}
OA
AI
DOI：10.1109/JIOT.2021.3082168.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Existing methods for wireless-sensor network (WSN) topology optimization employ simplifying assumptions of a fixed communication radius between network nodes, which is ill-suited for IoT networks deployed in complex terrain. This article proposes a data-driven approach to WSN topology optimization, employing a Bayesian link classifier trained on LIDAR-derived terrain characteristics and an in-situ survey of link quality. The classifier is trained to predict where good network links (packet-delivery ratio, PDR>0.5) are likely to form in a region given complex terrain attributes. Then, given numerous candidate wireless node placements throughout the domain, the classifier is used to construct an undirected weighted graph of the potential connectivity across the domain. Edge weights in the connectivity graph are proportional to the probability of forming a good link between the nodes. A novel modified cycle-union (MCyU) algorithm for generating a 2-vertex-connected, Steiner minimal network is then applied to the undirected weighted graph of potential network element placements. This ensures a survivable network design, while maximizing the probability of good links within the final network. The total number and spatial distribution of network elements produced by the algorithm is compared to an existing WSN, deployed for environmental monitoring in remote regions. In addition, the MCyU algorithm has been evaluated in three graph test cases to compare with state-of-the-art solutions, where MCyU outperforms in terms of weight minimization and computation time.
Keywords:
Wireless sensor networks
Vegetation mapping
Bayes methods
Laser radar
Wireless communication
Network topology
Topology
Bayesian approach
experimental validation
machine learning
topology optimization
wireless sensor networks (WSNs)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W