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Movable Platform-Based Topology Detection for a Geographic Routing Wireless Sensor Network

With the increasing adoption of the Internet-of-Things (IoT), the wireless sensors network (WSN), as an underlying application of IoT, has attracted increasing attention. Topology, the working structure used to observe WSN, is the most instinctive form in troubleshooting and has great significance t...

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Detalles Bibliográficos
Autores principales: Li, Runzhi, Wang, Jian, Chen, Jiongyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374310/
https://www.ncbi.nlm.nih.gov/pubmed/32635280
http://dx.doi.org/10.3390/s20133726
Descripción
Sumario:With the increasing adoption of the Internet-of-Things (IoT), the wireless sensors network (WSN), as an underlying application of IoT, has attracted increasing attention. Topology, the working structure used to observe WSN, is the most instinctive form in troubleshooting and has great significance to WSN management and safety. To this end, it is imperative to recover WSN topology for the purpose of network management and non-cooperative network detection. Traditional network topology recovery mainly relies on the monitoring modules installed in nodes, or an extra network attached. However, these two approaches have several limitations, such as high energy consumption for monitoring nodes, time synchronization problems, reuse failure, limitation to specific targeted networks and high cost. In this paper, we present a new approach to recover the topology of WSN that adopts location-based routing protocols, based on movable platforms. Our observation is that the network topology is consistent with the node routing, as the nodes choose the next hop according to the geological position of neighbor nodes. Hence, we calculate the cost parameters of choosing routing nodes for the targeted network according to the partial connection of the nodes. Based on those cost parameters, we can determine the topology of the whole network. More specifically, by collecting the geological position and data packets of the nodes from movable platforms, we are able to infer the topology of the WSN according to the recovered partial connection of nodes. Our approach can be easily adopted to many scenarios, especially for non-cooperative large-scale networks. The evaluation of 30 simulations shows that the accuracy of recovery is above 90%.