Research on sensor networks based on probability graph model

Author(s):  
Chundong Wang ◽  
Yinghui Chen ◽  
Junfeng Wei ◽  
Xiuliang Mo
2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Changjian Deng

Based on the complex network theory, robustness analysis of condition monitoring wireless sensor network under uncertain interference is present. In the evolution of the topology of sensor networks, the density weighted algebraic connectivity is taken into account, and the phenomenon of removing and repairing the link and node in the network is discussed. Numerical simulation is conducted to explore algebraic connectivity characteristics and network robustness performance. It is found that nodes density has the effect on algebraic connectivity distribution in the random graph model; high density nodes carry more connections, use more throughputs, and may be more unreliable. Moreover, the results show that, when network should be more error tolerant or robust by repairing nodes or adding new nodes, the network should be better clustered in median and high scale wireless sensor networks and be meshing topology in small scale networks.


2020 ◽  
Vol 79 (45-46) ◽  
pp. 34029-34043
Author(s):  
Jinguang Sun ◽  
Tao Li ◽  
Hua Yan ◽  
Xiangjun Dong

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2866 ◽  
Author(s):  
Mario Ruz ◽  
Juan Garrido ◽  
Jorge Jiménez ◽  
Reino Virrankoski ◽  
Francisco Vázquez

Within the context of the Internet of Things (IoT) and the Location of Things (LoT) service, this paper presents an interactive tool to quantitatively analyze the performance of cooperative localization techniques for wireless sensor networks (WSNs). In these types of algorithms, nodes help each other determine their location based on some signal metrics such as time of arrival (TOA), received signal strength (RSS), or a fusion of them. The developed tool is intended to provide researchers and designers a fast way to measure the performance of localization algorithms considering specific network topologies. Using TOA or RSS models, the Crámer-Rao lower bound (CRLB) has been implemented within the tool. This lower bound can be used as a benchmark for testing a particular algorithm for specific channel characteristics and WSN topology, which allows determination if the necessary accuracy for a specific application is possible. Furthermore, the tool allows us to consider independent characteristics for each node in the WSN. This feature allows the avoidance of the typical “disk graph model,” which is usually applied to test cooperative localization algorithms. The tool allows us to run Monte-Carlo simulations and generate statistical reports. A set of basic illustrative examples are described comparing the performance of different localization algorithms and showing the capabilities of the presented tool.


2015 ◽  
Vol 12 (2) ◽  
pp. 895-909 ◽  
Author(s):  
Jian Shu ◽  
Shandong Jiang ◽  
Qun Liu ◽  
Linlan Liu ◽  
Xiaotian Geng

Connectivity is one of the most important parameters in network monitoring. The connectivity model of Opportunistic Sensor Networks (OSN) can hardly be established by traditional graph models due to the fact that its connectivity is timing correlative and evolutionary, which makes it extremely difficult to monitor an OSN. In order to solve the monitoring problem, this paper builds an evolving graph model based on the theory of evolving graph as a description of an OSN. It defines a series of parameters to measure the connectivity of the OSN and establishes an monitoring model. Meanwhile, this paper gives the key algorithms in building the model, the Evolving-Graph-Modeling (EGM) algorithm and the Connected-Journey (CJ) algorithm. The rationality of the monitoring model has been proven by a prototype system and the simulation results. Extensive simulation results show that the proposed connectivity monitoring model can indicate real circumstances of OSN? connectivity, and it is applicable to monitoring an opportunistic sensor network.


Sign in / Sign up

Export Citation Format

Share Document