scholarly journals Cooperating Mobile GIS and Wireless Sensor Networks for Managing Transportation Infrastructures in Urban areas

2013 ◽  
Vol XL-1/W3 ◽  
pp. 469-474
Author(s):  
R. Shad ◽  
N. Abazari ◽  
A. Alizadeh ◽  
M. Choghooni
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Urban Areas ◽  
Wireless Sensor ◽  
Mobile Gis ◽  
Transportation Infrastructures

Optimization of wireless sensor network deployment for electromagnetic situation monitoring

2018 ◽  
Vol 10 (7) ◽  
pp. 746-753 ◽  
Author(s):  
Krzysztof Malon ◽  
Paweł Skokowski ◽  
Jerzy Lopatka
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Urban Areas ◽  
Environmental Parameters ◽  
Probability Of Detection ◽  
Wireless Sensor ◽  
Electromagnetic Spectrum ◽  
Radio Receivers ◽  
Effective Performance ◽  
Performance Area

Wireless sensor networks are an increasingly popular tool for monitoring various environmental parameters. They can also be used for monitoring the electromagnetic spectrum. Wireless sensors, due to their small size, typically have simplified radio receivers with reduced sensitivity and use small antennas. As a result, their effective performance area is similarly limited. This is especially important in urban areas where there are various kinds of adverse propagation phenomena related to area coverage. The aim of this paper is to present the phenomena in the wireless sensor networks and propose criteria and methods to optimize their deployment to ensure maximizing the probability of detection of emissions, minimization of unmonitored areas, and to provide the necessary hardware redundancy in the priority areas. Influence of detection parameters, number of sensors and range constraints between sensors on received outcomes are also presented.

scholarly journals Anchor-Free Localization Using a Deep Neural Network in Wireless Sensor Networks with Multiple Sinks

2021 ◽  
Author(s):  
Arunanshu Mahapatro ◽  
V CH Sekhar Rao Rayavarapu
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Urban Areas ◽  
Deep Neural Network ◽  
Wireless Sensor ◽  
Range Estimation ◽  
Node Localization ◽  
Free Node ◽  
Channel Classification

<div>Wireless sensor networks (WSNs) is one of the vital part of the Internet of Things (IoT) that allow to acquire and provide information from interconnected sensors. Localization-based services are among the most appealing applications associated to the IoT. The deployment of WSNs in the indoor environments and urban areas creates obstacles that lead to the Non-Line-of-Sight (NLOS) propagation. Additionally, the localization accuracy is minimized by the NLOS propagation. The main intention of this paper is to develop an anchor-free node localization approach in multi-sink WSN under NLOS conditions using three key phases such as LOS/NLOS channel classification, range estimation, and anchor-free node localization. The first phase adopts Heuristicbased Deep Neural Network (H-DNN) for LOS/NLOS channel classification. Further, the same H-DNN s used for the range estimation. The hidden neurons of DNN are optimized using the proposed Adaptive Separating Operator-based Elephant Herding Optimization (ASO-EHO) algorithm. The node localization is formulated as a multi-objective optimization problem. The objectives such as localization error, hardware cost, and energy overhead are taken into consideration. ASO-EHO is used for node localization. The suitability of the proposed anchor-free node localization model is validated by comparing over the existing models with diverse counts of nodes. </div>

scholarly journals Anchor-Free Localization Using a Deep Neural Network in Wireless Sensor Networks with Multiple Sinks

2021 ◽  
Author(s):  
Arunanshu Mahapatro ◽  
V CH Sekhar Rao Rayavarapu
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Urban Areas ◽  
Deep Neural Network ◽  
Wireless Sensor ◽  
Range Estimation ◽  
Node Localization ◽  
Free Node ◽  
Channel Classification

<div>Wireless sensor networks (WSNs) is one of the vital part of the Internet of Things (IoT) that allow to acquire and provide information from interconnected sensors. Localization-based services are among the most appealing applications associated to the IoT. The deployment of WSNs in the indoor environments and urban areas creates obstacles that lead to the Non-Line-of-Sight (NLOS) propagation. Additionally, the localization accuracy is minimized by the NLOS propagation. The main intention of this paper is to develop an anchor-free node localization approach in multi-sink WSN under NLOS conditions using three key phases such as LOS/NLOS channel classification, range estimation, and anchor-free node localization. The first phase adopts Heuristicbased Deep Neural Network (H-DNN) for LOS/NLOS channel classification. Further, the same H-DNN s used for the range estimation. The hidden neurons of DNN are optimized using the proposed Adaptive Separating Operator-based Elephant Herding Optimization (ASO-EHO) algorithm. The node localization is formulated as a multi-objective optimization problem. The objectives such as localization error, hardware cost, and energy overhead are taken into consideration. ASO-EHO is used for node localization. The suitability of the proposed anchor-free node localization model is validated by comparing over the existing models with diverse counts of nodes. </div>

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