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.

Design of Environment Monitoring System Based on CAN Bus and Wireless Sensor Networks for Greenhouse

2014 ◽  
Vol 1049-1050 ◽  
pp. 1163-1166
Author(s):  
Bo Chang ◽  
Xin Rong Zhang ◽  
Li Hong Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Can Bus ◽  
Environmental Parameters ◽  
Wireless Sensor ◽  
Area Network ◽  
Environment Monitoring ◽  
Effective Solution ◽  
Greenhouse Crops ◽  
Backbone Network

In order to accurately collect the environmental parameters (such as temperature, humidity, illumination, etc.), which influence growth of greenhouse crops, the paper proposed a design for greenhouse environment monitoring based on CAN bus and wireless sensor networks (WSNs). The communication network of the system consists of two parts: the backbone network being constructed by CAN bus and area network being constructed by WSNs. At the same time, the designed of hardware and software about the system is illustrated in detail. System architecture indicates that the system is an effective solution for greenhouse environment monitoring.

scholarly journals Wireless Sensor Networks for Indoor Environment Monitoring Using LabVIEW

2011 ◽  
pp. 87-90
Author(s):  
Mrutyunjay Rout ◽  
Dr. Harish Kumar Verma ◽  
Subhashree Das
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Indoor Environment ◽  
Environmental Parameters ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Environment Monitoring ◽  
Wireless Sensor Nodes ◽  
Local Decision ◽  
Mems Technology

Wireless sensor networks (WSNs) have gained worldwide attention in recent years, particularly with the rapid progress in Micro-Electro-Mechanical Systems (MEMS) technology which has facilitated the development of smart sensors. These sensors are small, with limited processing and computing resources, and they are inexpensive compared to traditional sensors. These sensor nodes can sense, measure, and gather information from the environment and, based on some local decision process, they can transmit the sensed data to the user. WSNs are large networks made of a numerous number of sensor nodes with sensing, computation, and wireless communication capabilities. In present work we provide a brief summary of the state-ofthe- art in wireless sensor networks, investigate the feasibility of indoor environment monitoring using crossbow wireless sensor nodes. Here we used nesC programming language and TinyOS operating system for programming Crossbow sensor nodes and LabVIEW GUI is used for displaying different indoor environmental parameters such as temperature, humidity and light acquired from different Wireless sensor nodes. These sensor readings can help building administrators to monitor the physical conditions of the environment in a building for creating optimized energy usage.

Intelligent Analogy on Wireless Communication Link Performance of Industry Wireless Sensor Networks

2011 ◽  
Vol 317-319 ◽  
pp. 366-369
Author(s):  
Guang Zhu Chen ◽  
Cheng Ming Luo
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fuzzy Neural Network ◽  
Environmental Parameters ◽  
Wireless Sensor ◽  
Neural Network Modeling ◽  
Communication Link ◽  
Key Factor ◽  
Fuzzy Neural

The received signal strength indication (RSSI) is the key factor in the communication link for industry wireless sensor networks, while it is very difficult to model the value of RSSI to the distance of two communication nodes. This paper presented a fuzzy neural network modeling method to solve the shortcoming of the theoretical modeling. After the value of RSSI and the distance value of two communication nodes are fuzzed by Gaussian membership function, a fuzzy controlling rule is also presented, and then the output value of fuzzy neural network, namely the error distance of two communication nodes can be attained. Finally, simulation results show that without correcting the environmental parameters, the estimated error value of the distance of two communication nodes through RSSI in fuzzy neural network model is less than in quadratic fit method. So, the method presented by this paper can provide precise data support for wireless sensor networks for industry environment.

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 A Novel Adjacent Sensors-Based Mechanism to Increase Performance of Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mohammed Al-Shalabi ◽  
Jafar Ababneh ◽  
Waled Abdulraheem
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Network Performance ◽  
Environmental Parameters ◽  
Base Station ◽  
Decision Makers ◽  
Wireless Sensor ◽  
Adaptive Clustering ◽  
Leach Protocol ◽  
And Performance

Wireless Sensor Networks are widely used nowadays to support the decision-makers in different applications by monitoring and collecting the environmental parameters in specific areas. Sensors are deployed in such areas either randomly or formally. In a high-density Wireless Sensor Network, several sensors are randomly deployed in a small area. This will make the adjacent sensors collect same data and send them to the sink, which will increase the power consumption in those sensors. Adjacent sensors are considered critical because of their effect on the network performance. In this paper, the effect of the adjacent sensors is minimized because of the above-mentioned criticality and performance influence of these sensors. The proposed mechanism is evaluated by using MATLAB simulator and is then compared with the low-energy adaptive clustering hierarchy (LEACH) protocol. Results prove that the proposed mechanism outperforms the LEACH protocol by 21% in terms of the network lifetime and by 18% in terms of the number of the transmitted packets to the cluster heads and reduces the number of the transmitted packets to the base station by approximately 3% by avoiding the duplicated packets.

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