Wireless Sensor Networks and Monitoring of Environmental Parameters in Precision Agriculture

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.

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A Decentralized Multi Competitive Clustering in Wireless Sensor Networks for the Precision Agriculture

2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC) ◽

10.1109/ctceec.2017.8455019 ◽

2017 ◽

Author(s):

A Venkateshwar ◽

Venkanagouda C Patil

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Precision Agriculture ◽

Wireless Sensor

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Wireless Sensor Networks for Indoor Environment Monitoring Using LabVIEW

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2011.1076 ◽

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.

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Optimization of wireless sensor network deployment for electromagnetic situation monitoring

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078718000211 ◽

2018 ◽

Vol 10 (7) ◽

pp. 746-753 ◽

Cited By ~ 1

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.

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