Smart Environmental Monitoring Using Wireless Sensor Networks

El-Bendary, Nashwa; Fouad, Mohamed Mostafa; Ramadan, Rabie A.; Banerjee, Soumya; Hassanien, Aboul Ella

doi:10.1201/b15425-33

Cited by 29 publications

(13 citation statements)

References 23 publications

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“…In weather monitoring system distributed sensor network plays very important role in predicting the climate condition. Wireless distributed sensor network plays very important role in collecting the information [11][12][13]. The information is processed at microcontroller [14,15] and further sent to the cloud, with which the user able to take wise decision in their business or in busy traffic on the road.…”

Section: Related Workmentioning

confidence: 99%

Providing enhanced security in IoT based smart weather system

Rao¹,

Chandra

Revathi

et al. 2020

IJEECS

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Weather reporting system consist multiple devices which are playing dynamic role in producing dynamic environmental parameters such as temperature, humidity, and air pollution. It was a tedious task to bring all the devices together and make them interleaving to produce relevant measurement. Recent trends have proven that the IoT has brought all the devices and peripherals in one place to make more flexible and smart measurement. In the traditional weather monitoring system measurement method is not real time, not continuous and also tedious job to take continuous measurement. But, the IoT has completely changed the measurement scenario and improved the consistency of the measurement. In the present work the environmental parameters such rainfall, temperature, humidity, and density of carbon dioxide in the air are measured with sensors. The Arduino Uno card gathers all information from the devices which are associated with its port pins. The information is sent to cloud server for record and future retrieving. At the same time the data security in cloud been assured with encryption and decryption data while retrieving the information from cloud. This enhances the security, ease of accessing the cloud data from mobile applications, provides wise predictions and minimizes the communication overhead.

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Section: Related Workmentioning

confidence: 99%

Providing enhanced security in IoT based smart weather system

Rao¹,

Chandra

Revathi

et al. 2020

IJEECS

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“…The agricultural WSNs monitoring of the air temperature, air humidity, soil temperature, soil moisture, leaf wetness, atmospheric pressure, solar radiation, wind direction, amount rainfall and etc. [8].…”

Section: Literature Surveymentioning

confidence: 99%

Application of Light Sensors Amplifier and Wireless Networking Sensor for Ambient Light Data to the Android Platform

Lapyga

2019

IJRTE

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Modern technologies of growing different plants types in greenhouses envisage automated systems use for monitoring climatic parameters, on which the ripening of fruits and yields depend. At the development and application of such automated systems, the focus is on measuring the accuracy of temperature and humidity soil, whereas to determine the level of illumination used sensors, that measure the total illumination or Photosynthetic Active Radiation (400-700 nm) with error up to 20%. Under conditions of insufficient photon flux density, even at the optimal moisture level and temperature in the greenhouse, fruiting and crop yields may not achieve high performance. This article proposes a greenhouse lighting control system in which the LI-200R Pyranometer and LI-190R-BL Quantum Sensor measure not only total illumination from the wavelength of 400–1000 nm, but also PPFD from a wavelength of 400–700 nm. Light Sensor Amplifier provides an increase in the source signal of the measuring sensors and reduces their measurement error to 1%. The applied SMART Connectivity Platform ESP8266 Version ESP 07 is more robust to interference and ensures the transfer of measured data to the server. Created software application for smartphones with Android OS, ensures remote user access to the database via the Internet for remote control and analysis of climatic indicators in the greenhouse. The proposed illumination control system in the greenhouse contributes to the improvement of precision farming

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“…The SHM designs discussed in Reference [21] is an acute process, while taking into account cloud integration and real-time operations of machine learning algorithms. SHM implementations using wireless sensors networks for Internet of Things (IoT) models [22,23] need improvement in their UC aspect, that is, there must be some algorithms and data processing that can assist Open System Interconnection (OSI) model, which should be application layer (layer 6), and presentation layer (layer 7) devices and applications. Deep Learning (DL) has been implemented on raspberry pi but still needs improvement for cloud compatibility and pairing with mathematical techniques mentioned in [24].…”

Section: Introductionmentioning

confidence: 99%

Geographical Area Network—Structural Health Monitoring Utility Computing Model

Tariq

Tahir

Touati

et al. 2019

IJGI

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In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner in form of a SHM geo-informatics system. The proposed UCM consists of networked SHM systems that send geometrical SHM variables to SHM-UCM gateways. Every gateway is routing the data to SHM-UCM servers running a geo-spatial patch health assessment and prediction algorithm. The inputs of the prediction algorithm are geometrical variables, environmental variables, and payloads. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This has been tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU routers and zone calculation models and were then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

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Smart Environmental Monitoring Using Wireless Sensor Networks

Cited by 29 publications

References 23 publications

Providing enhanced security in IoT based smart weather system

Providing enhanced security in IoT based smart weather system

Application of Light Sensors Amplifier and Wireless Networking Sensor for Ambient Light Data to the Android Platform

Geographical Area Network—Structural Health Monitoring Utility Computing Model

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