Готові списки джерел за темами / Environmental monitoring network

Добірка наукової літератури з теми "Environmental monitoring network"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "Environmental monitoring network"

1

Sundareshwar, P. V., R. Murtugudde, G. Srinivasan, S. Singh, K. J. Ramesh, R. Ramesh, S. B. Verma, et al. "ENVIRONMENT: Environmental Monitoring Network for India." Science 316, no. 5822 (April 13, 2007): 204–5. http://dx.doi.org/10.1126/science.1137417.

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Alemayehu, B., M. Mckinzie, T. Cochran, D. Sythe, R. Randrup, and E. Lafargue. "Citizen-based environmental radiation monitoring network." Journal of Radioanalytical and Nuclear Chemistry 314, no. 2 (September 21, 2017): 1095–101. http://dx.doi.org/10.1007/s10967-017-5479-4.

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3

Madda, Dheeraj R. "Distributed Environmental Monitoring using Wireless Sensor Network." International Journal for Research in Applied Science and Engineering Technology 7, no. 7 (July 31, 2019): 633–36. http://dx.doi.org/10.22214/ijraset.2019.7101.

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4

Sousa, P. J., R. Tavares, P. Abreu, and M. Teresa Restivo. "NSensor – Wireless Sensor Network for Environmental Monitoring." International Journal of Interactive Mobile Technologies (iJIM) 11, no. 5 (July 24, 2017): 25. http://dx.doi.org/10.3991/ijim.v11i5.7067.

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<p class="0abstract">This paper reports the development and integration of a wireless sensor network for environmental monitoring. The main goals of this system include modularity, low power consumption and ease of expansion. The system includes three main elements: sensor nodes, gateways and a server. Each sensor node can only connect to a gateway, resulting in a star network layout. Data collected from the different sensor nodes is stored in a database within the server. A web-based user interface for this system was developed and made available online.</p>
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5

Corbellini, S., E. Di Francia, S. Grassini, L. Iannucci, L. Lombardo, and M. Parvis. "Cloud based sensor network for environmental monitoring." Measurement 118 (March 2018): 354–61. http://dx.doi.org/10.1016/j.measurement.2017.09.049.

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6

Vairamani, K., N. Mathivanan, K. Arun Venkatesh, and U. Dinesh Kumar. "Environmental parameter monitoring using wireless sensor network." Instruments and Experimental Techniques 56, no. 4 (July 2013): 468–71. http://dx.doi.org/10.1134/s0020441213040118.

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7

Rus, C., R. Marcus, L. Pellegrini, M. Leba, M. Rebrisoreanu, and A. Constandoiu. "Electric cars as environmental monitoring IoT Network." IOP Conference Series: Materials Science and Engineering 572 (August 2, 2019): 012091. http://dx.doi.org/10.1088/1757-899x/572/1/012091.

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Vairamani, K., N. Mathivanan, K. Arun Venkatesh, and U. Dinesh Kumar. "Environmental Parameter Monitoring Using Wireless Sensor Network." Приборы и техника эксперимента 2013, no. 4 (2013): 108–11. http://dx.doi.org/10.7868/s0032816213040149.

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9

Lombardo, Luca, Simone Corbellini, Marco Parvis, Ahmed Elsayed, Emma Angelini, and Sabrina Grassini. "Wireless Sensor Network for Distributed Environmental Monitoring." IEEE Transactions on Instrumentation and Measurement 67, no. 5 (May 2018): 1214–22. http://dx.doi.org/10.1109/tim.2017.2771979.

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10

Zhu, Li, and Jianwu Li. "WSN Optimization Algorithm for Traffic Environmental Monitoring." International Journal of Online and Biomedical Engineering (iJOE) 14, no. 12 (December 23, 2018): 72. http://dx.doi.org/10.3991/ijoe.v14i12.9495.

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In accordance with the features of variety and disparity of traffic information, using wireless sensor network to monitor traffic environment, this paper proposes an optimization algorithm of applying wireless sensor to monitor traffic environment. In this paper, to optimize the coverage, we analyze the irregularity of network sensing areas based on environmental factors and propose a wireless sensor network optimization algorithm. According to the irregularity of network node sensing areas, we build an irregular network coverage model, divide the node monitoring area to improve the network coverage and make corrections to the coverage according to the network coverage connectivity and other features. The simulation test proves that the method proposed in this paper can avoid too many redundant nodes - it only requires a reasonable number of network nodes. This will reduce network energy costs and increase network connectivity without affecting the coverage, thus improving the network performance. the algorithm can satisfy the requirements of real-time acquisition, processing and remote supervision of traffic information.
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Дисертації з теми "Environmental monitoring network"

1

Grubinger, Michael, and Felix Strohmeier. "AUTONOMOUS ACQUISITION OF ENVIRONMENTAL DATA IN A GLOBAL NETWORK ENVIRONMENT." International Foundation for Telemetering, 2001. http://hdl.handle.net/10150/607597.

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Анотація:
International Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, Nevada
This paper presents the results of a feasibility study undertaken by the University of Salzburg (Austria), investigating the autonomous acquisition of environmental data in a global network. A suggested application which is used as the basis of this paper is a volcano monitoring system which would be able to track the activity of a volcano and act as a disaster warning system. The background Volcano observation data required for such a system is covered, before discussing the concepts for sensor data acquisition, storage and processing. A final analysis is then presented of the opportunities for the transmission by packet radio (both terrestrial and satellite).
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2

Narayan, Raghu B. (Raghu Bangalore) 1977. "Wireless sensor network for ground-water monitoring." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/84823.

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Анотація:
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.
Leaf 78 blank.
Includes bibliographical references (leaves 76-77).
by Raghu B. Narayan.
M.Eng.
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3

Han, Wei. "Three-tier wireless sensor network infrastructure for environmental monitoring." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/9183.

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Анотація:
Doctor of Philosophy
Department of Biological & Agricultural Engineering
Naiqian Zhang
A two-tier wireless data communication system was developed to remotely monitor sediment concentration in streams in real time. The system used wireless motes and other devices to form a wireless sensor network to acquire data from multiple sensors. The system also used a Stargate, a single-board computer, as a gateway to manage and control data flow and wireless data transfer. The sensor signals were transmitted from an AirCard on the Stargate to an Internet server through the General Packet Radio Service (GPRS) provided by a commercial GSM cellular carrier. Various types of antennas were used to boost the signal level in a radio-hostile environment. Both short- and long-distance wireless data communications were achieved. Power supplies for the motes, Stargate, and AirCard were improved for reliable and robust field applications. The application software was developed using Java, C, nesC, LabView, and SQL to ensure seamless data transfer and enable both on-site and remote monitoring. Remote field tests were carried out at different locations with different GPRS signal strengths and a variety of landscapes. A three-tier wireless sensor network was then developed and deployed at three military installations around the country – Fort Riley in Kansas, Fort Benning in Georgia, and Aberdeen Proving Ground in Maryland - to remotely monitor sediment concentration and movement in real time. Sensor nodes, gateway stations, repeater stations, and central stations were strategically deployed to insure reliable signal transmissions. Radio signal strength was tested to analyze effects of distance, vegetation, and topographical barriers. Omni- and Yagi-directional antennas with different gains were tested to achieve robust, long-range communication in a wireless-hostile environment. Sampling times of sensor nodes within a local sensor network were synchronized at the gateway station. Error detection algorithms were developed to detect errors caused by interference and other impairments of the transmission path. GSM and CDMA cellular modems were used at different locations based on cellular coverage. Data were analyzed to verify the effectiveness and reliability of the three-tier WSN.
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Koch, John R. "A hybrid sensor network for watershed monitoring." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2008. http://scholarsmine.mst.edu/thesis/pdf/jrk4y8_09007dcc804f8fe6.pdf.

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Анотація:
Thesis (M.S.)--Missouri University of Science and Technology, 2008.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 27, 2008) Includes bibliographical references (p. 84-86).
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5

Iyiola, Samuel Oluwagbemi. "Moteino-Based Wireless Data Transfer for Environmental Monitoring." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984271/.

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Data acquisition through wireless sensor networks (WSNs) has enormous potential for scalable, distributed, real-time observations of monitored environmental parameters. Despite increasing versatility and functionalities, one critical factor that affects the operation of WSNs is limited power. WSN sensor nodes are usually battery powered, and therefore the long-term operation of the WSN greatly depends on battery capacity and the node's power consumption rate. This thesis focuses on WSN node design to reduce power consumption in order to achieve sustainable power supply. For this purpose, this thesis proposes a Moteino-based WSN node and an energy efficient duty cycle that reduces current consumption in standby mode using an enhanced watchdog timer. The nodes perform radio communication at 915 MHz, for short intervals (180ms) every 10 minutes, and consume 6.8 mA at -14dBm. For testing, the WSN node monitored a low-power combined air temperature, relative humidity, and barometric pressure sensor, together with a typical soil moisture sensor that consumes more power. Laboratory tests indicated average current consumption of ~30µA using these short radio transmission intervals. After transmission tests, field deployment of a star-configured network of nine of these nodes and one gateway node provides a long-term platform for testing under rigorous conditions. A webserver running on a Raspberry Pi connected serially to the gateway node provides real-time access to this WSN.
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6

Spreeth, Gideon. "Design of a low power wireless sensor network for environmental monitoring." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/1606.

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Анотація:
Thesis (MScEng (Electrical and Electronic Engineering))--Stellenbosch University, 2008.
A WSN (wireless sensor network) consists of a collection of small, low power electronic devices that can sense their environment and communicate with each other in order to send data to a base station for logging and monitoring. Research done on WSNs has increased rapidly over the past few years, as the necessary RF hardware has become cheaper and smaller. The wealth of information and hardware available in this field has made it possible to design and deploy networks for a multitude of monitoring purposes, on almost any terrain, without an existing telecommunication infrastructure. This thesis presents research into some major aspects of WSNs and the implementation of a test system with wireless sensor motes, that can be used for environmental monitoring, conservation purposes, impact studies, early warning systems for floods, fires etc. The system also has a wide range of possible uses in agriculture, as more data and better control over crops can increase yield. The power constraint of sensor nodes is one of the biggest concerns, as batteries can be depleted quickly and render a system useless. For this reason, work was focused on reducing power consumption of the hardware by means of various methods. Power use was also simulated very successfully, giving a accurate way of predicting node lifetime with a variety of battery types. The system was implemented on the Tmote Sky hardware platform using the open source sensor network operating system, TinyOS.
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7

Khader, Abdelhaleem I. "Value of Information in Design of Groundwater Quality Monitoring Network under Uncertainty." DigitalCommons@USU, 2012. https://digitalcommons.usu.edu/etd/1325.

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The increasing need for groundwater as a source for fresh water and the continuous deterioration in many places around the world of that precious source as a result of anthropogenic sources of pollution highlights the need for efficient groundwater resources management. To be efficient, groundwater resources management requires efficient access to reliable information that can be acquired through monitoring. Due to the limited resources to implement a monitoring program, a groundwater quality monitoring network design should identify what is an optimal network from the point of view of cost, the value of information collected, and the amount of uncertainty that will exist about the quality of groundwater. When considering the potential social impact of monitoring, the design of a network should involve all stakeholders including people who are consuming the groundwater. This research introduces a methodology for groundwater quality monitoring network design that utilizes state-of-the-art learning machines that have been developed from the general area of statistical learning theory. The methodology takes into account uncertainties in aquifer properties, pollution transport processes, and climate. To check the feasibility of the network design, the research introduces a methodology to estimate the value of information (VOI) provided by the network using a decision tree model. Finally, the research presents the results of a survey administered in the study area to determine whether the implementation of the monitoring network design could be supported. Applying these methodologies on the Eocene Aquifer, Palestine indicates that statistical learning machines can be most effectively used to design a groundwater quality monitoring network in real-life aquifers. On the other hand, VOI analysis indicates that for the value of monitoring to exceed the cost of monitoring, more work is needed to improve the accuracy of the network and to increase people’s awareness of the pollution problem and the available alternatives.
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Ferdoush, Sheikh Mohammad. "A Low-cost Wireless Sensor Network System Using Raspberry Pi and Arduino for Environmental Monitoring Applications." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc500182/.

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Sensors are used to convert physical quantity into numerical data. Various types of sensors can be coupled together to make a single node. A distributed array of these nodes can be deployed to collect environmental data by using appropriate sensors. Application of low powered short range radio transceivers as a communication medium between spatially distributed sensor nodes is known as wireless sensor network. In this thesis I build such a network by using Arduino, Raspberry Pi and XBee. My goal was to accomplish a prototype system so that the collected data can be stored and managed both from local and remote locations. The system was targeted for both indoor and outdoor environment. As a part of the development a controlling application was developed to manage the sensor nodes, wireless transmission, to collect and store data using a database management service. Raspberry Pi was used as base station and webserver. Few web based application was developed for configuring the network, real time monitoring, and database management. Whole system functions as a single entity. The use of open source hardware and software made it possible to keep the cost of the system low. The successful development of the system can be considered as a prototype which needs to be expanded for large scale environmental monitoring applications.
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Abdou, Ahmed Abdallah. "An investigation of short range electromagnetic wave communication for underwater environmental monitoring utilising a sensor network platform." Thesis, Liverpool John Moores University, 2014. http://researchonline.ljmu.ac.uk/4543/.

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Current state of the art water communications systems rely on optical and acoustic propagation. But these have underperformed in many applications. Wireless Sensor Network (WSN) using radio communication underwater is state of the art. The frequency of operation and the antenna are the big challenges that if unlocked, will present many advantages. The aim of this research is to investigate short range electromagnetic wave communication for underwater environmental monitoring utilising a sensor network platform. Theoretical study and preliminary experiments have confirmed that ISM (industrial, scientific and medical) band at 433MHz was suitable for potable and freshwater communication. Traditional antennas have been constructed, tested and modelled in a High Frequency Simulator Structure (HFSS) but were found unsuitable for use underwater. A 433MHz bowtie antenna was modelled in HFSS and shown to perform well in both air and potable water without any matching circuit. The antenna was prototyped on a printed circuit board, waterproofed and tested successfully in a tank. Furthermore to eliminate RF crosstalk, a battery powered wireless transmitter that generated a carrier signal at 433MHz, was used successfully in the laboratory tank, and during experiments that were repeated in freshwater in Liverpool Stanley Canal. This range, in excess of 5m, was large enough to combine the bowtie antenna with off the shelf, low power transceivers operating at the 433MHz, and specific sensors to form a WSN for potable and freshwater applications. The contribution to knowledge is the experimental demonstration of reliable communication at 433MHz using a broadband antenna which unlocks the potential of underwater WSN applications, including applications in water quality measurement, using radio communication.
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Kridi, Douglas Santiago. "Monitoring thermic patterns in beehives via wireless sensor networks." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13059.

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nÃo hÃ
Swarming is the mass exodus of bees in a hive, whose most common causes are lack of food, stress, variations of humidity and especially high temperatures. Among the types of swarming, one in which the complete abandonment of the hive occurs has brought great harm to Brazilian beekeepers, particularly the Northeast. In the Northeast region, of great importance for the Brazilian beekeeping, and where high temperatures are common in most of the year, a large number of hives is lost due to the swarming through abandonment. In an attempt to mitigate this problem, we propose a proactive monitoring hives via a network of wireless sensors capable of identifying atypical heating indicative of a preswarming condition. By means of a sampling pattern obtained from the cyclical daily temperatures, we developed a predictive algorithm based on pattern recognition techniques capable of detecting the increase of temperature inside the beehive (microclimate) responsible for the typical stress bees culminating in swarming. Such a mechanism is also able to recognize and avoid sending redundant information over the network in order to reduce radio communication, thereby reducing costs of data transmission and energy.
EnxameaÃÃo à a saÃda em massa das abelhas de uma colmeia, cujas causas mais comuns sÃo a falta de alimentos, estresse, variaÃÃes da umidade do ar e principalmente as altas temperaturas. Dentre os tipos de enxameaÃÃo, aquela em que ocorre o abandono completo da colmeia tem trazido grandes prejuÃzos aos apicultores brasileiros, particularmente aos nordestinos. Na regiÃo Nordeste, de grande importÃncia para a produÃÃo apÃcola brasileira e onde altas temperaturas sÃo comuns na maior parte do ano, um grande nÃmero de colmeias à perdido em funÃÃo da enxameaÃÃo por abandono. Na tentativa de mitigar este problema, propomos aqui um monitoramento proativo de colmeias via uma rede de sensores sem fio capaz de identificar o aquecimento atÃpico indicativo de uma condiÃÃo prÃ-enxameatÃria. Por meio de um padrÃo de coletas obtido a partir do comportamento cÃclico de temperaturas diÃrias, elaboramos um algoritmo preditivo, baseado em tÃcnicas de reconhecimento de padrÃes, capaz de detectar o aumento da temperatura no interior da colmeia (microclima) responsÃvel pelo estresse tÃpico das abelhas que culmina na enxameaÃÃo. Tal mecanismo tambÃm à capaz de reconhecer e evitar o envio de informaÃÃes redundantes pela rede de modo a diminuir a comunicaÃÃo via rÃdio, consequentemente reduzindo custos de transmissÃo de dados e energia.
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Книги з теми "Environmental monitoring network"

1

Harmancioglu, Nilgun B. Water Quality Monitoring Network Design. Dordrecht: Springer Netherlands, 1999.

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2

Montana. Dept. of Environmental Quality. 2008 monitoring network plan. Helena, Mont: Montana Dept. of Environmental Quality, 2008.

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3

The Arctic Observing Network. New York: Nova Science, 2009.

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4

Dorp, A. L. C. van. Network interface unit "ARTEMIS product dissemination by e-mail". Amsterdam: National Aerospace Laboratory, 1994.

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5

Md.) National Mercury Monitoring Workshop (2008 Annapolis. MercNet: Establishing a comprehensive national mercury monitoring network : 2008 workshop report. Washington, DC: United States Environmental Protection Agency, 2008.

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6

Gain, W. Scott. An optimized network for phosphorus load monitoring for Lake Okeechobee, Florida. Tallahassee, Fla: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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7

Robertson, J. K. Design of the national trends network for monitoring the chemistry of atmospheric precipitation. [Reston, Va.?]: U.S. Dept. of the Interior, Geological Survey, 1985.

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8

Robertson, J. K. Design of the national trends network for monitoring the chemistry of atmospheric precipitation. [Reston, Va.?]: U.S. Dept. of the Interior, Geological Survey, 1985.

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9

Stone, Asako. Assessment of citizen perceptions and knowledge for a groundwater monitoring network design. Las Vegas, Nev.]: Desert Research Institute, 2009.

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10

Newlon, Karen Rachel. A reference wetland network for assessment and monitoring of Montana's herbaceous wetlands. Helena, (Mont.): Montana Natural Heritage Program, 2011.

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Частини книг з теми "Environmental monitoring network"

1

Zidek, James V., and Dale L. Zimmerman. "Monitoring network design." In Handbook of Environmental and Ecological Statistics, 499–522. Boca Raton : Taylor & Francis, 2018.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315152509-22.

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Harmancioglu, N. B., S. D. Ozkul, and M. N. Alpaslan. "Water Quality Monitoring and Network Design." In Environmental Data Management, 61–106. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-015-9056-3_4.

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3

Maniscalco, Umberto, Giovanni Pilato, and Filippo Vella. "Soft Sensor Network for Environmental Monitoring." In Smart Innovation, Systems and Technologies, 705–14. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39345-2_63.

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4

Yahya, Abid. "Climate: Environmental Monitoring Using Wireless Sensor Network System." In Emerging Technologies in Agriculture, Livestock, and Climate, 67–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33487-1_3.

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Yadav, Shiv Kumar, Manish Kumar Jain, and Dinesh Kumar Patel. "Monitoring of Air Pollution in Different Regions Along Road Network, Jharia Coalfield, Dhanbad, India." In Environmental Pollution, 125–34. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5792-2_10.

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Garcia, Miguel, and Jaime Lloret. "A Cooperative Group-Based Sensor Network for Environmental Monitoring." In Lecture Notes in Computer Science, 276–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04265-2_41.

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Brown, J., W. Haeberli, R. Barry, and F. E. Nelson. "The proposed International permafrost monitoring Network and service." In Permafrost Response on Economic Development, Environmental Security and Natural Resources, 601–6. Dordrecht: Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0684-2_41.

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Singh, Surjeet, Gopal Krishan, N. C. Ghosh, R. K. Jaiswal, T. Thomas, and T. R. Nayak. "Identification and Planning of Water Quality Monitoring Network in Context of Integrated Water Resource Management (IWRM)." In Environmental Pollution, 513–24. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5792-2_41.

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Wu, Fan, Christoph Rüdiger, Jean-Michel Redouté, and Mehmet Rasit Yuce. "A Wearable Multi-sensor IoT Network System for Environmental Monitoring." In Internet of Things, 29–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02819-0_3.

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An, Henan, Bing Hu, and Hui Fan. "Indoor Environmental Real-Time Monitoring System Based on 6LoWPAN Network." In Lecture Notes in Electrical Engineering, 798–805. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_103.

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Тези доповідей конференцій з теми "Environmental monitoring network"

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Zhang, Huai Qiang, Qing Cheng Liu, and Yu Juan Liu. "Study on Network Radiation Environmental Monitoring System." In 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-15487.

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With the application of nuclear technology in many fields and the development of nuclear power, the people pay more and more attention to which the radioactive substances are harmful to the human, environmental pollution and nuclear security. In order to prevent the dangers of radioactive substances on the professional staff and the public, the pollution of the environment, it is particularly important to establish a scientific and perfect radiation environment monitoring system. In this paper, the radiation environmental monitoring system based on network is designed for environment radiation situation of the original enterprise, such as the uranium mining, nuclear power plant, uranium hydrometallurgical plant, nuclear fuel processing plant. The network technology is introduced in the monitoring system which links the field monitoring system, data center service system, data transmission system and client system. the worker can control the on-site monitoring points, data acquisition and transmission in any of the network interface functions, complete displaying, analyzing and processing in the remote server, meanwhile the network power supply technology is applied in the monitoring system which can supply electricity to on-site monitoring points. According to the test, the radiation environmental monitoring system which based on network technology is easy to apply and maintain, with a certain degree of practicality and economy.
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Renner, Christian, Benjamin Meyer, Daniel Bimschas, Alexander Gabrecht, Sebastian Ebers, Thomas Tosik, Ammar Amory, Erik Maehle, and Stefan Fischer. "Hybrid underwater environmental monitoring." In SenSys '14: The 12th ACM Conference on Embedded Network Sensor Systems. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2668332.2668354.

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Lombardo, Luca, Simone Corbellini, Ahmed Elsayed, Emma Angelini, Marco Parvis, and Sabrina Grassini. "Sensor network for museum environmental monitoring." In 2017 IEEE International Workshop on Measurements & Networking (M&N). IEEE, 2017. http://dx.doi.org/10.1109/iwmn.2017.8078394.

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Boydstun, Daniel, Matthew Farich, John McCarthy III, Silas Rubinson, Zachary Smith, and Ioannis Rekleitis. "Drifter Sensor Network for Environmental Monitoring." In 2015 12th Conference on Computer and Robot Vision (CRV). IEEE, 2015. http://dx.doi.org/10.1109/crv.2015.10.

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Telci, Ilker T., Kijin Nam, Jiabao Guan, and Mustafa M. Aral. "Real Time Optimal Monitoring Network Design in River Networks." In World Environmental and Water Resources Congress 2008. Reston, VA: American Society of Civil Engineers, 2008. http://dx.doi.org/10.1061/40976(316)336.

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Quarto, Alessandro, Domenico Soldo, Antonella Giove, and Alberto Amato. "Social network for human-based environmental monitoring." In 2010 IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS 2010). IEEE, 2010. http://dx.doi.org/10.1109/eesms.2010.5634171.

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Gocen, R. Kerem, Ege M. Sari, and Aysegul Tuysuz Erman. "Noisemap: An environmental noise monitoring sensor network." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7496203.

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Cho, Hyuntae. "Personal Environmental Monitoring System and network platform." In 2015 9th International Conference on Sensing Technology (ICST). IEEE, 2015. http://dx.doi.org/10.1109/icsenst.2015.7438496.

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Ren, Zhihong, and Yong Qii. "The Overlay Network Optimization for Environmental Monitoring." In 2013 Fourth International Conference on Digital Manufacturing & Automation (ICDMA). IEEE, 2013. http://dx.doi.org/10.1109/icdma.2013.357.

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Bezanic, Nikola, and Ivan Popovic. "Service-oriented sensor network for environmental monitoring." In 2012 20th Telecommunications Forum Telfor (TELFOR). IEEE, 2012. http://dx.doi.org/10.1109/telfor.2012.6419515.

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Звіти організацій з теми "Environmental monitoring network"

1

Susca, Sara, Sonia Martinez, and Francesco Bullo. Monitoring Environmental Boundaries with a Robotic Sensor Network. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada459072.

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2

Case, James F. Coastal Bioluminescence Prediction Network (BPN): An Economical Development by Supplementation of Existing Environmental Monitoring Resources. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada629665.

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3

Bowles, David, Michael Williams, Hope Dodd, Lloyd Morrison, Janice Hinsey, Tyler Cribbs, Gareth Rowell, Michael DeBacker, Jennifer Haack-Gaynor, and Jeffrey Williams. Protocol for monitoring aquatic invertebrates of small streams in the Heartland Inventory & Monitoring Network: Version 2.1. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2284622.

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The Heartland Inventory and Monitoring Network (HTLN) is a component of the National Park Service’s (NPS) strategy to improve park management through greater reliance on scientific information. The purposes of this program are to design and implement long-term ecological monitoring and provide information for park managers to evaluate the integrity of park ecosystems and better understand ecosystem processes. Concerns over declining surface water quality have led to the development of various monitoring approaches to assess stream water quality. Freshwater streams in network parks are threatened by numerous stressors, most of which originate outside park boundaries. Stream condition and ecosystem health are dependent on processes occurring in the entire watershed as well as riparian and floodplain areas; therefore, they cannot be manipulated independently of this interrelationship. Land use activities—such as timber management, landfills, grazing, confined animal feeding operations, urbanization, stream channelization, removal of riparian vegetation and gravel, and mineral and metals mining—threaten stream quality. Accordingly, the framework for this aquatic monitoring is directed towards maintaining the ecological integrity of the streams in those parks. Invertebrates are an important tool for understanding and detecting changes in ecosystem integrity, and they can be used to reflect cumulative impacts that cannot otherwise be detected through traditional water quality monitoring. The broad diversity of invertebrate species occurring in aquatic systems similarly demonstrates a broad range of responses to different environmental stressors. Benthic invertebrates are sensitive to the wide variety of impacts that influence Ozark streams. Benthic invertebrate community structure can be quantified to reflect stream integrity in several ways, including the absence of pollution sensitive taxa, dominance by a particular taxon combined with low overall taxa richness, or appreciable shifts in community composition relative to reference condition. Furthermore, changes in the diversity and community structure of benthic invertebrates are relatively simple to communicate to resource managers and the public. To assess the natural and anthropo-genic processes influencing invertebrate communities, this protocol has been designed to incorporate the spatial relationship of benthic invertebrates with their local habitat including substrate size and embeddedness, and water quality parameters (temperature, dissolved oxygen, pH, specific conductance, and turbidity). Rigid quality control and quality assurance are used to ensure maximum data integrity. Detailed standard operating procedures (SOPs) and supporting information are associated with this protocol.
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4

Smith, S. L., S. Ye, and M. Ednie. Enhancement of permafrost monitoring network and collection of baseline environmental data between Fort Good Hope and Norman Wells, Northwest Territories. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2007. http://dx.doi.org/10.4095/224524.

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5

Balali, Vahid, Arash Tavakoli, and Arsalan Heydarian. A Multimodal Approach for Monitoring Driving Behavior and Emotions. Mineta Transportation Institute, July 2020. http://dx.doi.org/10.31979/mti.2020.1928.

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Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways.
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6

Bracewell, Jef. Coastal topography change at Gulf Islands National Seashore, Texas: 2018–2021 data summary. National Park Service, May 2022. http://dx.doi.org/10.36967/nrds-2293377.

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In the spring of 2018 and 2021, the Gulf Coast Network collected coastal topography data at Gulf Islands National Seashore as a part of the NPS Vital Signs Monitoring Program. Monitoring was conducted following methods detailed in Monitoring Coastal Topography at Gulf Coast Network Parks: Protocol Implementation Plan (PIP; Bracewell 2017). Key findings from this effort are as follows: In Florida, the Perdido Key unit showed higher losses in profile area as well as retreat in dune crest and shoreline position than in the Fort Pickens unit. Because of unfavorable weather conditions and a compressed survey window, six of 16 transects in Mississippi were not surveyed in 2021. The highest rates of loss in profile area on Horn Island were at the western end. Three pilot monitoring transects were added in 2021 at Fort Pickens area, updrift, or east of the Gulf Coast Network's established effort. This expands survey coverage about 3 kilometers (1.9 miles [mi]) and incorporates a portion of the narrower, washover-prone section of the unit. This project is in the early phases of implementation and will benefit from future surveys to better understand the influence of slight changes in survey timing and other environmental variations.
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Bracewell, Jeff. Shoreline change at Gulf Islands National Seashore, Florida and Mississippi: 2018–2021 data summary. National Park Service, March 2022. http://dx.doi.org/10.36967/nrr-2293103.

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In May and June 2018, and April 2021, the Gulf Coast Network (GULN) surveyed shoreline position at Gulf Islands National Seashore (GUIS) as a part of the NPS Vital Signs Monitoring Program. Monitoring was conducted following methods detailed in Monitoring Shoreline Position at Gulf Coast Network Parks: Protocol Implementation Plan (PIP; Bracewell 2017). Shoreline change was calculated using the Digital Shoreline Analysis System developed by USGS (Theiler et al. 2008). Key findings from this effort are as follows: In Florida, the mean shoreline change rate from 2018 to 2021 was -7.10 meters/year (-23.3 feet[ft]/year) with a standard deviation of 5.01 meters (16.4 ft) with approximately 95% of transects exhibiting landward retreat. In Mississippi, the mean change in island width from 2018 to 2021 was -7.46 meters/year (-24.5 ft/year) with a standard deviation of 12.49 meters (41.0 ft) with approximately 73% of transects exhibiting a loss in width. This project is in the early phases of implementation and will benefit from future surveys to better understand the influence of slight changes in survey timing and other environmental variations.
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Bracewell, Jeff, and Jane Carlson. Coastal topography change at Padre Island National Seashore, Texas: 2017–2021 data summary. National Park Service, March 2022. http://dx.doi.org/10.36967/nrds-2293032.

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In May and June 2017, April 2019, and May 2021, the Gulf Coast Network (GULN) collected coastal topography data at Padre Island National Seashore (PAIS) as a part of the NPS Vital Signs Monitoring Program. Monitoring was conducted following methods detailed in Monitoring Coastal Topography at Gulf Coast Network Parks: Protocol Implementation Plan (PIP; Bracewell 2017). Key findings from this effort are as follows: A fixed set of 23 topographic transects distributed along the Gulf shore were surveyed. The most accelerated change in profile area occurred in the transect groups located near Mile Markers 15 and 37. This is likely due to the impacts from Hurricane Hanna. The transects on the south end of Padre Island National Seashore near Mansfield Channel showed short-term losses in profile area (2019 to 2021) that are less than the longer-term losses (2017 to 2021). The transect nearest the channel showed substantial recovery, which is related to the deposition of dredged material. This project is in the early phases of implementation and will benefit from future surveys to better understand the influence of slight changes in survey timing and other environmental variations.
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Wang, Anbo. Embedded Active Fiber Optic Sensing Network for Structural Health Monitoring in Harsh Environments. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1406405.

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10

Bolton, Laura. Criminal Activity and Deforestation in Latin America. Institute of Development Studies (IDS), December 2020. http://dx.doi.org/10.19088/k4d.2021.003.

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This review examines evidence on criminal deforestation activity in Latin America (particularly, but not exclusively the Amazon) and draws from the literature on the lessons learned in combatting criminal deforestation activity. This review focuses on Brazil as representative of the overwhelming majority of literature on criminal activity in relation to deforestation in the Amazon. The literature notes that Illegal deforestation occurs largely through criminal networks as they have the capacity for coordination, processing, selling, and the deployment of armed men to protect operations. Bribery, corruption, and fraud are deeply ingrained in deforestation. Networks may bribe geoprocessing experts, police, and public officials. Members of the criminal groups may become council members, mayors, and state representatives. Land titles are fabricated and trading documentation fraudulent. The literature also notes some interventions to combat this criminal deforestation activity: monitoring and law enforcement; national systems for registry and monitoring; legal enforcement for compliance of environmental law; International agreements and action; and Involving indigenous communities in combatting deforestation.
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