B. Potter, Gina Valentino, Laura Yates, T. Benzing, A. Salman
{"title":"使用无人机无线传感器网络进行环境监测","authors":"B. Potter, Gina Valentino, Laura Yates, T. Benzing, A. Salman","doi":"10.1109/SIEDS.2019.8735615","DOIUrl":null,"url":null,"abstract":"Embedded electronic devices and sensors are playing a major role in bridging the gap between the physical world and the virtual world. Billions of devices such as smartphones, smart watches, wearables, medical implants, and wireless sensor nodes are considered building blocks in making the Internet of Things a reality. Such devices often carry sensitive or proprietary data and are used in critical applications, such as the use of wireless sensor nodes to remotely capture atmospheric greenhouse gas emissions data. Additionally, some of the devices used to collect data are being deployed in remote areas where accessibility is not easy and transmission of data for processing is not available due to the lack of network connectivity. Additionally, the use of wireless sensor nodes has been proven to making data collection faster, less labor intensive, and more cost effective. In this paper, we present an efficient method to remotely collect data from three sensors in a wireless sensor node. The intended purpose of this project is to remotely monitor a tributary to the South Fork of the Shenandoah River. The system makes use of an unmanned aerial vehicle to collect data from a remote stream site. We detail the methodology in which a customized unmanned aerial vehicle flies within range of connectivity of a wireless sensor node, establishing a communication channel to upload and store the data for pending analysis. The methodology utilized is shown through an environmental case study which illustrates the advantages of implementing a wireless sensor node which includes accessing a remote location, continuous data collection, and reduction of labor and costs associated with field data collection methodologies. We show that our node is efficient in terms on its power and energy consumption.","PeriodicalId":265421,"journal":{"name":"2019 Systems and Information Engineering Design Symposium (SIEDS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Environmental Monitoring Using a Drone-Enabled Wireless Sensor Network\",\"authors\":\"B. Potter, Gina Valentino, Laura Yates, T. Benzing, A. Salman\",\"doi\":\"10.1109/SIEDS.2019.8735615\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Embedded electronic devices and sensors are playing a major role in bridging the gap between the physical world and the virtual world. Billions of devices such as smartphones, smart watches, wearables, medical implants, and wireless sensor nodes are considered building blocks in making the Internet of Things a reality. Such devices often carry sensitive or proprietary data and are used in critical applications, such as the use of wireless sensor nodes to remotely capture atmospheric greenhouse gas emissions data. Additionally, some of the devices used to collect data are being deployed in remote areas where accessibility is not easy and transmission of data for processing is not available due to the lack of network connectivity. Additionally, the use of wireless sensor nodes has been proven to making data collection faster, less labor intensive, and more cost effective. In this paper, we present an efficient method to remotely collect data from three sensors in a wireless sensor node. The intended purpose of this project is to remotely monitor a tributary to the South Fork of the Shenandoah River. The system makes use of an unmanned aerial vehicle to collect data from a remote stream site. We detail the methodology in which a customized unmanned aerial vehicle flies within range of connectivity of a wireless sensor node, establishing a communication channel to upload and store the data for pending analysis. The methodology utilized is shown through an environmental case study which illustrates the advantages of implementing a wireless sensor node which includes accessing a remote location, continuous data collection, and reduction of labor and costs associated with field data collection methodologies. 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Environmental Monitoring Using a Drone-Enabled Wireless Sensor Network
Embedded electronic devices and sensors are playing a major role in bridging the gap between the physical world and the virtual world. Billions of devices such as smartphones, smart watches, wearables, medical implants, and wireless sensor nodes are considered building blocks in making the Internet of Things a reality. Such devices often carry sensitive or proprietary data and are used in critical applications, such as the use of wireless sensor nodes to remotely capture atmospheric greenhouse gas emissions data. Additionally, some of the devices used to collect data are being deployed in remote areas where accessibility is not easy and transmission of data for processing is not available due to the lack of network connectivity. Additionally, the use of wireless sensor nodes has been proven to making data collection faster, less labor intensive, and more cost effective. In this paper, we present an efficient method to remotely collect data from three sensors in a wireless sensor node. The intended purpose of this project is to remotely monitor a tributary to the South Fork of the Shenandoah River. The system makes use of an unmanned aerial vehicle to collect data from a remote stream site. We detail the methodology in which a customized unmanned aerial vehicle flies within range of connectivity of a wireless sensor node, establishing a communication channel to upload and store the data for pending analysis. The methodology utilized is shown through an environmental case study which illustrates the advantages of implementing a wireless sensor node which includes accessing a remote location, continuous data collection, and reduction of labor and costs associated with field data collection methodologies. We show that our node is efficient in terms on its power and energy consumption.