{"title":"部署在不同水体表面的无线网络链路质量波动","authors":"Waltenegus Dargie;Paulo Padrao;Leonardo Bobadilla;Christian Poellabauer","doi":"10.1109/JSEN.2024.3472850","DOIUrl":null,"url":null,"abstract":"Low-power Internet of Things (IoT) sensing nodes can be deployed on the surface of different water bodies for various purposes, including water quality monitoring and pollution detection. Two of the most formidable challenges toward such goals are: 1) making the nodes resilient against rough water and extreme weather conditions and 2) enabling the nodes to establish reliable wireless links. In this article, we share our experience in deploying low-power and resilient IoT nodes on the surface of different water bodies—on a small lake, North Biscayne Bay, Crandon Beach, and South Beach, in Miami, Florida. Furthermore, the article closely examines how link quality was affected by predeployment configurations as well as the characteristics and the motion of the waters. Based on the analyses of a vast amount of statistics, the article establishes a theoretical (mathematical) and generalized model to characterize and predict link quality fluctuations. We shall show that the realization of the model using the Kalman Filter enables link quality prediction with accuracy exceeding 90%.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 23","pages":"39789-39797"},"PeriodicalIF":4.3000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Link Quality Fluctuation in Wireless Networks Deployed on the Surface of Different Water Bodies\",\"authors\":\"Waltenegus Dargie;Paulo Padrao;Leonardo Bobadilla;Christian Poellabauer\",\"doi\":\"10.1109/JSEN.2024.3472850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Low-power Internet of Things (IoT) sensing nodes can be deployed on the surface of different water bodies for various purposes, including water quality monitoring and pollution detection. Two of the most formidable challenges toward such goals are: 1) making the nodes resilient against rough water and extreme weather conditions and 2) enabling the nodes to establish reliable wireless links. In this article, we share our experience in deploying low-power and resilient IoT nodes on the surface of different water bodies—on a small lake, North Biscayne Bay, Crandon Beach, and South Beach, in Miami, Florida. Furthermore, the article closely examines how link quality was affected by predeployment configurations as well as the characteristics and the motion of the waters. Based on the analyses of a vast amount of statistics, the article establishes a theoretical (mathematical) and generalized model to characterize and predict link quality fluctuations. We shall show that the realization of the model using the Kalman Filter enables link quality prediction with accuracy exceeding 90%.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"24 23\",\"pages\":\"39789-39797\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10713111/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10713111/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Link Quality Fluctuation in Wireless Networks Deployed on the Surface of Different Water Bodies
Low-power Internet of Things (IoT) sensing nodes can be deployed on the surface of different water bodies for various purposes, including water quality monitoring and pollution detection. Two of the most formidable challenges toward such goals are: 1) making the nodes resilient against rough water and extreme weather conditions and 2) enabling the nodes to establish reliable wireless links. In this article, we share our experience in deploying low-power and resilient IoT nodes on the surface of different water bodies—on a small lake, North Biscayne Bay, Crandon Beach, and South Beach, in Miami, Florida. Furthermore, the article closely examines how link quality was affected by predeployment configurations as well as the characteristics and the motion of the waters. Based on the analyses of a vast amount of statistics, the article establishes a theoretical (mathematical) and generalized model to characterize and predict link quality fluctuations. We shall show that the realization of the model using the Kalman Filter enables link quality prediction with accuracy exceeding 90%.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice