We developed L3SN, a scalable, longevous, adaptive, and internet accessible wireless sensor network system for agriculture information monitoring, which is meticulously designed to meet the requirement of thousands he...We developed L3SN, a scalable, longevous, adaptive, and internet accessible wireless sensor network system for agriculture information monitoring, which is meticulously designed to meet the requirement of thousands hectares coverage, years of time monitoring and the adverse environment. The system architecture, the agriculture sensor device, the mesh protocol, and the web-based information processing platform are introduced. We also presented some implementation experience. The mesh protocol (LayerMesh) is highlighted, in which “stair scheduling” and “distributed dynamic load-balancing” are proposed to response the scalability, longevity and adaptivity requirements. We believe the design of L3SN is useful to many other large-scale, longevous applications such as hydrologic monitoring, geological monitoring etc.展开更多
Wireless sensor network(WSN)is considered as the fastest growing technology pattern in recent years because of its applicability in varied domains.Many sensor nodes with different sensing functionalities are deployed ...Wireless sensor network(WSN)is considered as the fastest growing technology pattern in recent years because of its applicability in varied domains.Many sensor nodes with different sensing functionalities are deployed in the monitoring area to collect suitable data and transmit it to the gateway.Ensuring communications in heterogeneous WSNs,is a critical issue that needs to be studied.In this research paper,we study the system performance of a heterogeneous WSN using LoRa–Zigbee hybrid communication.Specifically,two Zigbee sensor clusters and two LoRa sensor clusters are used and combined with two Zigbee-to-LoRa converters to communicate in a network managed by a LoRa gateway.The overall system integrates many different sensors in terms of types,communication protocols,and accuracy,which can be used in many applications in realistic environments such as on land,under water,or in the air.In addition to this,a synchronous management software on ThingSpeak Web server and Blynk app is designed.In the proposed system,the token ring protocol in Zigbee network and polling mechanism in LoRa network is used.The system can operate with a packet loss rate of less than 0.5%when the communication range of the Zigbee network is 630 m,and the communication range of the LoRa network is 3.7 km.On the basis of the digital results collected on the management software,this study proves tremendous improvements in the system performance.展开更多
Forest fire prediction constitutes a significant component of forestmanagement. Timely and accurate forest fire prediction will greatly reduce property andnatural losses. A quick method to estimate forest fire hazard ...Forest fire prediction constitutes a significant component of forestmanagement. Timely and accurate forest fire prediction will greatly reduce property andnatural losses. A quick method to estimate forest fire hazard levels through knownclimatic conditions could make an effective improvement in forest fire prediction. Thispaper presents a description and analysis of a forest fire prediction methods based onmachine learning, which adopts WSN (Wireless Sensor Networks) technology andperceptron algorithms to provide a reliable and rapid detection of potential forest fire.Weather data are gathered by sensors, and then forwarded to the server, where a firehazard index can be calculated.展开更多
A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, ...A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, assuming that our network is composed by Anchor nodes （special sensors with known position） and simple Sensor nodes, the latter are supposed to estimate their own position after being placed within the coverage area with the minimum Anchor nodes needed to ＇feed＇ them with the necessary information. The goal is then to assist decision-makers in selecting among different alternatives to deploy the networks, according to resources features and availability, hence this method provides an estimate value of how many Anchor nodes should be deployed in a given area to trigger the location algorithm in the greatest possible number of Sensor nodes in the network.展开更多
文摘We developed L3SN, a scalable, longevous, adaptive, and internet accessible wireless sensor network system for agriculture information monitoring, which is meticulously designed to meet the requirement of thousands hectares coverage, years of time monitoring and the adverse environment. The system architecture, the agriculture sensor device, the mesh protocol, and the web-based information processing platform are introduced. We also presented some implementation experience. The mesh protocol (LayerMesh) is highlighted, in which “stair scheduling” and “distributed dynamic load-balancing” are proposed to response the scalability, longevity and adaptivity requirements. We believe the design of L3SN is useful to many other large-scale, longevous applications such as hydrologic monitoring, geological monitoring etc.
文摘Wireless sensor network(WSN)is considered as the fastest growing technology pattern in recent years because of its applicability in varied domains.Many sensor nodes with different sensing functionalities are deployed in the monitoring area to collect suitable data and transmit it to the gateway.Ensuring communications in heterogeneous WSNs,is a critical issue that needs to be studied.In this research paper,we study the system performance of a heterogeneous WSN using LoRa–Zigbee hybrid communication.Specifically,two Zigbee sensor clusters and two LoRa sensor clusters are used and combined with two Zigbee-to-LoRa converters to communicate in a network managed by a LoRa gateway.The overall system integrates many different sensors in terms of types,communication protocols,and accuracy,which can be used in many applications in realistic environments such as on land,under water,or in the air.In addition to this,a synchronous management software on ThingSpeak Web server and Blynk app is designed.In the proposed system,the token ring protocol in Zigbee network and polling mechanism in LoRa network is used.The system can operate with a packet loss rate of less than 0.5%when the communication range of the Zigbee network is 630 m,and the communication range of the LoRa network is 3.7 km.On the basis of the digital results collected on the management software,this study proves tremendous improvements in the system performance.
文摘Forest fire prediction constitutes a significant component of forestmanagement. Timely and accurate forest fire prediction will greatly reduce property andnatural losses. A quick method to estimate forest fire hazard levels through knownclimatic conditions could make an effective improvement in forest fire prediction. Thispaper presents a description and analysis of a forest fire prediction methods based onmachine learning, which adopts WSN (Wireless Sensor Networks) technology andperceptron algorithms to provide a reliable and rapid detection of potential forest fire.Weather data are gathered by sensors, and then forwarded to the server, where a firehazard index can be calculated.
文摘A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, assuming that our network is composed by Anchor nodes （special sensors with known position） and simple Sensor nodes, the latter are supposed to estimate their own position after being placed within the coverage area with the minimum Anchor nodes needed to ＇feed＇ them with the necessary information. The goal is then to assist decision-makers in selecting among different alternatives to deploy the networks, according to resources features and availability, hence this method provides an estimate value of how many Anchor nodes should be deployed in a given area to trigger the location algorithm in the greatest possible number of Sensor nodes in the network.
