On the Development of a Sensor Network-Based System for Wildfire Prevention

Vicente-Charlesworth, Luis; Galmés, Sebastià

doi:10.1007/978-3-642-23734-8_8

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Several strategies were provided for wildland fire prediction and detection using WSNs [30]. For example, a wireless sensor network allowed 24-hour monitoring of meteorological factors [31], and a wildfire prevention system based on a time-driven wireless sensor was introduced [32]. Sabit et al [33] built a TrueTime model of WSN architecture for wildfire prediction and analyzed the network latency, energy consumption, and scalability.…”

Section: Literature Reviewmentioning

confidence: 99%

Rating Wildfire Danger by Big Weather Data and Machine Learning: A Case Study in Eastern China

Gao

et al. 2022

Preprint

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Wildfire, as a natural phenomenon, shapes global ecosystems and threatens human communities. The meteorological conditions of the forest environment is one of the critical factors, which exists obvious effect on the risk of wildfire for a given landscape and fuel type. It is high challenge to predict wildland fire risks owing to the huge amount of meteorological paramenters with volume,variety,value and velocity. Driven by the emerging Artificial Intelligence and Big Data analytics, this work proposes a machine learning model (Fuzzy C-Means algorithm) to assess the probability of wildland fire. By training the historical weather and fire data in Eastern China (Jiangsu Province), the complex relationships between the weather parameter and the rating of wildland fire danger are quantified. The Fire Weather Index system are applied to evaluate the accuracy of predictions and compare them with the standards in the literature. This system further reveals that the spring season is the peak period for wildland fires in Eastern China. Such a prediction is also in accordance with the practice of wildfire occurrence that, in practice, can provide early warning to local residences and forest services.

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Section: Literature Reviewmentioning

confidence: 99%

Rating Wildfire Danger by Big Weather Data and Machine Learning: A Case Study in Eastern China

Gao

et al. 2022

Preprint

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“…There are a few cases where measurement collection frequency might need to be significantly increased, as synchronization accuracy is extremely important. These include wildfire monitoring, as wildfires can spread at incredibly high velocity and unpredictable directions [96]; controlled-environment farming, such as hydroponic cultivations [97,98]; and finally olive grove irrigation scheduling [99], which can also be used in wildfire prevention [100]. Such strict requirements define the presented work's objective for precise synchronization even in time-critical applications.…”

Section: Proposed Measurement Synchronization Schemementioning

confidence: 99%

Wireless Sensor Network Synchronization for Precision Agriculture Applications

et al. 2020

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The advent of Internet of Things has propelled the agricultural domain through the integration of sensory devices, capable of monitoring and wirelessly propagating information to producers; thus, they employ Wireless Sensor Networks (WSNs). These WSNs allow real time monitoring, enabling intelligent decision-making to maximize yields and minimize cost. Designing and deploying a WSN is a challenging and multivariate task, dependent on the considered environment. For example, a need for network synchronization arises in such networks to correlate acquired measurements. This work focuses on the design and installation of a WSN that is capable of facilitating the sensing aspects of smart and precision agriculture applications. A system is designed and implemented to address specific design requirements that are brought about by the considered environment. A simple synchronization scheme is described to provide time-correlated measurements using the sink node’s clock as reference. The proposed system was installed on an olive grove to assess its effectiveness in providing a low-cost system, capable of acquiring synchronized measurements. The obtained results indicate the system’s overall effectiveness, revealing a small but expected difference in the acquired measurements’ time correlation, caused mostly by serial transmission delays, while yielding a plethora of relevant environmental conditions.

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“…A distributed detection method on a binary target was considered to send local decisions to a fusion center with wireless sensors by Kim et al, [3], which proposed two new concepts called detection outage and detection diversity in the long-term system performance. Luis and Sebastia, [4] considered that the strong components of the system could provide a cost-effective, real-time, and high resolution means of wildfire prevention in a sensor network.…”

Section: Introductionmentioning

confidence: 99%

Resource Symmetric Dispatch Model for Internet of Things on Advanced Logistics

2016

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Business applications in advanced logistics service are highly concurrent. In this paper, we propose a resource symmetric dispatch model for the concurrent and cooperative tasks of the Internet of Things. In the model, the terminals receive and deliver commands, data, and information with mobile networks, wireless networks, and sensor networks. The data and information are classified and processed by the clustering servers in the cloud service platform. The cluster service, resource dispatch, and load balance are cooperative for management and monitoring of every application case during the logistics service lifecycle. In order to support the high performance of cloud service, resource symmetric dispatch algorithm among clustering servers and load balancing method among multi-cores in one server, including NIO (Non-blocking Input/Output) and RMI (Remote Method Invocation) are utilized to dispatch the cooperation of computation and service resources.

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On the Development of a Sensor Network-Based System for Wildfire Prevention

Cited by 4 publications

References 8 publications

Rating Wildfire Danger by Big Weather Data and Machine Learning: A Case Study in Eastern China

Rating Wildfire Danger by Big Weather Data and Machine Learning: A Case Study in Eastern China

Wireless Sensor Network Synchronization for Precision Agriculture Applications

Resource Symmetric Dispatch Model for Internet of Things on Advanced Logistics

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