Optimum sensors allocation for a forest fires monitoring system

Every year forest fires destroy millions of hectares of land worldwide. Detecting forest fire ignition in the early stages is fundamental to avoid forest fires catastrophes. In this approach, Wireless Sensor Network is explored to develop a monitoring system to send alert to authorities when a fire ignition is detected. The study of sensors allocation is essential in this type of monitoring system since its performance is directly related to the position of the sensors, which also defines the coverage region. In this paper, a mathematical model is proposed to solve the sensor allocation problem. This model considers the sensor coverage limitation, the distance, and the forest density interference in the sensor reach. A Genetic Algorithm is implemented to solve the optimisation model and minimise the forest fire hazard. The results obtained are promising since the algorithm could allocate the sensor avoiding overlaps and minimising the total fire hazard value for both regions considered.This research received no external funding.info:eu-repo/semantics/publishedVersio

Comparing consumer perceptions of appliances' electricity use to appliances' actual direct-metered consumption

Many strategies for reducing residential energy consumption—including product labelling programs, subsidies for the purchase of efficient devices, behavioral programs that encourage efficient energy use, and others—rely on building owners and end users to make informed investment and operational decisions. These strategies may be ineffective if consumers are unaware of how much electricity is used by different devices in their homes and buildings. This study therefore compares consumers' perceptions of their appliances' electricity use to these appliances' actual direct-metered electricity consumption. Using an online survey, 118 homeowners from Austin, Texas were asked to estimate the energy consumption of six household devices which were monitored in the participants' homes. Homeowners were randomly assigned to assess their appliance-specific electricity use in terms of energy units (kWh/month) or energy cost units ($/month) for an average summer month. Consistent with previous studies, participants overestimated the energy consumed by their low energy consuming devices and slightly underestimated that of their most energy-consuming device. Results also showed that responses of the experimental groups estimating their consumption in energy units and energy cost units were similar, the accuracy of the two groups' perceptions was similar, and levels of confidence in the two groups were similar. These results suggest that targeted information campaigns focused on air conditioning energy consumption and device power reduction opportunities could improve consumer decision-making to save energy and reduce demand

A nonsense mutation in the LIMP-2 gene associated with progressive myoclonic epilepsy and nephrotic syndrome

Abstract The main clinical features of two siblings from a consanguineous marriage were progressive myoclonic epilepsy without intellectual impairment and a nephrotic syndrome with a strong accumulation of C1q in capillary loops and mesangium of kidney. The biochemical analysis of one of the patients revealed a normal beta-glucocerebrosidase activity in leukocytes, but a severe enzymatic deficiency in cultured skin fibroblasts. This deficiency suggested a defect in the intracellular sorting pathway of this enzyme. The sequence analysis of the gene encoding LIMP-2 (SCARB2), the sorting receptor for beta-glucocerebrosidase, confirmed this hypothesis. A homozygous nonsense mutation in codon 178 of SCARB2 was found in the patient, whereas her healthy parents were heterozygous for the mutation. Besides lacking immunodetectable LIMP-2, patient fibroblasts also had decreased amounts of beta-glucocerebrosidase, which was mainly located in the endoplasmic reticulum, as assessed by its sensitivity to Endo H. This is the first report of a mutation in the SCARB2 gene associated with a human disease, which, contrary to earlier proposals, shares no features with Charcot-Marie-Tooth disease both at the clinical and neurophysiological levels.Projeto financiado pela Fundação para a Ciência e Tecnologia(SFRH/BD/19496/2004 to A.B.

Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system

Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.This work has been supported by SAFe Project through PROMOVE—Fundação La Caixa. The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for finan cial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Thadeu Brito is supported by FCT PhD Grant Reference SFRH/BD/08598/2020, and Beatriz Flamia Azevedo is supported by FCT PhD Grant Reference SFRH/BD/07427/2021.info:eu-repo/semantics/publishedVersio

An integer programming approach for sensor location in a forest fire monitoring system

Forests worldwide have been devastated by fires. Forest fires cause incalculable damage to fauna and flora. In addition, a forest fire can lead to the death of people and financial damage in general, among other problems. To avoid wildfire catastrophes is fundamental to detect fire ignitions in the early stages, which can be achieved by monitoring ignitions through sensors. This work presents an integer programming approach to decide where to locate such sensors to maximize the coverage provided by them, taking into account different types of sensors, fire hazards, and technological and budget constraints. We tested the proposed approach in a real-world forest with around 7500 locations to be covered and about 1500 potential locations for sensors, showing that it allows obtaining optimal solutions in less than 20 min.This work has been supported by FCT Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020 and within project PCIF/GRF/0141/2019 “O3F - An Optimization Framework to reduce Forest Fire” and also the project UIDB/05757/2020 and Forest Alert Monitoring System (SAFe) Project through PROMOVE - Funda¸c˜ao La Caixa. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021, Thadeu Brito was supported by FCT PhD grant SFRH/BD/08598/2020

Map coverage of LoRaWAN signal’s employing GPS from mobile devices

Forests are remote areas with uneven terrain, so it is costly to map the range of signals that enable the implementation of systems based on wireless and long-distance communication. Even so, the interest in Internet of Things (IoT) functionalities for forest monitoring systems has increasingly attracted the attention of several researchers. This work demonstrates the development of a platform that uses the GPS technology of mobile devices to map the signals of a LoRaWAN Gateway. Therefore, the proposed system is based on concatenating two messages to optimize the LoRaWAN transmission using the Global Position System (GPS) data from a mobile device. With the proposed approach, it is possible to guarantee the data transmission when finding the ideal places to fix nodes regarding the coverage of LoRaWAN because the Gateway bandwidth will not be fulfilled. The tests indicate that different changes in the relief and large bodies drastically affect the signal provided by the Gateway. This work demonstrates that mapping the Gateway’s signal is essential to attach modules in the forest, agriculture zones, or even smart cities.This work has been supported by Fundação La Caixa and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/5757/2020. Thadeu Brito is supported by FCT PhD Grant Reference SFRH/BD/08598/2020. Beatriz Flamia Azevedo is supported by FCT PhD Grant Reference SFRH/BD/07427/2021.info:eu-repo/semantics/publishedVersio

Data acquisition filtering focused on optimizing transmission in a LoRaWAN network applied to the WSN forest monitoring system

Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.This work has been supported by SAFe Project through PROMOVE—Fundação La Caixa. The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). Thadeu Brito is supported by FCT PhD Grant Reference SFRH/BD/08598/2020, and Beatriz Flamia Azevedo is supported by FCT PhD Grant Reference SFRH/BD/07427/2021info:eu-repo/semantics/publishedVersio

Detection of intestinal parasites on field-grown strawberries in the Federal District of Brazil.

This study evaluated the presence of pathogenic human prasites on field-grown strawberries in the Federal District of Brazil