Distributed Forest Fire Monitoring Using Wireless Sensor Networks

Serna, M. Angeles; Casado, Rafael; Bermúdez, Aurelio; Pereira, Nuno; Tennina, Stefano

doi:10.1155/2015/964564

Cited by 11 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature, a variety of WSN systems have been widely adopted in fire hazard detection and prediction [17][18][19][20][21]. Therefore, in this work, in order to validate the functionality of our proposed software framework, we applied our developed WSN system to detect indoor fire hazards in an office building.…”

Section: Experimental Validation and Measurement Resultsmentioning

confidence: 99%

A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring

Huang

Rodriguez

2019

Applied Sciences

View full text Add to dashboard Cite

A wireless sensor network (WSN) is typically composed of spatially distributed miniature sensors that help collect large amounts of real-time environmental data from buildings. These environmental data (e.g., temperature, humidity, CO2 concentration) can assist a series of heating, ventilation, and air conditioning (HVAC) equipment to increase the building energy efficiency. From a system design perspective, heterogeneous wireless sensor networks need to address two challenges. First, sensor data acquisition, conversion, fusion, and packaging involve a series of software processing. Since each type of environmental sensor typically has unique processing requirements, it is difficult to develop an efficient software framework to combine the processing of multiple heterogeneous sensors. Second, during normal operation of a heterogeneous wireless sensor network, if users insert or remove some environmental sensors, the entire WSN system should operate normally. In this work, in order to solve the above two system design challenges, we have developed a low-power, low-cost, small form-factor WSN development platform, and its software framework can perform efficient data acquisition, conversion, fusion, and packaging for multiple heterogeneous sensors. Our proposed software framework enables easy and rapid WSN system deployment without affecting the overall functionality of each node. The proposed design differs from existing WSN platforms in that it emphasizes advanced high-level usability and reduces time to market without sacrificing low-level features. The proposed WSN system has been implemented and tested in an office building for indoor fire hazard detection. The experimental results show that our software framework can successfully complete data acquisition, conversion, fusion, and packaging tasks for three heterogeneous environmental sensors. In addition, we have verified that our software framework supports robust system operation when inserting or removing sensors from an existing heterogeneous WSN system.

show abstract

Section: Experimental Validation and Measurement Resultsmentioning

confidence: 99%

A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring

Huang

Rodriguez

2019

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The applicability of our strategy is immense, for example, vast horticultural fields where environmental parameters, such as atmospheric temperature and humidity, soil moisture, soil temperature, and soil pH, are to be measured [42]. In addition, it can be applied to any event detection application, such as wildfire detection [43] and seismic monitoring [44]. Apart from these, it can also be used in structural health monitoring where parameters such as humidity, temperature, stress, and strain are required to be This grid-based approach is popularly known for its features, namely uniformity in energy consumption, scalability, and simplicity [20,33,38].…”

Section: Network Setupmentioning

confidence: 99%

Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications

Mishra

Gupta

Gui

2021

Sensors

View full text Add to dashboard Cite

The application of the Internet of Things (IoT) in wireless sensor networks (WSNs) poses serious challenges in preserving network longevity since the IoT necessitates a considerable amount of energy usage for sensing, processing, and data communication. As a result, there are several conventional algorithms that aim to enhance the performance of WSN networks by incorporating various optimization strategies. These algorithms primarily focus on the network layer by developing routing protocols to perform reliable communication in an energy-efficient manner, thus leading to an enhanced network life. For increasing the network lifetime in WSNs, clustering has been widely accepted as an important method that groups sensor nodes (SNs) into clusters. Additionally, numerous researchers have been focusing on devising various methods to increase the network lifetime. The prime factor that helps to maximize the network lifetime is the minimization of energy consumption. The authors of this paper propose a multi-objective optimization approach. It selects the optimal route for transmitting packets from source to sink or the base station (BS). The proposed model employs a two-step approach. The first step employs a trust model to select the cluster heads (CHs) that manage the data communication between the BS and nodes in the cluster. Further, a novel hybrid algorithm, combining a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), is proposed to determine the routes for data transmission. To validate the efficacy of the proposed hybrid algorithm, named PSOGA, simulations were conducted and the results were compared with the existing LEACH method and PSO, with a random route selection for five different cases. The obtained results establish the efficiency of the proposed approach, as it outperforms existing methods with increased energy efficiency, increased network throughput, high packet delivery rate, and high residual energy throughout the entire iterations.

show abstract

“…Wireless sensor networks (WSNs) have been used to effectively monitor various physical phenomena in real-time, where they collect, transmit, and process information in an on-line manner [1]. Their applications include environmental monitoring, [2][3][4][5][6], border security [7], target tracking and localization [8][9][10], urban search and rescue (USAR) [11,12] and lost person detection in wilderness search and rescue (WiSAR) [13,14]. Research in the field, however, often assumes omni-directional sensing models for network-topography planning.…”

Section: Introductionmentioning

confidence: 99%

Directional-Sensor Network Deployment Planning for Mobile-Target Search

et al. 2020

View full text Add to dashboard Cite

In this paper, a novel time-phased directional-sensor network deployment strategy is presented for the mobile-target search problem, e.g., wilderness search and rescue (WiSAR). The proposed strategy uses probabilistic target-motion models combined with a variation of a standard direct search algorithm to plan the optimal locations of directional-sensors which maximize the likelihood of target detection. A linear sensing model is employed as a simplification for directional-sensor network deployment planning, while considering physical constraints, such as on-time sensor deliverability. Extensive statistical simulations validated our method. One such illustrative experiment is included herein to demonstrate the method’s operation. A comparative study was also carried out, whose summary is included in this paper, to highlight the tangible improvement of our approach versus three traditional deployment strategies: a uniform, a random, and a ring-of-fire type deployment, respectively.

show abstract

Distributed Forest Fire Monitoring Using Wireless Sensor Networks

Cited by 11 publications

References 48 publications

A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring

A Software Framework for Heterogeneous Wireless Sensor Network Towards Environmental Monitoring

Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications

Directional-Sensor Network Deployment Planning for Mobile-Target Search

Contact Info

Product

Resources

About