Modelling of wireless sensor networks for detection land and forest fire hotspot

Indonesia located in South East Asia countries with tropical region, forest fires in Indonesia is one of big issue and disaster because it happens in almost of every year, this is because of some of region consist of peat land that high risk for fire especially in dry season. Riau Province is one of region that regularly incident of forest fire with affected the length and breadth of Indonesia. Propose development of Wireless Sensor Networks (WSNs) for detection of land and forest fire hotspot in Indonesia as well as one of the main consents in this research, case location in Riau province is at one of the regions that high risk forest fire in dry season. WSNs technology used for ground sensor system to collect environmental data. Data training for fire hotspot detection is done in data center to determine and conclude of fire hotspot then potential to become big fire. The deployment of sensors located at several locations that has potential for fire incident, especially as data shown in previous case and forecast location with potential fire happen. Mathematical analysis is used in this case for modelling number of sensors required to deploy and the size of forest area. The design and development of WSNs give high impact and feasibility to overcome current issues of forest fire and fire hotspot detection in Indonesia. The development of this system used WSNs highly applicable for early warning and alert system for fire hotspot detection

Collaborative sensor network algorithm for predicting the spatiotemporal evolution of hazardous phenomena

We present a novel decentralized Wireless Sensor Network (WSN) algorithm which can estimate both the speed and direction of an evolving diffusive hazardous phenomenon (e.g. a wildfire, oil spill, etc.). In the proposed scheme we approximate a progressing hazard’s front as a set of line segments. The spatiotemporal evolution of each line segment is modeled by a modified 2D Gaussian function. As the phenomenon evolves, the parameters of this model are updated based on the analytical solution of a Kullback – Leibler (KL) divergence minimization problem. This leads to an efficient WSN distributed parameters estimation algorithm that can be implemented by dynamically formed clusters (triplets) of collaborating sensor nodes. Computer simulations show that our approach is able to track the evolving phenomenon with reasonable accuracy even if a percentage of sensors fails due to the hazard and/or the phenomenon has a time varying speed

Distributed Particle Filters for Data Assimilation in Simulation of Large Scale Spatial Temporal Systems

Assimilating real time sensor into a running simulation model can improve simulation results for simulating large-scale spatial temporal systems such as wildfire, road traffic and flood. Particle filters are important methods to support data assimilation. While particle filters can work effectively with sophisticated simulation models, they have high computation cost due to the large number of particles needed in order to converge to the true system state. This is especially true for large-scale spatial temporal simulation systems that have high dimensional state space and high computation cost by themselves. To address the performance issue of particle filter-based data assimilation, this dissertation developed distributed particle filters and applied them to large-scale spatial temporal systems. We first implemented a particle filter-based data assimilation framework and carried out data assimilation to estimate system state and model parameters based on an application of wildfire spread simulation. We then developed advanced particle routing methods in distributed particle filters to route particles among the Processing Units (PUs) after resampling in effective and efficient manners. In particular, for distributed particle filters with centralized resampling, we developed two routing policies named minimal transfer particle routing policy and maximal balance particle routing policy. For distributed PF with decentralized resampling, we developed a hybrid particle routing approach that combines the global routing with the local routing to take advantage of both. The developed routing policies are evaluated from the aspects of communication cost and data assimilation accuracy based on the application of data assimilation for large-scale wildfire spread simulations. Moreover, as cloud computing is gaining more and more popularity; we developed a parallel and distributed particle filter based on Hadoop & MapReduce to support large-scale data assimilation

Design and Field Test of a WSN Platform Prototype for Long-Term Environmental Monitoring

Long-term wildfire monitoring using distributed in situ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application. The gateways are designed to operate with resources that are generally used for sensor nodes, while the requirements and cost of the sensor nodes are significantly lower. We define and test in simulation and in the field experiment a simple, but effective communication protocol for this application. It helps to lower the cost of the nodes and field deployment procedure, while extending the theoretical lifetime of the sensor nodes to over 16 years on a single 1 Ah lithium battery

Integrating Wildfires Propagation Prediction Into Early Warning of Electrical Transmission Line Outages

Wildfires could pose a significant danger to electrical transmission lines and cause considerable losses to the power grids and residents nearby. Previous studies of preventing wildfire damages to electrical transmission lines mostly analyze wildfire and power system security independently due to their differences in disciplines and cannot satisfy the requirement of the power grid for active and timely responses. In this paper, we have designed an integrated wildfire early warning system framework for power grids, taking prediction of wildfires and early warning of line outage probability together. First, the proposed model simulates the spatiotemporal process of wildfires via a geography cellular automata model and predicts when and where wildfires initially get into the security buffer of an electrical transmission line. It is developed in the context of electrical transmission line operating with various situations of topography, vegetation, wind and, especially, multiple ignition points. Second, we have proposed a line outage model (LOM), based on wildfire prediction and breakdown mechanisms of the air gap, to predict the breakdown probability varying with time and the most vulnerable poles at the holistic line scale. Finally, to illustrate the validation and rationality of our proposed system, a case study for a 500-kV transmission line near Miyi county, China, is presented, and the results under various wildfire situations are studied and compared. By integrating wildfire prediction into the LOM and alarming the holistic line breakdown probability along time, this paper makes a significant contribution in the early warning system to prevent transmission lines to be damaged by wildfires, illustrating the related breakdown mechanisms at the line operation level rather than laboratory experiments only. Meanwhile, the implementation of cellular automata model under comprehensive environmental conditions and simulation of the breakdown probability for the 500-kV transmission line could serve as references for other studies in the community

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

The numbers of natural disaster events are continuously affecting human and the world economics. For coping with disaster, several sectors try to develop the frameworks, systems, technologies and so on. However, there are little researches focusing on the usage behavior of Information Technology (IT) for disaster risk management (DRM). Therefore, this study investigates the affecting factors on the intention to use IT for mitigating disaster’s impacts. This study conducted a systematic review with the academic researches during 2011-2018. Two important factors from the Technology Acceptance Model (TAM) and others are used in describing individual behavior. In order to investigate the potential factors, the technology platforms are divided into nine types. According to the findings, computer software such as GIS applications are frequently used for simulation and spatial data analysis. Social media is preferred among the first choices during disaster events in order to communicate about situations and damages. Finally, we found five major potential factors which are Perceived Usefulness (PU), Perceived Ease of Use (PEOU), information accessibility, social influence, and disaster knowledge. Among them, the most essential one of using IT for disaster management is PU, while PEOU and information accessibility are more important in the web platforms

A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification

Forest and rural fires are one of the main causes of environmental degradation in Mediterranean countries. Existing fire detection systems only focus on detection, but not on the verification of the fire. However, almost all of them are just simulations, and very few implementations can be found. Besides, the systems in the literature lack scalability. In this paper we show all the steps followed to perform the design, research and development of a wireless multisensor network which mixes sensors with IP cameras in a wireless network in order to detect and verify fire in rural and forest areas of Spain. We have studied how many cameras, sensors and access points are needed to cover a rural or forest area, and the scalability of the system. We have developed a multisensor and when it detects a fire, it sends a sensor alarm through the wireless network to a central server. The central server selects the closest wireless cameras to the multisensor, based on a software application, which are rotated to the sensor that raised the alarm, and sends them a message in order to receive real-time images from the zone. The camera lets the fire fighters corroborate the existence of a fire and avoid false alarms. In this paper, we show the test performance given by a test bench formed by four wireless IP cameras in several situations and the energy consumed when they are transmitting. Moreover, we study the energy consumed by each device when the system is set up. The wireless sensor network could be connected to Internet through a gateway and the images of the cameras could be seen from any part of the world