International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 6 (2018) pp. 4326-4332 Š Research India Publications. http://www.ripublication.com
Forest Fire Detection using Proportional Conflict Redistribution Rule2 P. Sudha1, A. Murugan2 1
2
Research Scholar, Dept. of Comp. Sc. and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India. Associate Professor & Head, PG & Research Dept. of Comp. Sc., Dr Ambedkar Government Arts College, Vyasarpadi, Chennai, India.
There are numerous interconnected hardware elements in the network that even if one critical component flops it could cause an outage. It could be a complete or partial failure of any number of devices, such as a router, gateway or network controller [3]. During fire detection evolution in the forest using wireless sensor network there are prospects of node and link failure in the network. Using Proportional Conflict Redistribution rule a combination of data has been achieved to improve the accuracy of detection of fire in the forest.
Abstract The objective of this paper is to detect fire in the forest where some plants and trees are prone to fire very easily. The detection of forest fire preserves economic and environmental wealth of forest and defends human life. Wireless Sensor Networks (WSNs) are used to monitor tactical and hazardous sites inside the forest. Failure detection of sensor nodes in this specified application is a major concern. Furthermore, in WSN systems failures are unavoidable due to hardware constraints, unattended distribution areas in the forest and limited resources. This paper introduces Proportional Conflict Redistribution Rule2 (PCR2) rule, which deals better for vague, ambiguous and potentially highly contradictory sources of information due to the failure of nodes and links. For the data of high inconsistent sources of information due to misclassification or network/node failure, the PCR1 rules provides a reliable result. However, for the same high conflicting data, the new combination rule PCR2 provides both dependable and judicious results. The experimental analysis shows that the accuracy of PCR2, while using the incident data received from the failure links and nodes, is more reasonable than that of PCR1 in the framework of forest fire detection and is more consistent and vigorous in combining highly conflicting sources.
Section II springs a gesture on wireless sensor network. Section III bounces an awareness on some of the other forest fire detection algorithm and its inadequacies. Section IV articulates about the failure of node or link that occurs in the forest while detecting the fire. Section IV expresses an idea about Proportional Conflict Redistribution (PCR) rule and also explicates the concept of PCR1 and PCR2. An algorithm for PCR2 is designed and an experimental study of PCR1 and PCR2 is engendered once there is a link or node failure in the network while detecting the fire in the forest. Section V elasticities the conclusion during the failure or no failure of link or node, while using PCR1 and PCR2 combination rule.
WIRELESS SENSOR NETWORK
Keywords: Uncertainty, Belief entropy, Proportional Conflict Redistribution Rule1, Proportional Conflict Redistribution Rule2
With the recent advancement in the area of wireless sensor networks, electronic components used in the networking have become dramatically inexpensive. This has empowered the development of low-cost and multifunctional sensors that are smaller in size and interconnect very effectively and can be deployed anywhere in the forest easily [4].
INTRODUCTION Nowadays, the utmost threat in forests is fire and forest fire can be a great peril to the people who live in forests as well as wild life. It is an unrestrained fire happening in nature, which obliterates a forested area [1]. As forest fire has spread over a large area, making its control and stoppage is very tough and even incredible and dreadful at times. Early detection of forest fires is the only way to curtail the damages and casualties apart from defensive measures, using the wireless sensor network systems.
Wireless sensor network encompasses several tiny sensor nodes that have more computation power. The tiny sensor nodes of low cost and low battery power sensor devices are deployed in the forest. A sensing unit which is the core component of wireless sensor network is used to capture events of consideration and another significant component called wireless transceiver is used to transform the captured events back to the base station which is called as sink node [5]. Sensor nodes collaborate with one another nodes to accomplish tasks of data classifying, data communication, and data processing.
Wireless sensor networks comprise of numerous sensors nodes, which can be used to collect the data in the forest. These captured events from the nodes are sent to the cluster heads. All the cluster heads are connected to a sink, which in turn are connected to a manager node [2]. The collected data are classified using the classifiers with the help of attributes and the conflicting data are distributed to the relevant classes using the combination rule, Proportional Conflict Redistribution (PCR) rule and a decision is taken on the forest fire data regarding fire or no fire.
In the wireless sensor network, sensor nodes collected the Incident data and the poised data are delivered to the sink for the productive monitoring of forest. The consistency of individual link performance and the communication in the network are very crucial in forest fire detection to elude any unexploited detection [6]. The utmost notable advantage of sensor networks is its augmented computation ability to
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