Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection

Saoudi, Massinissa; Bounceur, Ahcène; Euler, Reinhardt; Kechadi, Tahar

doi:10.1145/2896387.2900323

Cited by 22 publications

(12 citation statements)

References 13 publications

(11 reference statements)

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“…In [43], data mining technique is used in the clustered WSN to detect fire in the forest. Each individual node is responsible for fire detection using data miningbased classifier.…”

Section: Related Researchmentioning

confidence: 99%

A Novel Association Rule-Based Data Mining Approach for Internet of Things Based Wireless Sensor Networks

et al. 2020

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Wireless Sensor Network (WSN) is one of the fundamental technologies used in the Internet of Things (IoT) which is deployed for diverse applications to carry out precise real-time observations. The limited resources of WSN with massive volume of fast-flowing IoT data make the aggregation and analytics of data more challenging. Recently, data mining-based solutions have been proposed to effectively handle the data being generated by the sensors and to analyze the data patterns for deducing the required information from it. The increasing need of these techniques motivated us to propose a distributed and efficient data mining technique that not only handles the massive and rapidly generated data by the nodes, but also increases the life span of the network. In this paper, we propose a novel scheme for the IoT based WSN that mines the sensor data using association rule without moving it to any Cluster Head (CH) or Base Station (BS). The new proposed scheme enables sensors to perform computations locally and only the minimum higher-level statistical summaries of the data at Cluster Members (CMs) are exchanged with their CH. This considerably reduces the communication overhead which ultimately prolongs the network lifetime. The proposed scheme is evaluated via extensive simulations and the results obtained demonstrate that the integration of the proposed scheme in the existing protocols significantly reduces the communication overhead which ultimately prolongs the network lifetime and stability.

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“…In [43], data mining technique is used in the clustered WSN to detect fire in the forest. Each individual node is responsible for fire detection using data miningbased classifier.…”

Section: Related Researchmentioning

confidence: 99%

A Novel Association Rule-Based Data Mining Approach for Internet of Things Based Wireless Sensor Networks

et al. 2020

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“…The data collected can be very diverse depending on the application. Saoudi et al presents in [22] an example of data collection applied to WSN networks for the early detection of forest fires verification. To this end, measurements of temperature, humidity, luminosity and smoke are made.…”

Section: Data Collection Functionmentioning

confidence: 99%

Evaluation of CupCarbon Network Simulator for Wireless Sensor Networks

Lopez-Pavon

Sendra

Valenzuela-Valdés

2018

NPA

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Wireless sensor networks are a technology in full evolution with great future and a huge quantity of applications. This document lists some of the limitations existing in WSN networks. In addition, several existing techniques are collected to maximize the network's lifetime, demonstrating that efficient use of energy is very important to achieve this goal. Due to the importance of knowing the behaviour of this type of network before its launch, a comparative is established for the options offered by the WSN network simulators available in the market, compared to those offered by CupCarbon. The objective of this project is the evaluation of the CupCarbon simulator through the implementation of an algorithm that aims to maximize the life time of the network, as well as collecting the results offered by this algorithm. In this way, a Dijkstra routing algorithm is developed whose selection of routes depends on the battery level contained in the nodes that constitute the network. This algorithm is referred as dynamic Dijkstra in the present document. To achieve the objectives described, we pretend to simulate the dynamic Dijkstra in CupCarbon, for this aim, the source code is modified, since it is a free code tool. As a result, it is possible to create the original Dijkstra, but certain problems are presented, to develop the part that depends on the energy, related to the functionalities offered by CupCarbon. As an alternative to determine the results associated to the created algorithm, theoretical analyses are accomplished. Consequently, it is concluded that the evaluation of CupCarbon is not satisfactory enough, while the algorithm created maximizes the network's lifetime.

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“…This type of energy depriving attack can damage network reliablity and functionality which triggers event hara@ist.osaka-u.ac.jp losses. Even worse, such an attack may lead to severe risks, particularly for real-time and safety-critical applications, such as extreme weather monitoring [20], water quality monitoring [7], and forest fire alarming [36]. Valuable insight is therefore needed for dealing with attacks and security issues in EHC-WSNs.…”

Section: Introductionmentioning

confidence: 99%

Detecting Energy Depriving Malicious Nodes by Unsupervised Learning in Energy Harvesting Cooperative Wireless Sensor Networks

Gao

Amagata

Maekawa

et al. 2020

Journal of Information Processing

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This paper presents an unsupervised learning-based method for detecting energy depriving malicious nodes in an energy harvesting cooperative wireless sensor network (EHC-WSN). In EHC-WSNs, nodes wirelessly transfer a portion of their energy to their neighboring nodes if their neighboring nodes lack energy. A malicious energy depriving mode may falsibly show that it has little energy and in this way obtains energy from neighboring nodes, thus depriving them of energy. To detect these malicious nodes, we utilize a clustering method. In our method, each node first observes the energy of its neighboring nodes, then it utilizes this information to obtain data points for the clustering. After clusters are formed, each node judges on a cluster of data points from malicious nodes and makes a malicious node determination. We investigate the performance of our method and confirm that our method outperforms the baseline method in terms of detection accuracy and false detection rate.

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Data Mining Techniques Applied to Wireless Sensor Networks for Early Forest Fire Detection

Cited by 22 publications

References 13 publications

A Novel Association Rule-Based Data Mining Approach for Internet of Things Based Wireless Sensor Networks

A Novel Association Rule-Based Data Mining Approach for Internet of Things Based Wireless Sensor Networks

Evaluation of CupCarbon Network Simulator for Wireless Sensor Networks

Detecting Energy Depriving Malicious Nodes by Unsupervised Learning in Energy Harvesting Cooperative Wireless Sensor Networks

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