FED: Fuzzy Event Detection model for Wireless Sensor Networks

Malazi, HadiTabatabaee; Zamanifar, Kamran; Dulman, Stefan

doi:10.5121/ijwmn.2011.3603

Cited by 11 publications

(8 citation statements)

References 38 publications

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“…Event detection [9] is one of the main components in numerous WSN applications. The network should be able to detect if particular events of interest have already occurred or are about to occur.…”

Section: Overview Of Fuzzy Event Detectionmentioning

confidence: 99%

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Design and Implementation of Fuzzy Event-detection Algorithm for Border Monitoring on FPGA

Ahmed

El-Khatib

Adly

et al. 2015

Int. J. Fuzzy Syst.

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The use of wireless sensor networks to protect sensitive facilities or international borders has recently attracted more and more attention. It has become a highpriority issue in many countries. In addition to the physical fences built for stopping illegal intruders from crossing the border, smart fencing has been proposed to extend intrusion-detection capabilities. Event detection is a central component in numerous wireless sensor network applications. In spite of this, the area of event description has not received enough attention. The majority of current event description approaches rely on using precise values to specify event thresholds. However, these crisp values cannot adequately handle the imprecise sensor readings. Therefore, in this paper, a hybrid event-detection algorithm based on two layers of 12-bit-resolution fuzzy logic system is used, which significantly improves the accuracy of event detection. Each sensor node has sensors to improve the precision of the detection system, as well as reducing the false alarm rate especially in a noisy environment. The proposed system was modeled using Matlab and designed using VHDL. The proposed system's VHDL code has been compared with the Matlab model, which provided very close results to this software-based controller model, and also with two other VHDL-based designs, results of which are also close and comparable to the proposed system. The proposed design is then implemented using a FPGA kit, and the prototype is connected to a graphical user interface. Finally, a testing experiment of this proposed system shows an event-detection ratio capability of 98.4 % and the false alarm ratio of 1.6 %.

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Section: Overview Of Fuzzy Event Detectionmentioning

confidence: 99%

“…Therefore, a suitable control technique is needed to describe alarming-level invasion events in a way that sensor nodes can understand them. Most previous works on event detection in WSNs use precise, also called crisp, values to specify the parameters that characterize an event [8][9][10]. However, sensor readings are not always precise.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Fuzzy Event-detection Algorithm for Border Monitoring on FPGA

Ahmed

El-Khatib

Adly

et al. 2015

Int. J. Fuzzy Syst.

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“…The experiment results showed that the fuzzy based faulty readings detection algorithm significantly reduces computational complexity of the calculation and shows very high accuracy comparing with the other existing algorithms. In paper [39] authors have introduced a fuzzy model called FED for distributed detection of composite events. This method supports the heterogeneity of sensor types.…”

Section: Event Detection and Decision Makingmentioning

confidence: 99%

Fuzzy logic and Wireless Sensor Networks – A survey

Maksimović

Vujović

Milosevic

2014

Journal of Intelligent &Amp; Fuzzy Systems

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This paper presents a survey of applications of fuzzy logic in real Wireless Sensor Networks (WSNs). The use of fuzzy logic in WSNs is shown to be a promising technique since it allows combining and evaluating diverse parameters in an efficient manner. Fuzzy logic is a good approach due to the execution requirements can be easily supported by sensor nodes, while it is able to improve the overall network performance. The potential of fuzzy logic goes beyond traditional systems and can be used in many research fields (statistics, quality control, optimization techniques . . . ), allowing multidisciplinary approaches and performance improvements. In WSNs, fuzzy logic has been used to improve decision-making, reduce resource consumption, and generally increase performance through efficient deployment, localization, clustering and cluster head election, routing, data aggregation, security, etc.

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“…The credibility value then represents the possibility that an event occurs within each cluster. FED [68] is another event detection approach that groups sensor nodes into clusters and detects events distributedly within each cluster. Each sensor node detect events using a threshold-based approach to detect events locally.…”

Section: Fuzzy-based Techniquesmentioning

confidence: 99%

“…We use this table to highlight the shortcomings of the exiting techniques and to identify a set of guidelines for designing optimal event detection techniques for WSNs. Threshold-base [48] Threshold-base [5] Threshold-based [51] Model-based [64] Model-based [54] Model-based [55] Model-based [56] Model-based [57] Model-based [58] Model-based [59] Model-based [60] Pattern-matching [61] Pattern-matching [62] Pattern-matching [63] Pattern-matching [64] Pattern-matching [66] AI-based [67] AI-based [68] AI-based [69] AI-based [70] AI-based [71] AI-based [72] AI-based [74] AI-based [75] AI-based [76] AI-based Considering Table 2.1 and Table 2.2, we can generally summarize the shortcomings of the existing techniques as the following:…”

Section: Comparison Of Event Detection Techniques For Wsns and Guidelmentioning

confidence: 99%

Artificial intelligence based event detection in wireless sensor networks

Bahrepour¹

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Wireless sensor networks (WSNs) are composed of large number of small, inexpensive devices, called sensor nodes, which are equipped with sensing, processing, and communication capabilities. While traditional applications of wireless sensor networks focused on periodic monitoring, the focus of more recent applications is on fast and reliable identification of out-of-ordinary situations and events. This new functionality of wireless sensor networks is known as event detection. Due to the fact that collecting all sensor data centrally to perform event detection is inefficient in many occasions, the new trend in event detection in wireless sensor networks is to perform detection in the network. Design of in-network event detection methods for wireless sensor networks is by no means straightforward, as it needs to efficiently cope with various challenges and concerns including unreliability, heterogeneity, adaptability, and resource constraints. In this thesis, we tackle this problem by proposing fast, accurate, innetwork, and intelligent event detection methods using artificial intelligence (AI) and machine learning (ML) approaches. To this end, the main objective of this thesis is to analyze, investigate applicability, and optimize artificial intelligence (AI) and machine learning (ML) methods for efficient, distributed, local and in-network event detection in wireless sensor networks (WSNs). The main contributions of the thesis can be summarized as: Feature extraction and reduction in three datasets for event detection in WSNs. We choose three datasets containing sensory data from real V Acknowledgment Give light, and the darkness will disappear of itself. Desiderius Erasmus (1466-1536) known as Erasmus of Rotterdam, Dutch Renaissance humanist, Catholic priest, social critic, teacher, and theologian.

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FED: Fuzzy Event Detection model for Wireless Sensor Networks

Abstract: ABSTRACT

Cited by 11 publications

References 38 publications

Design and Implementation of Fuzzy Event-detection Algorithm for Border Monitoring on FPGA

Design and Implementation of Fuzzy Event-detection Algorithm for Border Monitoring on FPGA

Fuzzy logic and Wireless Sensor Networks – A survey

Artificial intelligence based event detection in wireless sensor networks

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