Design and implementation of flood detector using wireless sensor network with mamdani's fuzzy logic method

Pratama, Ari; Munadi, Rendy; Mayasari, Ratna

doi:10.1109/icitisee.2017.8285493

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…A WSN is generally configured towards a specific use case, which sometimes can result in a lack of flexibility in terms of applications. One example of a novel application of WSN is a flood detector system [22], where the authors employ a multi-hop WSN consisting of ultrasonic and water flow sensors to monitor the water levels. A mamdani fuzzy logic system is proposed to process the sensor readings and output flood warnings, with a 96.96% accuracy achieved experimentally.…”

Section: Wireless Sensor Network Vs Virtual Sensor Networkmentioning

confidence: 99%

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection

Sternharz

Skackauskas

Elhalwagy

et al. 2022

Sensors

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This paper introduces a procedure to compare the functional behaviour of individual units of electronic hardware of the same type. The primary use case for this method is to estimate the functional integrity of an unknown device unit based on the behaviour of a known and proven reference unit. This method is based on the so-called virtual sensor network (VSN) approach, where the output quantity of a physical sensor measurement is replicated by a virtual model output. In the present study, this approach is extended to model the functional behaviour of electronic hardware by a neural network (NN) with Long-Short-Term-Memory (LSTM) layers to encapsulate potential time-dependence of the signals. The proposed method is illustrated and validated on measurements from a remote-controlled drone, which is operated with two variants of controller hardware: a reference controller unit and a malfunctioning counterpart. It is demonstrated that the presented approach successfully identifies and describes the unexpected behaviour of the test device. In the presented case study, the model outputs a signal sample prediction in 0.14 ms and achieves a reconstruction accuracy of the validation data with a root mean square error (RMSE) below 0.04 relative to the data range. In addition, three self-protection features (multidimensional boundary-check, Mahalanobis distance, auxiliary autoencoder NN) are introduced to gauge the certainty of the VSN model output.

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Section: Wireless Sensor Network Vs Virtual Sensor Networkmentioning

confidence: 99%

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection

Sternharz

Skackauskas

Elhalwagy

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The study also utilised the scale-invariant feature transform (SIFT) algorithm for the autonomous monitoring of flood. In a similar approach, Pratama et al [43] utilised Mamdani fuzzy logic along with ZigBee and water level sensors to detect and transmit the flood-related data. The study suggests that the maximum error for the proposed approach falls within an acceptable range of five percent.…”

Section: Computer Vision and Iot Sensors For Early Warning Systemsmentioning

confidence: 99%

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Arshad

Ogie

Barthélemy

et al. 2019

Sensors

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Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.

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“…Recent work [17] compares SMAC, TMAC and 802.11 in terms of energy consumption and the results show that TMAC saves 25% more energy than SMAC. In another project [18], the same protocols are compared, however, more performance metrics are looked into such as end-to-end delay, packet delivery ratio and throughput and similarly, TMAC does better than SMAC.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Denial-of-Sleep Attack-Prone MAC Protocols in Wireless Sensor Networks

Udoh

Getov

2018

2018 UKSim-AMSS 20th International Conference on Computer Modelling and Simulation (UKSim)

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Wireless sensor networks which form part of the core for the Internet of Things consist of resource constrained sensors that are usually powered by batteries. Therefore, careful energy awareness is essential when working with these devices. On the other hand, the presence as well as the absence of security features implemented in resource constrained sensors can have negative effects on their energy consumption. Indeed, the introduction of security techniques such as authentication and encryption, to ensure confidentiality and integrity of data, can place higher energy load on the sensors. However, the absence of security protection could give room for energy-drain attacks such as denial-of-sleep attacks which has a higher negative impact on the life span (availability) of the sensors than the presence of security techniques. This paper focuses on denial-of-sleep attacks by simulating three Media Access Control (MAC) protocols -Sensor-MAC, Timeout-MAC and TunableMAC -under different network sizes. We evaluate, compare, and analyse the received signal strength and the link quality indicators for each of these protocols. The results of our simulation provide insight into how these parameters can be used to detect a denial-of-sleep attack. Finally, we propose a novel architecture for tackling denialof-sleep attacks by propagating relevant knowledge via intelligent agents.

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Design and implementation of flood detector using wireless sensor network with mamdani's fuzzy logic method

Cited by 6 publications

References 7 publications

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection

Self-Protected Virtual Sensor Network for Microcontroller Fault Detection

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

Performance Analysis of Denial-of-Sleep Attack-Prone MAC Protocols in Wireless Sensor Networks

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