Design and Implementation of a Cloud Enabled Random Neural Network-Based Decentralized Smart Controller With Intelligent Sensor Nodes for HVAC

Javed, Abbas; Larijani, Hadi; Ahmadinia, Ali; Emmanuel, Rohinton; Mannion, Mike; Gibson, D. R.

doi:10.1109/jiot.2016.2627403

Cited by 77 publications

(41 citation statements)

References 46 publications

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“…A novel class of artificial neural networks has been proposed by Gelenbe named as Random Neural Network (RNN) [13]. RNNs have been used extensively for pattern recognition [8], communication as well as implementations of Heating, ventilation, and air conditioning (HVAC) [16] [17] to enhance energy efficiency. However no research has been reported to analyse the effectiveness of RNN's for intrusion detection systems using NSL-KDD dataset (to the best of our knowledge).…”

Section: A Random Neural Network Modelmentioning

confidence: 99%

A Novel Random Neural Network Based Approach for Intrusion Detection Systems

Qureshi

Larijani

Ahmad

et al. 2018

2018 10th Computer Science and Electronic Engineering (CEEC)

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Computer security and privacy of user specific data is a prime concern in day to day communication. The mass use of internet connected systems has given rise to many vulnerabilities which includes attacks on smart devices. Regular occurrence of such events has made the availability of scalable Intrusion Detection System (IDS) a perilous challenge. An intelligent IDS should be able to stop the malicious activity before it destabilizes the core network and to achieve this goal we propose a novel Random Neural Network based Intrusion Detection System (RNN-IDS) in this paper. The performance is evaluated by training different numbers of input and hidden layer neurons with learning rates on benchmark NSL-KDD dataset for binary classification. To validate the feasibility of proposed scheme, results were compared with existing systems and its performance was evaluated by the detection of novel attacks while obtaining an accuracy of 94.50%.

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Section: A Random Neural Network Modelmentioning

confidence: 99%

A Novel Random Neural Network Based Approach for Intrusion Detection Systems

Qureshi

Larijani

Ahmad

et al. 2018

2018 10th Computer Science and Electronic Engineering (CEEC)

Self Cite

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“…Testing the model confirmed that the proposed IDS had a good anomaly detection rate for 2-class and an accuracy of 81.2%, which was higher than the other reported models. In order to reduce computational times, random neural networks (RNN) [17] have generated significant results on several platforms [18][19][20][21][22][23]. RNN are easier to execute on hardware [24].…”

Section: Preliminary Workmentioning

confidence: 99%

“…A novel class of artificial neural networks was proposed by Gelenbe, named random neural network (RNN) [17]. RNNs have been used extensively for pattern recognition [27] and communication, as well as applied in the heating, ventilation, and air conditioning (HVAC) context [20,22] to enhance energy efficiency. However, little research has been reported which has analyzed the effectiveness of RNNs for intrusion detection systems using the NSL-KDD data set.…”

Section: Random Neural Network Modelmentioning

confidence: 99%

RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection

et al. 2019

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The exponential growth of internet communications and increasing dependency of users upon software-based systems for most essential, everyday applications has raised the importance of network security. As attacks are on the rise, cybersecurity should be considered as a prime concern while developing new networks. In the past, numerous solutions have been proposed for intrusion detection; however, many of them are computationally expensive and require high memory resources. In this paper, we propose a new intrusion detection system using a random neural network and an artificial bee colony algorithm (RNN-ABC). The model is trained and tested with the benchmark NSL-KDD data set. Accuracy and other metrics, such as the sensitivity and specificity of the proposed RNN-ABC, are compared with the traditional gradient descent algorithm-based RNN. While the overall accuracy remains at 95.02%, the performance is also estimated in terms of mean of the mean squared error (MMSE), standard deviation of MSE (SDMSE), best mean squared error (BMSE), and worst mean squared error (WMSE) parameters, which further confirms the superiority of the proposed scheme over the traditional methods.

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“…4. System Development For HVAC application, reliability, easy installation and power consumptions are the major factor to select WSN TOPOLOGY [8]- [10]. In this project we are using mesh topology.…”

Section: B Hvac Duct Sensormentioning

confidence: 99%

Cloud Enabled Neural Network with Intelligent Sensor nodes for HVAC

Karthik*,

Reddy,

Kumar

2019

IJITEE

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HVAC (Heating, Ventilation and Air Conditioning) is the technology of indoor and vehicular environmental comfort and to control these systems. The status of building energy consumption is increasingly prominent. Indoor air pollution is 10times danger than outdoor due to incorrect functionality of heating, ventilation, and air condition system. For indoor environment quality, a novel real-time method for HVAC system operation is developed. Internet of things is used to monitor the indoor air quality by using embedded electronics, software and sensors and connectivity. This project aims to integrate air condition, ventilation and protected system on a single embedded system that alerts early warning for the unpredictable dangers. The wireless sensor nodes have limited processing power and memory. In order to embed intelligence into sensor nodes, a hybrid algorithm is proposed containing RNN (Random Neural Network) and LNP (Linear Non-linear Poisson) cascade model.

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Design and Implementation of a Cloud Enabled Random Neural Network-Based Decentralized Smart Controller With Intelligent Sensor Nodes for HVAC

Cited by 77 publications

References 46 publications

A Novel Random Neural Network Based Approach for Intrusion Detection Systems

A Novel Random Neural Network Based Approach for Intrusion Detection Systems

RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection

Cloud Enabled Neural Network with Intelligent Sensor nodes for HVAC

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