Abbas Javed

Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology

Smart homes reduce human intervention in controlling the heating ventilation and air conditioning (HVAC) systems for maintaining a comfortable indoor environment. The embedded intelligence in the sensor nodes is limited due to the limited processing power and memory in the sensor node. Cloud computing has become increasingly popular due to its capability of providing computer utilities as internet services. In this study, a model for the intelligent controller by integrating internet of things (IoT) with cloud computing and web services is proposed. The wireless sensor nodes for monitoring the indoor environment and HVAC inlet air, and wireless base station for controlling the actuators of HVAC have been developed. The sensor nodes and base station communicate through RF transceivers at 915 MHz. Random neural network (RNN) models are used for estimating the number of occupants, and for estimating the predicted-mean-vote-based setpoints for controlling the heating, ventilation, and cooling of the building. Three test cases are studied (Case 1—Data storage and implementation of RNN models on the cloud, Case 2—RNN models implementation on base station, Case 3—Distributed implementation of RNN models on sensor nodes and base stations) for determining the best architecture in terms of power consumption. The results have shown that by embedding the intelligence in the base station and sensor nodes (i.e., Case 3), the power consumption of the intelligent controller was 4.4% less than Case 1 and 19.23% less than Case 2.

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

Building energy management systems (BEMSs) monitor and control the heating ventilation and air conditioning (HVAC) of buildings in addition to many other building systems and utilities. Wireless sensor networks (WSNs) have become the integral part of BEMS at the initial implementation phase or latter when retro fitting is required to upgrade older buildings. WSN enabled BEMS, however, have several challenges which are managing data, controllers, actuators, intelligence, and power usage of wireless components (which might be battery powered). The wireless sensor nodes have limited processing power and memory for embedding intelligence in the sensor nodes. In this paper, we present a random neural network (RNN)-based smart controller on a Internet of Things (IoT) platform integrated with cloud processing for training the RNN which has been implemented and tested in an environment chamber. The IoT platform is modular and not limited to but has several sensors for measuring temperature, humidity, inlet air coming from the HVAC duct and PIR. The smart RNN controller has three main components: 1) base station; 2) sensor nodes; and 3) the cloud with embedded intelligence on each component for different tasks. This IoT platform is integrated with cloud processing for training the RNN. The RNN-based occupancy estimator is embedded in sensor node which estimates the number of occupants inside the room and sends this information to the base station. The base station is embedded with RNN models to control the HVAC on the basis of setpoints for heating and cooling. The HVAC of the environment chamber consumes 27.12% less energy with smart controller as compared to simple rule-based controllers. The occupancy estimation time is reduced by our proposed hybrid algorithm for occupancy estimation that combines RNN-based occupancy estimator with door sensor node (equipped with PIR and magnetic reed switch). The results show that accuracy of hybrid RNN occupancy estimator is 88%.

Comparison of the Robustness of RNN, MPC and ANN Controller for Residential Heating System

In this paper, a novel random neural network (RNN) controller is proposed to maintain a comfortable indoor environment in a single storey residential building having four rooms fitted with radiators for heating. This controller considers the effect of outside temperature and solar radiations on the building and is capable of maintaining a comfortable indoor environment on the basis of a PMV-based set point. The RNN controller is trained with a 30 day dataset from the living room of the building and the performance of the controller is evaluated by testing the controller in all four rooms of the building for 100 days. It is found that the RNN controller is not only capable of maintaining comfortable indoor environment as suggested by PMV-based set point but can also adjust the room temperature to a lower set point (not included in the training set) required by the user for unoccupied rooms. The RNN controller is further compared with similar artificial neural network (ANN) controller and model predictive control (MPC) controller. The results show that for maintaining comfortable indoor environment, the performance of the RNN controller is approximately equivalent to the MPC controller for the set points not covered in the training set, while ANN controller failed to maintain accurate comfortable environment for the operating points not covered in the training phase.

Intelligent Intrusion Detection in Low-Power IoTs

Security and privacy of data are one of the prime concerns in today’s Internet of Things (IoT). Conventional security techniques like signature-based detection of malware and regular updates of a signature database are not feasible solutions as they cannot secure such systems effectively, having limited resources. Programming languages permitting immediate memory accesses through pointers often result in applications having memory-related errors, which may lead to unpredictable failures and security vulnerabilities. Furthermore, energy efficient IoT devices running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate IoT in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e., sensor nodes and base station) is destabilised by the attackers. In this article, we have presented an intrusion detection and prevention mechanism by implementing an intelligent security architecture using random neural networks (RNNs). The application’s source code is also instrumented at compile time in order to detect out-of-bound memory accesses. It is based on creating tags, to be coupled with each memory allocation and then placing additional tag checking instructions for each access made to the memory. To validate the feasibility of the proposed security solution, it is implemented for an existing IoT system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node within the system operating range and anomalous activity in the base station with an accuracy of 97.23%. Overall, the proposed security solution has presented a minimal performance overhead.

Critical Analysis of Learning Algorithms in Random Neural Network Based Cognitive Engine for LTE Systems

In this paper, we critically analyze the performance of an intelligent Long-Term Evolution-Uplink (LTE-UL) system having a cognitive engine (CE) embedded in e-NodeB. Performance characterization, optimal radio parameters prediction, and inter-cell-interference coordination (ICIC) are studied. The embedded CE allocates the optimal radio parameters to serving users and suggests the acceptable transmit power to users served by adjacent cells for ICIC. The desired cognition has been achieved with a novel random neural network (RNN) based CE architecture. To achieve the best learning performance, we critically analyzed three learning algorithms, gradient descent (GD), adaptive inertia weight particle swarm optimization (AIW-PSO) and differential evolution (DE). The analysis showed that AIW-PSO was 10.57% better than GD and 8.012% better than DE in terms of learning accuracy (based on MSE), but with considerable compromise on computational time as compared to GD. Moreover, in terms of convergence time (to achieve the MSE less than 1.04E-03), AIW-PSO took 60% less iterations than GD and 50% less than DE.

Energy demand prediction through novel random neural network predictor for large non-domestic buildings

Buildings are among the largest consumers of energy in the world. In developed countries, buildings currently consumes 40% of the total energy and 51% of total electricity consumption. Energy prediction is a key factor in reducing energy wastage. This paper presents and evaluates a novel RNN technique which is capable to predict energy utilization for a non-domestic large building comprising of 562 rooms. Initially, a model for the 562 rooms is developed using Integrated Environment Solutions Virtual Environment (IES-VE) software. The IES-VE model is simulated for one year and 10 essential data inputs i.e., air temperature, dry resultant temperature, internal gain, heating set point, cooling set point, plant profile, relative humidity, moisture content, heating plant sensible load, internal gain and number of people are measured. Datasets are generated from the measured data. RNN model is trained with this datasets for the energy demand prediction. Experiments are used to identify the accuracy of prediction. The results show that the proposed RNN based energy model achieves 0.00001 Mean Square Error (MSE) in just 86 epochs via Gradient Decent (GD) algorithm.

Random Neural Network Based Intelligent Intrusion Detection for Wireless Sensor Networks

Security and privacy of data are one of the prime concerns in todays embedded devices. Primitive security techniques like signature-based detection of malware and regular update of signature database are not feasible solutions as they cannot secure such systems, having limited resources, effectively. Furthermore, energy efficient wireless sensor modes running on batteries cannot afford the implementation of cryptography algorithms as such techniques have significant impact on the system power consumption. Therefore, in order to operate wireless embedded devices in a secure manner, the system must be able to detect and prevent any kind of intrusions before the network (i.e. sensor nodes and base station) is destabilized by the attackers. In this paper, we have presented an intrusion detection mechanism by implementing an intelligent security architecture using Random Neural Networks (RNN). To validate the feasibility of the proposed security solution, it is implemented for an existing wireless sensor network system and its functionality is practically demonstrated by successfully detecting the presence of any suspicious sensor node and anomalous activity in the base station with high accuracy and minimal performance overhead.

Random neural networks based cognitive controller for HVAC in non-domestic building using LoRa

The critical requirements for devices connected in Internet of Things (IoT) are long battery life, long coverage range, and low deployment cost. In our previous work, we developed cognitive controller for controlling the HVAC of non-domestic building using short range communication in an unlicensed spectrum (915 MHz). In this work, we have upgraded our cognitive controller with recently developed long range communication (LoRa) technology and compared it with short range RF communication in an indoor building. The comparison is made in terms of battery life, coverage range, control accuracy and memory size. The effect of changing the transmission power of LoRa on battery consumption of the sensor node is also evaluated. Results show that coverage range of LoRa is 60.4% more than short range communication inside a building.

Impact of Learning Algorithms on Random Neural Network based Optimization for LTE-UL Systems

This paper presents an application of context-aware decision making to the problem of radio resource management (RRM) and inter-cell interference coordination (ICIC) in long-term evolution-uplink (LTE-UL) system. The limitations of existing analytical, artificial intelligence (AI), and machine learning (ML) based approaches are highlighted and a novel integration of random neural network (RNN) based learning with genetic algorithm (GA) based reasoning is presented. In first part of the implementation, three learning algorithms (gradient descent (GD), adaptive inertia weight particle swarm optimization (AIWPSO), and differential evolution (DE)) are applied to RNN and two learning algorithms (GD and levenberg-marquardt (LM)) are applied to artificial neural network (ANN). In second part of the implementation, the GA based reasoning is applied to the trained ANN and RNN models for performance optimization. Finally, the ANN and RNN based optimization results are compared with the state-of-the-art fractional power control (FPC) schemes in terms of user throughput and power consumption. The simulation results have revealed that an RNN-DE (RNN trained with DE algorithm) based cognitive engine (CE) can provide up to 14% more cell capacity along with 6dBm and 9dBm less user power consumption as compared to RNN-GD (RNN trained with GD algorithm) and FPC methods respectively.

Modelling and optimization of residential heating system using random neural networks

In this paper, a novel random neural network (RNN) model based optimization process for radiator-based heating system is proposed to maintain a comfortable indoor environment in a living room of a single storey residential building. The predictive model of the living room is developed by training a feed forward RNN and then optimisation algorithms are used to calculate the optimal flowrate for the radiators. Three optimisation algorithms: Genetic Algorithm (GA), Particle swarm optimization (PSO) algorithm, and Sequential quadratic programming (SQP) optimization algorithm are investigated to calculate the optimal control input. The accuracy of the control scheme is verified by simulations using International Building Physics Toolbox (IBPT). It was found that mean squared error (MSE) for PSO is 38.87% less than GA and the MSE for PSO is 21.19% less than SQP. The RNN model based optimization technique is further compared with model predictive controller (MPC) designed for the radiator based heating system. The comparison results showed that the proposed RNN technique minimize the energy consumption and maintains accurate room thermal comfort according to the predicted mean vote (PMV) based setpoints.