Hadi Larijani

Evaluation of LoRa and LoRaWAN for wireless sensor networks

LoRa is a new ISM band wireless technology designed for low power, unlicensed, Long Range operation. LoRaWAN is a Wide Area Network protocol that incorporates the LoRa wireless into a networked infrastructure. The indoor and outdoor performance of these technologies, the physical layer wireless and multi-gateway wide area network, was evaluated across the central business district (CBD) of Glasgow city (Scotland). The results indicated that this technology can be a reliable link for low cost remote sensing applications.

Voice Quality in VoIP Networks Based on Random Neural Networks

The growth of Internet has led to the development of many new applications and technologies. Voice over Internet Protocol (VoIP) is one of the fastest growing applications. Calculating the quality of calls has been a complex task. The ITU E-Model gives a framework to measure quality of VoIP calls but the MOS element is a subjective measure. In this paper, we discuss a novel method using Random Neural Network (RNN) to accurately predict the perceived quality of voice and more importantly to perform this on real-time traffic to overcome the drawbacks of available methods. The novelty of this model is that RNN model provides a non-intrusive method to accurately predict and monitor perceived voice quality for both listening and conversational voice. This method has learning capabilities and this makes it possible for it to adapt to any network changes without human interference. Our novel model uses three input variables (neurons) delay, jitter, and packet loss and the codec used was G711.a. Results show a good degree of accuracy in calculating Mean Option Score (MOS), compared to Perceptual Evaluation of Speech Quality (PESQ) algorithm. WAN emulation software WANem was used to generate different samples for testing and training the RNN.

Evaluating perceived voice quality on packet networks using different random neural network architectures

Voice over Internet Protocol (VoIP) is one of the fastest growing technologies in the world. In VoIP speech signals are transmitted over the same network used for data communications. The internet is not a robust network and is subjected to delay, jitter, and packet loss. It is very important to measure and monitor the quality of service (QoS) the users experience in VoIP networks; this is not an easy task and usually requires subjective tests. In this paper we have analyzed three non-intrusive models to measure and monitor voice quality using Random Neural Networks (RNN). A RNN is an open queuing network with positive and negative signals. We have assessed the voice quality based on various parameters i.e. delay, jitter, packet loss, and codec. In our approach we have used the Mean Opinion Score (MOS) calculated using a Perceptual Evaluation of Speech Quality (PESQ) algorithm to generate data for training the RNN model. We have studied two feed-forward models and a recurrent architecture. We have found that the simple feed-forward architecture has produced the most accurate results compared to the other two architectures.

Non-intrusive method for video quality prediction over LTE using random neural networks (RNN)

Long Term Evolution (LTE) is the preliminary version of a fourth generation (4G) mobile communication system. Its aim is to support different services with high data rates and strict Quality of Experience (QoE) requirements of users. The main aim of this study is to present a prediction model based on Random Neural Networks (RNNs) for objective, non-intrusive prediction of video quality over LTE for video applications. A three layer feed-forward RNN model with gradient descent training algorithm has been developed. This model uses a combination of objective parameters in the application and network layers, such as Content Type (CT), Sender Bit Rate (SBR), resolution size, Frame Rate (FR), codec, and packet loss rate (PLR). The video quality was predicted in terms of the Mean Opinion Score (MOS). The results show an approximate 50% increase in accuracy using this model, compared to previous models. LTE-Sim software has been used to generate different samples for testing and training RNN model.

ANCH: A New Clustering Algorithm for Wireless Sensor Networks

The adaptable and distributed nature of wireless sensor networks has made them popular in a broad range of applications. Clustering is a widely accepted approach for organising nodes in sensor networks to address the network congestion and energy efficiency concerns. In clustering, the number and uniform distribution of the cluster heads are crucial for the effectiveness of an algorithm. In this paper, we propose a new clustering algorithm for wireless sensor networks that reduces the networks energy consumption and significantly prolongs its lifetime. This is achieved by optimising the distribution of cluster heads across the network. The results of our extensive simulation study show considerable reduction in network energy consumption and therefore prolonging network lifetime.

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.

A study on QoS of VoIP networks: a random neural network (RNN) approach

Voice over Internet Protocol (VoIP) is predicted to be the replacement of the traditional PSTN telephone system. Quality of Service (QoS) of VoIP systems are more difficult to measure and implement compared to PSTN systems. The nature of QoS in VoIP networks is very variable and hence it is important to be able to measure the QoS offered by the system in real time with a low computational cost. So it is very important to measure the quality of service offered by VoIP networks. In this paper we discuss a new novel model to calculate the perceived voice quality using Random Neural Network (RNN). The RNN is an open Markovian queuing model, motivated by spiking behaviour of biological neurons that has been used for several applications. We used the feedforward architecture with different numbers of hidden neurons to test the stability of our model. We study the RNN model with 4, 5, and 6 hidden layers of neurons. Our model shows a high degree of accuracy

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.