Intelligent MANET optimisation system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the literature, various Mobile Ad hoc NETwork (MANET) routing protocols proposed. Each performs the best under specific context conditions, for example under high mobility or less volatile topologies. In existing MANET, the degradation in the routing protocol performance is always associated with changes in the network context. To date, no MANET routing protocol is able to produce optimal performance under all possible conditions. The core aim of this thesis is to solve the routing problem in mobile Ad hoc networks by introducing an optimum system that is in charge of the selection of the running routing protocol at all times, the system proposed in this thesis aims to address the degradation mentioned above. This optimisation system is a novel approach that can cope with the network performance’s degradation problem by switching to other routing protocol. The optimisation system proposed for MANET in this thesis adaptively selects the best routing protocol using an Artificial Intelligence mechanism according to the network context. In this thesis, MANET modelling helps in understanding the network performance through different contexts, as well as the models’ support to the optimisation system. Therefore, one of the main contributions of this thesis is the utilisation and comparison of various modelling techniques to create representative MANET performance models. Moreover, the proposed system uses an optimisation method to select the optimal communication routing protocol for the network context. Therefore, to build the proposed system, different optimisation techniques were utilised and compared to identify the best optimisation technique for the MANET intelligent system, which is also an important contribution of this thesis. The parameters selected to describe the network context were the network size and average mobility. The proposed system then functions by varying the routing mechanism with the time to keep the network performance at the best level. The selected protocol has been shown to produce a combination of: higher throughput, lower delay, fewer retransmission attempts, less data drop, and lower load, and was thus chosen on this basis. Validation test results indicate that the identified protocol can achieve both a better network performance quality than other routing protocols and a minimum cost function of 4.4%. The Ad hoc On Demand Distance Vector (AODV) protocol comes in second with a cost minimisation function of 27.5%, and the Optimised Link State Routing (OLSR) algorithm comes in third with a cost minimisation function of 29.8%. Finally, The Dynamic Source Routing (DSR) algorithm comes in last with a cost minimisation function of 38.3%

Big data analytics correlation taxonomy

Big data analytics (BDA) is an increasingly popular research area for both organisations and academia due to its usefulness in facilitating human understanding and communication. In the literature, researchers have focused on classifying big data according to data type, data security or level of difficulty, and many research papers reveal that there is a lack of information on evidence of a real-world link of big data analytics methods and its associated techniques. Thus, many organisations are still struggling to realise the actual value of big data analytic methods and its associated techniques. Therefore, this paper gives a design research account for formulating and proposing a step ahead to understand the relation between the analytical methods and its associated techniques. Furthermore, this paper is an attempt to clarify this uncertainty and identify the difference between analytics methods and techniques by giving clear definitions for each method and its associated techniques to integrate them later in a new correlation taxonomy based on the research approaches. Thus, the primary outcome of this research is to achieve for the first time a correlation taxonomy combining analytic methods used for big data and its recommended techniques that are compatible for various sectors. This investigation was done through studying various descriptive articles of big data analytics methods and its associated techniques in different industries

Intelligent IoT wireless supervision system in oil pipeline grid

The security of oil transportation through pipeline grids is of paramount importance. One of the security methods currently used in this field is a monitoring and supervision system. This poster represents a proposal to blend Internet of Things (IoT) with artificial intelligence (AI) to create a knowledge-based system to monitor and supervise oil transportation through the pipeline grid. The reliability of IoT allows the user to gain lots of useful data to satisfy the needs, analyse health, gain the direction, achieve better communication and have a higher accuracy percentage. In this research, the Internet of things were the pressure and volume sensors near the valves and pumps. Nowadays, AI has proven its effectiveness in many disciplines and therefore Neuro-Fuzzy (NF) will be used as the prediction tool in the system. The supervision system is developed in phases; three simulation systems should be produced. Phase one; a wired centralized supervision system, phase two; a wired decentralized supervision system and finally phase three; a wireless decentralized supervision system. The first phase showed promising results, while the work will continue with the other two phases

UWTSD research seminar

Dr Nagham Saeed, a Senior Lecturer in Electrical Engineering with the School of Computing and Engineering, University of West London (UWL). She will be talking about her research in developing Diagnostic Systems for Electrocardiograms (ECGs) Readings and IoT in Monitoring and Controlling Water Pipelines in Buildings

AI applications in engineering

The YESI workshop aims to create a space for AI researchers from Poland and UK to create partnerships between individuals and institutions from both countries. This will start the search for funding opportunities, the implementation of research projects and the application of the research for social and economic benefit

IoT leak detection system for building hydronic pipes

Building’s Air Conditioning systems require moving liquids for dweller comfort. Clogged pipes, system degradation can cause pressure buildups, leaks and other faults which leads to damage to the building. Most of the leaks in the commercial building occur due to poor maintenance and/or material degradation. Visual inspection is most predominantly used to solve this problem in the industry. This paper introduces the Internet of Things technology to detect leakage in building’s hydronic pipes with the support of sensors, fault detection method and mechanical control. The system consists of: Microcontroller, Windows application and website application. Internet of Things technology was used to monitor and control the hydronics using microcontroller’s capability of connecting to main server which is used to transmit the data to the cloud. The prototype was successfully built and tested. Promising results show that leaks above 2ml/s could be detected after 4 seconds specifically for the built small-scale system while control and monitor feature could be implemented with Internet of Things technology

A new vision of a simple 1D Convolutional Neural Networks (1D-CNN) with Leaky-ReLU function for ECG abnormalities classification

Artificial Intelligence (AI) is increasingly impacting the healthcare field, due to its computational power that reduces time, cost and efforts for both healthcare professionals and patients. Diagnosing cardiac abnormalities using AI represents a very attractive subject for both medical and technical professionals. Cardiac abnormalities are characterized by the ECG signal, which is known by its variable morphology and intense affection by noises and artifacts. In this context, the presented study aims to propose a simple yet efficient version of Convolutional Neural Networks (CNN) to classify those abnormalities. This version increases the ability to detect several heart rate arrhythmias and severe cardiac abnormalities based only on the original 1D format of the ECG signal, which reserve the main feature of this signal and can be very suitable for ready-to-use and real-time applications. The main used training datasets are the MIT-BIH arrhythmias and the PTB databases. The proposed architectures are mainly inspired by the most recent CNN models and introduce several modifications on functions and layers, such as the use of the Leaky-ReLU instead of the ReLU activation function. The results of the proposed model are varying from an accuracy of 97%–99% in classifying Normal (n), Supraventricular (s), Ventricular (v), Fusion of ventricular and normal (f), and noisy (q) beats, in addition to the Myocardial Infarction (MI) case. A continuous performance was achieved while testing the model on real data, and after its migration to real mobile devices

Big data characteristics (V’s) in industry

In the new digital age, Data is the collection of the observation and facts in terms of events, thus data is continuously growing, getting denser and more varied by the minute across multiple channels. Nowadays, consumers generate mass amounts of data on a daily basis. Hence, Big Data (BD) emerged and is evolving rapidly, the various types of data being processed are huge, and ensuring that this data is being used efficiently is becoming increasingly more difficult. BD has been differentiated into several characteristics (the V’s) and many researchers have been developing more characteristics for new purposes over the past years. Therefore, it is shown from observation that there is a clear gap between researchers about the current status of the BD characteristics. Even after the introduction of newer characteristics, many papers are still proposing the use of 3 or 5 V’s, while some researchers are far more progressed and has reached up to 10V’s. This paper will provide an overview of the main characteristics that have been added over time and investigate the recent growth of Big Data Analytics (BDA) characteristics in each industry sector which will provide some detailed and general scope for most researchers to consider and learn from

RFID pet monitoring & identification system with RFID

(RFID) Radio Frequency Identification is a certain type of technology that uses communication via an electromagnetic field by magnetic waves to receive and transmit data from a terminal to and electromagnetic tag attached to an object of choice, for the importance of identifying and tracking objects. In this proposal it looks at the technology of Radio Frequency Identification and how it can be implemented into in world, the design of the RFID Pet Monitoring & identification system with RFID Tag Access Hatch is built with the idea of how this system can benefit pet owners and Vets, this system will allow pet owners to monitor their pets movement with alerts sent to their smart devices, it will inform the owner of when the pet enters and leaves the premises. When considering the benefits of the Vets research on what credentials the veterinary doctor requires on the pet is done, with this system the veterinary doctor will be able to pull up all the necessary information on the pet such as name, owner, previous appointments and medical issues

Monitoring and controlling water pipelines in buildings

Building design was always important in engineering field which embraces many different branches for great number of specialty engineers to work on one of the important aspects of the building is the water pipelines. It provides the building with heat, water, cooling and so on. It is important to control and monitor the pipelines in order to achieve maximum efficiency, minimal losses and maintenance costs. If the leak is not detected in time, it can bring huge costs to the building as internal building cavities and interior in general are not designed to withstand constant water contact. Monitoring and controlling water pipelines in buildings system is proposed to solve this issue within buildings and with some configuration it can be adapted in other fields such as oil transfer systems, gas pipelines, cooling systems in powerplants and others