An intelligent approach to quality of service for MPEG-4 video transmission in IEEE 802.15.1

Nowadays, wireless connectivity is becoming ubiquitous spreading to companies and in domestic areas. IEEE 802.15.1 commonly known as Bluetooth is high-quality, high-security, high-speed and low-cost radio signal technology. This wireless technology allows a maximum access range of 100 meters yet needs power as low as 1mW. Regrettably, IEEE 802.15.1 has a very limited bandwidth. This limitation can become a real problem If the user wishes to transmit a large amount of data in a very short time. The version 1.2 which is used in this project could only carry a maximum download rate of 724Kbps and an upload rate of 54Kbps In its asynchronous mode. But video needs a very large bandwidth to be transmitted with a sufficient level of quality. Video transmission over IEEE 802.15.1 networks would therefore be difficult to achieve, due to the limited bandwidth. Hence, a solution to transmit digital video with a sufficient quality of picture to arrive at the receiving end is required. A hybrid scheme has been developed in this thesis, comprises of a fuzzy logic set of rules and an artificial neural network algorithms. MPEG-4 video compression has been used in this work to optimise the transmission. This research further utilises an ‘added-buffer’ to prevent excessive data loss of MPEG-4 video over IEEE 802.15.1transmission and subsequently increase picture quality. The neural-fuzzy scheme regulates the output rate of the added-buffer to ensure that MPEG-4 video stream conforms to the traffic conditions of the IEEE 802.15.1 channel during the transmission period, that is to send more data when the bandwidth is not fully used and keep the data in the buffers if the bandwidth is overused. Computer simulation results confirm that intelligence techniques and added-buffer do improve quality of picture, reduce data loss and communication delay, as compared with conventional MPEG video transmission over IEEE 802.15.1

State of the art in privacy preservation in video data

Active and Assisted Living (AAL) technologies and services are a possible solution to address the crucial challenges regarding health and social care resulting from demographic changes and current economic conditions. AAL systems aim to improve quality of life and support independent and healthy living of older and frail people. AAL monitoring systems are composed of networks of sensors (worn by the users or embedded in their environment) processing elements and actuators that analyse the environment and its occupants to extract knowledge and to detect events, such as anomalous behaviours, launch alarms to tele-care centres, or support activities of daily living, among others. Therefore, innovation in AAL can address healthcare and social demands while generating economic opportunities. Recently, there has been far-reaching advancements in the development of video-based devices with improved processing capabilities, heightened quality, wireless data transfer, and increased interoperability with Internet of Things (IoT) devices. Computer vision gives the possibility to monitor an environment and report on visual information, which is commonly the most straightforward and human-like way of describing an event, a person, an object, interactions and actions. Therefore, cameras can offer more intelligent solutions for AAL but they may be considered intrusive by some end users. The General Data Protection Regulation (GDPR) establishes the obligation for technologies to meet the principles of data protection by design and by default. More specifically, Article 25 of the GDPR requires that organizations must "implement appropriate technical and organizational measures [...] which are designed to implement data protection principles [...] , in an effective manner and to integrate the necessary safeguards into [data] processing.” Thus, AAL solutions must consider privacy-by-design methodologies in order to protect the fundamental rights of those being monitored. Different methods have been proposed in the latest years to preserve visual privacy for identity protection. However, in many AAL applications, where mostly only one person would be present (e.g. an older person living alone), user identification might not be an issue; concerns are more related to the disclosure of appearance (e.g. if the person is dressed/naked) and behaviour, what we called bodily privacy. Visual obfuscation techniques, such as image filters, facial de-identification, body abstraction, and gait anonymization, can be employed to protect privacy and agreed upon by the users ensuring they feel comfortable. Moreover, it is difficult to ensure a high level of security and privacy during the transmission of video data. If data is transmitted over several network domains using different transmission technologies and protocols, and finally processed at a remote location and stored on a server in a data center, it becomes demanding to implement and guarantee the highest level of protection over the entire transmission and storage system and for the whole lifetime of the data. The development of video technologies, increase in data rates and processing speeds, wide use of the Internet and cloud computing as well as highly efficient video compression methods have made video encryption even more challenging. Consequently, efficient and robust encryption of multimedia data together with using efficient compression methods are important prerequisites in achieving secure and efficient video transmission and storage.This publication is based upon work from COST Action GoodBrother - Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living (CA19121), supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. www.cost.e

Quality of service differentiation for multimedia delivery in wireless LANs

Delivering multimedia content to heterogeneous devices over a variable networking environment while maintaining high quality levels involves many technical challenges. The research reported in this thesis presents a solution for Quality of Service (QoS)-based service differentiation when delivering multimedia content over the wireless LANs. This thesis has three major contributions outlined below: 1. A Model-based Bandwidth Estimation algorithm (MBE), which estimates the available bandwidth based on novel TCP and UDP throughput models over IEEE 802.11 WLANs. MBE has been modelled, implemented, and tested through simulations and real life testing. In comparison with other bandwidth estimation techniques, MBE shows better performance in terms of error rate, overhead, and loss. 2. An intelligent Prioritized Adaptive Scheme (iPAS), which provides QoS service differentiation for multimedia delivery in wireless networks. iPAS assigns dynamic priorities to various streams and determines their bandwidth share by employing a probabilistic approach-which makes use of stereotypes. The total bandwidth to be allocated is estimated using MBE. The priority level of individual stream is variable and dependent on stream-related characteristics and delivery QoS parameters. iPAS can be deployed seamlessly over the original IEEE 802.11 protocols and can be included in the IEEE 802.21 framework in order to optimize the control signal communication. iPAS has been modelled, implemented, and evaluated via simulations. The results demonstrate that iPAS achieves better performance than the equal channel access mechanism over IEEE 802.11 DCF and a service differentiation scheme on top of IEEE 802.11e EDCA, in terms of fairness, throughput, delay, loss, and estimated PSNR. Additionally, both objective and subjective video quality assessment have been performed using a prototype system. 3. A QoS-based Downlink/Uplink Fairness Scheme, which uses the stereotypes-based structure to balance the QoS parameters (i.e. throughput, delay, and loss) between downlink and uplink VoIP traffic. The proposed scheme has been modelled and tested through simulations. The results show that, in comparison with other downlink/uplink fairness-oriented solutions, the proposed scheme performs better in terms of VoIP capacity and fairness level between downlink and uplink traffic

Accessible Integration of Physiological Adaptation in Human-Robot Interaction

Technological advancements in creating and commercializing novel unobtrusive wearable physiological sensors have generated new opportunities to develop adaptive human-robot interaction (HRI). Detecting complex human states such as engagement and stress when interacting with social agents could bring numerous advantages to creating meaningful interactive experiences. Bodily signals have classically been used for post-interaction analysis in HRI. Despite this, real-time measurements of autonomic responses have been used in other research domains to develop physiologically adaptive systems with great success; increasing user-experience, task performance, and reducing cognitive workload. This thesis presents the HRI Physio Lib, a conceptual framework, and open-source software library to facilitate the development of physiologically adaptive HRI scenarios. Both the framework and architecture of the library are described in-depth, along with descriptions of additional software tools that were developed to make the inclusion of physiological signals easier for robotics frameworks. The framework is structured around four main components for designing physiologically adaptive experimental scenarios: signal acquisition, processing and analysis; social robot and communication; and scenario and adaptation. Open-source software tools have been developed to assist in the individual creation of each described component. To showcase our framework and test the software library, we developed, as a proof-of-concept, a simple scenario revolving around a physiologically aware exercise coach, that modulates the speed and intensity of the activity to promote an effective cardiorespiratory exercise. We employed the socially assistive QT robot for our exercise scenario, as it provides a comprehensive ROS interface, making prototyping of behavioral responses fast and simple. Our exercise routine was designed following guidelines by the American College of Sports Medicine. We describe our physiologically adaptive algorithm and propose an alternative second one with stochastic elements. Finally, a discussion about other HRI domains where the addition of a physiologically adaptive mechanism could result in novel advances in interaction quality is provided as future extensions for this work. From the literature, we identified improving engagement, providing deeper social connections, health care scenarios, and also applications for self-driving vehicles as promising avenues for future research where a physiologically adaptive social robot could improve user experience

Simulation and modeling of the behavior in the four-stroke spark ignition engine by using CFD simulation

Computational fluid dynamics (CFD) is a branch of fluid mechanics that use numerical analysis and data structures to analyze and solves problems that involve fluid flows. CFD have been applied to a wide range of research and engineering problems in many fields of study and industries, including engine and combustion analysis. The objective of this review paper is to analyze the behavior in the four-stroke Spark Ignition (SI) engine by using CFD simulation. To get the require result a few methods have been used to analyze the behavior in the engine such as using CAD geometric model where the solid works software have been prepared. Then, in the CAD geometric model also have ANSYS software to perform analysis in engine module. To predict the behavior of the engine during its working two type of analysis can be performed namely port flow simulation and combustion simulation. So, in first part of this report, the CFD analysis is carried out to analyze the performance parameter, including intake stroke, compression stroke, power stroke and exhaust stroke with hexane fuel combustion. For the results, some details of the engine model and some predicted results including temperature, flow time and pressure profiles. With the existence of CFD simulation it can help many fields of study and industries by predict and analyze the possibility that can be happened in the future. At the same time, serves as a quick and economical way of future engine designs and concepts

An efficient telemetry system for restoring sight

PhD ThesisThe human nervous system can be damaged as a result of disease or trauma, causing conditions such as Parkinson’s disease. Most people try pharmaceuticals as a primary method of treatment. However, drugs cannot restore some cases, such as visual disorder. Alternatively, this impairment can be treated with electronic neural prostheses. A retinal prosthesis is an example of that for restoring sight, but it is not efficient and only people with retinal pigmentosa benefit from it. In such treatments, stimulation of the nervous system can be achieved by electrical or optical means. In the latter case, the nerves need to be rendered light sensitive via genetic means (optogenetics). High radiance photonic devices are then required to deliver light to the target tissue. Such optical approaches hold the potential to be more effective while causing less harm to the brain tissue. As these devices are implanted in tissue, wireless means need to be used to communicate with them. For this, IEEE 802.15.6 or Bluetooth protocols at 2.4GHz are potentially compatible with most advanced electronic devices, and are also safe and secure. Also, wireless power delivery can operate the implanted device. In this thesis, a fully wireless and efficient visual cortical stimulator was designed to restore the sight of the blind. This system is likely to address 40% of the causes of blindness. In general, the system can be divided into two parts, hardware and software. Hardware parts include a wireless power transfer design, the communication device, power management, a processor and the control unit, and the 3D design for assembly. The software part contains the image simplification, image compression, data encoding, pulse modulation, and the control system. Real-time video streaming is processed and sent over Bluetooth, and data are received by the LPC4330 six layer implanted board. After retrieving the compressed data, the processed data are again sent to the implanted electrode/optrode to stimulate the brain’s nerve cells

Context-aware real-time assistant architecture for pervasive healthcare

The aging population in many countries brings into focus rising healthcare costs and pressure on conventional healthcare services. Pervasive healthcare has emerged as a viable solution capable of providing a technology-driven approach to alleviate such problems by allowing healthcare to move from the hospital-centred care to self-care, mobile care, and at-home care. The state-of-the-art studies in this field, however, lack a systematic approach for providing comprehensive pervasive healthcare solutions from data collection to data interpretation and from data analysis to data delivery. In this thesis we introduce a Context-aware Real-time Assistant (CARA) architecture that integrates novel approaches with state-of-the-art technology solutions to provide a full-scale pervasive healthcare solution with the emphasis on context awareness to help maintaining the well-being of elderly people. CARA collects information about and around the individual in a home environment, and enables accurately recognition and continuously monitoring activities of daily living. It employs an innovative reasoning engine to provide accurate real-time interpretation of the context and current situation assessment. Being mindful of the use of the system for sensitive personal applications, CARA includes several mechanisms to make the sophisticated intelligent components as transparent and accountable as possible, it also includes a novel cloud-based component for more effective data analysis. To deliver the automated real-time services, CARA supports interactive video and medical sensor based remote consultation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile-based Activity Recognition, (ii) Intelligent Healthcare Decision Support Systems and (iii) Home-based Remote Monitoring Systems

SCALABLE AND EFFICIENT VERTICAL HANDOVER DECISION ALGORITHMS IN VEHICULAR NETWORK CONTEXTS

A finales de los años noventa, y al comienzo del nuevo milenio, las redes inalámbricas han evolucionado bastante, pasando de ser sólo una tecnología prometedora para convertirse en un requisito para las actividades cotidianas en las sociedades desarrolladas. La infraestructura de transporte también ha evolucionado, ofreciendo comunicación a bordo para mejorar la seguridad vial y el acceso a contenidos de información y entretenimiento. Los requisitos de los usuarios finales se han hecho dependientes de la tecnología, lo que significa que sus necesidades de conectividad han aumentado debido a los diversos requisitos de las aplicaciones que se ejecutan en sus dispositivos móviles, tales como tabletas, teléfonos inteligentes, ordenadores portátiles o incluso ordenadores de abordo (On-Board Units (OBUs)) dentro de los vehículos. Para cumplir con dichos requisitos de conectividad, y teniendo en cuenta las diferentes redes inalámbricas disponibles, es necesario adoptar técnicas de Vertical Handover (VHO) para cambiar de red de forma transparente y sin necesidad de intervención del usuario. El objetivo de esta tesis es desarrollar algoritmos de decisión (Vertical Handover Decision Algorithms (VHDAs)) eficientes y escalables, optimizados para el contexto de las redes vehiculares. En ese sentido se ha propuesto, desarrollado y probado diferentes algoritmos de decisión basados en la infraestructura disponible en las actuales, y probablemente en las futuras, redes inalámbricas y redes vehiculares. Para ello se han combinado diferentes técnicas, métodos computacionales y modelos matemáticos, con el fin de garantizar una conectividad apropiada, y realizando el handover hacia las redes más adecuadas de manera a cumplir tanto con los requisitos de los usuarios como los requisitos de las aplicaciones. Con el fin de evaluar el contexto, se han utilizado diferentes herramientas para obtener información variada, como la disponibilidad de la red, el estado de la red, la geolocalizaciónMárquez Barja, JM. (2012). SCALABLE AND EFFICIENT VERTICAL HANDOVER DECISION ALGORITHMS IN VEHICULAR NETWORK CONTEXTS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17869Palanci

Cognitive virtual ad hoc mobile cloud-based networking architecture

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThis thesis proposed cognitive techniques and intelligent algorithms that offered adaptive and advanced facilities to cloud-based networking by using Virtual Ad Hoc Mobile Cloud Computing Networks architecture (VAMCCNs). This is presented as a working case to address their global network challenges and to add cognitive support to the network design and implementation for better meeting traffic management and application requirements in mission objectives. The thesis concentrates on three main contributions. Firstly, an adaptive model, namely: a Heterogeneous Mobile Cloud Computing Network (HMCCN), was proposed to integrate different cloud networks architectures into one workflow. The cognitive data offloading task and the routing decision methods were applied using two different approaches: Fuzzy Analytic Hierarchy system (FAH) as a first approach and cognitive Software Defined Network (SDN) model as a second centralised approach. Experimental results show improvement in network reliability and throughputs, minimised in both nodes’ energy consumption and network latency with efficient intelligent data load balance and network resources allocation with best cloud model selection. Secondly, based on a virtual Ad Hoc cloud network with a realistic Random Waypoint Motion (RWM) model, an innovative cognitive routing algorithm was presented to improve efficient and reliable route selection among multiple possible routes. Routing protocols based on conventional, Fuzzy logic used important parameters with two data collections and decisions techniques and a new adaptive Intelligent Hybrid Fuzzy-Neural routing protocol (IHFN) that included prior knowledge to the network of the underlying motion and energy parameters were all proposed and compared. Results with the new hybrid algorithm shown a significant improvement to solve the network end-to-end performance degradation problem. The new hybrid protocol improved network throughput with an average of 20% higher than traditional Ad Hoc On-Demand Distance Vector (AODV) Routing protocol, improved the usage of network resources and reduced the maintenance process in adynamic topologies network. Finally, based on datasets collected from a realistic motion RWM model in a virtual Ad Hoc cloud network, the performance behaviour of six selected deep learning algorithms to predict the next steps of positions, speed and residual battery energy values of these mobile nodes have been evaluated and compared. This work goes further by presenting two algorithm's training techniques to predict the next 300-time steps of position, speed, and energy. Results and dissuasion show the differences concerning prediction accuracy between using the single node dataset model or Multiple node's dataset model