Autonomous vehicles have huge potential in improving road safety and congestion. Towards the road map of full autonomy, each vehicle will be able to communicate with other vehicles within the network of vehicles to improve congestion and notify emergencies. Many architectures for communication between vehicles are centralised, typically using cloud servers. The security and trust of that communication is paramount. Therefore, the aim of this paper is to propose a novel method for encrypting and fragmenting data in various cloud providers in order to protect the anonymity and increase the uncertainty for an attacker having access to the data on cloud. Our experimental results seem promising and we were able to achieve good results with low overhead in transmission.

In this paper, Adaptive Neuro-Fuzzy Interference System (ANFIS) technique is used to develop models to predict two conditions commonly found in a Wireless Sensor Network's deployment; these conditions are failure due to (i) poorly deployed environment and (ii) human movements. ANFIS models are trained using parameters obtained from actual ZigBee PRO nodes' Neighbour Table experimented under the influence of associated network challenges. These parameters are Mean RSSI, Standard Deviation RSSI, Average Coefficient of Variation RSSI and Neighbour Table Connectivity. The individual and combined effects of parameters are investigated in-depth. Results showed the mean RSSI is a critical parameter and the combination of mean RSSI, ACV RSSI and NTC produced the best prediction results (~92%) for all ANFIS models.

The primary aim of this paper is to present a new content-based, non-intrusive quality of experience (QoE) prediction model for low bitrate and resolution (QCIF) H.264 encoded videos and to illustrate its application in video quality adaptation over Universal Mobile Telecommunication Systems (UMTS) networks. The success of video applications over UMTS networks very much depends on meeting the QoE requirements of users. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet such QoE requirements. Video quality is affected by distortions caused both by the encoder and the UMTS access network. The impact of these distortions is content dependent, but this feature is not widely used in non-intrusive video quality prediction models. In the new model, we chose four key parameters that can impact video quality and hence the QoE-content type, sender bitrate, block error rate and mean burst length. The video quality was predicted in terms of the mean opinion score (MOS). Subjective quality tests were carried out to develop and evaluate the model. The performance of the model was evaluated with unseen dataset with good prediction accuracy ( ~ 93%). The model also performed well with the LIVE database which was recently made available to the research community. We illustrate the application of the new model in a novel QoE-driven adaptation scheme at the pre-encoding stage in a UMTS network. Simulation results in NS2 demonstrate the effectiveness of the proposed adaptation scheme, especially at the UMTS access network which is a bottleneck. An advantage of the model is that it is light weight (and so it can be implemented for real-time monitoring), and it provides a measure of user-perceived quality, but without requiring time-consuming subjective tests. The model has potential applications in several other areas, including QoE control and optimization in network planning and content provisioning for network/service providers.

IP Multimedia Subsystem (IMS) offers a framework which enables the provisioning of multimedia services with Quality of Service (QoS) and mobility support across heterogeneous networks. The aim of this paper is twofold. First, to present a new fuzzy logic based Sender Bitrate (SBR) adaptation scheme at pre-encoding stage that is Quality of Experience (QoE) driven for video applications. The scheme was tested and evaluated in the NS2 based simulation access networks of third generation Universal Mobile Telecommunication System (UMTS) networks. Second, to demonstrate the implementation of the proposed adaptation scheme in our developed open Android-based IMS test bed. The test bed was developed to fully understand and manipulate the effects of network conditions on perceptual quality. The SBR adaptive scheme is evaluated in terms of the Mean Opinion Score (MOS). Extensive simulation and test bed results demonstrate the effectiveness of the proposed adaptation scheme especially at UMTS bottleneck access networks where perceived video quality is most affected. The proposed scheme was responsive to available network bandwidth and congestion and adapted the SBR accordingly maintaining acceptable quality in terms of the MOS. The proposed scheme has applications in network planning and content provisioning for network/service providers.

Universal Mobile Telecommunication System (UMTS) is a third generation mobile communication systems that supports wireless wideband multimedia applications. The objective of this paper is to present a new model for non-intrusive prediction of H.264 encoded video quality over UMTS networks and to illustrate their application to video quality monitoring and adaptation in mobile wireless streaming services. First, we present an efficient regression model for predicting video quality non-intrusively for all content types. The model is predicted from a combination of a set of objective parameters in the application and physical layer in terms of the Mean opinion Score (MOS). The application layer parameters considered are the content type, sender bitrate and frame rate and the physical layer parameters are the block error rate modeled with 2-state Markov model for a mean burst length of 1.75. The performance of the proposed metric is evaluated with unseen dataset with good prediction accuracy. Second, we illustrate the application of the model in mobile streaming services by presenting a new Sender Bitrate (SBR) adaptation scheme at pre-encoding stage that is Quality of Experience (QoE) driven. The scheme was tested and evaluated in the NS2 based UMTS simulation network. Extensive simulation results demonstrate the effectiveness of the proposed adaptation scheme in terms of the MOS and especially at the UMTS network bottleneck access where perceived video quality is most affected. The proposed scheme was responsive to available network bandwidth and congestion and adapted the SBR accordingly maintaining acceptable quality in terms of the MOS. The proposed scheme has applications in network planning and content provisioning for network/service providers.

In Universal Mobile Telecommunication System (UMTS) Radio Link Control (RLC) losses severely affect the Quality of Service (QoS) due to high error probability. Therefore, for any video quality prediction model, it is important to model the radio-link loss behaviour. In this paper we evaluate the impact of the radio access network on the end-to-end QoS for H.264 encoded video. In order to characterize the QoS level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) is proposed that takes into account the RLC loss models to predict the video quality in terms of the Mean Opinion Score (MOS). The RLC loss models considered are 2-state Markov models with variable mean burst lengths. The aim of the paper is two-fold. First, to find the impact of QoS parameters in both the physical and application layer on end-to-end video quality. Second, to propose a prediction model based on ANFIS to predict video quality over UMTS networks. ANFIS is well suited for video quality prediction over error prone and bandwidth restricted UMTS as it combines the advantages of neural networks and fuzzy systems. The ANFIS model is trained with a combination of application and physical layer parameters. The performance of the proposed model is validated with unseen dataset. These studies should help in the understanding of the impact of both the application and physical layer parameters on end-to-end video quality and in QoS control methods and adaptation.

Universal mobile telecommunication system (UMTS) is a third-generation mobile communications system that supports wireless wideband multimedia applications. The primary aim of this study is to present learning models based on neural networks for objective, non-intrusive prediction of video quality over wireless local area network (WLAN) and UMTS networks for video applications. The contributions of this study are two-fold: first, an investigation of the impact of parameters both in the application and physical layer on end-to-end video quality is presented. The parameters considered in the application layer are content type (CT), sender bitrate (SBR) and frame rate (FR), whereas in the physical layer block error rate (BLER) and link bandwidth (LBW) are considered. Secondly, learning models based on adaptive neural fuzzy inference system (ANFIS) are developed to predict the visual quality in terms of the mean opinion score for all contents over access networks of UMTS and WLAN. ANFIS is well suited for video quality prediction over error-prone and bandwidth restricted UMTS as it combines the advantages of neural networks and fuzzy systems. The ANFIS-based artificial neural network is trained using a combination of physical layer parameters such as BLER and LBW and application layer parameters of CT, SBR and FR. The proposed models are validated using unseen data set. The preliminary results show that good prediction accuracy was obtained from the models. This study should help in the development of a reference-free video prediction model and quality of service control methods for video over UMTS/WLAN networks.

User's perceived quality of service (QoS) or quality of experience (QoE) is likely to be the major determining factor in the success of new multimedia applications over wireless/mobile networks. The primary aim of this study is to present an adaptation scheme that is QoE-driven for optimising content provisioning and network resource utilisation for video applications over wireless networks. The proposed scheme encompasses the application of a QoE-driven model for optimising content provisioning and network resource utilisation. The content provisioning is optimised by the determination of initial content quality by adapting the video sender bitrate (SBR) according to users' QoE requirement. By finding the impact of the QoS parameters on end-to-end perceptual video quality, the optimum trade-off between SBR and frame rate is found and the benefits to network providers in maximising existing network resources are demonstrated. The QoE is measured in terms of the mean opinion score. The proposed scheme makes it possible for content providers to achieve optimum streaming (with an appropriate SBR) suitable for the network and content type for a requested QoE. The scheme is also beneficial for network providers for network resource provision and planning, and therefore maximising existing network infrastructure by providing service differentiation.

The aim of this paper is quality prediction for streaming MPEG4 video sequences over wireless networks for all video content types. Video content has an impact on video quality under same network conditions. This feature has not been widely explored when developing reference-free video quality prediction model for streaming video over wireless or mobile communications. In this paper, we present a two step approach to video quality prediction. First, video sequences are classified into groups representing different content types using cluster analysis. The classification of contents is based on the temporal (movement) and spatial (edges, brightness) feature extraction. Second, based on the content type, video quality (in terms of Mean Opinion Score) is predicted from network level parameter (packet error rate) and application level (i.e. send bitrate, frame rate) parameters using Principal Component Analysis (PCA). The performance of the developed model is evaluated with unseen datasets and good prediction accuracy is obtained for all content types. The work can help in the development of reference-free video prediction model and priority control for content delivery networks.