Efficient Approximate Big Data Clustering: Distributed and Parallel Algorithms in the Spectrum of IoT Architectures

Clustering, the task of grouping together similar items, is a frequently used method for processing data, with numerous applications. Clustering the data generated by sensors in the Internet of Things, for instance, can be useful for monitoring and making control decisions. For example, a cyber physical environment can be monitored by one or more 3D laser-based sensors to detect the objects in that environment and avoid critical situations, e.g. collisions.With the advancements in IoT-based systems, the volume of data produced by, typically high-rate, sensors has become immense. For example, a 3D laser-based sensor with a spinning head can produce hundreds of thousands of points in each second. Clustering such a large volume of data using conventional clustering methods takes too long time, violating the time-sensitivity requirements of applications leveraging the outcome of the clustering. For example, collisions in a cyber physical environment must be prevented as fast as possible.The thesis contributes to efficient clustering methods for distributed and parallel computing architectures, representative of the processing environments in IoT- based systems. To that end, the thesis proposes MAD-C (abbreviating Multi-stage Approximate Distributed Cluster-Combining) and PARMA-CC (abbreviating Parallel Multiphase Approximate Cluster Combining). MAD-C is a method for distributed approximate data clustering. MAD-C employs an approximation-based data synopsis that drastically lowers the required communication bandwidth among the distributed nodes and achieves multiplicative savings in computation time, compared to a baseline that centrally gathers and clusters the data. PARMA-CC is a method for parallel approximate data clustering on multi-cores. Employing approximation-based data synopsis, PARMA-CC achieves scalability on multi-cores by increasing the synergy between the work-sharing procedure and data structures to facilitate highly parallel execution of threads. The thesis provides analytical and empirical evaluation for MAD-C and PARMA-CC

Hybrid mobile computing for connected autonomous vehicles

With increasing urbanization and the number of cars on road, there are many global issues on modern transport systems, Autonomous driving and connected vehicles are the most promising technologies to tackle these issues. The so-called integrated technology connected autonomous vehicles (CAV) can provide a wide range of safety applications for safer, greener and more efficient intelligent transport systems (ITS). As computing is an extreme component for CAV systems,various mobile computing models including mobile local computing, mobile edge computing and mobile cloud computing are proposed. However it is believed that none of these models fits all CAV applications, which have highly diverse quality of service (QoS) requirements such as communication delay, data rate, accuracy, reliability and/or computing latency.In this thesis, we are motivated to propose a hybrid mobile computing model with objective of overcoming limitations of individual models and maximizing the performances for CAV applications.In proposed hybrid mobile computing model three basic computing models and/or their combinations are chosen and applied to different CAV applications, which include mobile local computing, mobile edge computing and mobile cloud computing. Different computing models and their combinations are selected according to the QoS requirements of the CAV applications.Following the idea, we first investigate the job offloading and allocation of computing and communication resources at the local hosts and external computing centers with QoS aware and resource awareness. Distributed admission control and resource allocation algorithms are proposed including two baseline non-cooperative algorithms and a matching theory based cooperative algorithm. Experiment results demonstrate the feasibility of the hybrid mobile computing model and show large improvement on the service quality and capacity over existing individual computing models. The matching algorithm also largely outperforms the baseline non-cooperative algorithms.In addition, two specific use cases of the hybrid mobile computing for CAV applications are investigated: object detection with mobile local computing where only local computing resources are used, and movie recommendation with mobile cloud computing where remote cloud resources are used. For object detection, we focus on the challenges of detecting vehicles, pedestrians and cyclists in driving environment and propose three methods to an existing CNN based object detector. Large detection performance improvement is obtained over the KITTI benchmark test dataset. For movie recommendation we propose two recommendation models based on a general framework of integrating machine learning and collaborative filtering approach.The experiment results on Netix movie dataset show that our models are very effective for cold start items recommendatio

Input Projection Algorithms Influence in Prediction and Optimization of QoS Accuracy

Regardless of new achievements in the research of prediction models, QoS is still a great issue for high quality web services and remains one of the key subjects that need to be studied. We believe that QoS should not only be measured, but have to be predicted in development and implementation phases. In this paper we assess how different input projection algorithms influence the prediction accuracy of a Multi-Layer Perceptron (MLP) trained with large datasets of web services QoS values

Machine Learning for Multimedia Communications

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise