Towards Energy - Efficient Qos-Aware Online Stream Data Processing for Internet of Things

Online data stream processing in Internet of Things (IoT) systems is an emerging paradigm that allows users to use resource-constrained IoT devices with the back- end of resourceful machines to process the data collected from the physical world in a real-time manner. The huge amount of generated sensor data can produce value- added information with different purposes for several applications. Techniques to pro- mote knowledge discovery from the raw data allow fully exploiting the potential usage of wide spread sensors in the IoT. In this context, using the energy of the resource- constrained IoT devices in an efficient way is a major concern. However, the appli- cation of QoS requirements should not be ignored to achieve the purpose of energy saving at any cost. In this thesis, we propose a framework that combines online stream data processing with adaptive system control to address both needs. The online algo- rithms are based on statistical methods to meet the needs of stream data processing. The result of the algorithms are then used to dynamically control the system behaviour to meet the needs of energy-saving. Simulation results show the effectiveness of our proposed framework

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Machine Learning Based Physical Activity Extraction for Unannotated Acceleration Data

Sensor based human activity recognition (HAR) is an emerging and challenging research area. The physical activity of people has been associated with many health benefits and even reducing the risk of different diseases. It is possible to collect sensor data related to physical activities of people with wearable devices and embedded sensors, for example in smartphones and smart environments. HAR has been successful in recognizing physical activities with machine learning methods. However, it is a critical challenge to annotate sensor data in HAR. Most existing approaches use supervised machine learning methods which means that true labels need be given to the data when training a machine learning model. Supervised deep learning methods have outperformed traditional machine learning methods in HAR but they require an even more extensive amount of data and true labels. In this thesis, machine learning methods are used to develop a solution that can recognize physical activity (e.g., walking and sedentary time) from unannotated acceleration data collected using a wearable accelerometer device. It is shown to be beneficial to collect and annotate data from physical activity of only one person. Supervised classifiers can be trained with small, labeled acceleration data and more training data can be obtained in a semi-supervised setting by leveraging knowledge from available unannotated data. The semi-supervised En-Co-Training method is used with the traditional supervised machine learning methods K-nearest Neighbor and Random Forest. Also, intensities of activities are produced by the cut point analysis of the OMGUI software as reference information and used to increase confidence of correctly selecting pseudo-labels that are added to the training data. A new metric is suggested to help to evaluate reliability when no true labels are available. It calculates a fraction of predictions that have a correct intensity out of all the predictions according to the cut point analysis of the OMGUI software. The reliability of the supervised KNN and RF classifiers reaches 88 % accuracy and the C-index value 0,93, while the accuracy of the K-means clustering remains 72 % when testing the models on labeled acceleration data. The initial supervised classifiers and the classifiers retrained in a semi-supervised setting are tested on unlabeled data collected from 12 people and measured with the new metric. The overall results improve from 96-98 % to 98-99 %. The results with more challenging activities to the initial classifiers, taking a walk improve from 55-81 % to 67-81 % and jogging from 0-95 % to 95-98 %. It is shown that the results of the KNN and RF classifiers consistently increase in the semi-supervised setting when tested on unannotated, real-life data of 12 people

Modeling And Dynamic Resource Allocation For High Definition And Mobile Video Streams

Video streaming traffic has been surging in the last few years, which has resulted in an increase of its Internet traffic share on a daily basis. The importance of video streaming management has been emphasized with the advent of High Definition: HD) video streaming, as it requires by its nature more network resources. In this dissertation, we provide a better support for managing HD video traffic over both wireless and wired networks through several contributions. We present a simple, general and accurate video source model: Simplified Seasonal ARIMA Model: SAM). SAM is capable of capturing the statistical characteristics of video traces with less than 5% difference from their calculated optimal models. SAM is shown to be capable of modeling video traces encoded with MPEG-4 Part2, MPEG-4 Part10, and Scalable Video Codec: SVC) standards, using various encoding settings. We also provide a large and publicly-available collection of HD video traces along with their analyses results. These analyses include a full statistical analysis of HD videos, in addition to modeling, factor and cluster analyses. These results show that by using SAM, we can achieve up to 50% improvement in video traffic prediction accuracy. In addition, we developed several video tools, including an HD video traffic generator based on our model. Finally, to improve HD video streaming resource management, we present a SAM-based delay-guaranteed dynamic resource allocation: DRA) scheme that can provide up to 32.4% improvement in bandwidth utilization

Recognition of elementary upper limb movements in an activity of daily living using data from wrist mounted accelerometers

In this paper we present a methodology as a proof of concept for recognizing fundamental movements of the humanarm (extension, flexion and rotation of the forearm) involved in ‘making-a-cup-of-tea’, typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering. Movements performed during ADL, forming part of the testing phase, are associated with each cluster label using a minimum distance classifier in a multi-dimensional feature space, comprising of features selected from a ranked set of 30 features, using Euclidean and Mahalonobis distance as the metric. Experiments were performed with four healthy subjects and our results show that the proposed methodology can detect the three movements with an overall average accuracy of 88% across all subjects and arm movement types using Euclidean distance classifier

The use of process data to examine reading strategies

Researchers are increasingly interested in the cognitive behaviors students display during tests. This interest has led researchers to look for innovative ways to collect this type of data. Due to the proliferation of computer-based assessments, process data has become popular for its ability to help show what students know, what students don’t know, and how students interact during assessments. Aim: The aims of the current study are 1) to use process data to identify potential reading strategies and 2) to examine if reading strategy is associated with gender, race/ethnicity, and differences in performance. Methods: Apply latent profile analysis (LPA) to extracted process data variables collected from US examinees who participated in the literacy section of the Program for the International Assessment of Adult Competencies (PIAAC). The variables are item response time and number of highlight events per item. Results: A two-class solution provided the best fit for the data in each testlet of the literacy section of the PIAAC. Class one progressed through items in each testlet faster than class two. Class one most closely resembled a skimming strategy while class two most closely resembled a full-reading strategy. However, there was not conclusive evidence to suggest that the classes were reminiscent of skimming and full-reading. Class assignment had no significant relationship with gender nor race/ethnicity, and there was no significant difference in literacy performance between the two classes, except in one case. Even then, both classes performed at a level two on the PIAAC literacy achievement scale. Discussion: Response time was found to be the only discriminating variable in the identification of patterns related to reading strategies. While there was some separation between classes, it was minimal in some cases. Response time was found to be useful but not enough to identify conclusive reading strategies. Further research is needed to identify process data variables with explanatory power other than response time to aid in the identification of reading strategies

On Pecuniary Resiliency, Early Warning, and Market Imitation Under Unrestricted Warfare

This study extends established financial market approaches to account for key econophysical attributes, low probability/high impact events, and a market\u27s potential use as early warning for threats. Any disparity between established financial practices and true market conditions may provide incentive for exploitation and may harm national security objectives and interests through cascading effects. These national security concerns may include, in particular, the health of a reserve currency for those countries whose currency serves as one. This is a preferred approach with Unrestricted Warfare-type operations as these techniques may not enable repudiation of the antagonist. Since this approach may remain a strong incentive for such tactics for the foreseeable future, it is imperative to develop techniques that hedge against financial miscalculations and subversive efforts. This research relaxes key assumptions of standard finance theory and applies these approaches to currency dynamics and portfolio selection which provides insight on areas of vulnerability. Early warning measures of threats are developed and compared to critical world events. Vulnerabilities to capital markets are studied, and their effects on reserve currencies are also analyzed. Lastly, a mathematical framework is developed that enables imitation of the aforementioned econophysical attributes in a simulated environment thereby bridging the divide between certain aspects of standard finance theory and econophysics for future study