Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS

Infrastructure-Assisted Smartphone-based ADL Recognition in Multi-Inhabitant Smart Environments

Ministry of Education, Singapore under its Academic Research Funding Tier

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

A PILOT STUDY TO DEVELOP DISCOURSE CODES SPECIFIC TO PREFRONTAL DYSFUNCTION

This pilot study developed a set of codes designed to capture the "nonaphasic" but characteristic discourse deficits that may be present following prefrontal cortex damage (PFCD). The codes were utilized based on narrative sample elicitation to investigate between-group differences in two study populations: patients with left, right, or bi-frontal PFCD and age and education-matched healthy comparison group participants. Narrative samples were coded on indices of content units, thematic units, story grammar features, and discourse errors, and analyzed using CLAN. Results of this study support the original deficit hypotheses. The coding schema demonstrated fair to good inter-rater reliability, stronger performances by the healthy comparison group across all four levels of analysis, and poorer performance overall on the retell phase than the tell phase. Qualitative analysis revealed relatively few discourse errors associated with the healthy comparison group, while various classic discourse errors were associated with the PFCD group

Sensor Networks and Their Applications: Investigating the Role of Sensor Web Enablement

The Engineering Doctorate (EngD) was conducted in conjunction with BT Research on state-of-the-art Wireless Sensor Network (WSN) projects. The first area of work is a literature review of WSN project applications, some of which the author worked on as a BT Researcher based at the world renowned Adastral Park Research Labs in Suffolk (2004-09). WSN applications are examined within the context of Machine-to-Machine (M2M); Information Networking (IN); Internet/Web of Things (IoT/WoT); smart home and smart devices; BT’s 21st Century Network (21CN); Cloud Computing; and future trends. In addition, this thesis provides an insight into the capabilities of similar external WSN project applications. Under BT’s Sensor Virtualization project, the second area of work focuses on building a Generic Architecture for WSNs with reusable infrastructure and ‘infostructure’ by identifying and trialling suitable components, in order to realise actual business benefits for BT. The third area of work focuses on the Open Geospatial Consortium (OGC) standards and their Sensor Web Enablement (SWE) initiative. The SWE framework was investigated to ascertain its potential as a component of the Generic Architecture. BT’s SAPHE project served as a use case. BT Research’s experiences of taking this traditional (vertical) stove-piped application and creating SWE compliant services are described. The author’s findings were originally presented in a series of publications and have been incorporated into this thesis along with supplementary WSN material from BT Research projects. SWE 2.0 specifications are outlined to highlight key improvements, since work began at BT with SWE 1.0. The fourth area of work focuses on Complex Event Processing (CEP) which was evaluated to ascertain its potential for aggregating and correlating the shared project sensor data (‘infostructure’) harvested and for enabling data fusion for WSNs in diverse domains. Finally, the conclusions and suggestions for further work are provided

A Knowledge-driven Distributed Architecture for Context-Aware Systems

As the number of devices increases, it becomes a challenge for the users to use them effectively. This is more challenging when the majority of these devices are mobile. The users and their devices enter and leave different environments where different settings and computing needs may be required. To effectively use these devices in such environments means to constantly be aware of their whereabouts, functionalities and desirable working conditions. This is impractical and hence it is imperative to increase seamless interactions between the users and devices,and to make these devices less intrusive. To address these problems, various responsive computing systems, called context- aware systems, have been developed. These systems rely on architectures to perceive their physical environments in order to appropriately and effortlessly respond. Currently, the majority of the existing architectures focus on acquiring data from sensors, interpreting and sharing it with these systems

Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications

Understanding and modeling humans and device mobility has fundamental importance in mobile computing, with implications ranging from network design and location-aware technologies to urban infrastructure planning. Today\u27s users carry a plethora of devices such as smartphones, laptops, tablets, and smartwatches, with each device offering a different set of services resulting in different usage and mobility leading to the research question of understanding and modeling multiple user device trajectories. Additionally, prior research on mobility focuses on outdoor mobility when it is known that users spend 80% of their time indoors resulting in wide gaps in knowledge in the area of indoor mobility of users and devices. Here, I try to fill the gaps in mobility modeling in the areas of understanding and modeling indoor-outdoor human mobility as well as multi-device mobility. In this thesis, I propose the characterization and modeling of human and device mobility. Further, I design and deploy mobility-aware applications for contact tracing of infectious diseases and energy-aware Heating, Ventilation, and Air Conditioning (HVAC) scheduling. I try and answer a sequence of four primary inter-related questions : (1) how is indoor and outdoor user mobility different, (2) are multiple device trajectories belonging to a single user correlated, (3) how to model indoor mobility of users and (4) how to design effective mobility aware applications that are easily deployable and align with long term goals of sustainability as well relay positive societal impact. The insights gained from each question serves as a base to build up on the next question in the series. I present answers to these questions across three main parts of my thesis. The first part comprises of characterization and analysis of human and device mobility. In this part I design and develop tool to extract device trajectories from WiFi system logs syslog and map devices to users. These extracted trajectories and device to user mapping are used to characterize and empirically analyze the mobility of users at varying spatial granularity (indoor, outdoor) and extract device mobility correlations between multiple devices of users and forms the first part of my thesis. In the second part, based on the insights gained from the multi-granular and multi-device mobility characterization stated above, I argue that mobility is inherently hierarchical in nature and propose novel indoor human mobility modeling approach. Third, I leverage the passively observed mobility to design mobility-aware applications that either look back or look ahead in time. WiFiTrace is a look back or backtracking application that is a network-centric contact tracing tool to aid healthcare workers in manual contact tracing of infectious diseases and iSchedule is a look ahead machine learning based mobility-aware energy-saving application that predicts Heating, Ventilation, and Air Conditioning (HVAC) schedule for higher energy savings while increasing user comfort

A Cognitive IoE (Internet of Everything) Approach to Ambient-Intelligent Smart Space

At present, the United Nations figures claim that the current world population would rise from 7.6 billion to 8.5 billion in 2030 and 9.7 billion in 2050. Therefore by the 2050, 65 percent of world’s population would be living in urban mega-cities and each megacity would be accommodating around 10 million inhabitants. Such massive urbanization of growing population would be known as 21st century’s ’Urban Age’. On the other side, by 2020 the growing population of elderly people above 65 years old would be increasing by 25 percent in EU countries and by 30 percent in other developing nations including Asia and North America. As a result, the growth of massive population and elderly inhabitants in urban cities would require an assisted living environment for independent and comfortable living experiences. As can be expected, a persuasive demand of assisted living environment would be vital to the humankind. The goal of an assisted living environment is to support the aging population and inhabitants to live independently in their own home and communities with the support of trained services and personal digital assistants. Therefore, the continuous growing demand of assisted living environment targets to improve the inhabitants comfort level and efficiency to do their ADL (Activity Daily Living) routine tasks by enabling the cooperation among various IoT smart objects and sensors which will understand the environmental surroundings and the inhabitant’s contextual needs in a proactive manner.In this work, a Cognitive IoE (Internet of Everything) framework with ambient intelligence capability is proposed to observe the inhabitant activities with heterogeneous IoT network objects and sensors in a time series manner to perceive the inhabitant intentions and situations in the environment. The predictive regression model forecasts the inhabitant’s activity patterns with regressive machine learning algorithms. The interconnected network objects (sensors and actuators) behave as agents to learn, think and adapt to contextual situations in the dynamic environment with no or minimum human intervention. Therefore, the first research challenge is to recognize the inhabitant’s intentional-situation in the environment, and it is achieved by the Ambient Cognition Model(ACM). The ACM not only performs IoT data-fusion but also applies a statistical model for threshold and weight scheme to extract contextual information in a more systematic manner. The second research challenge of automating the predictive regression model to forecast the time series activity patterns of inhabitants is addressed within the Ambient-Expert Model(AEM). The hidden activity state patterns are identified, trained and tested with the supervised machine learning method of Hidden Markov Model, Recurrent-Neural Network, and Naive Bayes classifier. In addition, a recursive training mechanism of DATAWELL is integrated with the architecture to train(re-train) the model over new datasets and perform predictive analysis in a proactive manner.Furthermore, the unified framework CAiSH (Cognitive Ambient Intelligent Smart Home), built upon the integration of ACMand AEM architectures to a provide an intelligent IoT framework for the ambient intelligence smart home environment. The trained model uses maximum likelihood posterior probabilities to forecast the inhabitant’s intentional activity states. The CAiSH works as a proactive digital assistant to the inhabitant provide a development platform for autonomous and enhanced assisted living services in the cognitive IoE environment. The research has been carried out on time-series data sets, deploying IoT lab to generate and collect time series data for the training and testing purpose and providing hands-on research experience on IoT prototype deployment. Overall, 5499 datasets of 30 SA (Spot-Activities) and 9 IA (Intention- Activities) data sets have been engaged for the training and evaluation. The result outputs are evaluated with MAE (Mean-Square Error), MAPE (Mean Absolute Percentage Error) and MAE (Mean Absolute Error) metrics for the prediction accuracy measures