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A challenge in Human-Robot Interaction is tailoring the social skills of robot companions to match those expected by individual humans during their rst encounter. Currently, large amounts of user data are needed to con gure robot companions with these skills. This creates the need of running long-term Human-Robot Interaction studies in domestic environments. A new approach using personas is explored to alleviate this arduous data collection task without compromising the level of interaction currently shown by robot companions. The personas technique was created by Alan Cooper in 1999 as a tool to de ne user archetypes of a system in order to reduce the involvement of real users during the development process of a target system. This technique has proven bene cial in Human-Computer Interaction for years. Therefore, similar bene ts could be expected when applying personas to Human-Robot Interaction. Our novel approach de nes personas as the key component of a computational behaviour model used to adapt robot companions to individual user's needs. This approach reduces the amount of user data that must be collected before a Human-Robot Interaction study, by associating new users to pre-de ned personas that adapt the robot behaviours through their integration with the computational behaviour model. At the same time that the current robot social interaction level expected by humans during the rst encounter is preserved. The University of Hertfordshire Robot House provided the naturalistic domestic environment for the investigation. After incorporating a new module, an Activity Recognition System, to increase the overall context-awareness of the system, a computational behaviour model will be de ned through an iterative research process. The initial de nition of the model was evolved after each experiment based on the iii ndings. Two successive studies investigated personas and determined the steps to follow for their integration into the targeted model. The nal model presented was de ned from users' preferences and needs when interacting with a robot companion during activities of daily living at home. The main challenge was identifying the variables that match users to personas in our model. This approach open a new discussion in the Human-Robot Interaction eld to de ne tools that help reduce the amount of user data requiring collection prior to the rst interaction with a robot companion in a domestic environment. We conclude that modelling people's preferences when interacting with robot companions is a challenging approach. Integrating the Human-Computer Interaction technique into a computational behaviour model for Human-Robot Interaction studies was more di cult than anticipated. This investigation shows the advantages and disadvantages of introducing this technique into Human-Robot Interaction, and explores the challenges in de ning a personas-based computational behaviour model. The continuous learning process experienced helps clarify the steps that other researchers in the eld should follow when investigating a similar approach. Some interesting outcomes and trends were also found among users' data, which encourage the belief that the personas technique can be further developed to tackle some of the current di culties highlighted in the Human-Robot Interaction literature.

The future smart home will be enhanced and driven by the recent advance of the Internet of Things (IoT), which advocates the integration of computational devices within an Internet architecture on a global scale [1, 2]. In the IoT paradigm, the smart home will be developed by interconnecting a plethora of smart objects both inside and outside the home environment [3-5]. The recent take-up of these connected devices within home environments is slowly and surely transforming traditional home living environments. Such connected and integrated home environments lead to the concept of the smart home, which has attracted significant research efforts to enhance the functionality of home environments with a wide range of novel services. The wide availability of services and devices within contemporary smart home environments make their management a challenging and rewarding task. The trend whereby the development of smart home services is decoupled from that of smart home devices increases the complexity of this task. As such, it is desirable that smart home services are developed and deployed independently, rather than pre-bundled with specific devices, although it must be recognised that this is not always practical. Moreover, systems need to facilitate the deployment process and cope with any changes in the target environment after deployment. Maintaining complex smart home systems throughout their lifecycle entails considerable resources and effort. These challenges have stimulated the need for dynamic auto-configurable services amongst such distributed systems. Although significant research has been directed towards achieving auto-configuration, none of the existing solutions is sufficient to achieve auto-configuration within smart home environments. All such solutions are considered incomplete, as they lack the ability to meet all smart home requirements efficiently. These requirements include the ability to adapt flexibly to new and dynamic home environments without direct user intervention. Fulfilling these requirements would enhance the performance of smart home systems and help to address cost-effectiveness, considering the financial implications of the manual configuration of smart home environments. Current configuration approaches fail to meet one or more of the requirements of smart homes. If one of these approaches meets the flexibility criterion, the configuration is either not executed online without affecting the system or requires direct user intervention. In other words, there is no adequate solution to allow smart home systems to adapt dynamically to changing circumstances, hence to enable the correct interconnections among its components without direct user intervention and the interruption of the whole system. Therefore, it is necessary to develop an efficient, adaptive, agile and flexible system that adapts dynamically to each new requirement of the smart home environment. This research aims to devise methods to automate the activities associated with customised service delivery for dynamic home environments by exploiting recent advances in the field of ubiquitous robotics and Semantic Web technologies. It introduces a novel approach called the Knowledge-based Auto-configuration Software Robot (Sobot) for Smart Home Environments, which utilises the Sobot to achieve auto-configuration of the system. The research work was conducted under the Distributed Integrated Care Services and Systems (iCARE) project, which was designed to accomplish and deliver integrated distributed ecosystems with a homecare focus. The auto-configuration Sobot which is the focus of this thesis is a key component of the iCARE project. It will become one of the key enabling technologies for generic smart home environments. It has a profound impact on designing and implementing a high quality system. Its main role is to generate a feasible configuration that meets the given requirements using the knowledgebase of the smart home environment as a core component. The knowledgebase plays a pivotal role in helping the Sobot to automatically select the most appropriate resources in a given context-aware system via semantic searching and matching. Ontology as a technique of knowledgebase representation generally helps to design and develop a specific domain. It is also a key technology for the Semantic Web, which enables a common understanding amongst software agents and people, clarifies the domain assumptions and facilitates the reuse and analysis of its knowledge. The main advantages of the Sobot over traditional applications is its awareness of the changing digital and physical environments and its ability to interpret these changes, extract the relevant contextual data and merge any new information or knowledge. The Sobot is capable of creating new or alternative feasible configurations to meet the system's goal by utilising inferred facts based on the smart home ontological model, so that the system can adapt to the changed environment. Furthermore, the Sobot has the capability to execute the generated reconfiguration plan without interrupting the running of the system. A proof-of-concept testbed has been designed and implemented. The case studies carried out have shown the potential of the proposed approach to achieve flexible and reliable auto-configuration of the smart home system, with promising directions for future research.

The 2008 Climate Change Act set legally-binding carbon reduction targets. Demand side management (DSM) includes energy use reduction and peak shaving and offers significant potential to reduce the amount of carbon used by the electricity grid. The demand side management (DSM) schemes that have tried to meet this challenge have been dominated by engineering-based approaches and so favour tools like automation (which aims to make shifting invisible) and pricing (which requires customer response) to shift demand. These approaches tend to focus on the tools for change and take little account of people and energy-use practices. This thesis argues that these approaches are limited and therefore unlikely to produce the level of response that will be needed in future. The thesis therefore investigates the potential for time-shifting domestic energy demand but takes a different angle by trying to understand how people use energy in their daily lives, whether this use can be shifted and some of the implications of shifting it. The centrepiece of the work is an empirical study of eleven households energy-use practices. The interdisciplinary methodology involved in-house observations, interviews, photographs, metered energy data and disruptive interventions. The data was collected in two phases. Initially, a twenty-four hour observation was carried out in each household to find out how energy was implicated in everyday practices. Next, a series of three challenges were carried out, aimed at assessing the implications of disrupting practices by time-shifting food preparation, laundry and work/ leisure. A practice theory approach is used to shift the focus of attention from appliances, tools for change, behaviour or even people, to practices. The central finding of this work is that practices were flexible. This finding is nuanced, in the light of the empirical research, by an extended discussion on the nature of practices; in particular, the relationship between practices and agency and the temporal-spatial locatedness of practices. The findings demonstrate that, in this study at least, expanding the range of demand response options was possible. The research suggests numerous possibilities for extending the potential of practices to shift in time and space, shift the energy used in practices or substitute practices for other non-energy-using practices, though there are no simple technological or behavioural fixes . More profoundly, however, the thesis concludes that infrastructures of provision , such as the electricity grid and the companies that run it, underpin and facilitate energy-use practices irrespective of the time of day and year. In this context technology-led demand response schemes may ultimately contribute to the problem they purport to solve. A more fundamental interrogation of demand and the infrastructures that serve it is therefore necessary and is almost entirely absent from the demand response debate.

A model to predict air temperatures inside a residential home was created, which used local and remote environmental sensor data. Space conditioning experiments were carried out in the residential home and models were created to describe the observed temperature data. Results from laboratory space conditioning experiments were used to create a new experimental prediction model. Static inputs of the experimental model were replaced with dynamic inputs and an improved model was created, capable of general prediction application in the residential home. A novel system for space conditioning control was designed, which applied the improved model for air temperature prediction. Initial prediction models showed prediction error margins of ±1°C. Residential home and laboratory space conditioning experiments were utilized to create non-linear temperature prediction models capable of application outside of the experimental scope. Improved prediction models were based on the experimental models and showed average error margins of ±0:12°C compared to observed temperature data. A novel system design was proposed, combining the improved model with a traditional heating control to create a new optimal start-stop heating application for residential homes.

This thesis introduces a novel approach in which multiple heterogeneous devices collaborate to provide useful applications in an ad-hoc network. This thesis proposes a smart home as a particular ubiquitous computing scenario considering all the requirements given by the literature for succeed in this kind of systems. To that end, we envision a horizontally integrated smart home built up from independent components that provide services. These components are described with enough syntactic, semantic and pragmatic knowledge to accomplish spontaneous collaboration. The objective of these collaboration is domestic use, that is, the provision of valuable services for home residents capable of supporting users in their daily activities. Moreover, for the system to be attractive for potential customers, it should offer high levels of trust and reliability, all of them not at an excessive price. To achieve this goal, this thesis proposes to study the synergies available when an ontological description of home device functionality is paired with a formal method. We propose an ad-hoc home network in which components are home devices modelled as processes represented as semantic services by means of the Web Service Ontology (OWL-S). In addition, such services are specified, verified and implemented by means of the Communicating Sequential Processes (CSP), a process algebra for describing concurrent systems. The utilisation of an ontology brings the desired levels of knowledge for a system to compose services in a ad-hoc environment. Services are composed by a goal based system in order to satisfy user needs. Such system is capable of understaning, both service representations and user context information. Furthermore, the inclusion of a formal method contributes with additional semantics to check that such compositions will be correctly implemented and executed, achieving the levels of reliability and costs reduction (costs derived form the design, development and implementation of the system) needed for a smart home to succeed.

People know about threats such as ransomware and data breaches potentially affecting healthcare providers through various articles on the news as well as being aware of the impact that the growing dependency of embedded devices has on our care. Given these two conditions, it’s fair to ask: what else is being done to ensure public safety when someone's in with a serious medical condition? Who’s ensuring that some entity isn’t getting in and remotely changing settings on critical care components that could in turn possibly cause someone to needlessly die?All of these pose excellent questions that deserve to be answered. Yet, there’s no easy solution out there. Very few enterprises, much less health systems, are capable of consistently providing more resources such as labor and tools as needed. While a majority of the nation’s 5,000 healthcare systems are technically not-for-profit, budget crunches still nonetheless have an adverse impact on security operations. This adverse impact on security operations often means cyberattacks still persist, and we continue to hear about data breaches in the news. It’s clear that something practical needs to be done, but what?

In modern urban landscape, the safety and efficiency of both home and city environments are paramount. Action Recognition (AR) has emerged as a pivotal technology to enhance these domains, particularly in the realms of Smart Home and Smart City applications. This dissertation delves into the intricacies of AR, underscoring its transformative role in monitoring and ensuring safety across diverse contexts.Diving into the realm of deep learning, its transformative impact on action recognition over recent years becomes evident. Notwithstanding these advances, inherent challenges remain, particularly when addressing specific AR tasks that rely on limited datasets. To navigate these complexities, our research introduces a novel, resource-efficient framework combining transfer learning techniques with Conv2D LSTM layers for tasks such as Smart Baby Care. This initiative resulted in the creation of a benchmark dataset and an automated model tailored for recognizing and predicting baby activities, setting new standards in computational efficiency and performance.From the intimate confines of smart baby care within homes, we broadened our lens to encompass the bustling streets of urban landscapes. Complementing home safety, the safety of these urban environments became a pivotal focus of our research. Through an empirical analysis, we delved into the intricacies of accident detection. Identifying and analyzing prevailing techniques, taxonomies, and algorithms showcased the central role of AR in accident detection and autonomous transportation. Furthermore, by leveraging data from reputable sources like the NHTSA Crash Report Sampling System, we provided a holistic view of traffic accident trends, underlining the dire need for robust accident detection systems.Our seminal contribution is the introduction of the I3D-CONVLSTM2D model architecture, uniquely designed for accident detection in smart city with 87% mean average precision and 80% for detecting traffic accidents. Empirical studies validated our model's superiority, especially in dealing with diverse traffic scenarios and data imbalances. In essence, this dissertation bridges the gap between foundational AR principles and their real-world applications, providing tools and insights to foster safer homes and cities in an increasingly connected world.

Smart homes can operate as a distributed energy resource (DER), when equipped with controllable high-efficiency appliances, solar photovoltaic (PV) generators, electric vehicles (EV) and energy storage systems (ESS). The high penetration of such buildings changes the typical electric power load profile, which without appropriate controls, may become a “duck curve” when the surplus PV generation is high, or a “dragon curve” when the EV charging load is high. A smart home may contribute to an optimal solution of such problems through the energy storage capacity, provided by its by battery energy storage system (BESS), heating, ventilation, and air conditioning (HVAC) system, and electric water heater (EWH), and the advanced controls of an home energy management (HEM). The integrated modeling of home energy usage and electric power distribution system, developed as part of this dissertation research, provides a testbed for HEM control methods and prediction of long-term scenarios. A hybrid energy storage system including batteries and a variable power EWH was proposed. It was demonstrated that when the operation of the proposed hybrid energy storage system was coordinated with PV generation, the required battery capacity would be substantially reduced while still maintaining the same functionality for smart homes to operate as dispatchable generators. A newly developed co-simulation framework, INSPIRE+D, enables the dynamic simulation of smart homes and their connection to the grid. The equivalent thermal model of a reference house was proposed with parameters based on the systematic study of experimental data from fully instrumented field demonstrators. Energy storage capacity of HVAC systems was calculated and an equivalent state-of-charge (SOC) was defined. The aggregated HVAC load was calculated based on special HVAC parameters and a sequential DR scheme was proposed to reduce both ramping rate and peak power, while maintaining human comfort according to ASHRAE standards. A long short-term memory (LSTM) method was applied to for the identification of HVAC system from the aggregated data. The generic water heater load curves based on the data retrieved from large experimental projects for resistive EWHs and heat pump water heaters (HPWHs) were created. A community-level digital twin with scalability has been developed to capture the aggregated hot water flow and average hot temperature in the tanks. The potential electricity saving of shifting from EWH to HPWH was calculated. The energy storage capacities for both EWHs and HPWHs were calculated. Long term load prediction by considering different fractions of smart homes with HEM for at the power system was provided based on one of the largest rural field smart energy technology demonstrators located in Glasgow, KY, US. Also demonstrates was the ability of EWH to provide ancillary services while maintaining customer comfort. The minimum participation rates for EWH and batteries were calculated and compared with respect to different peak reduction targets. The aggregated charging load for EV in a community was calculated based on data from the National Travel Household Survey (NHTS). The EV charging and RESS operation were scheduled to reduce the daily utility charge. Building resilience was quantified by analyzing the self-sustainment duration for all possible power outages throughout an entire year based on the annual electricity usage of a typical California residence. The influence of factors such as energy use behavioral patterns, BESS capacity, and an availability of EV was evaluated. A concept of generalized energy storage (GES) model for BESS, EWH and HVAC systems was proposed. The analogies, including SOC versus water/indoor temperature differential, were identified and explained, and models-in-the-loop (MIL) were introduced, which were compatible with the Energy Star and Consumer Technology Association (CTA)-2045 general specifications and command types. A case study is included to illustrate that the “energy content” and “energy take” for BESS and EWH. The main original contributions of this dissertation include the comprehensive simulation of the total building energy usage and the development of the co-simulation framework incorporating building and power system simulators. Another contribution of the dissertation is the quantification of building resilience based on the building energy usage model. The dissertation also contributes to the concept of GES which regards the HVAC and EWH as virtual energy storage and their unified controls with BESS. The GES facilitates the employment of industrial standards, e.g., CTA-2045, and the hybrid ESS reduces required BESS capacity. This dissertation contributes to the modeling of aggregated load for EWH, HVAC, and EV using different methods and long term forecasting of power profile at the system level. The aggregated generic load for EWH was calculated based on large amount of field data, the aggregated EV charging load was estimated based on national survey results, and the aggregated HVAC load was simulated based on the modeling of every residences, where the model parameters were populated according to special distributions. The methods based LSTM for the identification of HVAC power from the aggregated load was developed.

Smart homes, equipped with sensing, actuation, communication, and computation capabilities, enable automation and adaptation according to the occupants' needs. These capabilities work together to build holistic spatial and living experiences for the occupants. Smart technologies significantly impact spatial experiences, making smart home design an architectural problem along with a technological problem. Nevertheless, smart home research focuses primarily on standalone technological solutions, where the spatial/architectural aspect is largely absent. We argue that addressing the technological aspects isolated from the spatial context leads to reduced experiences for the users/occupants, as this practice blocks the pathways to develop holistic and innovative smart home solutions. Hence, we focus on bridging the gap between architectural and technological components in smart home research. To this end, we studied the design of smart homes from related disciplines, i.e., architecture, human-computer interaction, human--building interaction, industrial manufacturing, and modular assembly. Our research used the triangulation technique to consult with subject matter experts (researchers, practitioners, and professors of related disciplines) to understand current design processes. We conducted ethnographic studies, focus group studies, and in-depth interviews and identified challenges and best practices for smart home design process. Our investigation recognizes a nascent research problem where the technological and architectural aspects come together in the design thinking of smart home designers. We expanded the scope of design thinking to include three primary elements of smart homes- embedded technology, architectural elements, and occupants' needs. This multidisciplinary and complex process requires a well-defined design framework to methodically address all the issues associated with it. Hence, we developed a user-centered design framework, ArTSE, through an iterative Delphi study to guide the smart home design process. ArTSE stands for "Architecture and Technology in Smart Home DEsign". This framework guides user requirements collection using HCI models, technology decision making, interaction modalities selection, the decision support system for schematic design, technology infrastructure development, and production of the necessary documentation. This framework is an evolution of the normative theory in the architectural design process that caters to the needs of smart home design. For defining implementation strategies, we applied the framework to a case study-- a smart reconfigurable space design project. Overall, we document different aspects of the smart home design process and provide a comprehensive guideline for designers, researchers, and practitioners in this area.

The IoT-embedded smart homes have a high level of home automation and could change many aspects of the residents' daily lives, such as control, convenience, comfort, and energy-saving. The rise of voice-based virtual assistants like Amazon's Alexa, Google assistants in the past five years has brought new potentials to provide occupants with a convenient and intuitive interface to interact with smart homes through conversations. However, the one-way communications in the form of user commands to control building systems does not result in the optimal course of actions. As such, in this thesis, we proposed the concept of proactive smart home assistants and explored the occupants' perception towards smart home assistants proactively providing suggestions to adapt them into energy-saving behaviors. We also investigated the impact of occupants' personal features on their intention in taking energy-saving behaviors. A comprehensive data collection was conducted through online surveys, in which 307 valid responses with participant's personal profile information, their perceptions of smart home assistants, and their feedback to our designed messages were collected. The first manuscript compared participants' responses to traditional plain-text energy-saving suggestions and suggestions provided by smart home assistants. The nudging effect of smart home assistants was justified to be significant in affecting occupant's energy-saving behaviors. Occupant's thermal comfort range, smart home device previous experience, values and beliefs were then proved to have significant impact on their intention in taking the smart home assistant's suggestions. The second manuscript fitted 21 personal characteristics features in machine learning models (SVM, Random Forest, Logistic Regression) to predict occupant's intention and attitude towards energy-saving suggestions. The results indicated that occupant's beliefs about interests in taking actions and beliefs about energy expenses, occupant's education level, residence occupancy type, thermal comfort ranges, and smart home device experiences are important features in occupants' energy-saving behavior intention prediction. This research demonstrates the effect of proactive smart home assistants in human-building interaction as well as the impact of personal characteristic features on occupant's energy-saving behaviors, paving a path to the future development of bi-directional human-building communication.

In the United States, the proportion of senior citizens is expected to rise significantly over the next few decades. This increasing number of senior citizens combined with the increasing demand for at home healthcare workers is putting a strain on the elderly healthcare system. Smart-home healthcare technology { such as smart medication dispensers, fall detection systems, smart pantry systems, etc. { has the potential to alleviate this strain on the elderly healthcare system. Smart devices can give an individual more autonomized and personalized surveillance of their health and well-being. While these devices are beneficial as standalone devices, they would be more accessible and extensible under a single, modular smart-home system. For these reasons, the GeronTechnology Lab at West Virginia University is developing the Residents Aware Network for Intelligent Assistance (RANIA) system. RANIA is a modular smart-home system that is designed to integrate numerous types of devices for aiding elderly residents in aging at home. This thesis presents a generalized framework for integrating RANIA devices within the RANIA smart-home system. The novel aspects of this project are the one-click device connection/disconnection and automated package management for devices connected to the RANIA Hub. Other functionalities outlined include system messaging and Amazon Alexa Echo system integration.

Internet of things (IoT) applications in our daily lives has made life easier, but also comes with associated security threats. The vulnerability of the IoT system stems from the vulnerability of each connected device and transmission of threats through an interconnected home network. Smart homes are one of the applications of IoT, which is comprised of connected devices for easier interaction. An isolated IoT system with no internet connection has some level of safety from attacks because it is not exposed to the internet, although these devices have their innate vulnerabilities from the manufacturer. IoT gateways connecting IoT devices to the internet can create a backdoor into the smart home system that an attacker can exploit. Therefore, Internet-connected IoT devices have a high-security risk and one of the ways to detect an intrusion into an IoT gateway is through anomalies in the traffic passing through it. This thesis introduces early work on an intrusion detection system (IDS) by detecting anomalies in the smart home network using Extreme Learning Machine and Artificial Immune System (AIS-ELM). AIS uses the Clonal Algorithm for the optimization of the input parameters, and ELM analyzes the input parameter for better convergence in detecting anomalous activity. The larger implications of this work are the potential to apply this approach to a smart home network gateway and combine it with a push notification system that will allow the homeowner to identify any abnormalities in the smart home network and take appropriate action to mitigate threats.

Nowadays, technology has evolved to be in our daily lives to assist in making our lives easier. We now have technology helping us in our lives at home. Devices used to create our “smart home” have done a great deal in making our lives at home less burdensome, but sadly, these devices have secured our personal lives to be more accessible to outsiders. In this paper, the security of home smart devices and their communication will be researched by using other academic articles to support facts found. The operation of the devices will be discussed along with security risks and future trends on security attacks. The results found will be crucial to knowing exactly how well our own home is protected. After understanding where the risks lie and a demonstration of how hackers can take control of our smart home, solutions will be given to shield ourselves from security attacks. We protect our homes from physical threats by locking doors, but it is time we guard ourselves from cyber threats as well.

The increasing penetration of renewable sources of energy has resulted in an increased likelihood of power over-generation and ramp rate requirements at the electricity supplier end. By incorporating temporally varying costs of electricity provided to the customer, the grid supplier may choose to offer demand-response programs that encourage the customer to defer high load activities to periods of low grid load, effectively overcoming these challenges and increasing machine life. Smart homes optimally activate appliances at the appropriate time with an objective to minimize load at high-price periods, so that at the user end, the total electricity price is lowered.The work presented in this thesis focuses first on the development of models for energy demand and generation associated with electric vehicle (EV) charging and solar power generation, and their integration in an existing residential energy modeling framework. For this enhanced residential power demand model, machine learning (ML) techniques are used to develop a prediction of the user activities for single-resident and multi-resident households. The predicted power demand can be integrated into the smart home algorithm to enhance the optimal activation of appliances to minimize electricity cost and inconvenience.

In this work, we view smart home from 3 different sides: devices, platforms, and apps. We first attempt to better understand the problems on each side and later explore new methods and techniques to better guarantee security, privacy, and safety of smart homes. On the devices side, we discovered that smart home devices are vulnerable to passive inference attacks based on network traffic, even in the presence of encryption. We first present this passive inference attack and our techniques that we developed to exploit this vulnerability on smart home devices. We created PingPong, a tool that can automatically extract packet-level signatures for device events (e.g., light bulb turning ON/OFF) from network traffic. We evaluated PingPong on popular smart home devices ranging from smart plugs and thermostats to cameras, voice-activated devices, and smart TVs. We were able to: (1) automatically extract previously unknown signatures that consist of simple sequences of packet lengths and directions; (2) use those signatures to detect the devices or specific events with an average recall of more than 97%; (3) show that the signatures are unique among hundreds of millions of packets of real world network traffic; (4) show that our methodology is also applicable to publicly available datasets; and (5) demonstrate its robustness in different settings: events triggered by local and remote smartphones, as well as by home-automation systems. Furthermore, we also present our discussion and evaluation on existing techniques (e.g., packet padding) as possible defenses against passive inference attacks and their analyses.On the platforms side, smart home platforms such as SmartThings enable homeowners to manage devices in sophisticated ways to save energy, improve security, and provide conveniences. Unfortunately, we discovered that smart home platforms contain vulnerabilities, potentially impacting home security and privacy. Aside from the traditional defense techniques to enhance the security and privacy of smart home devices, we also created Vigilia, a system that shrinks the attack surface of smart home IoT systems by restricting the network access of devices. As existing smart home systems are closed, we have created an open implementation of a similar programming and configuration model in Vigilia and extended the execution environment to maximally restrict communications by instantiating device-based network permissions. We have implemented and compared Vigilia with forefront IoT-defense systems; our results demonstrate that Vigilia outperforms these systems and incurs negligible overhead.On the apps side, smart home platforms allow developers to write apps to make smart home devices work together to accomplish tasks, e.g., home security and energy conservation—smart home devices provide the convenience of remotely controlling and automating home appliances. A smart home app typically implements narrow functionality and thus to fully implement desired functionality homeowners may need to install multiple apps. These different apps can conflict with each other and these conflicts can result in undesired actions such as locking the door during a fire. We study conflicts between apps on Samsung SmartThings, the most popular platform for developing and deploying smart home IoT devices. By collecting and studying 198 official and 69 third-party apps, we found significant app conflicts in 3 categories: (1) close to 60% of app pairs that access the same device, (2) more than 90% of app pairs with physical interactions, and (3) around 11% of app pairs that access the same global variable. Our results suggest that the problem of conflicts between smart home apps is serious and can create potential safety risks. We then developed an automatic conflict detection tool that uses model checking to automatically detect up to 96% of the conflicts.

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems. This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms. Advisers: Hamid Bagheri and Qiben Yan

Privacy concerns have been identified as an important barrier to the growth of IoT. These concerns are exacerbated by the complexity of manually setting privacy preferences for numerous different IoT devices. Hence, there is a demand to solve the following, urgent research question: How can we help users simplify the task of managing privacy settings for IoT devices in a user-friendly manner so that they can make good privacy decisions? To solve this problem in the IoT domain, a more fundamental understanding of the logic behind IoT users’ privacy decisions in different IoT contexts is needed. We, therefore, conducted a series of studies to contextualize the IoT users’ decision-making characteristics and designed a set of privacy-setting interfaces to help them manage their privacy settings in various IoT contexts based on the deeper understanding of users’ privacy decision behaviors. In this dissertation, we first present three studies on recommending privacy settings for different IoT environments, namely general/public IoT, household IoT, and fitness IoT, respectively. We developed and utilized a “data-driven” approach in these three studies—We first use statistical analysis and machine learning techniques on the collected user data to gain the underlying insights of IoT users’ privacy decision behavior and then create a set of “smart” privacy defaults/profiles based on these insights. Finally, we design a set of interfaces to incorporate these privacy default/profiles. Users can apply these smart defaults/profiles by either a single click or by answering a few related questions. The biggest limitation of these three studies is that the proposed interfaces have not been tested, so we do not know what level of complexity (both in terms of the user interface and the in terms of the profiles) is most suitable. Thus, in the last study, we address this limitation by conducting a user study to evaluate the new interfaces of recommending privacy settings for household IoT users. The results show that our proposed user interfaces for setting household IoT privacy settings can improve users’ satisfaction. Our research can benefit IoT users, manufacturers, and researchers, privacy-setting interface designers and anyone who wants to adopt IoT devices by providing interfaces that put their most prominent concerns in the forefront and that make it easier to set settings that match their preferences.

The increasing population of elderly people has been one of the characteristics of this moderncentury. This ever-increasing population of elderly people is predicted to outnumber thepopulation of youths and caregivers in a few years’ time. Old age is mostly associated with anumber of problems like memory issues, cognitive disabilities, disorganized behavior and manymore, quite often leading to Mild Cognitive Impairment (MCI) and other forms of disabilitieswhich cause problems for elderly people to accomplish their daily routine activities.This increased need for healthcare and daily care services that is associated with old age, causesa huge burden on the world economy to sustain and meet the needs of elderly people worldwide.To ensure that these varying needs of elderly people arising from their differing diversities aremet, and they have a better quality of life, it is essential for modern day technologies to adaptaccording to the specific needs of individual users and develop an assisted living environmentthat can improve both physical and mental wellbeing and foster independent living of olderadults in the context of their day to day goals and tasks.The integration of Affect Aware Technologies with Activity Centric Computing in the contextof Human-Computer Interaction and Internet of Things (IoT)-based technologies holdsimmense potential to act as a long-term solution to address these ever-increasing needs forelderly care. This serves as the primary interest for this research work. In this work, aFramework for a Context Aware Adaptive Intelligent Assistant for Activities of Daily Living(ADLs) has been proposed. This framework integrates methodologies for complex activity recognition, user performance augmentation for respective activities and complex activityrecommendation, based on the users affective state and daily routine. The ability of thisframework to adapt according to the dynamic needs of specific users and help them have betteruser experiences in the context of ADLs is aimed to address the above-mentioned needs ofelderly care and contribute towards healthy aging in place to sustain and foster an improvedquality of life for older adults in the context of their day to day goals.This proposed framework has been tested on 21 different complex activities from three differentuser interaction datasets and the results are presented and discussed. A comparative study isalso included that discusses the superiority of this framework for modelling “specific users”over the traditional approaches of designing systems for “average users” - which quite oftenfail to address the varying needs of specific users arising from universal diversity.The results presented in this work discuss the ability of this Proposed Adaptive IntelligentAssistant to act in a personalized manner and help elderly people perform ADLs in the contextof a smart and connected IoT-based environment with a high degree of accuracy and reliability.Several potential applications of this framework are also discussed which uphold the relevancefor implementation of the same for creation of an adaptive and assisted living environment toensure that elderly people as well as other categories of users with disabilities, have better userexperiences in the context of ADLs.

Internet of Things (IoT) is a promising technology with great potential for individuals, society, governments, and the economy. IoT is expected to become ubiquitous and influence every aspect of everyday experience. Thus, IoT represents an important phenomena for both organizational and behavioral information system (IS) researchers. This dissertation seeks to contribute to IS research by studying the aspects that influence IoT adoption and use at both consumer and organizational levels. This dissertation achieves this purpose in a series of three essays. The first essay focuses on IoT acceptance in the context of smart home. The second essay focuses on examining the effect of artificial intelligence (AI) capabilities on consumers' IoT perceptions and intentions. Finally, the third essay focuses on the organizational investment and adoption of IoT technologies.

Smart home Internet-of-Things (IoT) accompanied by smart home apps has witnessed tremendous growth in the past few years. Yet, the security and privacy of the smart home IoT devices and apps have raised serious concerns, as they are getting increasingly complicated each day, expected to store and exchange extremely sensitive personal data, always on and connected, and commonly exposed to any users in a sensitive environment. Nowadays wireless smart home IoT devices rely on electromagnetic wave-based radio-frequency (RF) technology to establish fast and reliable quality network connections. However, RF has its limitations that can negatively affect the smart home user experience and even cause serious security issue, such as crowded spectrum resources and RF waves leakage. To overcome those limitations, people have to use technology with sophisticated time and frequency division management and rely on the assumptions that the attackers have limited computational power. In this thesis we propose URadio, a wideband ultrasonic communication system, using electrostatic ultrasonic transducers. We design and develop two different types of transducer membranes using two types of extremely thin materials, Aluminized Mylar Film (AMF) and reduced Graphene Oxide (rGO), for assembling transducers, which achieve at least 45 times more bandwidth than commercial transducers. Equipped with the new wideband transducers, an OFDM communication system is designed to better utilize the available 600 kHz wide bandwidth. Our experiments show that URadio can achieve an unprecedentedly 4.8 Mbps data rate with a communication range of 17 cm. The attainable communication range is increased to 31 cm and 35 cm with data rates of 1.2 Mbps and 0.6 Mbps using QPSK and BPSK, respectively. Although the current wideband system only supports short-range communication, it is expected to extend the transmission range with better acoustic engineering. Also, by conducting experiments to measure the ultrasonic adversaries' eavesdropping and jamming performance, we prove that our system is physically secure even when exchanging plaintext data. Adviser: Qiben Yan

Apart from the dominant environmental factors such as relative humidity, radiant, and ambient temperatures, studies have confirmed that thermal comfort significantly depends on internal personal parameters such as metabolic rate, age, and health status. This study reviews the sensitivity of the Predicted Mean Vote (PMV) thermal comfort model relative to its environmental and personal parameters of a group of people in a space. PMV model equations adapted in ASHRAE Standard 55–Thermal Environmental Conditions for Human Occupancy, are used in this investigation to conduct a parametric study by generating and analyzing multi-dimensional comfort zone plots. It has been found that personal parameters such as metabolic rate and clothing have the highest impact. Current and newly emerging advancements in state of the art wearable technology have made it possible to continuously acquired biometric information. This work proposes to access and exploit this data to build a new innovative thermal comfort model. Relying on various supervised machine-learning methods, a thermal comfort model has been produced and compared to a general model to show its superior performance. Finally, the study represents an architecture to employ new thermal comfort model in inexpensive, responsive and extensible smart home service. Advisor: Fadi Alsaleem

Smart home solutions are utilized in a different way by each user according to the user’s unique needs and preferences over his unique home setting. User – device interactions themselves carry some implicit characteristics of the context the smart devices are used. In order to better exploit Internet of Things technologies in smart home environments, the user’s interaction history can be leveraged to generate knowledge of his behavior habits. With a purpose of achieving personalized decision making of smart lighting environments, this thesis presents three learning model approaches, which are based solely on the individual’s interaction habits with the devices, adaptive to changes in inhabitant’s device usage behavior, and do not require preset data for initialization. Individual interactions with smart light devices are contextualized based on the timestamp of the interaction, and ambient light intensity reading of the room at that instant. Moreover, the thesis introduces a Java simulator, which interactively demonstrates the behavior of a personalized smart home setting with the integrated learning models with a feedback mechanism. The simulator leads up to a way of evaluating adaptive learning models in smart home environments, reducing the immediate need of testing their behavior in a real life smart home, which is both hard and costly to construct and maintain. The learning approaches, namely K-Nearest Neighbor and Softmax Regression with batch learning and online learning algorithms, are evaluated with different datasets representing different scenarios, and the results show that all three methods are able to perfectly capture the usage pattern when the interactions with distinct “things”, smart light devices, are separable in terms of their corresponding context. For more complex datasets, which have overlaps between the usage context of distinct devices and big changes in user behavior over time, the online learning algorithm needs more data in order to catch the performance of KNN and Softmax Regression with batch learning algorithms.

The introduction of dynamic electricity pricing structures such as Time of Use (TOU) rates and Day Ahead Pricing (DAP) in residential markets has created the possibility for customers to reduce their electric bills by using energy storage systems for load shifting and/or peak load shaving. While there are numerous system designs and model formulations for minimizing electric bills under these rate structures the use of these systems has the potential to cause an increase in emissions from the electricity system. The Increase in emissions is linked to the difference in fuel mix of marginal generators throughout the day as well as inefficiencies associated with energy storage systems. In this work a multi-objective optimization model is designed to optimize reduction in cost of electricity as well as reduction in carbon dioxide (CO2) emissions from the electricity used by residential customers operating a battery energy storage system under dynamic pricing structures. A total of 22 different regions in the US are analyzed. Excluding emissions from the model resulted in an annual increase of CO2 emissions in all but one region ranging from 60-2000kg per household. The multi-objective model could be used to economically reduce these additional emissions in most regions by anywhere from 5 – 1300kg of CO2 per year depending on the region. When using the multi-objective model several regions had a net decrease in CO2 emissions compared to not using a battery system but most had a net increase.

The rapid growth of the Internet of Things (IoT) has led to a situation where individual manufacturers develop their own communication protocols and frameworks that are often incompatible with other systems. Part of this is due to the use of incompatible communication hardware, and part is due to the entrenched proprietary systems. This has created a heterogeneous communication landscape, where it is difficult for devices to coordinate their efforts. To remedy this, a number of IoT Frameworks have been proposed to provide a common interface between IoT devices. There are many approaches to common frameworks, each with their strengths and weaknesses, but there is no clear winner among them. This thesis presents a virtual network testbed for implementing smart home IoT Frameworks. It consists of a simulated home network made up of multiple Virtual Machines (VM), simulated smart home devices and an implementation of the OpenHAB framework to integrate the devices. Simulated devices are designed to be network- accurate representations of actual devices, a LIFX smart lightbulb was developed and an existing Nest thermostat simulation was integrated. The demonstrated setup serves as a proof of concept for the idea of a home network testbed. Such a testbed could allow for the development of new IoT frameworks or the comparison of existing ones, and it could also serve as an education aid to illustrate how smart home IoT devices communicate with one another.

Virtual energy storage (VES) refers to an indirect method of storing energy without using a battery. In a residential setting, VES uses the building structure interior appurtenances together with its physical properties as an energy storage device. It represents a methodology in energy storage mechanisms to help with load management in residential microgrids. It is an approach that is critical to the necessary paradigm shift from the less flexible and more costly "demand response" energy market of the present to the more flexible and potentially less costly "availability response" energy market of the future. This work quantifies VES monetary cost-savings potential for residential homes, as part of an effort to develop smart systems (using power sensors, and simple computation and control mechanisms) to assist individuals in making decisions about energy use that will save energy and, consequently, electricity costs. The project also compares the cost-effectiveness of VES to that of battery energy storage (BES)¿currently the more traditional and widely-advocated-for approach to energy storage for load management. In addition, this project devises a load management framework for a residential microgrid, where strategies that enable energy and cost savings for both utilities and consumers are tested. To make a home act as its own storage device, we need to intelligently control its heating, ventilation, and air conditioning (HVAC) system. Through this control, we can harness the house's thermal storage abilities by methods such as preheating or precooling the house (with due consideration to user comfort) during periods when energy is less expensive so that this heat or coolness will be retained during higher-cost periods. A well-insulated residential home equipped with sensing technology and intermittent generation resources will be utilized as a testbed for this project. Using a testbed is advantageous as it provides realistic results as well as a platform where behavior of the home can be learned. By combining modeling techniques with test results from a live testbed, cost-saving solutions can be simulated and later evaluated. This work provides a means to determine how to reduce peak demand and save costs for both utilities and consumers by changing consumer behavior, while respecting consumer thermal comfort preferences. Additionally, by creating the aforementioned modeling framework, we provide the load management community with tools by which they can readily test their optimization algorithms. By so doing, more efficient algorithms can be developed (potentially leading to increased residential energy efficiency).

Smart home IoT devices are becoming increasingly popular and increasingly prevalent in people’s homes. These devices create new potential attack surfaces in people’s homes, and therefore it is important that the manufacturers are taking the appropriate measures to secure these devices. The motivation for this work was to determine if these measures were being taken since people could be unknowingly purchasing smart home IoT devices with security or privacy vulnerabilities.Smart home IoT devices that are available to consumers were purchased and analyzed for this paper. Some of these devices were found to contain privacy vulnerabilities. Therefore, some smart home IoT devices on the market contain a privacy vulnerability, which is they do not encrypt transmitted data over a local WiFi network, and therefore can be subject to a man in the middle attack.The privacy and security of seven different smart home IoT devices were analyzed including smart light bulbs, WiFi thermostats, a smart plug, and the Amazon Echo. It was found that four of the seven devices do not encrypt transmitted data over the local WiFi network connection, which is a privacy vulnerability. For two of these devices, the transmitted data could be visible in plain text, which can be easily deciphered by an attacker. Three of the four devices that contain a privacy vulnerability were also vulnerable to replay attacks, meaning replaying recorded packets causes the device to perform an action such as turn the lights on. It is discussed how the data obtained due to this privacy vulnerability can be used to track a user’s lifestyle habits. From this data an attacker can infer if the user is currently home or away. Lastly, solutions to these vulnerabilities are presented, which includes encrypting the communication data that is transmitted between the different nodes of the smart home IoT devices. For devices that use a point-to-point type of architecture, lightweight encryption techniques are needed and discussed.

Our homes, hospitals, cities, and industries are being enhanced with devices that have computational and networking capabilities. This emerging network of connected devices, or Internet of Things (IoT), promises better safety, enhanced management of patients, improved energy efficiency, and optimized manufacturing processes. Although there are many such benefits, security vulnerabilities in these systems can lead to user dissatisfaction (e.g., from random bugs), privacy violation (e.g., from stolen information), monetary loss (e.g., denial-of-service attacks or ``ransomware''), or even loss of life (e.g., from malicious actors manipulating critical processes in a hospital). Security design flaws may manifest at several layers of the IoT software/hardware stack. This work focuses on design flaws that arise in IoT platforms---software systems that manage devices, data analysis results and control logic. Specifically, we show that empirical security-oriented analyses of personal IoT platforms lead to: (1) an understanding of design flaws that can be leveraged in long-range and device-independent attacks; (2) the development of security mechanisms that limit the potential for these attacks. Concretely, we contribute empirical analyses for two categories of personal IoT platforms---Hub-Based (Samsung SmartThings), and Cloud-First (If-This-Then-That). Our analyses reveal overprivilege as a main enabler for attacks, and we propose a set of information flow control techniques (FlowFence and Decoupled-IFTTT) to manage privilege better in these platforms, therefore reducing the potential for attacks.

The boom of in-home monitoring technology offers unprecedented information about an individual’s interaction with the environment. A variety of low cost sensors can continuously and unobtrusively collect information about activities in the living space. Capturing early changes in the daily routines of vulnerable older adults residing in these “smart homes” may allow clinicians to predict and prevent negative health consequences through timely intervention. However, the current state of science is hampered by the lack of theoretically driven approaches to analyze sensor data in relation to clinically meaningful health outcomes. The aims of the study are 1) to characterize an older adult’s daily routine, as captured with smart home sensors, 2) to assess if deviations from it are indicative of changes in their health trajectory, such as falls, ER visits or unplanned hospitalizations, and 3) identify between person factors that affect the characteristics of the daily routine. It used previously collected data from 10 residents of TigerPlace, a unique retirement facility that evaluates health technology affiliated with University of Missouri, Columbia. Older adults live in apartments equipped with network of motion, depth and bed sensors that unobtrusively collect information about daily activity of its resident. Thirty months of continuous sensor data were analyzed in the context of bi-annual clinical assessments and nursing notes extracted from the electronic health record. A retrospective multiple case study approach is guided by the conceptual model developed for this study that is grounded in nursing and gerontological literature. Changes in the temporality and frequency of daily activity were found for common geriatric symptoms, such as urinary symptoms and confusion. Seasonal and weekly effect was evident across participants in the duration of time spent in various areas of the apartment. Participants varied in their baseline daily routines, but for the majority of symptoms there were prodromal changes in at home activity that was detected with sensors. As the cost of technology adoption decreases, nurses can use these innovative tools to coordinate care and intervene early to prevent or mitigate the functional decline associated with vulnerable older adults.

One challenge facing the utility is management of peak demands since electricity cannot be stored in bulk so the utility has to match demand at any point in time. Demand response management has proven to be a significant way of reducing these peaks. Scheduling of electrical appliances and proper design of the electricity tariffs are some of the mechanisms used in demand response management to reduce these peaks. With proper design demand response scheme, the consumer will be motivated to either shift their loads from peaking periods or reduce their consumption. It was the aim of this study to come out with scheduling scheme to reduce peaking in the grid taking into account occupancy error detection factor and combining wind energy and solar to reduce the intermittency nature of these renewables. In this study a mixed integer linear programming based smart appliance scheduling scheme with real-time pricing and algorithm to service as motion sensor were proposed. It can be said that the proposed scheduling scheme and pricing scheme (real-time pricing) provided enough incentives for the customers to encourage them to accept the proposed scheme. Significant savings on electricity bill of the community was realized using the proposed schemes. Savings made on occupancy error detection alone was not significant as compared to the total power consumed by the community, but it must be realized that this is just a conceptual study and it does not represent reality. It was therefore recommended that the proposed scheduling scheme incorporating occupancy error detection mechanism should be deployed in a real community of residential housing and simulate in realistic conditions.

The emergence of multiple home computers and the need to network them together are dramatically increasing in our society. At the same time, more and more methods of networking home computers are being developed. As our society becomes more reliant on using digital devices in our everyday lives, the possibility of a "smart" home can actually become a reality. My thesis will research currently available home automation products, and will propose a complete smart home system using current and possible future technologies. It will also include the various services that can be offered via home networking and automation. There are several component areas that must be considered and combined to create the complete system. Some of these areas include communications, device control, temperature, security, and most important, the user interface. One of the main components of the system will be the concept of a "console", which will be the primary interface. My thesis will explore different kinds of consoles using existing and proposed technology, how they can be internetworked together to form a complete communications system, and how they can be connected to the outside world. The feasibility of offering home automation services using these consoles is another area that will be explored. For instance, can a home be considered secure if doors and locks are automated and integrated into the smart home? With the smart home, a user should be able to enter the house without a key, solely by communicating with the system. It could even be possible to lock a door from any console within the house, in case the user forgot. I believe the smart home is becoming a reality, and can propel our society into a new way of life as an advanced civilization. My thesis will attempt to provide various ways of making it a reality using current and possible future technologies. Although it will not provide a concrete example of a complete smart home system, that area of expansion exists for future research. Perhaps one day it will become a standard in new home development, and will change how we perform everyday tasks at home.