There is a growing need for at-home healthcare and real-time monitoring in elderly care. The increase in elderly and chronically ill patients causes the need forhigh-demand services in healthcare. There is a lack of research regarding the elderly and technology in general. The Internet of Things provides several advantages and functionalities which could be applied to the healthcare domainand provide invaluable services. Healthcare revolves around confidential patient information which raises skepticism about the natural heterogenous devices and network infrastructure IoT relies upon in terms of security and privacy. This research aims to identify the impact of IoT and how privacy is included in the field of cybersecurity in Sweden's elderly care. Researchers, security experts, and IT departments may utilize the research to evaluate the advantages and issues of realizing IoT in elderly care. The research is based upon a systematic literature review, the data were acquired and collected using three different index services. The services were ACM digital library, IEEExplorer, and ScienceDirect. The acquired articles were then analyzed using thematic coding based on the user-centric aligned McCumber Cube framework. This provided relevant themes based on the framework and relevant articles to the research aim. In total, the review provided 47 articles, divided into three themes that connected nine categories. When the thematic coding had been applied, the study could provide feasible answers to the research aim. The study showed that privacy aligns with cybersecurity and personal information, it was barely accounted for in personal communication and behavior.

The Internet of Things (IoT) is a network of interconnected devices and objects that have various functions,such as sensing, identifying, computing, providing services and communicating. It is estimated that by the year 2030, there will be approximately 29.42 billion IoT devices globally, facilitating extensive data exchange among them. In response to this rapid growth of IoT, Artificial Intelligence (AI) has become a pivotal technology in automating key aspects of IoT systems, including decision-making, predictive data analysis among others. The widespread use of AI across various industries has brought about significant transformations in business ecosystems. Despite its immense potential, IoT systems still face several challenges. These challenges encompass concerns related to privacy and security, data management, standardization issues, trust among others. Looking at these challenges, AI emerges as an essential enabler, enhancing the intelligence and sophistication of IoT systems. Its diverse applications offer effective solutions to address the inherent challenges within IoT systems. This, in turn, leads to the optimization of processes and the development of more intelligent and smart IoT systems.This thesis presents a semi-systematic literature review (SSLR) that aims to explore the role of AI in IoT systems. A systematic search was performed on three (3) databases (Scopus, IEEE-Xplore and the ACM digital library), 29 scientific and peer reviewed studies published between 2018-2022 were selected and examined to provide answers to the research questions. This study also encompasses an additional study within the context of AI and trustworthiness in IoT systems, user acceptance within IoT systems and AIoT's impact on sustainable economic growth across industries. This thesis also presents the DIMACERI Framework which encompasses eight dimensions of IoT challenges and concludes with recommendations for stakeholders in AIoT systems. AI is strategically integrated across the DIMACERI dimensions to create reliable, secure and efficient IoT systems.

The emerging scenarios and use cases for the Internet of Things (IoT) and the latest developments of 5G and beyond networks envision a connected world with diverse stakeholders as an integral and dynamic part of it. The synergistic coming together of the different stakeholders also brings along with it different values, requirements, and policies that need to be orchestrated in an automated and agile manner. This presents a requirement for a dynamic and context-aware system that can provide the assimilation of the different policies and requirements of the stakeholders and perform a dynamic orchestration in the system. Artificial intelligence has a major role in bringing in the desired adaptability and automation in the context-aware system. Besides, with the advent of fog computing for IoT and multi-access edge computing (MEC) in 5G, the execution location also introduces benefits and challenges bringing along different engineering trade-offs such as for execution performance, maintainability, and usability. This thesis aims at exploring and analyzing the engineering trade-offs of employing (artificial intelligence) AI-based and a static rule-based system for these orchestration requirements. It further explores the engineering trade-offs of employing the proposed systems at a cloud-only or fog setup. Scenarios are taken from different industry experts and evolved further. Different architecture solutions are proposed and a scenario-based architecture assessment is performed. A PERT (Program Evaluation Review Technique) analysis is also performed for the systems and the change scenarios for them. Two prototypes are developed using C++ and an expert system (for AI) and measurements are captured and evaluated for the different scenarios and configurations. The performance trade-offs of the execution of the scenarios on the two prototypes while executing over fog or cloud-only setups are evaluated. Execution performance is measured in terms of the time taken for the execution and a comparative analysis is done with graphs and charts explaining the various execution trade-offs. Maintainability and usability trade-offs are also discussed in the light of the two reference systems executing on the two locations, viz. fog, and cloud. As the next-generation IoT and telecommunication systems have diverse and strict quality of service (QoS) requirements, the challenges of learning in the system are evaluated and discussed in the thesis. Bayesian networks and probabilistic programming are explored and evaluated for validating the input of small data for evolving an existing expert system model.

[pt] Um sistema de rastreamento que armazena dados de alto volume, tais como indicadores de mobilidade, status de agentes e cargas, é de interesse tanto para empresas quanto para consumidores. Um sistema como esse permite verificar informações, como os locais visitados em um shopping center, a duração das estadias dos funcionários em determinadas partes de um prédio de escritórios ou fábrica, se um ônibus parou em cada ponto de ônibus em uma rota, a rota do entregador ou se uma equipe de seguranças está executando a ronda especificada em um bairro. Esta dissertação tem como objetivo investigar a eficiência de identificar a posição atual de uma entidade móvel e propor uma solução para transmitir, adicionar e recuperar informações como estas. A solução proposta é um framework chamado ARTIIMoR, que armazena dados de maneira segura, imutável e transparente usando um sistema de Distributed Ledger Technology (DLT) multicamadas. Três camadas de DLT são utilizadas para armazenar informações de localização em diferentes níveis de abstração e agregação. O sistema visa permitir que empresas e consumidores registrem informações de posição e movimento com confiabilidade, escalabilidade e rastreabilidade. / [en] A tracking system capable of storing high volume data, such as mobility indicators, agent and cargo statuses, is of interest to both companies and consumers. Such a system enables the checking of information, such as the locations visited in a shopping center, the duration of employees stays incertain parts of an office building or factory, whether a bus stopped at each bus stop on a route, the route of a delivery driver, or whether a security team is performing the specified patrol in a neighborhood. This dissertation aims to investigate the efficiency of identifying the current position of amobile entity and propose a solution for transmitting, adding, and retrieving information like this. The proposed solution is a framework called ARTIIMoR,which stores data securely, immutably, and transparently using a multilayer Distributed Ledger Technology (DLT) system. Three layers of DLT are used to store location information at different levels of abstraction and aggregation.The system aims to allow companies and consumers to record position andmovement information with reliability, scalability, and traceability.

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24221号 / 工博第5049号 / 新制||工||1788(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 清水 芳久, 教授 米田 稔, 准教授 松田 知成 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Networked systems integrating software with the physical world are known as cyber-physical systems (CPSs). CPSs have been used in diverse sectors, including power generation and distribution, transportation, industrial systems, and building management. The diversity of applications and interdisciplinary nature make CPSs exciting to design and build but challenging to manage once deployed. Deployed CPSs must adapt to changes in the operating environment or the system's architecture, e.g., when outdated or malfunctioning components need to be replaced. Skilled human operators have traditionally performed such adaptations using centralized management protocols. As the CPS grows, management tasks become more complex, tedious, and error-prone. This dissertation studies management challenges in deployed CPSs. It is based on practical research with CPSs of various sizes and diverse application domains, from the large geographically dispersed electrical grid to small-scale consumer Internet of Things (IoT) systems. We study the management challenges unique to each system and propose network services and protocols specifically designed to reduce the amount of management overhead, drawing inspiration from autonomic systems and networking research. We first introduce PhoenixSEN, a self-managing ad hoc network designed to restore connectivity in the electrical grid after a large-scale outage. The electrical grid is a large, heterogeneous, geographically dispersed CPS. We analyze the U.S. electrical grid network subsystem, propose an ad hoc network to temporarily replace the network subsystem during a blackout, and discuss the experimental evaluation of the network on a one-of-a-kind physical electrical grid testbed. The novel aspects of PhoenixSEN lie in a combination of existing and new network technologies and manageability by power distribution industry operators. Motivated by the challenges of running unmodified third-party applications in an ad hoc network like PhoenixSEN, we propose a geographic resource discovery and query processing service for federated CPSs called SenSQL. The service combines a resource discovery protocol inspired by the LoST protocol with a standard SQL-based query interface. SenSQL aims to simplify the development of applications for federated or administratively decoupled autonomous cyber-physical systems without a single administrative or technological point of failure. The SenSQL framework balances control over autonomous cyber-physical devices and their data with service federation, limiting the application's reliance on centralized infrastructures or services. We conclude the first part of the dissertation by presenting the design and implementation of a testbed for usability experiments with mission-critical voice, a vital communication modality in PhoenixSEN, and during emergency scenarios in general. The testbed can be used to conduct human-subject studies under emulated network conditions to assess the influence of various network parameters on the end-user's quality of experience. The second dissertation part focuses on network enrollment of IoT devices, a management process that is often complicated, frustrating, and error-prone, particularly in consumer-oriented systems. We motivate the work by reverse-engineering and analyzing Amazon Echo's network enrollment protocol. The Echo is one of the most widely deployed IoT devices and, thus, an excellent case study. We learn that the process is rather complicated and cumbersome. We then present a systematic study of IoT network enrollment with a focus on consumer IoT devices in advanced deployment scenarios, e.g., third-party installations, shared physical spaces, or evolving IoT systems. We evaluate existing frameworks and their shortcoming and propose WIDE, a network-independent enrollment framework designed to minimize user interactions to enable advanced deployment scenarios. WIDE is designed for large-scale or heterogeneous IoT systems where multiple independent entities cooperate to set the system up. We also discuss the design of a human-subject study to compare and contrast the usability of network enrollment frameworks. A secure network must authenticate a new device before it can be enrolled. The authentication step usually requires physical device access, which may be impossible in many advanced deployment scenarios, e.g., when IoT devices are installed by a specialist in physically unreachable locations. We propose Lighthouse, a visible-light authentication protocol for physically inaccessible IoT devices. We discuss the protocol's design, develop transmitter and receiver prototypes, and evaluate the system. Our measurements with off-the-shelf components over realistic distances indicate authentication times shorter or comparable with existing methods involving gaining physical access to the device. We also illustrate how the visible-light authentication protocol could be used as another authentication method in other network enrollment frameworks.

Background: With the global decline of honey bee populations, safeguarding these vital pollinators has become crucial. Varroa destructor mites are a primary threat, weakening bees and facilitating the spread of diseases, which can decimate colonies and disrupt ecosystems. This thesis investigates the application of a Wireless sensor network (WSN) for the monitoring and control of varroasis spread within bee colonies across large geographical areas. Objectives: The main objective of this research is to develop an integrated method that combines biological insights into varroasis with WSN functionalities for real-time disease monitoring and control. By doing so, the study aims to contribute to the development of a scalable and sustainable approach to apiculture and disease management. Methods: A multi-phase methodological approach was employed, encompassing the modelling of biological phenomena, formulation of WSN functionalities, and the design of a scalable WSN architecture. Simulation studies were conducted, followed by the development of a theoretical framework to support the practical application of the proposed WSN system. A key aspect of the methodology is the introduction of energy estimation models to evaluate the operational feasibility of the WSN. Results: The results indicate that the WSN is capable of dynamically adjusting its monitoring rate in response to changes in infection dynamics, effectively identifying and managing varroa mite populations. The system demonstrated adaptability to various infection rates, with the potential to improve the timely and targeted treatment of infested colonies. Energy consumption data further affirms the operational viability of the WSN. Conclusions: The study concludes that integrating WSNs with biological models is a viable solution for the real-time monitoring and management of varroasis. The proposed WSN system holds promise for enhancing the health and productivity of bee colonies on a broad scale, offering a novel contribution to the fields of apiculture and environmental monitoring.

Internet of Things (IoT) devices are becoming more and more common in homes, making the security and privacy of these increasingly important. Previous research has found that home IoT users can become a threat to themselves if they lack knowledge of their devices and awareness of potential threats. To investigate how the users’ security and privacy practices can be improved, it is necessary to understand the current everyday practices and what impacts these. This is examined in 10 interviews, revealing that the practices are primarily influenced by convenience, motivation and the effort required from the user. Using these insights, this thesis suggests that tangible interaction needs to be used as a complement to digital solutions to improve the security and privacy practices. By having a physical object that in a simple way can inform everyone of the current security and privacy situation and is equally accessible for all members of a household, the security and privacy can become more attainable for all users no matter their level of knowledge and experience. / Internet of Things (IoT) enheter har blivit vanligt förekommande i hem vilket gör deras säkerhet och integritet allt viktigare. Det har tidigare visats att användare av IoT i hemmet kan utgöra ett hot mot sig själva om de saknar kunskap om enheterna och kännedom om potentiella hot. För att undersöka hur användarnas vanor kring säkerhet och integitet kan förbättras är det först nödvändigt att utforska de nuvarande vanorna och vad som påverkar dessa. Detta undersöks i tio intervjuer som visar att vanorna främst påverkas av bekvämlighet, motivation och ansträngningen som krävs av användaren. Utifrån dessa insikter föreslås det att fysisk interaktion används som ett komplement till digitala lösningar för att förbättra vanorna kring säkerhet och integritet. Genom att ha ett fysiskt objekt som på ett enkelt sätt kan förmedla enheternas nuvarande status och är lika tillgängligt för alla medlemmar i ett hushåll kan säkerhet och integritet bli mer uppnåeligt för alla användare, oavsett deras nivå av kunskap och erfarenhet.

IoT (Internet of Things) systems implement event-driven architectures that are deployed on an ever-increasing scale as more and more devices (things) become connected to the internet. Consequently, IoT cloud platforms are becoming increasingly distributed and complex as they adapt to handle larger amounts of user requests and device data. The complexity of such systems makes it close to impossible to predict how they will handle failures that inevitably occur once they are put into production. Chaos engineering, the practice of deliberately injecting faults in production, has successfully been used by many software companies as a means to build confidence in that their complex systems are reliable for the end-users. Nevertheless, its applications in the scope of IoT systems remain largely unexplored in research. Modern IoT cloud platforms are built cloud native with containerized microservices, container orchestration, and other cloud native technologies, much like any other distributed cloud computing system. We therefore investigate cloud native chaos engineering technology and its applications in IoT cloud platforms. We also introduce a framework for getting started with using cloud native chaos engineering to verify and improve the resilience of IoT systems and evaluate it through a case study at a commercial home appliance manufacturer. The evaluation successfully reveals unknown system behavior and results in the discovery of potential resilience improvements for the case study IoT system. The evaluation also shows three ways to measure the resilience of IoT cloud platforms with respect to perturbations, these are: (1) success rate of user requests, (2) system health, and (3) event traffic. / IoT(Sakernas Internet)-system implementerar händelsedrivna arkitekturer som driftsätts i allt större skala i och med att allt fler enheter (saker) blir anslutna till internet. IoT-molnplattformar blir därmed alltmer distribuerade och komplexa i takt med att de anpassas till att hantera större mängder användarförfrågningar och enhetsdata. Komplexiteten hos sådana system gör det nära omöjligt att förutsäga hur de hanterar problem som oundvikligen inträffar när de väl körs i produktionsmiljö. Kaosteknik, att avsiktligt injicera fel medans ett system körs i produktionsmiljön, har framgångsrikt använts av många mjukvaruföretag som ett sätt att bygga förtroende för att deras komplexa system är tillförlitliga för slutanvändarna. Trots det är dess tillämpningar inom ramen för IoT-system i stort sett outforskade inom dataforskning. Moderna IoT-molnplattformar byggs molnäkta med containeriserade mikrotjänster, containerorkestering, och andra molnäkta teknologier, precis som andra distribuerade molntjänstsystem. Vi undersöker därför molnäkta kaosteknik och dess tillämpningar i IoT-molnplattformar. Vi introducerar även ett ramverk för att komma igång med att använda molnäkta kaosteknik för att verifiera och förbättra motståndskraften hos IoT-system och utvärderar det genom en fallstudie hos en kommersiell tillverkare av hushållsapparater. Utvärderingen lyckas avslöja okänt systembeteende och resulterar i upptäckten av potentiella motståndskraftsförbättringar för IoT-systemet i fallstudien. Utvärderingen visar också tre sätt att mäta motståndskraften hos IoT-molnplattformar med hänsyn till störningar, dessa är: (1) andel framgångsrika användarförfrågningar, (2) systemhälsa och (3) händelsetrafik.

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