I takt med den exponentiella tillväxten av Internet of Things (IoT) har utmaningen att hantera den enorma mängden genererade data blivit allt större. Denna studie undersöker paradigmen med distribuerade dimdatorer, där kostnadseffektiva IoT-enheter används som dimnoder, som en potentiell lösning på de utmaningarna som det centraliserade molnet står inför. Skalbarheten och prestandan hos ett dimdatorsystem utvärderades under en rad olika arbetsbelastningar genererade av beräkningsintensiva uppgifter. Resultaten visade att en ökning av antal dimnoder förbättrade systemets skalbarhet och minskade den totala latensen. Dock visade det sig att konfigurationer med färre dimnoder presterade bättre vid lägre arbetsbelastningar, vilket understryker vikten av balansen mellan beräkningsuppgifter och kommunikationskostnaden. Sammantaget framhäver denna studie dimdatorkonceptets genomförbarhet som en effektiv och skalbar lösning för beräkningsintensiva databearbetning inom IoT. Trots att studiens fokus låg på latens, kan de insikter som vunnits vägleda framtida design och implementering av dimdatorsystem och bidra till de pågående diskussionerna om strategier för datahantering inom IoT. / With the exponential growth of the Internet of Things (IoT), managing the enormous amount of data generated has become a significant challenge. This study investigates the distributed paradigm of fog computing, using cost-effective IoT devices as fog nodes, as a potential solution for the centralized cloud. The scalability and performance of a fog computing system were evaluated under a range of workloads, using computationally intensive tasks reflective of real-world scenarios. Results indicated that with an increase in the number of fog nodes, system scalability improved, and the overall latency decreased. However, at lower workloads, configurations with fewer fog nodes outperformed those with more, highlighting the importance of the balance between computation and communication overheads. Overall, this study emphasizes the viability of fog computing as an efficient and scalable solution for data processing in IoT systems. Although the study primarily focused on latency, the insights gained could guide future design and implementation of fog computing systems and contribute to the ongoing discussions on IoT data processing strategies.

In the era of Industry 4.0, the Internet-of-Things (IoT) plays the driving role comparable to steam power in the first industrial revolution. IoT provides the potential to combine machine-to-machine (M2M) interaction and real time data collection within the field of manufacturing. Therefore, the adoption of IoT in industry enhances dynamic optimization, control and data-driven decision making. However, the domain suffers due to interoperability issues, with massive numbers of IoT devices connecting to the internet despite the absence of communication standards upon. Heterogeneity is pervasive in IoT ranging from the low levels (device connectivity, network connectivity, communication protocols) to high levels (services, applications, and platforms). The project investigates the current state of industrial IoT (IIoT) ecosystem, to draw a comprehensive understanding on interoperability challenges and current solutions in supporting of IoT-based smart manufacturing. Based upon a literature review, IIoT interoperability issues were classified into four levels: technical, syntactical, semantic, and organizational level interoperability. Regarding each level of interoperability, the current solutions that addressing interoperability were grouped and analyzed. Nine reference architectures were compared in the context of supporting industrial interoperability. Based on the analysis, interoperability research trends and challenges were identified. FIWARE Generic Enablers (FIWARE GEs) were identified as a possible solution in supporting interoperability for manufacturing applications. FIWARE GEs were evaluated with a scenario-based Method for Evaluating Middleware Architectures (MEMS).  Nine key scenarios were identified in order to evaluate the interoperability attribute of FIWARE GEs. A smart manufacturing use case was prototyped and a test bed adopting FIWARE Orion Context Broker as its main component was designed. The evaluation shows that FIWARE GEs meet eight out of nine key scenarios’ requirements. These results show that FIWARE GEs have the ability to enhance industrial IoT interoperability for a smart manufacturing use case. The overall performance of FIWARE GEs was also evaluated from the perspectives of CPU usage, network traffic, and request execution time. Different request loads were simulated and tested in our testbed. The results show an acceptable performance in terms with a maximum CPU usage (on a Macbook Pro (2018) with a 2.3 GHz Intel Core i5 processor) of less than 25% with a load of 1000 devices, and an average execution time of less than 5 seconds for 500 devices to publish their measurements under the prototyped implementation. / I en tid präglad av Industry 4.0, Internet-of-things (IoT) spelar drivande roll jämförbar med ångkraft i den första industriella revolutionen. IoT ger potentialen att kombinera maskin-till-maskin (M2M) -interaktion och realtidsdatainsamling inom tillverkningsområdet. Därför förbättrar antagandet av IoT i branschen dynamisk optimering, kontroll och datadriven beslutsfattande. Domänen lider dock på grund av interoperabilitetsproblem, med enorma antal IoT-enheter som ansluter till internet trots avsaknaden av kommunikationsstandarder på. Heterogenitet är genomgripande i IoT som sträcker sig från de låga nivåerna (enhetskonnektivitet, nätverksanslutning, kommunikationsprotokoll) till höga nivåer (tjänster, applikationer och plattformar). Projektet undersöker det nuvarande tillståndet för det industriella IoT (IIoT) ekosystemet, för att få en omfattande förståelse för interoperabilitetsutmaningar och aktuella lösningar för att stödja IoT-baserad smart tillverkning. Baserat på en litteraturöversikt klassificerades IIoT-interoperabilitetsfrågor i fyra nivåer: teknisk, syntaktisk, semantisk och organisatorisk nivå interoperabilitet. När det gäller varje nivå av driftskompatibilitet grupperades och analyserades de nuvarande lösningarna för adressering av interoperabilitet. Nio referensarkitekturer jämfördes i samband med att stödja industriell driftskompatibilitet. Baserat på analysen identifierades interoperabilitetstrender och utmaningar. FIWARE Generic Enablers (FIWARE GEs) identifierades som en möjlig lösning för att stödja interoperabilitet för tillverkningstillämpningar. FIWARE GEs utvärderades med en scenariebaserad metod för utvärdering av Middleware Architectures (MEMS). Nio nyckelscenarier identifierades för att utvärdera interoperabilitetsattributet för FIWARE GEs. Ett smart tillverkningsfodral tillverkades med prototyper och en testbädd som antog FIWARE Orion Context Broker som huvudkomponent designades. Utvärderingen visar att FIWARE GE uppfyller åtta av nio krav på nyckelscenarier. Dessa resultat visar att FIWARE GE har förmågan att förbättra industriell IoT-interoperabilitet för ett smart tillverkningsfodral. FIWARE GEs totala prestanda utvärderades också utifrån perspektivet för CPU-användning, nätverkstrafik och begär exekveringstid. Olika förfrågningsbelastningar simulerades och testades i vår testbädd. Resultaten visar en acceptabel prestanda i termer av en maximal CPU-användning (på en Macbook Pro (2018) med en 2,3 GHz Intel Core i5-processor) på mindre än 25% med en belastning på 1000 enheter och en genomsnittlig körningstid på mindre än 5 sekunder för 500 enheter att publicera sina mätningar under den prototyperna implementateringen.

Internet of Things (IoT) är en växande teknologi som spelar en allt större roll i samhället. Den innefattar ett nätverk av internetanslutna enheter som samlar in och utbyter data. Samtidigt som IoT växer uppstår utmaningar kring hantering av stora datamängder och säkerhetsaspekter. Företaget Softhouse står inför utmaningen att välja en effektiv tidsseriedatabas för hantering av temperatur- och geopositionsdata från värmesystem i privata bostäder, där både prestanda och dataintegritet via kryptering är av stor vikt. Detta examensarbete har därför utfört en prestandajämförelse mellan AWSTimestream och InfluxDB, där olika tester har använts för att mäta exekveringstiden för inskrivning av sensordata och databasfrågor. Jämförelsen inkluderar AWS Timestream i krypterad form mot InfluxDB i dess AWS-molnversion i krypterad form, samt InfluxDB AWS i krypterad form mot InfluxDB i okrypterad form. Syftet med studien var att ge riktlinjer för valet av tidsseriedatabaser med fokus på prestanda och säkerhetsaspekter, inklusivekryptering. Studien undersökte även hur valet av rätt databas påverkar företag som Softhouse, både i termer av kvantitativa och kvalitativa fördelar, samt att ge en bedömning av kostnaderna. Resultatet visade att InfluxDB i dess AWS-molnversion generellt presterade bättre än AWS Timestream och InfluxDB i dess standardversion. Det fanns tydliga skillnader i prestanda mellan AWS Timestream och InfluxDB i dess AWS-molnversion, men inte lika tydliga skillnader i prestanda mellan InfluxDB i dess AWS-molnversion och standardversionen. Med hänsyn till både prestanda och säkerhet framstår InfluxDB i dess AWS-molnversion som det mest lämpliga alternativet. Det är emellertid av stor vikt att ta kostnadaspekten i beaktande, då AWS Timestream visar sig vara avsevärt mer kostnadseffektivt än InfluxDB. / The Internet of Things (IoT) is a growing technology that plays an increasingly significant role in society. It encompasses a network of internet-connected devices that collect and exchange data. As IoT continues to expand, challenges arise regarding the management of large volumes of data and security aspects. The company Softhouse faces the challenge of choosing an efficient time-series database for handling temperature and geoposition data from heating systems in homes, where both performance and data integrity through encryption are of great importance. Therefore, this thesis has conducted a performance comparison between AWS Timestream and InfluxDB, using various tests to measure the execution times for data ingestion of sensor data and database queries. The comparison includes AWS Timestream in encrypted form versus InfluxDB in its AWS cloud version in encrypted form, as well as InfluxDB AWS in encrypted form versus InfluxDB in unencrypted form. The aim of the study was to provide guidelines for the selection of time-series databases with a focus on performance and security aspects, including encryption. The study also explored how the choice of the right database affects companies like Softhouse, both in terms of quantitative and qualitative benefits, and provided an assessment of costs. The results showed that InfluxDB in its AWS cloud version generally outperformed AWS Timestream and InfluxDB in its standard version. There were clear performance differences between AWS Timestream and InfluxDB in its AWS cloud version, but not as pronounced differences in performance between InfluxDB in itsAWS cloud version and the standard version. Considering both performance and security, InfluxDB in its AWS cloud version appears to be the most suitable option. However, it is crucial to consider the cost aspect, as AWS Timestream proves to be significantly more cost-effective than InfluxDB.

This thesis introduces the circuit and system design of interactive Radio-Frequency Identification (RFID) for Internet of Things (IoT) applications. IoT has the vision of connectivity for anything, at anytime and anywhere. One of the most important characteristics of IoT is the automatic and massive interaction of real physical world (things and human) with the virtual Internet world.RFID tags integrated with sensors have been considered as one suitable technology for realizing the interaction. However, while it is important to have RFID tags with sensors as the input interaction, it is also important to have RFID tags with displays as the output interaction.Display interfaces vary based on the information and application scenarios. On one side, remote and centralized display interface is more suitable for scenarios such as monitoring and localization. On the other side, tag level display interface is more suitable for scenarios such as object identification and online to offline propagation. For tag level display, though a substantial number of researches have focused on introducing sensing functionalities to low power Ultra-High Frequency (UHF) RFID tags, few works address UHF RFID tags with display interfaces. Power consumption and integration with display of rigid substrate are two main challenges.With the recent emerging of Electronic Paper Display (EPD) technologies, it becomes possible to overcome the two challenges. EPD resembles ordinary ink on paper by characteristics of substrate flexibility, pattern printability and material bi-stability. Average power consumption of display is significantly reduced due to bi-stability, the ability to hold color for certain periods without power supplies. Among different EPD types, Electrochromic (EC) display shows advantage of low driving voltage compatible to chip supply voltage.Therefore this thesis designs a low power UHF RFID tag integrated in 180 nm CMOS process with inkjet-printed EC polyimide display. For applications where refresh rate is ultra-low (such as electronic label in retailing and warehouse), the wireless display tag is passive and supplied by the energy harvested from UHF RF wave. For applications where refresh rate is not ultra-low (such as object identification label in mass customized manufacturing), the wireless display tag is semi-passive and supplied by soft battery. It works at low average power consumption and with out-of-battery alert. For remote and centralized display, the limitations of uplink (from tags to reader) capacity and massive-tag information feedback in IoT scenarios is the main challenge. Compared to conventional UHF RFID backscattering whose data rate is limited within hundreds of kb/s, Ultra-wideband (UWB) transmission have been verified with the performance of Mb/s data rate with several tens of pJ/pulse energy consumption.Therefore, a circuit prototype of UHF/UWB RFID tag replacing UHF backscattering with UWB transmitter is implemented. It also consists of Analog-to-Digital Converter (ADC) and Electrocardiogram (ECG) electrodes for healthcare applications of real-time remote monitoring of multiple patients ECG signals. The ECG electrodes are fabricated on paper substrate by inkjet printing to improve patient comfort. Key contribution of the thesis includes: 1) the power management scheme and circuit design of passive UHF/UWB RFID display tag. The tag sensitivity (the input RF power) is -10.5 dBm for EC display driving, comparable to the performance of conventional passive UHF RFID tags without display functions, and -18.5 dBm for UWB transmission, comparable to the state-of-the-art performance of passive UHF RFID tag. 2) communication flow and circuit design of UHF/UWB RFID tag with ECG sensing. The optimum system throughout is 400 tags/second with 1.5 KHz ECG sampling rate and 10 Mb/s UWB pulse rate. / <p>QC 20151012</p>

abstract: With the increasing user demand for low latency, elastic provisioning of computing resources coupled with ubiquitous and on-demand access to real-time data, cloud computing has emerged as a popular computing paradigm to meet growing user demands. However, with the introduction and rising use of wear- able technology and evolving uses of smart-phones, the concept of Internet of Things (IoT) has become a prevailing notion in the currently growing technology industry. Cisco Inc. has projected a data creation of approximately 403 Zetabytes (ZB) by 2018. The combination of bringing benign devices and connecting them to the web has resulted in exploding service and data aggregation requirements, thus requiring a new and innovative computing platform. This platform should have the capability to provide robust real-time data analytics and resource provisioning to clients, such as IoT users, on-demand. Such a computation model would need to function at the edge-of-the-network, forming a bridge between the large cloud data centers and the distributed connected devices. This research expands on the notion of bringing computational power to the edge- of-the-network, and then integrating it with the cloud computing paradigm whilst providing services to diverse IoT-based applications. This expansion is achieved through the establishment of a new computing model that serves as a platform for IoT-based devices to communicate with services in real-time. We name this paradigm as Gateway-Oriented Reconfigurable Ecosystem (GORE) computing. Finally, this thesis proposes and discusses the development of a policy management framework for accommodating our proposed computational paradigm. The policy framework is designed to serve both the hosted applications and the GORE paradigm by enabling them to function more efficiently. The goal of the framework is to ensure uninterrupted communication and service delivery between users and their applications. / Dissertation/Thesis / Masters Thesis Computer Science 2015

The use of Internet-of-Things (IoT) makes it possible to inter-connect Information Technology (IT) and Operational Technology (OT) into a completely new system. This convergence is often known as Industrial IoT (IIoT). IIoT brings a lot of benefits to industrial assets, such as improved efficiency and productivity, reduced cost, and depletion of human error. However, the high inter-connectivity opens new possibilities for cyber incidents. These incidents can cause major damage like halting of production on the manufacturing line, or catastrophic havoc to companies, communities, and countries causing power outages, floods, and fuel shortages. Such incidents are important to be predicted, stopped, or alleviated at no cost. Moreover, these incidents are a great motive for researchers and practitioners to investigate known security problems and find potential moderation strategies.  In this thesis work, we try to identify what types of IIoT systems have been investigated in the literature. We seek out to find if software-related issues can yield security problems. Also, we make an effort to perceive what are the proposed methods to mitigate the security threats.We employ the systematic literature review (SLR) methodology to collect this information. The results are gathered from papers published in the last five years and they show an increased interest in research in this domain. We find out software vulnerabilities are a concern for IIoT systems, mainly firmware vulnerabilities and buffer overflows, and there are a lot of likely attacks that can cause damage, mostly injection and DDoS attacks. There are a lot of different solutions which offer the possibility to stop the identified problems and we summarize them. Furthermore, the research gap considering the update process in these systems and devices, as well as a problem with the unsupervised software supply chain is identified.

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.

<p>Developing resiliency in supply chains became vital in the recent years due to global diversification and vulnerability to risks. Firms need to identify, evaluate, and mitigate risks in supply chain to maintain continuity and create competitive advantage. Although the problem of supply chain disruptions has existed for a long time, less attention has been given by researchers in exploring the adoption of advanced technologies to build resilient supply chains. This study explored the potential of mitigating supply chain disruptions with the use of Industry 4.0 technologies such as Internet of Things (IoT) and Supply chain data analytics platform which develops digital twin environment for supply chains. </p> <p><br></p> <p>This research gathered expert’s opinion on the resilience capabilities developed in supply chain by digital twin adoption, stages and practices involved in digital twin assimilation through Delphi survey with subject matter experts and supply chain practitioners. Semi-structured interviews were conducted with participants to attain deep understanding on the resilience capabilities gained by digital twin and stages in digital twin adoption. Comparison of the results from Delphi survey and interviews was carried out to synthesize the results to yield a comprehensive understanding of resilience capabilities gained through digital twin and adoption stages of supply chain digital twin. This research has conducted interviews with 21 subject matter experts and completed three rounds of Delphi survey (with participants n = 15, 11, 11 in three rounds respectively) to develop a framework for digital twin adoption to enhance supply chain resilience. </p> <p><br></p> <p>This research determined that digital twin develops real-time monitoring and sensing capabilities, planning and decision support system, and automating decisions and action execution capabilities in supply chain. In addition, digital twin positively impacts resilience elements such as agility, supply chain reconfiguration, robustness, and collaboration in supply chain, which improves the supply chain performance. The results from this study were utilized to develop a framework for enabling supply chain resilience through digital twin. The framework included antecedents, consequences, and various moderators that impact digital twin adoption and diffusion in supply chains. Finally, this research developed a five-stage roadmap for adopting digital twin capabilities in supply chain. </p>

Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

IoT is an abbreviation of the term Internet of Things. The term describes everydayitems such as light bulbs that are connected to the Internet. IoT is a field that isgrowing very quickly with some researchers and industry leaders predicting thatthere will be up to 200 billion connected IoT devices in the world by 2020. Many IoTdevices are developed by smaller companies looking to capitalize on a specific needin the market. Because of this, the companies may favor launching a product as fastas possible which could mean that the devices may have not been adequately testedfor different vulnerabilities.The IoT and Smart Home market is currently experiencing rapid growth and all signspoint towards that continuing in the future. This thesis focuses on testing forvulnerabilities to Denial of Service attacks in common-off-the-shelf IoT devices thatcan be found in a smart home environment. The purpose of this thesis is to createmore knowledge about the vulnerabilities that can be found in Internet connecteddevices that are used daily.This thesis includes experiments using OpenVAS, which is a vulnerability scannerdeveloped by Greenbone Security used to test for vulnerabilities to Denial of Serviceattacks in IoT devices. The devices that are tested are Sony PlayStation 4, IKEATrådfri Smart Lighting, Google Chromecast (First Generation), Apple TV (ThirdGeneration) and D-Link DCS-930LB Wi-Fi IP-Camera. The firmware/software of allthe devices are updated as of April of 2019.The results of the conducted experiments show that all the tested devices besidesChromecast and IKEA Trådfri had vulnerabilities to Denial of Service attacks.PlayStation 4 was the device with the highest amount of vulnerabilities (9) and thevulnerability with highest possible severity (10.0). The effects of a Denial of Serviceattack range from an annoyance, when a gaming console is unavailable, to a securityrisk when an IP camera can be temporarily disabled.