Vi lever i en snabbt utvecklande teknologisk värld och mängden internet-uppkopplade enheter ökar hastigt. År 2025 förväntas mängden IoT-enheter öka till 41.6 miljarder. En generell säkerhetsbrist bland dessa enheter har medfört att de blivit favoriserade mål och medel för nätverksattacker. Syftet med denna rapport är att undersöka kunskapen hos privatpersoner gällande säkerhet i IoT-enheter, vilka åtgärder de tagit för att säkra sina hemnätverk samt var de anser ansvaret ligga när det kommer till säkerhet i de enheter de köper. Rapporten undersöker även återförsäljares kunskap när det gäller säkerhet i IoT-enheter, tillverkares synpunkt samt vad lagen säger gällande ansvarsskyldighet. I vår undersökning fann vi att kunskapen om säkerhet och åtgärder för säkerhet är bristfällig. Samtidigt så finns ett stort förtroende till tillverkare att säkerheten i IoT-enheter på marknaden har en tillräckligt god implementerad säkerhet. Denna rapport visar att detta inte alltid är sant. Säkerhet kommer ofta i andra hand vid tillverkning och en stor efterfrågan har påskyndat utvecklingsarbetet av IoT-enheter vilket har skadat säkerhetstänket. Ett stort problem är användandet av fabriksinställda användarnamn och lösenord utan krav på konsumenten att åtgärda detta vid uppstart av en ny enhet. Dessa enheter blir lättåtkomliga offer för cyberattacker och möjligheterna för angripare växer hastigt. Krav måste ställas på tillverkare att hålla en minimum standard av säkerhetsimplementation och IoT som begrepp måste definieras med tydliga riktlinjer om vilka enheter som omfattas av begreppet. Svenska lagar som omfattar nätverksattacker är bristfälliga och reflekterar inte de möjligheter som finns idag för angripare. Fler funktioner i privatpersoners hem blir uppkopplade och kan manipuleras på nya sätt men det finns inga tydliga lagar som reflekterar detta problem.

Creating visualizations for Internet of Things (IoT) data can be a challenge due to the uncertain and voluminous nature of this type of data. Many different tools have been made to visualize voluminous data. One such tool is Amazon Web Service (AWS) Quicksight. Quicksight is marketed as a scalable system for big data. For IoT developers, the visualization task is only a smaller subtask, which is often not the focus for the developer. Therefore it is in the developer’s best interest to use an easy tool for visualizing their data. To study if Quicksight is a viable tool for developers of IoT systems a user study was executed. Both the perceived usability and measured understanding when using the system were studied. This was done by using the System Usability Scale (SUS) for the perceived usability. For measured understanding some questions regarding trends and errors were asked and a statistical classification was made. The study showed a tendency for the users to rely too much on the visual structure Autograph as well as their difficulty to extract information from aggregated visualizations. In addition it was cumbersome to connect data between different datasets. / Att skapa visualiseringar för IoT data kan vara utmanande på Internet of Things (IoTs) oförutsägbara och voluminösa natur. Det finns många olika verktyg för att visualisera data. Ett sådant verktyg är Amazon Web Service (AWS) Quicksight. Quicksight marknadsförs som ett skalbart system för big data. För IoT utvecklare är dock visualisering oftast en mindre deluppgift som inte ligger i fokus. Därför är det viktigt för utvecklare att använda ett lättanvänt verktyg för att visualisera data. För att undersöka ifall Quicksight är ett bra alternativ som verktyg för utvecklare av IoT system så utfördes en användarstudie. Både den uppfattade och uppmätt förståelsen av systemet studerades. System Usability Scale (SUS) användes för att mäta den uppfattade användbarheten. För att mäta den uppmätta förståelsen ställdes frågor om trender och fel i data som sedan delades in i statistisk klassificering. Studien visade att användare tenderar att förlita sig för mycket på den visuella strukturen Autograph samt att dem hade svårigheter att utvinna information från aggregerad visualisering. Därtill ansåg användarna att det var besvärligt att koppla samman flera dataset i en visualisering.

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Cybersecurity is currently an important and relevant issue, as more and more industries are taking advantage of the accessibility of storing information online. To create a secure system one must know the potential risks and attacks on that system, making risk analysis a very potent tool. In this study, we performed such an analysis using the risk analysis method OCTAVE Allegro on a company providing a cloud-based service to find out what risks a cloud service provider might be exposed to, and the usefulness of said risk analysis method in this circumstance. We found that OCTAVE Allegro is suitable to use on smaller companies and applicable to cloud services, and the most severe risks identified were connected to leakage of client data with a consequence of damaging the company's reputation. Common areas of concern for these risks were third parties hacking the cloud server or other company systems to gain access to sensitive information. Such risks will likely be found at any company providing a cloud service that manages sensitive data, increasing the importance of risk analysis at these companies.

With the increase of devices on the internet that comes coupled with the growing IoT field, there is a high amount of research being conducted on the topic. Whilst much has been done to make these systems more scalable and resilient by replacing the current standard architecture with a decentralized one, the applied models mostly focus on the implementation details of such a system, and little thought is placed on the algorithms used to structure the architecture itself. Instead, one of the many, already defined protocols is used, and the system is built around this. These protocols, whilst elegant and outright ingenious in their own nature are initially intended for other applications, and hence do not take any advantage of the domain specifics of IoT, and hence the implemented solutions are sub-optimal in terms of performance and overhead. This thesis attempts to bridge that gap by first providing data on an existing IoT system, and then using the data to leverage the modifications of the prevailing protocol for decentralized peer-to-peer architectures. This is done by introducing groups in the ID scheme of the system, and thus greatly modifying the internal structure, forcing devices with interest in each other to be placed closely in the structure. The consequence of this is that there is a major reduction of overhead in searching for devices, bringing the total number of devices required to be contacted for normal use-cases down substantially.

Activities of daily living (ADLs) are everyday routine tasks which provide insight into the physical and cognitive wellbeing of older adults. ADLs are commonly self-reported to clinicians, which can lead to overestimation and underestimation of a patients’ functional abilities. Remote health monitoring is an emerging field aimed at utilizing technology for monitoring ADLs remotely, improving clinical accuracy and enabling older adults to age safely within their homes. In this dissertation, we report a Smart Home platform and two indoor positioning systems (IPSs) – (i) a hybrid Bluetooth Low Energy (BLE) and radar motion sensor system and (ii) a hybrid radio-frequency identification (RFID) and infrared (IR) range-finding system for tracking occupant mobility, the primary predictor of falls among older adults. For the Smart Home platform, the design methodology and technological features were explained. As for the IPSs’, position accuracy of multiple occupants within multiple rooms of a residential apartment was evaluated. The systems were also evaluated for cost, implementation ease, and scalability, which, upon reviewing literature, were identified as key metrics for developing an IPS for enabling aging in place. Both IPSs enforced a decentralized localization architecture and performed well, achieving high localization accuracy for multiple occupants. / Thesis / Master of Applied Science (MASc) / By 2031, the number of people aged 65 and over is expected to nearly double. This population shift is concerning for healthcare providers as limited resources become increasingly constrained. Resultantly, older adults, the largest consumers of healthcare, face longer wait times and reduced quality of care. Remote health monitoring is an emerging field aimed at utilizing technology for monitoring older adults within their homes. In this thesis, we report a Smart Home platform and two indoor positioning systems (IPSs) for tracking resident mobility, the primary predictor of falls among older adults. For the Smart Home platform, the design methodology and technological features were explained. As for the IPSs’, position accuracy of multiple occupants within multiple rooms of a residential apartment was evaluated. Upon reviewing literature system cost, implementation ease, and scalability, were identified as key metrics for developing an IPS for enabling aging in place. Both IPSs performed well, achieving high localization accuracy for multiple occupants.

El desarrollo de tecnología agropecuaria involucra actores de todas las disciplinas cientícas, principalmente de las ciencias naturales, seguido por las ciencias formales y en menor medida, las ciencias humanas o sociales. Es a partir de la colaboración interdisciplinaria de distintos grupos de investigación que permitió la elaboración del presente trabajo, en el cual se abordan diversas cuestiones respecto de la incorporación de sistemas computacionales con distintas funcionalidades y características en pos de brindar soporte a las actividades agropecuarias, particularmente aquellas que predominan en el sector del Sudoeste de la Provincia de Buenos Aires. Se intenta cubrir los aspectos m as relevantes referidos al diseño e implementación de dichos sistemas, desde un punto de vista técnico y con un especial enfoque hacia el empleo de metodologías de diseño con cierto grado de optimalidad. El eje central de la tesis se divide en tres partes con la finalidad de separar las problemáticas relacionadas a la adquisición, transmisión y el análisis o procesamiento de los datos. En la cuarta y ante ultima parte de este trabajo, se integran los conceptos anteriormente expuestos y se exploran las arquitecturas donde interactúan sistemas m as complejos del que forman parte tanto las máquinas como las personas. Dentro de este ultimo grupo se engloban los sistemas colaborativos, para los cuales se analizan dos propuestas diferentes, con un enfoque hacia los aspectos de comunicación por un lado y por el otro, el diseño de un sistema que tiene el objetivo de mejorar cuestiones de logística en el sector agropecuario, especialmente en zona rural. / The development of agricultural technology involves actors from all scienti c disciplines, mainly from the natural sciences, followed by the formal sciences and to a lesser extent, the human or social sciences. It is from the interdisciplinary collaboration of different research groups that allowed the accomplishment of this work, in which certain issues are addressed regarding the incorporation of computer systems with different functionalities and characteristics in order to provide support to agricultural activities, particularly those that predominate in the Southwest sector of the Buenos Aires Province. It is intended to cover all technical aspects related to the design and implementation of said systems, from a technical point of view and with a special focus on the use of design methodologies with a certain degree of optimality. The central axis of the current thesis is divided into three parts in order to separate the problems related to the data acquisition, transmission and analysis or processing. In the fourth and penultimate part of this work, the previously exposed concepts are integrated and the architectures where more complex systems interacts, of which both machines and people are part, are explored. Collaborative systems are included in this last group, for which two different proposals are analyzed, with a focus on communication aspects on the one hand and on the other, the design of a system that aims to improve logistics issues in the agricultural sector, especially in rural areas.

Yes / Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms. / European Regional Development Fund Interreg project Smart Cities and Open Data REuse (SCORE).

Yes

Reaping the benefits of the Internet of Things (IoT) system is contingent upon developing IoT-specific security and privacy solutions. Conventional security and authentication solutions often fail to meet IoT requirements due to the computationally limited and portable nature of IoT objects. Privacy in IoT is a major issue especially in the light of current attacks on Facebook and Uber. Research efforts in both the academic and the industrial fields have been focused on providing security and privacy solutions that are specific to IoT systems. These solutions include systems to manage keys, systems to handle routing protocols, systems that handle data transmission, access control for devices, and authentication of devices. One of these solutions is Blockchain, a trust-less peer-to-peer network of devices with an immutable data storage that does not require a trusted party to maintain and validate data entries in it. This emerging technology solves the problem of centralization in systems and has the potential to end the corporations control over our personal information. This unique characteristic makes blockchain an excellent candidate to handle data communication and storage between IoT devices without the need of oracle nodes to monitor and validate each data transaction. The peer-to-peer network of IoT devices validates data entries before being added to the blockchain database. However, accurate authentication of each IoT device using simple methods is another challenging problem. In this dissertation, a complete novel system is proposed to authenticate, verify, and secure devices in IoT systems. The proposed system consists of a blockchain framework to collect, monitor, and analyze data in IoT systems. The blockchain based system exploits a method, called Sharding, in which devices are grouped into smaller subsets to provide a scalable system. In addition to solving the scalability problem in blockchain, the proposed system is secured against the 51% attack in which a malicious node tries to gain control over the majority of devices in a single shard in order to disrupt the validation process of data entries. The proposed system dynamically changes the assignment of devices to shards to significantly decrease the possibility of performing 51% attacks. The second part of the novel system presented in this work handles IoT device authentication. The authentication framework uses device-specific information, called fingerprints, along with a transfer learning tool to authenticate objects in the IoT. The framework tracks the effect of changes in the physical environment on fingerprints and uses unique IoT environmental effects features to detect both cyber and cyber-physical emulation attacks. The proposed environmental effects estimation framework showed an improvement in the detection rate of attackers without increasing the false positives rate. The proposed framework is also shown to be able to detect cyber-physical attackers that are capable of replicating the fingerprints of target objects which conventional methods are unable to detect. In addition, a transfer learning approach is proposed to allow the use of objects with different types and features in the environmental effects estimation process. The transfer learning approach was also implemented in cognitive radio networks to prevent primary users emulation attacks that exist in these networks. Lastly, this dissertation investigated the challenge of preserving privacy of data stored in the proposed blockchain-IoT system. The approach presented continuously analyzes the data collected anonymously from IoT devices to insure that a malicious entity will not be able to use these anonymous datasets to uniquely identify individual users. The dissertation led to the following key results. First, the proposed blockchain based framework that uses sharding was able to provide a decentralized, scalable, and secured platform to handle data exchange between IoT devices. The security of the system against 51% attacks was simulated and showed significant improvements compared to typical blockchain implementations. Second, the authentication framework of IoT devices is shown to yield to a 40% improvement in the detection of cyber emulation attacks and is able to detect cyber-physical emulation attacks that conventional methods cannot detect. The key results also show that the proposed framework improves the authentication accuracy while the transfer learning approach yields up to 70% additional performance gains. Third, the transfer learning approach to combine knowledge about features from multiple device types was also implemented in cognitive radio networks and showed performance gains with an average of 3.4% for only 10% relevant information between the past knowledge and the current environment signals. / Doctor of Philosophy / The Internet of things (IoT) system is anticipated to reach billions of devices by the year 2020. With this massive increase in the number of devices, conventional security and authentication solutions will face many challenges from computational limits to privacy and security challenges. Research on solving the challenges of IoT systems is focused on providing lightweight solutions to be implemented on these low energy IoT devices. However these solutions are often prone to different types of attacks. The goal of this dissertation is to present a complete custom solution to secure IoT devices and systems. The system presented to solve IoT challenges consists of three main components. The first component focuses on solving scalability and centralization challenges that current IoT systems suffer from. To accomplish this a combination of distributed system, called blocchain, and a method to increase scalability, called Sharding, were used to provide both scalability and decentralization while maintaining high levels of security. The second component of the proposed solution consists of a novel framework to authenticate the identity of each IoT device. To provide an authentication solution that is both simple and effective, the framework proposed used a combination of features that are easy to collect, called fingerprints. These features were used to model the environment surrounding each IoT device to validate its identity. The solution uses a method called transfer learning to allow the framework to run on different types of devices. The proposed frameworks were able to provide a solution that is scalable, simple, and secured to handle data exchange between IoT devices. The simulation presented showed significant improvements compared to typical blockchain implementations. In addition, the frameworks proposed were able to detect attackers that have the resources to replicate all the device specific features. The proposed authentication framework is the first framework to be able to detect such an advanced attacker. The transfer learning tool added to the authentication framework showed performance gains of up to 70%.