Internet of Things require a continuous power supply for longevity and energy harvesting from ambient sources enable sustainable operation of such embedded devices. Using selfpowered power supply gives raise two scenarios, where there is surplus or scarce harvested energy. In situations where the harvester is capable of harvesting beyond its storage capacity, the surplus energy is wasted. In situations where the harvester does not have sufficient resources, the sparse harvested energy can only transiently power the device. Transiently powered devices, referred to as intermittent computing devices, ensure forward progress by storing checkpoints of the device state at regular intervals. Irrespective of the availability of energy, the device should have adequate security. This thesis addresses the security of energy harvested embedded devices in both energy scenarios. First, we propose precomputation, an optimization technique, that utilizes the surplus energy. We study two cryptographic applications, namely bulk encryption and true random number generation, and we show that precomputing improves energy efficiency and algorithm latency in both applications. Second, we analyze the security pitfalls in transiently powered devices. To secure transiently powered devices, we propose the Secure Intermittent Computing Protocol. The protocol provides continuity to underlying application, atomicity to protocol operations and detects replay and tampering of checkpoints. Both the proposals together provide comprehensive security to self-powered embedded devices. / Master of Science / Internet of Things(IoT) is a collection of interconnected devices which collects data from its surrounding environment. The data collected from these devices enable emerging technologies like smart home and smart cities, where objects are controlled remotely. With the increase in the number of such devices, there is a demand for self-powered devices to conserve electrical energy. Energy harvesters are suitable for this purpose because they convert ambient energy into electrical energy to be stored in an energy buffer, which is to be used when required by the device. Using energy harvesters as power supply presents us with two scenarios. First, when there is sufficient ambient energy, the surplus energy, which is the energy harvested beyond the storage capacity of the buffer, is not consumed by the device and thus, wasted. Second, when the harvested energy is scarce, the device is forced to shutdown due to lack of power. In this thesis, we consider the overall security of an energy harvested IoT device in both energy scenarios. We optimize cryptographic algorithms to utilize the surplus energy and design a secure protocol to protect the device when the energy is scarce. Utilizing both the ideas together provides adequate security to the Internet of Things.

No / Abstract: Internet of Things (IoT) aims to enable the interconnection of a large number of smart devices (things) using a combination of networks and computing technologies. But an influx of interconnected things makes a greater demand on the underlying communication networks and affects the quality of service (QoS). This paper investigates into the QoS of delay sensitive things and the corresponding traffic they generate over the network. Things such as security alarms, cameras, etc, generate delay sensitive information that must be communicated in a real time. Such things have heterogeneous features with limited buffer capacity, storage and processing power. Thus the most commonly used Best Effort service model cannot be an attractive mechanism to treat delay sensitive traffic. This paper proposes a cost-effective analytical model for a finite capacity queueing system with pre-emptive resume service priority and push-out buffer management scheme. Based on the analytical model various simulation results are generated in order to analyse the mean queue length and the blocking probability of high and low priority traffic for system with various capacities.

Yes / Web of Things (WoT) can be considered as a merger of newly emerging paradigms of Internet of Things (IoT) and cloud computing. Rapidly varying, highly volatile and heterogeneous data traffic is a characteristic of the WoT. Hence, the capture, processing, storage and exchange of huge volumes of data is a key requirement in this environment. The crucial resources in the WoT are the sensing devices and the sensing data. Consequently, access control mechanisms employed in this highly dynamic and demanding environment need to be enhanced so as to reduce the end-to-end latency for capturing and exchanging data pertaining to these underlying resources. While there are many previous studies comparing the advantages and disadvantages of access control mechanisms at the algorithm level, vary few of these provide any detailed comparison the performance of these access control mechanisms when used for different data handling procedures in the context of data capture, processing and storage. This study builds on previous work on token-based access control mechanisms and presents a comparison of two different approaches used for handling sensing devices and data in the WoT. It is shown that the aggregated data submission approach is around 700% more efficient than the serial payload submission procedure in reducing the round-trip response time.

Yes / Technological advancements have been evolving for so long, particularly Internet of Things (IoT) technology that has seen an increase in the number of connected devices surpass non IoT connections. It has unlocked a lot of potential across different organisational settings from healthcare, transportation, smart cities etc. Unfortunately, these advancements also mean that cybercriminals are constantly seeking new ways of exploiting vulnerabilities for malicious and illegal activities. IoT is a technology that presents a golden opportunity for botnet attacks that take advantage of a large number of IoT devices and use them to launch more powerful and sophisticated attacks such as Distributed Denial of Service (DDoS) attacks. This calls for more research geared towards the detection and mitigation of botnet attacks in IoT systems. This paper proposes a feature selection approach that identifies and removes less influential features as part of botnet attack detection method. The feature selection is based on the frequency of occurrence of the value counts in each of the features with respect to total instances. The effectiveness of the proposed approach is tested and evaluated on a standard IoT dataset. The results reveal that the proposed feature selection approach has improved the performance of the botnet attack detection method, in terms of True Positive Rate (TPR) and False Positive Rate (FPR). The proposed methodology provides 100% TPR, 0% FPR and 99.9976% F-score.

The manufacturing domain has been experiencing several revolutions over the years that have been shaping not only the design and management of processes but also their core drivers and value propositions. Industry 4.0 unleashes many enabling technologies such as the Internet of Things sensors and machine learning algorithms to boost industries’ productivity through data-driven process monitoring, rather than relying on operation manager experience. However, this fourth revolution does not set as strategic goals sustainability drivers (e.g., social and environmental) triggered by external forces that undermine modern societies. European policymakers address this structural limitation by defining the Industry 5.0 paradigm focused on human-centric and sustainable value creations. In this fast-paced landscape, this doctoral thesis targets the limitations of Industry 4.0 related contributions and defines three research questions to demonstrate the competitive advantages in designing cyber-physical systems to monitor the efficiency and sustainability of human-centric manufacturing environments. The human-centricity is an important feature of this work because, despite the rise of automation, workers represent a strategic and fragile resource in industrial plants. Therefore, Internet of Things technologies are leveraged to achieve a digital representation of workers. The acquired measurements are fed into computational algorithms to appreciate data-driven managerial insights based on the returned Key Performance and Risk indicators. The contributions of this thesis can be conceptually divided into two separate streams. The first demonstrates the relevance of enhancing the operational visibility of in-plant operations by exploiting Real Time Locating Systems acquisition layers. Although this technology indoor locates whichever (manufacturing) entity and asset in a defined coverage area, the returned workers’ positions fail to evaluate systems’ performances and sustainability. For this purpose, density-based machine learning algorithms and neural networks are introduced and validated to embed operational metrics into Decision Support Systems. Multidimensional managerial insights prove the consistency of this methodology in three different manufacturing environments. Considering production settings, managers appreciate the uptimes of workers and resource utilizations while evaluating the layout configurations and the related efficiency in manual material handling activities. This twofold level of analysis enables to eventually increase in-plant productivity while optimizing workers’ efforts in replenishment routes. The logistic investigation offers similar takes by monitoring the Overall Equipment Effectivness of manual forklifts and the distribution of picking/depositing activities in storage areas. Potential inefficiencies provide valid input to optimize the performances while reducing the energy consumption of logistics vehicles. The second stream focuses on workers’ physical resilience during task executions. To achieve this purpose, ergonomic indices are largely adopted to mitigate work-related musculoskeletal disorders in the workforce. The European Assembly Worksheet screening tool is the most complete one focusing on several parameters ranging from working postures to exterted forces. The developed cyber-physical system mirrors in digital spaces workers’ operations through a multi-device acquisition layer. While a four-channel surface ElectroMyoGraphy and a network of markerless cameras acquire muscular contractions in upper limbs and body joints, a radio-frequency-based smart glove detects process interactions such as tool usages and component pickings and thus segments production activities. These digital measurements are fed into computational algorithms to automate the mentioned ergonomic assessment. The experimental campaign validates the proposed cyber-physical systems and draws several managerial insights. For instance, strong bending postures may highlight a poor workplace design suggesting the need of self-adjustable workstations to accommodate a diverse workforce. At the same time, worrisome exerted forces could require line rebalancing to fairly redistribute muscular activity rates among operators. In summary, this thesis represents a significant advancement in digital manufacturing, offering ready-to-deploy systems while outlining future research opportunities and applications.

The preservation of the vaccine cold chain is crucial in order to ensure the proper preservation of vaccines throughout transport in a controlled environment. Maintaining the vaccine at suitable temperature and humidity levels during transportation will significantly significantly impacts vaccine effectiveness and quality. However, exposure to high temperature or improper conditions will result in vaccine degradation and public health concerns. Moreover, centralized real-time monitoring systems often suffer from issues related to integrity, transparency, availability, and single points of failure. This paper aims to transition data infrastructure from centralized to decentralized to enhance confidentiality, integrity, and availability (CIA). The proposed system, InoculLedger, employs IOTA and smart contracts to monitor and control environmental parameters, taking into account the vaccine's manufacturing process until the patient receives it. The system ensures transparency at every stage the vaccine undergoes and disseminates this information to all stakeholders, including the patient. The system utilizes IOTA for data infrastructure and employs smart contracts for data management. Additionally, the proposed system uses the Internet of Things (IoT) to monitor environmental parameters in real time.

Internet of Things växer fort och förutspås bli en del av vardagen. Detta öppnar möjligheter för att skapa produkter som förenklar vardagslivet. Automatisk objektsidentifiering kombinerad med en automatiserad lagerstatus kan underlätta inventering, något som kan användas till exempel i smarta kylskåp för att göra vardagen enklare genom Internet of Things.Detta examensarbete studerar metoder inom objektsidentifikation för att ta fram ett system som automatiskt kan identifiera objekt och hantera lagerstatus. En prototyp framställdes och testades för att se vilka möjligheter som finns. Systemet använder en Raspberry Pi som basenhet, vilken använder Dlib-bibliotek för att identifiera objekt som har blivit fördefinierade. Vid okända objekt identifierar användaren objekt i en mobilapplikation, systemet kan genom detta lära sig identifiera nya objekt. Samma applikation används för att se lagerstatusen på de olika objekt som har registrerats av systemet. Prototypen klarar av att identifiera kända objekt samt att lära sig nya, enligt projektets mål. / Internet of Things is growing fast and is predicted to become a part of everyday life. This can be used to create products which will make everyday life easier. Automated object detection combined with an automated inventory check can make it easier to manage what is in stock, this is something that can be used in smart refrigerators as an example, to make life more convenient through Internet of Things. This Bachelor thesis studies methods regarding object detection with the purpose to build a system which automatically identifies objects and manages the inventory status. A prototype was built and tested to see what the possibilities there is with such a system. The Prototype uses a Raspberry Pi as core unit, which uses Dlib libraries to identify predefined objects. The user will identify unknown objects via the mobile phone application, which makes it possible for the system to learn how to identify new objects. The same application is used to check the inventory status for the different objects that has been identified by the system. The prototype can identify objects and learn to identify new ones, according to the goals of the project.

Streetlights are usually lit during all dark hours even though vehicles or other objects are not using the road. Instead of wasting energy on keeping the streetlights lit when no vehicles are using the road, the streetlights should be lit whenever vehicles are in proximity of the streetlights and turned off otherwise. A distributed network can be used to handle the communication between streetlights for sharing information about vehicles in proximity. There are streetlight systems that adapt from the environment and handles communication but are still not optimized for country roads with low frequency of vehicles. Therefore, distributed communication for streetlight systems is implemented, by letting the streetlights be a part of a distributed system. Each streetlight is represented with a Zolertia RE-Mote, a sensor for detecting objects and an LED. The representation of the streetlights are wirelessly connected as a mesh network where they can communicate with each other and forward data packets to nodes more far away in the network. The concept of having the streetlights in a distributed system is believed to work and can be considered to be applied on streetlights at country roads to save energy. / Gatlyktor är oftast tända under alla timmar då det är mörkt ute, även fast det inte är något fordon eller annat objekt som använder vägen. Istället för att slösa energi på att ha gatlyktorna tända när det inte är några fordon som använder vägen, bör gatlyktorna vara tända när fordon är i närheten av dem och släckta annars. Ett distribuerat nätverk kan användas för att hantera kommunikationen mellan gatlyktor till att dela information om fordon i närheten. Det finns gatlyktsystem som anpassar efter miljön och hanterar kommunikationen, men är inte optimerat för landsvägar med låg trafik. Därför är distribuerad kommunikation för gatlyktsystem implementerat genom att låta gatlyktorna vara en del av ett distribuerat system. Varje gatlykta är representerad med en Zolertia RE-Mote, en sensor för detektering av objekt och en LED. Representationen är trådlöst kopplat som ett meshnätverk där de kan kommunicera med varandra och skicka vidare datapaket till noder längre bort i nätverket. Konceptet att ha gatlyktorna i ett distribuerat system tros fungera och kan tänkas att appliceras på gatlyktor på landsvägar för att spara energi.

The extraction of metal and other natural resources has historically been and is still today an important part of Swedish society and its development. To keep pace with demand and at the sametime increase security, more and more digital technology has been implemented into the miningindustry. However, transforming an industry with the help of digital technology is no easy task and the mining industry faces unique challenges to overcome because of the complexity of the mining environment. Against this background the purpose of this study is to describe and analyse the challenges and enabling factors a small enterprise must face to implement Internet of things inan electronic filter indicator in the mining industry. To answer our research questions, we have useda qualitative case study with semi-structured interviews. The results show that the main challengesthat can prohibit an implementation of a filter indicator that has IoT qualities are environmentalaspects of mines such as gas, dust, and water along with challenges of network, connectivity, and power. Further challenges also exist connected to the development and implementation processes where qualified competence and skills are crucial for the success of a functional IoT product. Whatthis study shows are several enabling factors for implementing an IoT product in a specific mining company such as management of servers and data of various kinds, and an extensive network withseveral different connected products.

The manufacturing industry is quickly adapting to new technologies. Some of these trending technologies are virtual commissioning, virtual reality, and cloud-based technologies. This project summarizes these three technologies and aims to create a commissioning tool adapted to the 4.0 Industry. The project’s methodology was to analyse a problem and consequently create a solution that solves it. The process of designing and developing was repeated iteratively, each time an evaluation was made. The final product developed has shown that it is worth spending time introducing the cloudbased technologies inside many applications since it saves time and allows to work remotely. Applying virtual reality to virtual commissioning has proven to add efficiency. At the same time, it gives an immersive experience with a real-time display of quantitative data and the process itself in a visual mode without interfering with the actual production. With these two technologies, virtual commissioning evolves and goes a step further. This project also proved that the user experience and interface in this type of immersive applications need much attention on creating a comfortableinterface that does not fatigue or cause rejection in the user.