<p>Despite the many security and privacy concerns associated with Internet-of-Things (IoT) devices, we continue to be barraged by new IoT devices every day. These devices have infiltrated almost every aspect of our lives, from government and corporations to our homes, and now, on and within our person, in the form of smartphones and wearables. These personal IoT devices can collect some of the most intimate pieces of data about their user. For instance, a smartwatch can record its wearer's heart rate, skin temperature, physical activity, and even GPS location data. At the same time, a smartphone has access to almost every piece of information related to its user, including text messages, social media activity, web browser history, and application-specific data. Due to the quantity and quality of data these personal IoT devices record, these devices have become critical sources of evidence during forensic investigations. However, there are instances in which digital forensic investigators need to make doubly sure that the data obtained from these smart devices, in fact, belong to the alleged owner of the smart device and not someone else. To that end, this dissertation provides the first look at using personal IoT device handling as a user identification technique with machine learning models to aid forensic investigations. The results indicated that this technique is capable of significantly differentiating device owners with performance metrics of .9621, .9618, and .9753, for accuracy, F1, and AUC, respectively, when using a smartwatch with statistical time-domain features. When considering the smartphone performance, the performance was only marginally acceptable with accuracy, F1, and AUC values of .8577, .8560, and .8891, respectively.  The results also indicate that female users handled their devices notably differently from male users. This study thus lays the foundation for performing user identification during a forensic investigation to determine whether the smart device owner did, in fact, use the device at the time of the incident.</p>

Varje dag kopplas tusentals godsvagnar om på de olika rangerbangårdarna i Sverige. För att kunna automatiskt bromsa vagnarna tillräckligt mycket är det nödvändigt att veta deras hastigheter. En teknik som har blivit populär på sistone är Light Detection and Ranging (LiDAR) som använder ljus för att mäta avstånd till objekt. Den här rapporten diskuterar design- och implementationsprocessen av ett trådlöst sensornätverk bestående av en LiDARutrustad sensornod. Designprocessen gav en insikt i hur LiDAR-sensorer bör placeras för att täcka en så stor yta som möjligt. Sensornoden var programmerad att bestämma avståndet av objekt genom att använda Random Sample Consensus (RANSAC) för att ta bort outliers och sen linjär regression på de inliers som detekterats. Implementationen utvärderades genom att bygga ett litet spår med en låda som kunde glida fram och tillbaka över spåret. LiDAR- sensorn placerades med en vinkel vid sidan om spåret. Resultaten visade att implementationen både kunde detektera objekt på spåret och också hastigheten av objekten. En simulation gjordes också med hjälp av en 3D-modell av en tågvagn för att se hur väl algoritmen hanterade ojämna ytor. LiDAR-sensorn i simuleringen hade en strålavvikelse på 0_. 30% av de simulerade mätvärdena gjordes om till outliers för att replikera dåliga väderförhållanden. Resultaten visade att RANSAC effektivt kunde ta bort outliers men att de ojämna ytorna på tåget ledde till felaktiga hastighetsmätningar. En slutsats var att en sensor med en divergerande stråle möjligtvis skulle leda till bättre resultat. Framtida arbete inkluderar att utvärdera implementationen på en riktig bangård, hitta optimala parametrar för algoritmen samt evaluera algoritmer som kan filtrera data från ojämn geometri. / Every day, thousands of train wagons are coupled on the multiple classification yards in Sweden. To be able to automatically brake the wagons a sufficient amount, it is a necessity to determine the speed of the wagons. A technology that has been on the rise recently is Light Detection and Ranging (LiDAR) that emits light to determine the distance to objects. This report discusses the design and implementation of a wireless sensor network consisting of a LiDAR-equipped sensor node. The design process provided insight into how LiDAR sensors may be placed for maximum utilization. The sensor node was programmed to determine the speed of an object by first using Random Sample Consensus (RANSAC) for outlier removal and then linear regression on the inliers. The implementation was evaluated by building a small track with an object sliding over it and placing the sensor node at an angle to the side of the track. The results showed that the implementation could both detect objects on the track and also track the speed of the objects. A simulation was also made using a 3D model of a wagon to see how the algorithm performs on non-smooth surfaces. The simulated LiDAR sensor had a beam divergence of 0_. 30% of the simulated measurements were turned into outliers to replicate bad weather conditions. The results showed that RANSAC was efficient at removing the outliers but that the rough surface of the wagon resulted in some incorrect speed measurements. A conclusion was made that a sensor with some beam divergence could be beneficial. Future work includes testing the implementation in real-world scenarios, finding optimal parameters for the proposed algorithm, and to evaluate algorithms that can filter rough geometry data.

[pt] A Internet das Coisas (do inglês, IoT) está se popularizando cada dia mais, se expandindo para aplicações em quase todos os setores de nossa socidade, impactando a economia e a vida cotidiana dos cidadãos. Com o crescimento das aplicações IoT, também ocorre uma expansão na demanda por novos dispositivos com capacidades de atuação, como lâmpadas inteligentes, fechaduras inteligentes, aparelhos de climatização (HVAC), máquinas industriais com capacidade de comunicação, robôs, drones, etc. Muitas plataformas de middleware estão sendo desenvolvidas para suportar o desenvolvimento de aplicativos de IoT distribuídos e facilitar a comunicação de sensores para a nuvem e recursos de edge processing. Mas surpreendentemente, muito pouco foi feito para fornecer mecanismos de suporte genéricos e em nível de middleware para detectar dispositivos controláveis e executar comandos de atuação, ou seja, transferi-los para o dispositivo. Este trabalho apresenta uma extensão ao middleware ContextNet que provê suporte à atuação remota e genérica sobre dispositivos inteligentes conectados pela Internet das Coisas. / [en] The Internet of Things (IoT) is becoming increasingly popular, expanding into applications in almost every sector of our society, impacting the economy and daily life of citizens. As IoT applications grow, so does the demand for new devices with actuation capabilities, such as smart light bulbs, HVAC devices, smart locks, communication-capable industrial machines, robots, drones, and so on. Many software platforms are being developed to support the development of distributed IoT applications and to facilitate cloud sensor communication and edge processing capabilities. But surprisingly, very little has been done to provide middleware-level support and generic mechanisms for discovering devices and their interfaces, and executing activation commands, i.e. transferring them to the device. This work presents an extension to the ContextNet Middleware to support generic and remote actuation on devices connected by the Internet of Things.

The need for energy efficiency in wireless communication is prevalent in all areas, but to an even greater extent in low-power and lossy networks that rely on resource-constrained devices. This degree project seeks to address the problem of modelling the battery lifetime of a duty-cycled node, participating in a wireless sensor network that is typically used in smart home and building applications. Modelling in MATLAB combined with experimentation are employed to predict the life expectancy and to validate using a hardware implementation. Various scenarios including sleepy end devices in a wireless sensor network are modelled and validated; these range from variable wake-up frequency and packet payload transmission to increasing network contention with the addition of network load. A comprehensive analysis of the main factors contributing to wasteful energy usage is provided in this thesis project, and it can be concluded that the model can estimate the battery lifetime under different testing scenarios with an error less than 5 %. / Det finns ett stort behov av energieffektivitet inom trådlös kommunikation, särskilt inom nätverk med bortfall och låg strömförbrukning där resursbegränsade enheter nyttjas. Det här examensarbetet eftersträvar att lösa problemet med att modellera batterilivslängden hos en sensoranordning med en låg driftcykel, som en del av ett trådlöst sensornätverk avsett för att tillämpas i smarta hus och byggnader. Modellering i MATLAB kombinerat med experimentering används för att förutsäga den förväntade livslängden samt för att validera en hårdvaruimplementering. Flertalet scenarier med sovande noder modelleras och valideras, med allt från variabel uppvakningsfrekvens och paketöverföring till ökande resurskonflikter med ytterligare belastning på nätverket. I detta examensprojekt inkluderas en heltäckande analys av huvudorsakerna till energislöseriet hos enheterna och slutsatsen kan dras att modellen kan beräkna batterilivslängden för olika testscenarier med mindre än 5 % fel.

The smart society, including smart infrastructures is developing quickly. The smart monitoring of the water distribution systems is a significant part of this development, trying to address possible problems in the Water Distribution Networks (WDNs) such as water leakages, pressure instability and water contamination. By sampling and sensing important parameters in the water distribution infrastructure and sending these data to control systems through a low power wide area network (LPWAN), a Cyber Physical System (CPS) of a digitalized WDN can be built. This thesis provides support for the design of such a CPS, by the design of a simulation framework, which includes coordinated WDN and communication network simulators. First, we make a selection on the communication network simulators and protocol stacks. Network simulator 3 (NS-3) is chosen as the simulation tool and Long Range (LoRa)/long range wide-area network (LoRaWAN) is chosen as the network protocol. The EPANET software is used as WDN simulator. Then, the existing implementation of the LoRaWAN protocol stack in NS-3 is modified, to allow the network simulator run parallel to the WDN simulator, and to transmit WDN sampling data to a control center. Finally, the effect of the communication network properties on the performance of the WDN monitoring system is evaluated, and it is shown that the communication delays can affect the monitoring performance even in small systems. The thesis provides the first steps of the development of a simulation environment of the cyber physical system of digitalized water distribution networks, and is expected to support further research in the area. / Det smarta samhället, inklusive smarta infrastrukturer, utvecklas snabbt. Den smarta övervakningen av vattendistributionssystemen är en betydande del av denna utveckling, som försöker lösa eventuella problem i vattendistributionsnäten (WDN) såsom vattenläckage, tryckinstabilitet och vattenförorening. Genom att ta prov och känna av viktiga parametrar i vattendistributionsinfrastrukturen och genom att skicka dessa data till ett kontrollsystem via ett LPWAN, kan ett CPS av ett digitaliserat WDN byggas. Detta examensarbete ger stöd för designen av en sådan CPS, genom utformningen av ett simuleringsramverk, som inkluderar koordinerade simulatorer för WDN och kommunikationsnätverk. Först gör vi ett urval på simulatorer för protokollstackar och kommunikationsnätverk. NS-3 väljs som simuleringsverktyg och LoRa/LoRaWAN väljs som nätverksprotokoll. EPANET-mjukvaran används som WDN-simulator. Sedan modifieras den befintliga implementeringen av LoRaWAN-protokollstacken i NS-3, för att tillåta nätverkssimulatorn att köras parallellt med WDN-simulatorn och för att överföra WDN-samplingsdata till ett kontrollcenter. Slutligen utvärderas effekten av kommunikationsnätverkets egenskaper på prestandan hos WDN-övervakningssystemet, och det visas att förseningarna i kommunikationsnätet kan påverka övervakningsprestandan även i små system. Avhandlingen ger de första stegen i utvecklingen av en simuleringsmiljö av det cyberfysiska systemet för digitaliserade vattendistributionsnät, och förväntas stödja ytterligare forskning inom området.

Digitala tvillingar har på de senaste åren blivit ett väldigt aktuellt och omtalat begrepp. I det här examensarbetet undersöks det om termen enbart är en hype eller om det faktiskt är verktyget för att utveckla olika branscher till något bättre. Projektet fokuserar främst på produktions- och utvecklingsindustrin, men även inom exempelvis stadsplanering och motorsport förklaras det vad digitala tvillingar används till. En viktig del i projektet är även Industri 4.0 och Sakernas Internet. Syftet (och resultatet) för det utförda examensarbetet är att ta reda på vad företag anser gynnsamt, såväl som missgynnsamt med digitala tvillingar, samt fastställa vilka barriärer det finns hos ett företag för att börja använda sig av digitala tvillingar. Syftet är även att utforska hur digitala tvillingar införs i ett företags produktutvecklingsprocess, samt vilken roll en fysisk provning får hos ett företag som introducerar digitala tvillingar. I projektets syfte ingår också att ta reda på mer om digitala tvillingar ur ett utvecklingsperspektiv, för att utreda hur kapaciteten förefaller hos en produkt som är testad och utvecklad i kombination med digital och verklig miljö. Vad finns det för data att utläsa, och i vilka fall finns det potential för en digital tvilling? För att nå önskade resultat med projektet har en tablettmaskin skapats i CAD. Med hjälp av den tredimensionella modellen visas det hur en digital tvilling kan användas för produktionsanläggningar (specifikt process- och tillverkningsindustri) genom att montera sensorer på den tredimensionella modellen, som även ska monteras identiskt på en fysisk motsvarighet. Under projektets genomförande undersöks även Gartners hypekurva och därmed vad som är hype och inte hype. I slutet av rapporten diskuteras bland annat vad som är möjligt att simulera med digitala tvillingar och vad som inte är det. Slutsatsen gällande om en digital tvilling är en hype eller inte, resulterar i att digitala tvillingar är ett verktyg som blir vad det skapas till. Anses en digital tvilling vara en hype, ja då kanske det egentligen är själva förväntningarna som är överskattade, och inte den digitala tvillingen i sig. / Digital twins have in recent years become a very current and discussed concept. This thesis examines whether the term is merely a hype or whether it is the tool for developing different industries for something better. The project focuses mainly on the manufacturing and development industry, but also what digital twins are used for in e.g. urban planning and motorsport. An important part of the project is also Industry 4.0 and the Internet of Things (IoT). The purpose (and result) of this Bachelor thesis is to find out what companies consider favourable, as well as unfavourable with digital twins, and to determine what barriers a company encounters to start using digital twins. The purpose is also to explore how digital twins are introduced into a company's product development process, as well as which role a physical test at a company that introduces digital twins has. The purpose of the project also includes finding out more about digital twins from a development perspective, to investigate how the capacity of a product tested and developed in combination with digital and real environment appears. What kind of data is there to read, and in what cases is there a potential for a digital twin? To achieve the desired results, a tablet machine has been created in CAD. The threedimensional model shows how a digital twin can be used for production facilities (specific process- and manufacturing industry) by mounting sensors on the three-dimensional model, which should also be mounted equally on a physical counterpart. During the project's implementation, Gartner's hype curve is also examined and thus what is hype and not hype. At the end of the report there is a discussion including e.g. what is possible to simulate with digital twins and what is not. The conclusion regarding whether a digital twin is a hype or not, results in digital twins being a tool that becomes what it is created for. Is a digital twin considered a hype, well then maybe it is actually the expectations that are overestimated, and not the digital twin itself.

<p dir="ltr">With the ability to connect to networks and send and receive data, Internet of Things (IoT) devices involve associated security risks and threats, for a given environment. These threats are even more of a concern in a Smart Home network, where there is a lack of a dedicated security IT team, unlike a corporate environment. While efficient user interface(UI) and ease of use is at the front and center of IoT devices within Smart Home which enables its wider adoption, often security and privacy have been an afterthought and haven’t kept pace when needed. Therefore, a unsafe possibility exists where malicious actors could exploit vulnerable devices in a domestic home environment.</p><p dir="ltr">This thesis involves a detailed study of the cybersecurity for a Smart Home and also examines the various types of cyberthreats encountered, such as DDoS, Man-In-Middle, Ransomware, etc. that IoT devices face. Given, IoT devices are commonplace in most home automation scenarios, its crucially important to detect intrusions and unauthorized access. Privacy issues are also involved making this an even more pertinent topic. Towards this, various state of the art industry standard tools, such as Nmap, Nessus, Metasploit, etc. were used to gather data on a Smart Home environment to analyze their impacts to detect security vulnerabilities and risks to a Smart Home. Results from the research indicated various vulnerabilities, such as open ports, password vulnerabilities, SSL certificate anomalies and others that exist in many cases, and how precautions when taken in timely manner can help alleviate and bring down those risks.</p><p dir="ltr">Also, an IoT monitoring dashboard was developed based on open-source tools, which helps visualize threats and emphasize the importance of monitoring. The IoT dashboard showed how to raise alerts and alarms based on specific threat conditions or events. In addition, currently available cybersecurity regulations, standards, and guidelines were also examined that can help safeguard against threats to commonly used IoT devices in a Smart Home. It is hoped that the research carried out in this dissertation can help maintain safe and secure Smart Homes and provide direction for future work in the area of Smart Home Cybersecurity.</p>

This study addresses the challenges faced by industries requiring precise water quality monitoring by developing and evaluating a modular multi-sensor system. Existing solutions often lack scalability and flexibility, necessitating multiple devices for comprehensive analysis. The methodology employed a recursive prototype development approach, integrating various hardware and software components, including microcontrollers and a user-friendly mobile application. The prototype facilitated real-time data acquisition and management through a dedicated server, supporting essential water quality parameters such as pH, temperature, and conductivity. Results indicate that the system significantly enhances measurement accuracy and operational efficiency. However, the implementation of a smart home connectivity standard was unsuccessful, highlighting the complexities associated with integrating new communication protocols. Despite this, the system offers a scalable, cost-effective solution for continuous water quality monitoring, presenting significant improvements over existing technologies in terms of flexibility, user engagement, and data reliability. / Denna studie behandlar de utmaningar som industrier står inför som kräver noggrann övervakning av vattenkvalitet genom att utveckla och utvärdera ett modulärt multisensorsystem. Befintliga lösningar saknar ofta skalbarhet och flexibilitet, vilket kräver flera enheter för omfattande analys. Metodiken använde en rekursiv prototyputvecklingsansats, som integrerade olika hårdvaru- och mjukvarukomponenter, inklusive mikrokontroller och en användarvänlig mobilapplikation. Prototypen underlättade insamling och hantering av realtidsdata genom en dedikerad server, som stödde viktiga vattenkvalitetsparametrar såsom pH, temperatur och konduktivitet. Resultaten indikerar att systemet avsevärt förbättrar mätnoggrannhet och operationell effektivitet. Dock var implementeringen av smarta hem-anslutningsstandard inte framgångsrik, vilket belyser komplexiteten med att integrera nya kommunikationsprotokoll. Trots detta erbjuder systemet en skalbar, kostnadseffektiv lösning för kontinuerlig övervakning av vattenkvalitet, med betydande förbättringar jämfört med befintliga teknologier när det gäller flexibilitet, användarengagemang och datareliabilitet.

Noise is defined as any undesired sound. Urban noise and its effect on citizens area significant environmental problem, and the increasing level of noise has become a critical problem in some cities. Fortunately, noise pollution can be mitigated by better planning of urban areas or controlled by administrative regulations. However, the execution of such actions requires well-established systems for noise monitoring. In this thesis, we present a solution for noise measurement and classification using a low-power and inexpensive IoT unit. To measure the noise level, we implement an algorithm for calculating the sound pressure level in dB. We achieve a measurement error of less than 1 dB. Our machine learning-based method for noise classification uses Mel-frequency cepstral coefficients for audio feature extraction and four supervised classification algorithms (that is, support vector machine, k-nearest neighbors, bootstrap aggregating, and random forest). We evaluate our approach experimentally with a dataset of about 3000 sound samples grouped in eight sound classes (such as car horn, jackhammer, or street music). We explore the parameter space of the four algorithms to estimate the optimal parameter values for the classification of sound samples in the dataset under study. We achieve noise classification accuracy in the range of 88% – 94%.

[pt] À medida que a população cresce, mais alimentos precisarão ser produzidos nas próximas quatro décadas do que nos últimos 10.000 anos. No entanto, o mundo moderno ainda depende da produção de monoculturas de alto rendimento, cada vez mais ameaçada por condições climáticas incomuns, escassez de água e terra insuficiente. A fim de superar esses problemas e alimentar o mundo, é necessário um caminho prático para fornecer alimentos frescos, com qualidade e em escala, com mínima dependência do clima e com uso de água e pegada de carbono reduzidos. Uma abordagem razoável é construir fazendas verticais dentro das cidades em um ambiente fechado repleto de sensores e iluminação artificial controlada por software para uma produção e gestão eficiente do plantio de alimentos. Esta tese propõe a instanciação de um modelo, chamado Ciclo do Software dos Lugares (SoPC), que é capaz de responder a estímulos ambientais em um sistema fechado de produção de plantas com iluminação artificial que possibilite a criação de ambientes com auto-aprendizagem. Esta tese descreve o SoPC, as abordagens e processos de implementação de uma mini fábrica de plantas com iluminação artificial com base na discussão em cinco ciclos de pesquisa-ação. / [en] As the population grows, more food will need to be produced in the next four decades than has been in the past 10,000 years. However, the modern world still depends on high yield monoculture production which is increasingly threatened by unusual weather, water shortages, and insufficient land. In order to overcome these problems and feed the world, a practical path to provide quality fresh healthy food at scale with minimal weather dependency, water usage and reduced carbon footprint is necessary. One reasonable approach is to build vertical farms inside the cities in a close environment full of sensors and artificial lighting controlled by software for efficient production of food crops. This thesis proposes a model, entitled Software of Places Cycle (SoPC), that should be able to answer to environmental stimuli in a closed plant production system using artificial lighting in order to create a self-learning environment. This thesis describes the SoPC, the approaches and processes of implementing a mini Plant Factory using Artificial Lighting based on the discussion on five action-research cycles. The thesis main contribution is a conceptual model to guide the development and maintenance of a mini-PFAL (m-PFAL), a minor contribution is the deployment of the SoP, i.e., the very notion of having software dedicated to a specific place.