Connected vehicle networks and future autonomous driving systems call for characterization of risky driving events to improve safety applications and autonomous driving features. Precision of driving event characterization (\gls{dec}) systems in connected vehicles has become increasingly important with the responsive connectivity that is available to the modern vehicles. While risky behavior patterns entail potential safety issues on road networks, the advent of vehicular sensing and vehicular networks cannot guarantee accurate characterization of driving/movement behavior of vehicles and the precision of such systems still remains an open issue. Additionally, artificial intelligence-backed solutions are vital components towards highly accurate characterization systems in the modern transportation. However, such solutions require significant volume of driving event data for an acceptable level of performance. With this in mind, the proposal of this thesis is three-fold: 1) a reliable methodology to generate representative data under the scarcity of diverse anomalous sensory data, 2) classification of mobility/driving events of vehicles with attention-based deep learning methods, and 3) a modular prior-knowledge input method to the characterization methodologies in order to further improve the trustworthiness of the systems. Implementing the proposed steps, we are able to not only increase the consistency in the training process but also reduce the false positive detection instances compared to the previous models. One of the roadblocks against robust event characterization systems in connected vehicles that is tackled in this thesis is the scarcity of anomalous driving data to make the training of event classification models robust. To mitigate this issue an optimized deep recurrent neural network-based encoding model is introduced to extract the precise feature representation of the anomalous data. The utilization of the encoded input to the previous network allowed for a 12\% accuracy improvement. Furthermore, we introduced a framework for precise risky driving behavior detection that takes advantage of an attention-based neural networks model. Ultimately, the combination of prior knowledge modelling with our network and some optimizations to the network structure, the model outperforms the state-of-the-art solutions by reaching an average accuracy of 0.96 and F1-score of 0.92.

Low-Power Lossy Networks (LLNs) are a type of Internet of Things (IoT) meshnetwork that collaboratively interact and perform various tasks autonomously. TheRouting Protocol for Low-power and Lossy Network (RPL) is the most used rout-ing protocol for LLNs. Recently, we have been witnessing a tremendous increasein attacks on Internet infrastructures using IoT devices as a botnet (IoT botnet).This thesis focuses on two parts: designing an ML-based IDS for 6LoWPAN, andgenerating a new larger labeled RPL attack dataset by implementing various non-attack and attack IoT network scenarios in the Cooja simulator. The collected rawdata from simulations is preprocessed and labeled to train the Machine Learningmodel for Intrusion Detection System (IDS). We used Deep Neural Network (DNN),Random Forest Classifier (RFC), and Support Vector Machines with Radial-BasisFunction kernel (SVM-RBF) learning algorithms to detect attack in RPL based IoTmesh networks. We achieved a high accuracy (96.7%) and precision (95.7%) usingthe RFC model. The thesis also reviewed the possible placement strategy of IDSfrom cloud to edge.

When focusing on the general area of data mining, high-utility itemset mining (HUIM) can be defined as an offset of frequent itemset mining (FIM). It is known to emphasize more factors critically, which gives HUIM its intrinsic edge. Due to the flourishing development of the IoT technique, the uncertainty patterns mining is also attractive. Potential high-utility itemset mining (PHUIM) is introduced to reveal valuable patterns in an uncertainty database. Unfortunately, even though the previous methods are all very effective and powerful to mine, the potential high-utility itemsets quickly. These algorithms are not specifically designed for a database with an enormous number of records. In the previous methods, uncertainty transaction datasets would be load in the memory ultimately. Usually, several pre-defined operators would be applied to modify the original dataset to reduce the seeking time for scanning the data. However, it is impracticable to apply the same way in a big-data dataset. In this work, a dataset is assumed to be too big to be loaded directly into memory and be duplicated or modified; then, a MapReduce framework is proposed that can be used to handle these types of situations. One of our main objectives is to attempt to reduce the frequency of dataset scans while still maximizing the parallelization of all processes. Through in-depth experimental results, the proposed Hadoop algorithm is shown to perform strongly to mine all of the potential high-utility itemsets in a big-data dataset and shows excellent performance in a Hadoop computing cluster.

Internet of Things har sedan 1990-talet varit ett hett ämne, men på senare tid har begreppetblivit allt viktigare medan teknologin utvecklas mer än någonsin. IoT används i dagslägetbland annat inom sjukvården, för att skapa smarta städer och i effektiviseringen av processeroch verksamheter. I takt med att denna teknologi växer, uppstår frågor kring säkerhet ochhantering av den massiva mängd data som samlas in.Forskningen kring IoT, i samband med brottsbekämpning, är i dagsläget relativt begränsad dåteknologin i sig fortfarande är ung. Den aktuella studien har, genom att intervjua experterinom området, utforskat möjligheter för att skapa ett tryggare samhälle med hjälp av IoT.Studien inkluderar dessutom samhällets perspektiv där vi genom en enkät samlat ininformation om vad människor känner inför att polisen använder IoT för att effektiviserabrottsbekämpning. Vi ställer oss följande fråga:Hur värderas frågor om integritet i förhållande till brottsbekämpning genom IoT? / Internet of Things has been a hot topic ever since the 1990s´ but lately the term has becomemore and more important as the technology is evolving more than ever before. Today IoT isused in order to improve healthcare, to create smart cities and in order to create moreefficient businesses and processes. With the growth of this technology there is now newquestions regarding safety and the handling of the massive amount of data that is gathered.The research within IoT, in connection to crime prevention, is quite limited since thetechnology itself is relatively young. This study has, through interviewing experts within thefield, explored opportunities to create a safer society with the help of IoT. The study alsoincludes the public´s perspective through the use of a questionnaire. This helped us gatherdata regarding what the public feels about law enforcement using IoT to become moreefficient when it comes to crime prevention. We ask the following question:How are privacy issues measured when compared to crime prevention through IoT?

Purchase management of medical supplies is a critical and important process that affects the services provision quality. Nonetheless, it is facing a growing pressure to provide visibility and traceability of the purchase, to reduce fraud, to improve flexibility and to ensure communication between everyone involved. Currently, private health institutions in Peru choose to implant different software products within the same company with restricted visibility access to other concerned parties and based on information from a single source. A new alternative is Blockchain technology, since it provides a single source of shared truth to all participants and ensures that the information cannot be altered, thus offering high levels of transparency that, together with IoT technology, creates not only visibility about where things are, but also traceability, showing the current state of things. / Revisón por pares

Internet växer idag konstant och det förväntas finnas fler än 50 miljarder enheter anslutna till internet efter år 2020. Flertalet av dessa enheter kommer vara små, inbäddade enheter som är anslutna och kommunicerar via Internet of Things. Att försäkra att dessa enheter är säkra och skyddade från obehörig åtkomst har varit något som väckt oro ända sedan så kallade botnets visat sig kapabla till att ta över och utnyttja hundratusentals Internet of Things anslutna enheter för att utföra Distributed Denial-of-Service attacker. Målet med denna studie har varit att ställa frågan samt svara på hur stor påverkan Internet of Things enheter har när de utnyttjas för att utföra en Distributed Denial-of-Service attack i ett lokalt trådlöst nätverk. För att besvara denna fråga har denna avhandling forskat kring områden som rör cybersäkerhet, Internet of Things, samt metoder för att utföra Distributed Denial-of-Service attacker. Denna studie har implementerat ett scenario som mäter påverkan vid en Distributed Denial-of-Service attack när upp till sex emulerade Internet of Things enheter som attackerar en ensam offerdator via TCP, UDP och HTTP flood metoder i ett lokalt nätverk. Flertalet test har utförts samt analyserats. Resultatet från denna studie presenteras och jämförs vilket visar att offerdatorn är relativt kapabel till att försvara sig mot TCP och HTTP floods med upp till sex Internet of Things enheter vid respektive attack.  Det implementerade scenariot och metoden är huruvida kapabel till att tungt överbelasta offerdatorn när UDP flood används för samtliga sex Internet of Things enheter. / The internet is constantly growing, we are expecting there to be more than 50 billion devices on the internet past 2020. Many of these devices will be small, embedded devices connected and communicating using the Internet of Things. Keeping these devices secure and protected from unauthorized access has been a raising concern in part due to botnets that have proven capable of exploiting hundreds of thousands of Internet of Things devices to carry out Distributed Denial-of-Service attacks in the past. The objective of this study has been to answer how big of an impact compromised IoT devices might have when exploited to carry out a Distributed Denial-of-Service attack in a Wireless Local Area Network. To answer this question this thesis has done research in the fields concerning cyber-security, the Internet of Things, and methods of distributing Denial-of-Service attacks. This study implements a scenario that measures the impact of a Distributed Denial-of-Service attack utilizing up to six emulated IoT devices that attack a single victim computer using a TCP, UDP or HTTP flood. Several tests have been performed and analyzed. The results from this work are presented and compared and shows that the victim computer is relatively capable of mitigating and defending against the TCP and HTTP flood with up to six utilized IoT devices in each attack. In the implemented scenario and method are however capable of heavily congesting and overwhelming a single victim computer when utilizing a UDP flood with all six IoT devices simultaneously attacking.

Indiana University-Purdue University Indianapolis (IUPUI) / The Internet of Things (IoT) is a natural evolution of the Internet and is becoming more and more ubiquitous in our everyday home, enterprise, healthcare, education, and many other aspects. The data gathered and processed by IoT networks might be sensitive and that calls for feasible and adequate security measures. The work in this thesis describes the use of the Wi-Fi technology in the IoT connectivity, then proposes a new approach, the Proactive Wireless Protected Access (PWPA), to protect the access networks. Then a new end to end (e2e) IoT security model is suggested to include the PWPA scheme. To evaluate the solutions security and performance, rstly, the cybersecurity triad: con dentiality, integrity, and availability aspects were discussed, secondly, the solutions performance was compared to a counterpart e2e security solution, the Secure Socket Layer security. A small e2e IoT network was set up to simulate a real environment that uses HTTP protocol. Packets were then collected and analyzed. Data analysis showed a bandwidth e ciency increase by 2% (Internet links) and 12% (access network), and by 344% (Internet links) and 373% (access network) when using persistent and non-persistent HTTP respectively. On the other hand, the analysis showed a reduction in the average request-response delay of 25% and 53% when using persistent and non-persistent HTTP respectively. This scheme is possibly a simple and feasible solution that improves the IoT network security performance by reducing the redundancy in the TCP/IP layers security implementation.

Indiana University-Purdue University Indianapolis (IUPUI) / The nanotechnology materials have been used for high sensitivity sensing devices due to their ability to alter their properties in response to the environmental parameters such as temperature, pressure, gas, electromagnetic, and chemicals. The features of employing nanoparticles on top of graphene thin film have driven the hypothesis of achieving high sensing nanotechnology devices. This study demonstrates a novel approach for designing a low noise nanoparticle based gas sensing device with internet of things (IoT) capability. The system is capable of minimizing cross-talk between multiple channels of amplifiers arranged on one chip using guard rings. Graphene mono-layer is utilized as sensing material with the sensitivity catalyzed by addition of gold nano-particles on its surface. The signal from the sensing unit is received by an offset cancellation amplifying system using a system on chip (SoC) approach. IoT capability of the sensing device is developed using FRDM K64f micro-controller board which sends messages on IoT platform when a gas is sensed. The message is received by an application created and sent as an email or message to the user. This study details the mathematical models of the graphene based gas sensing devices, and the interface circuitry that drives the differential potentials, resulting from the sensing unit. The study presents the simulation and practical model of the device, detailing the design approach of the processing unit within the SoC system and wireless implementation of it. The sensing device was capable of sensing gas concentration from 5% to 100% using both the resistive and capacitive based models. The I-V characteristics of the FET sensing device was in agreeable with the other models. The SoC processing unit was designed using cadence tools, and simulation results showed very high CMRR that enable the amplifier to sense a very low signal received from the gas sensors. The cross talk noise was reduced by surrounding guard rings around the amplifier circuits. The layout was accomplished with 45nm technology and simulation showed an offset voltage of 17μV.

Teknologi påverkar alla delar av samhället, inte minst företag och organisationer somtvingas till digitalisering för att förbli relevanta. Internet of Things (IoT) är en populärteknologi för effektivisering och automation av processer.Den teknologiska utvecklingen sker i en takt som gör att det kan vara svår att integrerateknologi i kulturella och sociala sammanhang. För att hantera de utmaningar somdigitalisering medför behöver variabler som samarbete utvecklas för att man ska kunnadra nytta av teknologins utveckling i miljöer som arbetsplatser och städer.Denna studie har därför tagit ansats i ett kommunalt samarbete rörandeimplementeringen av en verksamhetsgemensam IoT-plattform. Studien har ämnatundersöka de omständigheter som ligger till grund för interkommunala samarbetenmellan förvaltningar och bolag vars gemensamma mål är att utgöra en smart stad (eng.smart city) med hjälp av Internet of Things.Detta arbete har bedrivits som en fallstudie vars föremål för undersökning har varitJönköpings kommun. Undersökningen har tagit en kvalitativ ansats och haft sin grundi semistrukturerade intervjuer vars subjekt har utgjorts av ett urval av representanter förnoga utvalda verksamheter inom kommunens regi.Resultaten av datainsamlingen visade att det finns en stark koppling mellanvälfungerande interkommunala samarbeten och ett övergripande gemensamt synsättmed etablerade informationskanaler. En avgörande faktor för framgångsrikasamarbeten har visat sig vara att involvera samtliga delar av organisationen för att dranytta av insikter och kompetenser som finns i olika skikt av verksamheterna ochetablera ett gemensamt tankesätt. Resultatet har lett fram till upptäckten att nyttogradenav samarbeten framför allt ligger i att identifiera verksamheter med gemensammaintressen och behov.Denna studie har lagt grunden för att utreda aspekter av samarbete mellan olikaverksamheter och öppnar upp för vidare forskning på samarbeten inom organisationeroch företag med fokus på den interna verksamhetens struktur. / Technology impacts all aspects of society, especially businesses and organizations thatare forced to undergo digitalization to remain relevant. Internet of Things (IoT) is apopular technology used for streamlining and automation of processes.The rapid pace of technological development makes it challenging to integrate it intocultural and social contexts. To address the challenges brought by digitalization,variables such as collaboration need to be developed in order to take advantage oftechnological advancements in environments such as workplaces and cities.This study has therefore focused on a municipal collaboration regarding theimplementation of a shared IoT platform. The study aimed to examine thecircumstances underlying intermunicipal collaborations between departments andcompanies with a common goal of creating a smart city using the Internet of Things.This work has been conducted as a case study with Jönköping Municipality as thesubject of investigation. The study took a qualitative approach and was based on semi-structured interviews with representatives from selected departments within themunicipality.The results of the data collection showed a strong connection between well-functioningintermunicipal collaborations and an overall shared mindset with establishedcommunication channels. A crucial factor for successful collaborations has been theinvolvement of all parts of the organization in order to leverage insights and expertisefrom different layers of the operations and establish a common mindset. The findingshave led to the discovery that the degree of novelty in collaborations primarily lies inidentifying operations with common interests and needs.This study has laid the foundation for investigating aspects of collaboration betweendifferent operations and opens for further research on collaborations withinorganizations and companies with a focus on the internal operational structure.

Disc golf is one of the fastest growing sports in Sweden and the countrywide playerbase is steadily growing. In order to meet this increased demand, municipalities and sports associations alike have built new courses all around the country, which all require maintenence. Without an accurate way of determining course usage, it's difficult to guage how much money should be put towards maintaining and developing additional courses. The aim of this project was to design and test a people-counting system for disc golf couses which could provide this information to both players and course owners. Computer vision, wireless communication and sensor technologies were core topics explored during the development of the working prototype.