<p>Internet of Things (IOT) has become the key theme of the world since 2009 and been considered as the third wave in the information industry after the waves of Computer, Internet and Mobile Radio Communication. It is expected to have a strong influence on small and medium companies (SMEs). However, little research on what and how the influence of IOT on the SME’s development can be found in the literature. The purpose of the thesis is to examine how IOT influences the organizational changes in SMEs. Finally, suggestions for strategy developments will be proposed to assist the SMEs in making their organization changes successfully. Two main models are applied for this part: change model (OD model) and business model. Moreover, the SWOT theory is adopted to identify the SMEs position in IOT era. We collect the primary data through launching the survey on internet. After data documentation, we apply “Approximation of the Probability Hypothesis Testing” Method to conduct data analysis. After researching, we found out the IOT brings “revolution” change to the Logistics of SMEs while only “adapting” change for Manufactures’ business development. Compare to logistic industry, the manufacturing SMEs seldom adopts IT technologies for their selling channel because they are lacking of resources and knowledge for the new technologies. We suggest the logistic SMEs should establish logistic network between the logistic companies to enhance information and resource integration. For the manufacturing SMEs should apply knowledge management and change the companies into learning organizations. In future, IOT will bring radical changes for manufacturers, which are the biggest area with application of IOT technology. Nevertheless, the logistics industry might go out of fashion. In other words, logistics industry may die out or change to other functions.</p>

Internet of Things (IOT) has become the key theme of the world since 2009 and been considered as the third wave in the information industry after the waves of Computer, Internet and Mobile Radio Communication. It is expected to have a strong influence on small and medium companies (SMEs). However, little research on what and how the influence of IOT on the SME’s development can be found in the literature. The purpose of the thesis is to examine how IOT influences the organizational changes in SMEs. Finally, suggestions for strategy developments will be proposed to assist the SMEs in making their organization changes successfully. Two main models are applied for this part: change model (OD model) and business model. Moreover, the SWOT theory is adopted to identify the SMEs position in IOT era. We collect the primary data through launching the survey on internet. After data documentation, we apply “Approximation of the Probability Hypothesis Testing” Method to conduct data analysis. After researching, we found out the IOT brings “revolution” change to the Logistics of SMEs while only “adapting” change for Manufactures’ business development. Compare to logistic industry, the manufacturing SMEs seldom adopts IT technologies for their selling channel because they are lacking of resources and knowledge for the new technologies. We suggest the logistic SMEs should establish logistic network between the logistic companies to enhance information and resource integration. For the manufacturing SMEs should apply knowledge management and change the companies into learning organizations. In future, IOT will bring radical changes for manufacturers, which are the biggest area with application of IOT technology. Nevertheless, the logistics industry might go out of fashion. In other words, logistics industry may die out or change to other functions.

With technology advancements and the prices of electronic components reducing over the last fifteen years, many devices and systems that would have been proprietary only for large companies or industry giants are becoming an everyday household item. Various areas of technology have been benefiting from this but one of the biggest is the Internet of Things (IoT).With the prevalence of IoT, it has been integrated into houses, small businesses, farms, agriculture, building automation, etc. and the user population is now a resource to the industry as they complete personal projects. Within any project there are always limitations, this might be a limited time, limited funds, limited distance, or limitations of the devices being used. This study proposes to evaluate two low-powered networks, Narrowband Internet of Things (NB-IoT)and Long-Range Wide-Area Network(LoRaWAN), in different environments with the goal of understanding where the signal propagation is better and what distances can be reached despite obstructions. Distances and signal propagations, when measured by the manufacturers are often evaluated in ideal conditions which is rarely the case when utilized in the field. This creates a gap in the deployment and the end-users are frequently faced with diminished performances. As IoT is predominantly employed in urban and rural areas this study will focus on those two settings by testing the Received Signal Strength Indicator (RSSI)at various distances. The evaluation testing of the two systems showed each system performing more consistently in rural areas but neither had 100% coverage at any locations.

The Internet of Things (IoT) is spearheading a significant revolution in the realm of computing systems for the next generation. IoT has swiftly permeated various domains, including healthcare, manufacturing, military, and transportation, becoming an essential component of numerous smart devices and applications. However, as the number of IoT devices proliferates, security concerns have surged, resulting in severe attacks in recent years. Consequently, it is imperative to conduct a comprehensive investigation into IoT networks to identify and address vulnerabilities in order to preempt potential adversarial activities. The aim of this research is to examine different IoT-based systems and comprehend their security weaknesses. Additionally, the objective is to develop effective strategies to mitigate vulnerabilities and explore the security loopholes inherent in IoT-based systems, along with a plan to rectify them. IoT-based systems present unique challenges due to the expanding adoption of IoT technology across diverse applications, accompanied by a wide array of IoT devices. Each IoT network has its own limitations, further compounding the challenge. For instance, IoT devices used in sensor networks often face constraints in terms of resources, possessing limited power and computational capabilities. Moreover, integration of IoT with existing systems introduces security issues. A prime example of this integration is found in connected cars, where traditional in-vehicle networks, designed to connect internal car components, must be highly robust to meet stringent requirements. However, modern cars are now connected to a wide range of IoT nodes through various interfaces, thus creating new security challenges for professionals to address. This work offers a comprehensive investigation plan for different types of IoT-based systems with varying constraints to identify security vulnerabilities. We also propose security measures to mitigate the vulnerabilities identified in our investigation, thereby preventing adversarial activities. To facilitate the exploration and investigation of vulnerabilities, our work is divided into two parts: resource-constrained IoT-based systems (sensor networks, smart homes) and robustness-constrained IoT-based systems (connected cars). In our investigation of resource-constrained IoT networks, we focus on two widely used service-oriented IoT protocols, namely Universal Plug and Play (UPnP) and Message Queue Telemetry Transport (MQTT). Through a structured phase-by-phase analysis of these protocols, we establish a comprehensive threat model that explains the existing security gaps in communications. The threat models present security vulnerabilities of service-oriented resource-constrained IoT networks and the corresponding security attacks that exploit these vulnerabilities. We propose security solutions to mitigate the identified vulnerabilities and defend against potential security breaches. Our security analysis demonstrates that the proposed measures successfully thwart adversarial activities, and our experimental data supports the feasibility of the proposed models. For robustness-constrained IoT-based systems, we investigate the in-vehicle networks of modern cars, specifically focusing on the Controller Area Network (CAN) bus system, which is widely adopted for connecting Electronic Control Units (ECUs) in vehicles. To uncover vulnerabilities in these in-vehicle networks, we leverage fuzz testing, a method that involves testing with random data. Fuzz testing over the CAN bus is a well-established technique for detecting security vulnerabilities in in-vehicle networks. Furthermore, the automatic execution of test cases and assessment of robustness make CAN bus fuzzing a popular choice in the automotive testing community. However, a major drawback of fuzz testing is the generation of a large volume of execution reports, often containing false positives. Consequently, all execution reports must be manually reviewed, which is time-consuming and prone to human errors. To address this issue, we propose an automatic investigation mechanism to identify security vulnerabilities from fuzzing logs, considering the class, relative severity, and robustness of failures. Our proposed schema utilizes artificial intelligence (AI) to identify genuine security-critical vulnerabilities from fuzz testing execution logs. Additionally, we provide mechanisms to gauge the relative severity and robustness of a failure, thereby determining the criticality of a vulnerability. Moreover, we propose an AI-assisted vulnerability scoring system that indicates the criticality of a vulnerability, offering invaluable assistance in prioritizing the mitigation of critical issues in in-vehicle networks. / Computer and Information Science

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%.

Distributed computing allows effective data storage, processing and retrieval but it poses security and privacy issues. Sensors are the cornerstone of the IoT-based pipelines, since they constantly capture data until it can be analyzed at the central cloud resources. However, these sensor nodes are often constrained by limited resources. Ideally, it is desired to make all the collected data features private but due to resource limitations, it may not always be possible. Making all the features private may cause overutilization of resources, which would in turn affect the performance of the whole system. In this thesis, we design and implement a system that is capable of finding the optimal set of data features to make private, given the device’s maximum resource constraints and the desired performance or accuracy of the system. Using the generalization techniques for data anonymization, we create user-defined injective privacy encoder functions to make each feature of the dataset private. Regardless of the resource availability, some data features are defined by the user as essential features to make private. All other data features that may pose privacy threat are termed as the non-essential features. We propose Dynamic Iterative Greedy Search (DIGS), a greedy search algorithm that takes the resource consumption for each non-essential feature as input and returns the most optimal set of non-essential features that can be private given the available resources. The most optimal set contains the features which consume the least resources. We evaluate our system on a Fitbit dataset containing 17 data features, 4 of which are essential private features for a given classification application. Our results show that we can provide 9 additional private features apart from the 4 essential features of the Fitbit dataset containing 1663 records. Furthermore, we can save 26:21% memory as compared to making all the features private. We also test our method on a larger dataset generated with Generative Adversarial Network (GAN). However, the chosen edge device, Raspberry Pi, is unable to cater to the scale of the large dataset due to insufficient resources. Our evaluations using 1=8th of the GAN dataset result in 3 extra private features with up to 62:74% memory savings as compared to all private data features. Maintaining privacy not only requires additional resources, but also has consequences on the performance of the designed applications. However, we discover that privacy encoding has a positive impact on the accuracy of the classification model for our chosen classification application. / Distribuerad databehandling möjliggör effektiv datalagring, bearbetning och hämtning men det medför säkerhets- och sekretessproblem. Sensorer är hörnstenen i de IoT-baserade rörledningarna, eftersom de ständigt samlar in data tills de kan analyseras på de centrala molnresurserna. Dessa sensornoder begränsas dock ofta av begränsade resurser. Helst är det önskvärt att göra alla insamlade datafunktioner privata, men på grund av resursbegränsningar kanske det inte alltid är möjligt. Att göra alla funktioner privata kan orsaka överutnyttjande av resurser, vilket i sin tur skulle påverka prestanda för hela systemet. I denna avhandling designar och implementerar vi ett system som kan hitta den optimala uppsättningen datafunktioner för att göra privata, med tanke på begränsningar av enhetsresurserna och systemets önskade prestanda eller noggrannhet. Med hjälp av generaliseringsteknikerna för data-anonymisering skapar vi användardefinierade injicerbara sekretess-kodningsfunktioner för att göra varje funktion i datasetet privat. Oavsett resurstillgänglighet definieras vissa datafunktioner av användaren som viktiga funktioner för att göra privat. Alla andra datafunktioner som kan utgöra ett integritetshot kallas de icke-väsentliga funktionerna. Vi föreslår Dynamic Iterative Greedy Search (DIGS), en girig sökalgoritm som tar resursförbrukningen för varje icke-väsentlig funktion som inmatning och ger den mest optimala uppsättningen icke-väsentliga funktioner som kan vara privata med tanke på tillgängliga resurser. Den mest optimala uppsättningen innehåller de funktioner som förbrukar minst resurser. Vi utvärderar vårt system på en Fitbit-dataset som innehåller 17 datafunktioner, varav 4 är viktiga privata funktioner för en viss klassificeringsapplikation. Våra resultat visar att vi kan erbjuda ytterligare 9 privata funktioner förutom de 4 viktiga funktionerna i Fitbit-datasetet som innehåller 1663 poster. Dessutom kan vi spara 26; 21% minne jämfört med att göra alla funktioner privata. Vi testar också vår metod på en större dataset som genereras med Generative Adversarial Network (GAN). Den valda kantenheten, Raspberry Pi, kan dock inte tillgodose storleken på den stora datasetet på grund av otillräckliga resurser. Våra utvärderingar med 1=8th av GAN-datasetet resulterar i 3 extra privata funktioner med upp till 62; 74% minnesbesparingar jämfört med alla privata datafunktioner. Att upprätthålla integritet kräver inte bara ytterligare resurser utan har också konsekvenser för de designade applikationernas prestanda. Vi upptäcker dock att integritetskodning har en positiv inverkan på noggrannheten i klassificeringsmodellen för vår valda klassificeringsapplikation.

This degree project is a design project that investigates digitization in the real estate industry and examines how far the real estate industry has come regarding the integration of new technology. The study highlights how companies work today and what problems and needs can be facilitated through digitization, more specifically the technology IoT (Internet Of Things). IoT makes it possible to connect different devices and sensors to the Internet in order to read information or control the functionality. The study is based on qualitative interviews which are analyzed through methods within User Experience (UX). The target group for the study is people who work with operation and maintenance of real estates. By creating personas and scenarios for the relevant target group, a greater understanding is created of how they work today along with what problems and needs they experience within their professional roles. Through methods within the User Interface (UI) a prototype is created which illustrates the information that is significant to the target group. The information from the properties is communicated via IoT devices. To enable communication between devices and prototype, the system Yggio is used, an API (Application Programming Interface) which is provided by the company Sensative. The study shows how a user-friendly interface can look, function and facilitate work in the real estate industry.

Embedded systems have been present in our daily lives for some time, but trends clearly show a rise in inter-connectivity in such devices. This presents promising new applications and possibilities, but also opens up a lot attack surface. Our goal in this thesis is to find out how you can develop such interconnected embedded systems in a way that guarantees the three major components of information security: Confidentialy, Integrity and Availability. The main focus of security is networked security. In this thesis, a dual approach is taken: investigate the development process of building secure systems, and perform such an implementation. The artifacts produced as byproducts, the software itself, deployment instructions and lessons learned are all presented. It is shown that the process used helps businesses find a somewhat deterministic approach to security, have a higher level of confidence, helps justify the costs that security work entails and helps in seeing security as a business decision. Embedded systems were also shown to present unforeseen obstacles, such as how the lack of a motherboard battery clashes with X.509. In the end, a discussion is made about how far the system can guarantee information security, what problems still exist and what could be done to mitigate them.

<p>How LoRa/LoRaWAN performance evaluation in various environmental scenarios has been an active research topic for researchers, and there are many existing works carried out in outdoor scenarios. On top of that, it is necessary to study how LoRa/LoRaWAN performs in indoor environments as one of the fast-growing IoT network mechanisms. However, few studies are found to work on LoRa and LoRaWAN performance evaluation in indoor scenarios. This study focuses on a real-world experiment to understand how LoRa radio signals behave according to its physical layer parameter settings.</p><p>Data is collected through real-world experiments in a campus environment. The experiments for data sample collection were conducted in September 2021 in the Purdue Campus area in West Lafayette, Indiana, United States. LoRa transceivers with the SX1276 module are deployed operating in the 915MHz frequency band on both LoRa RX and TX end nodes in this study. The data transmitted between LoRa transmitter and LoRa receiver is packet-sized (17 bytes) messages. </p><p>For data collection, LoRa module is configured with 36 PHY parameter settings – three spreading factors (7, 9, 11), three signal bandwidths (125kHz, 250kHz, 500kHz), and four coding rates (4/5, 4/6, 4/7, 4/8). Test devices are the Dragino LoRa shields equipped with SX1276 radio modules in 915MHz frequency bands. The experiment is conducted at three different distances – 10m, 20m, and 40m – between LoRa TX node and LoRa RX node in indoor office buildings in Purdue University West Lafayette Campus, US.</p> <p>The RSSI and SNR are measured to characterize the link performance of Lora. The Received Signal Strength Indication (RSSI) and Signal-to-Noise Ratio (SNR) are two Physical level indicators available on wireless radio chips. In addition to them, the LoRa communication reliability is calculated based on the Received Packet Ratio (RPR) out of transmitted packets with different PHY settings at each distance.</p>