Context: Smart farming, agritech, is growing in popularity and is starting to develop rapidly with some already existing technology that is implemented in agriculture for both industrial and private use. Objectives: The goal of this thesis is to investigate the benefits and issues with implementing technology in agriculture, agritech. In this thesis the investigation and research is performed by conduction a literature study and an experiment. Realization: A prototype was created to monitor the soil moisture level and calculating the average soil moisture value, then water the plants when needed. This was then compared to a manually watered pot to investigate if agritech could reduce the water usage when maintaining plants. Results: The result of the experiment indicates that it is possible to improve the use of resources such as human labor, time spent on maintaining the plants and water usage. Conclusions: The conclusion of this thesis is with the help of agritech, human workers can spend more time on other tasks and maintain the technology implemented. Instead of observing the plants to see if they need watering and watering them manually. Water usage may also be minimized with the help of sensors that make sure the plants only get watered when needed by constantly checking the soil moisture level.

<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.

IoT platforms face huge challenges in deploying robust authentication mechanisms due to the fact that edge devices and resource-constrained devices may not have enough compute and storage capabilities to deploy and run existing mechanisms, which involve in general complex computations. Moreover, establishing end-to-end device authentication in the Internet of Things (IoT) networks is challenging because of the heterogeneous nature of IoT devices. One of the well-known challenges confronting the IoT infrastructure is related to authentication. Many IoT devices rely on weak authentication schemes, which has led in the last few years to several successful and widely publicized hacking incidents. According to the ISO/IEC 27002 standard, authentication is the process of determining whether something is, in fact, what it is declared to be. Authentication is considered the main gate to protect IoT networks from various security threats; determining who the entity is (authentication) is of high importance to establish a secure session between IoT devices. This dissertation identifies gaps in the literature and presents new authentication schemes and security mechanisms to improve IoT security and privacy against common attacks such as replay and impersonation. This research enhances IoT security and privacy by introducing a new lightweight mutual authentication and key exchange protocol for IoT based on dynamic identity and cumulative chained-hash. Nodes can anonymously and mutually authenticate and establish a session with the controller node using dynamic identities and temporary symmetric keys in an unlinkable and untraceable manner. Moreover, the enforcement of security policies between nodes is guaranteed by setting up virtual domain segregation and restricting node capabilities of sending and receiving data to or from other nodes. The Cumulative chained-hash technique is introduced as a way to ensure the identity of the sender (through challenge-response). Additionally, we introduce a new anonymous device- to-device mutual authentication and key exchange protocol based on the ZigBee technique. The proposed protocol relies on symmetric encryption and counter and enables IoT devices to authenticate in the network and agree on a shared secret session key when communicating with each other via a trusted intermediary (home controller). To achieve forward secrecy, the session keys are changed frequently after every communication session. The proposed scheme achieves secure, anonymous authentication with the unlinkability and untraceability of IoT device transactions. The security of the protocols is evaluated and simulated using three different methods: informal analysis, formal analysis using the Burrows–Abadi–Needham logic (BAN), and model-checking using the automated validation of Internet security protocols and applica- tions (AVISPA) toolkit. The overhead and efficiency of the proposed schemes are analyzed and compared with other related schemes. The results showed that our protocols are in general more efficient. / Graduate

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%.

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 / Welcome to this special issue of the Future Generation Computer Systems (FGCS) journal. The special issue compiles seven technical contributions that significantly advance the state-of-the-art in exploration of Internet of Things (IoT) generated big data using semantic web techniques and technologies.

Yes / The rapid proliferation of internet-connected devices in public and private spaces offers humanity numerous conveniences, including many safety benefits. However, unlocking the full potential of the Internet of Things (IoT) would require the assurance that IoT devices and applications do not pose any safety hazards to the stakeholders. While numerous efforts have been made to address security-related challenges in the IoT environment, safety issues have yet to receive similar attention. The safety attribute of IoT systems has been one of the system’s vital non-functional properties and a remarkable attribute of its dependability. IoT systems are susceptible to safety breaches due to a variety of factors, such as hardware failures, misconfigurations, conflicting interactions of devices, human error, and deliberate attacks. Maintaining safety requirements is challenging due to the complexity, autonomy, and heterogeneity of the IoT environment. This article explores safety challenges across the IoT architecture and some application domains and highlights the importance of safety attributes, requirements, and mechanisms in IoT design. By analysing these issues, we can protect people from hazards that could negatively impact their health, safety, and the environment. / The full text will be available at the end of the publisher's embargo: 11th Feb 2025

Yes