I detta projekt, som gjorts på uppdrag av IoT Sverige, har en produkt skapats åt derasDemorum för att visa hur Internet of Things fungerar och vad man kan använda dettill. Rapporten beskriver hur arbetet har utformats fram till den färdiga produktensom, utifrån deras önskemål, består av en koldioxid- och en rörelsesensor koppladetill en enkortsdator. Med hjälp av kodade skript skickas den insamlade datan till enserver placerad på ett så kallat moln. I grafer på en hemsida kan man därefter se datan illustrativt och det enda man då behöver är en dator med internetuppkoppling.

The main goal of this thesis is to discover how an emerging technological megatrend, the Internet of Things, changes the business model of the Microsoft OEM team. OEM business is fully dependent on sales of Windows licenses; however, the PC market is declining and whole Microsoft is shifting into offering of cloud services while being financed by the OEM revenues. Nevertheless, the strategy of how the OEM team fits into the future of cloud offerings is clearly missing. To discover what changes should be made inside the current business model, 13 interviews with managers across Microsoft were conducted and analyzed. Identified themes were assigned to business model building blocks, ranked by the importance, and supported by relevant quotes from interviews as well as the theoretical background. The most important subjects of changes were identified as partners, activities and value proposition; however, changes will be seen in every aspect of the OEM business. As a result, specific changes in building blocks are described and the first steps towards the Internet of Things strategy suggested. That will ensure the relevance of the conservative OEM team in the connected future.

In the "Internet of Things" (IoT), the things will be able to sense, communicate, and interact. They will also exchange data, information and knowledge, and locate themselves and other things that surround them. In order to be able to interact, the things need to recognize that they are in proximity of other things. It is anticipated that the most widespread components of the IoT will be passive radio frequency identification (RFID) tags because they are inexpensive and provide automatic identification. However, passive RFID tags are not capable of performing complex operations, such as proximity detection and localization, which will be required in future networks. In this thesis, we describe existing problems with current RFID systems and survey potential solutions for localization and proximity detection. Then we present a new RFID device called "Sense-a-Tag" (ST) that can passively detect and decode backscattered signals from tags in its proximity. There have already been an attempt to use this device for tracking. However, detailed analysis of the performance of the ST especially for proximity detection has not been performed yet. We show that when STs are added to a standard RFID system, the problems of proximity detection and localization with RFID tags can readily be solved. Then we applied ST-based system for identifying people and object interactions. The potential uses of ST as an augmented device for IoT applications are discussed in this thesis. Advantages and limitations of an ST based RFID system have been investigated in details for each application. Results obtained from real experiments illustrate that an ST-based RFID system is feasible for proximity detection applications. In addition, a special software is developed in C\# to process the data and run a localization algorithm based on proximity detection information. The same software has been used for tracking people's activity. Different scenarios have been considered in the experiments. We tried to consider majority of factors that might affect the accuracy in the experiments including: angle and distance between the reader/ST and tags, timing in sending queries, presence of human body, etc. The simulations based on real experiments and results illustrates that an ST-based RFID system can be a realistic solution for proximity detection and localization for Location Positioning systems (LPS) and activity monitoring in future IoT.

This paper is situated at the border between privacy studies and law and media studies. More precisely, the research aims to find out how the discourse on privacy is reshaped in the context of the upcoming technological changes envisaged in the scenery of the IoT. In a world where potentially all items become connected, the era of the Web 2.0 seems to fade away and leave the floor for a new era where the machines are also empowered as to create human-related content. One of the dimensions of this technological shift is the ubiquity of data and the continuous flow of information it involves. In this new landscape, individual privacy is a construct that necessitates further reflection and content analysis. Where legislation sets up for being the patron of data protection, the European legal rules are undergoing a reform process aiming to adapt the legal framework to the social realities. In light of the above, this paper starts by mapping how privacy was conceptualized by analyzing different theories set up in various media contexts. It follows by sketching the new media context of the IoT and mainly how it functions and where it applies. In order to draw a conclusion on how the new type of communications under the IoT can carve the notion of privacy, this paper will analyze the legal texts that aim to regulate the field of privacy. Legal texts are chosen as empirical material because they are the best barometer of social realities. In addition, in this particular field, the European legal background is subject to a reformation aiming to impose stricter rules that mirror the need for a stronger protection of privacy under the fast technological changes. After the analysis of the empirical material, the research applies the findings on the IoT to the legal background in order to assess whether the legal regime is strong enough to protect personal data. After carrying out this examination, the theories presented at the beginning of the paper are tested under the IoT scenery in order to assess which one is the most appropriate for the new context. The analysis reveals that surveillance theories and especially the panspectric gaze theory are the most applicable in the IoT scenery.

Recently, the wide range of communication applications has greatly increased the number of connected devices, and this trend continues by emerging new technologies such as Internet-of-Things (IoT) and vehicular ad hoc networks (VANETs). The increase in the number of devices may sooner or later cause wireless spectrum shortage. Furthermore, with the limited wireless spectrum, transmission efficiency degrades when the network faces a super-dense situation. In IEEE 802.11ah-based networks whose channel access protocol is basically a contention-based one, the protocol loses its efficiency when the total number of contending users grows. VANETs suffer from the same problem, where broadcasting and receiving safety messages, i.e., beacons, are critical. An inefficient medium access control (MAC) can negatively impact the network's reliability. Effective resource management solutions are needed to improve the network's reliability and scalability considering the features of different types of networks. In this work, we address the resource management problem in dense wireless networks in vehicle-to-everything (V2X) systems and IoT networks. For IoT networks, e.g., sensor networks, in which the network topology is quite stable, the grouping technique is exploited to make the stations (STAs) compete in a group to mitigate the contention and improve the channel access quality. While, in VANETs, devices are mobile and the network topology changes over time. In VANETs, beacons should be broadcast periodically by each vehicle reliably to improve road safety. Therefore, how to share the wireless resources to ensure reliability and scalability for these dense static and mobile wireless networks is still a difficult and open problem. In static IoT networks, we apply the Max-Min fairness criterion to the STAs' throughput to group the STAs to ensure network performance and fairness. Formulation of the problem results in a non-convex integer programming optimization problem which avoids hidden terminals opportunistically. As solving the optimization problem has a high time complexity, the Ant Colony Optimization (ACO) method is applied to the problem to find the sub-optimal solution. To support reliable and efficient broadcasting in VANET, wireless resources are divided into basic resource units in the time and frequency domains, and a distributed and adaptive reservation-based MAC protocol (DARP) is proposed. For decentralized control in VANETs, each vehicle's channel access is coordinated with its neighbors to solve the hidden terminal problem. To ensure the reliability of beacon broadcasting, different kinds of preambles are applied in DARP to support distributed reservation, detect beacon collisions, and resolve the collisions. Once a vehicle reserves a resource unit successfully, it will not release it until a collision occurs due to topology change. Protocol parameters, including transmission power and time slots duration, can be adjusted to reduce collision probability and enhance reliability and scalability. Simulation of urban mobility (SUMO) is used to generated two different city traces to assess the DARP's performance. Then, a distributed network coding-based MAC protocol (NC-MAC) is proposed to support reliable single-hop vehicle-to-vehicle (V2V) beacon broadcasting. We combine the preamble-based feedback mechanism, retransmissions, and network coding together to enhance broadcasting reliability. We deploy the preamble mechanism to facilitate the negative acknowledgment (NACK) and retransmission request procedures. Moreover, linear combinations of missed beacons are generated according to the network coding (NC) principles. We also use SUMO to evaluate the NC-MAC's performance in highway and urban scenarios. Group-casting and applying multi-hop communication can ensure reliability in V2X systems. As an extension of the proposed NC-MAC, a distributed grouping and network coding-assisted MAC protocol (GNC-MAC) is proposed to support reliable group-casting and multi-hop communication, which can address blockchain protocols' requirements. We propose a new grouping protocol by combining preamble-based feedback mechanism, multi-hop communication, and network coding to improve group-casting reliability. The preamble mechanism is responsible for reporting a NACK and requesting retransmission due to beacon missing. The missed beacons are combined according to the NC principles and sent on a resource block. / Graduate

Internet of Things (IoT) has great potential in enabling many beneficial applications (i.e., connected vehicle applications). Named Data Networking (NDN) recently emerges as a promising networking paradigm in supporting IoT due to its data-centric architecture. In this dissertation, we present our research on design a scalable, efficient and secure ndn-based data retrieval framework for Internet of Things. Our work includes three parts:First, we envision an NDN-based Connected Vehicles (CV) application framework with a distributed data service model, as CV is a typical scenario of IoT. The scalability of the framework is greatly challenged by the fast mobility and vast moving area of vehicles. To handle such an issue, we develop a novel hyperbolic hierarchical NDN backbone architecture (H2NDN) by exploiting the location dependency of CV applications. H2NDN designs the backbone routers topology and the data/interest namespace by following the hierarchical architecture of geographic locations. The efficient data searching only requires static forwarding information base (FIB) configuration over NDN routers. To avoid overloading high-level routers, H2NDN integrates hyperbolic routing through carefully designed hyperbolic planes.Second, a distributed adaptive caching strategy is proposed to improve the efficiency of data caches on NDN routers. NDN provides native support to cache data at routers for future Interest packets. As we model the caching problem, the goal of cache allocation is to maximize the savings of Interest/Data forwarding hops under the limited cache space on each router. We discuss the impracticality of global optimization and provide the local caching method. Extensive ndnSIM based simulation with real traffic data proves the efficiency and scalability of the proposed H2NDN architecture.Finally, although NDN provides some security advantages such as secures data directly and uses name semantics to enable applications to reason about security, employing NDN to support IoT applications nevertheless presents some new challenges about security. In this dissertation, we focus on two resultant attacks that are not effectively handled in current studies, namely the targeted blackhole attack and the targeted content poisoning attack. We propose a lightweight and efficient approach named SmartDetour to tackle the two attacks. To ensure high scalability and collusion-resilience, SmartDetour lets each router respond to attacks (i.e., packet drops or corrupted data) independently in order to isolate attackers. The core solution contains a reputation-based probabilistic forwarding strategy and a proactive attacker detection algorithm. Extensive ndnSIM based simulation demonstrates the efficiency and accuracy of the proposed SmartDetour.

This master's thesis is about the design and development of a flower pot sensor, which monitors data related to the cultivation of houseplants. The sensor sends the data to the cloud, where the analysis is performed and the evaluated living conditions of the monitored plant are displayed to the user.

The continuing growth of Internet-connected devices presents exciting opportunities for future technology. These Internet of Things (IoT) products are being manufactured and interleaved with many everyday activities, which is creating a larger security concern. Sensors will collect previously unimaginable amounts of private and public data and transmit all of it through an easily observable wireless medium in order for other devices to perform data analytics. As more and more devices are produced, many are lacking a strong security foundation in order to be the "first to market." Moreover, current security techniques are based on protocols that were designed for more-capable devices such as desktop computers and cellular phones that have ample power, computational ability, and memory storage. Due to IoT's technological infancy, there are many security challenges without proper solutions. As IoT continues to grow, special considerations and protections must be in place to properly secure this data and protect the privacy of its users. This dissertation highlights some of the major challenges related to IoT and prioritizes their impacts to help identify where gaps are that must be filled. Focusing on these high priority concerns, solutions are presented that are tailored to IoT's constraints. A security feature-based framework is developed to help characterize classes of devices to help manage the heterogeneous nature of IoT devices and networks. A novel physical device authentication method is presented to show the feasibility in IoT devices and networks. Additional low-power techniques are designed and evaluated to help identify different security features available to IoT devices as presented in the aforementioned framework. / Doctor of Philosophy / The Internet has been gaining a foothold in our everyday lives. Smart homes, smart cars, and smart cities are becoming less science fiction and more everyday realities. In order to increase the public’s general quality of life, this new Internet of Things (IoT) technological revolution is adding billions of devices around us. These devices aim to collect unforeseen amounts of data to help better understand environments and improve numerous aspects of life. However, IoT technology is still in its infancy, so there are still many challenges still remaining. One major issue in IoT is the questionable security for many devices. Recent cyber attacks have highlighted the shortcomings of many IoT devices. Many of these device manufacturers simply wanted to be the first in a niche market, ignoring the importance of security. Proper security implementation in IoT has only been done by a minority of designers and manufacturers. Therefore, this document proposes a secure design for all IoT devices to be based. Numerous security techniques are presented and shown to properly protect the data that will pass through many of these devices. The overall goal for this proposed work aims to have an overall security solution that overcomes the current shortfalls of IoT devices, lessening the concern for IoT’s future use in our everyday lives.

The Internet of Things (IoT) is a promising networking technology that will interconnect a plethora of heterogeneous wireless devices. To support the connectivity across a massive-scale IoT, the scarce wireless communication resources must be appropriately allocated among the IoT devices, while considering the technical challenges that arise from the unique properties of the IoT, such as device heterogeneity, strict communication requirements, and limited device capabilities in terms of computation and memory. The primary goal of this dissertation is to develop novel resource management frameworks using which resource-constrained IoT devices can operate autonomously in a dynamic environment. First, a comprehensive overview on the use of various learning techniques for wireless resource management in an IoT is provided, and potential applications for each learning framework are proposed. Moreover, to capture the heterogeneity among IoT devices, a framework based on cognitive hierarchy theory is discussed, and its implementation with learning techniques of different complexities for IoT devices with varying capabilities is analyzed. Next, the problem of dynamic, distributed resource allocation in an IoT is studied when there are heterogeneous messages. Particularly, a novel finite memory multi-state sequential learning is proposed to enable diverse IoT devices to reallocate the limited communication resources in a self-organizing manner to satisfy the delay requirement of critical messages, while minimally affecting the delay-tolerant messages. The proposed learning framework is shown to be effective for the IoT devices with limited memory and observation capabilities to learn the number of critical messages. The results show that the performance of learning framework depends on memory size and observation capability of IoT devices and that the learning framework can realize low delay transmission in a massive IoT. Subsequently, the problem of one-to-one association between resource blocks and IoT devices is studied, when the IoT devices have partial information. The one-to-one association is formulated as Kolkata Paise Restaurant (KPR) game in which an IoT device tries to choose a resource block with highest gain, while avoiding duplicate selection. Moreover, a Nash equilibrium (NE) of IoT KPR game is shown to coincide with socially optimal solution. A proposed learning framework for IoT KPR game is shown to significantly increase the number of resource blocks used to successful transmit compared to a baseline. The KPR game is then extended to consider age of information (AoI), which is a metric to quantify the freshness of information in the perspective of destination. Moreover, to capture heterogeneity in an IoT, non-linear AoI is introduced. To minimize AoI, centralized and distributed approaches for the resource allocation are proposed to enable the sharing of limited communication resources, while delivering messages to the destination in a timely manner. Moreover, the proposed distributed resource allocation scheme is shown to converge to an NE and to significantly lower the average AoI compared to a baseline. Finally, the problem of dynamically partitioning the transmit power levels in non-orthogonal multiple access is studied when there are heterogeneous messages. In particular, an optimization problem is formulated to determine the number of power levels for different message types, and an estimation framework is proposed to enable the network base station to adjust power level partitioning to satisfy the performance requirements. The proposed framework is shown to effectively increase the transmission success probability compared to a baseline. Furthermore, an optimization problem is formulated to increase sum-rate and reliability by adjusting target received powers. Under different fading channels, the optimal target received powers are analyzed, and a tradeoff between reliability and sum-rate is shown. In conclusion, the theoretical and performance analysis of the frameworks proposed in this dissertation will prove essential for implementing an appropriate distributed resource allocation mechanisms for dynamic, heterogeneous IoT environments. / Doctor of Philosophy / The Internet of Things (IoT), which is a network of smart devices such as smart phones, wearable devices, smart appliances, and environment sensors, will transform many aspects of our society with numerous innovative IoT applications. Those IoT applications include interactive education, remote healthcare, smart grids, home automation, intelligent transportation, industrial monitoring, and smart agriculture. With the increasing complexity and scale of an IoT, it becomes more difficult to quickly manage the IoT devices through a cloud, and a centralized management approach may not be viable for certain IoT scenarios. Therefore, distributed solutions are needed for enabling IoT devices to fulfill their services and maintain seamless connectivity. Here, IoT device management refers to the fact that the system needs to decide which devices access the network and using which resources (e.g., frequencies). For distributed management of an IoT, the unique challenge is to allocate scarce communication resources to many IoT devices appropriately. With distributed resource management, diverse IoT devices can share the limited communication resources in a self-organizing manner. Distributed resource management overcomes the limitations of centralized resource management by satisfying strict service requirements in a massive, complex IoT. Despite the advantages and the opportunities of distributed resource management, it is necessary to address the challenges related to an IoT, such as analyzing intricate interaction of heterogeneous devices, designing viable frameworks for constrained devices, and quickly adapting to a dynamic IoT. Furthermore, distributed resource management must enable IoT devices to communicate with high reliability and low delay. In this regard, this dissertation investigates these critical IoT challenges and introduces novel distributed resource management frameworks for an IoT. In particular, the proposed frameworks are tailored to realistic IoT scenarios and consider different performance metrics. To this end, mathematical frameworks and effective algorithms are developed by significantly extending tools from wireless communication, game theory, and machine learning. The results show that the proposed distributed wireless resource management frameworks can optimize key performance metrics and meet strict communication requirements while coping with device heterogeneity, massive scale, dynamic environment, and scarce wireless resources in an IoT.

The growth of the internet towards the Internet of Things (IoT) has impacted the way we live. Intelligent (smart) devices which can act autonomously has resulted in new applications for example industrial automation, smart healthcare systems, autonomous transportation to name just a few. These applications have dramatically improved the way we live as citizens. While the internet is continuing to grow at an unprecedented rate, this has also been coupled with the growing demands for new services e.g. machine-to machine (M2M) communications, smart metering etc. Transmission Control Protocol/Internet Protocol (TCP/IP) architecture was developed decades ago and was not prepared nor designed to meet these exponential demands. This has led to the complexity of the internet coupled with its inflexible and a rigid state. The challenges of reliability, scalability, interoperability, inflexibility and vendor lock-in amongst the many challenges still remain a concern over the existing (traditional) networks. In this study, an evolutionary approach into implementing a "Scalable IoT Data Transmission Network" (S-IoT-N) is proposed while leveraging on existing transport networks. Most Importantly, the proposed evolutionary approach attempts to address the above challenges by using open (existing) standards and by leveraging on the (traditional/existing) transport networks. The Proof-of-Concept (PoC) of the proposed S-IoT-N is attempted on a physical network testbed and is demonstrated along with basic network connectivity services over it. Finally, the results are validated by an experimental performance evaluation of the PoC physical network testbed along with the recommendations for improvement and future work.