Advancements in technology have brought about a wide variety of devices, such as embedded devices with sensors and actuators, personal computers, smart devices, and health devices. Many of these devices are categorized as “wearables,” meaning that they are intended to be carried and used on one’s body. As this category increases in popularity and functionality, developers will need a convenient way for these devices to communicate with each other and store information in a standardized and ecient manner. The Fog protocol and FogKit framework developed and demonstrated for this thesis address these issues by providing a set of powerful features, including data posting, data querying, event notifications, and network status requests. These features are defined as convenient JSON formatted messages which can be communicated between Bluetooth peripherals using an iOS device running FogKit as router and server.

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.

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Environmental monitoring using wireless sensors plays a key role in detecting hotspots or over-cooling conditions in a data center (DC). Despite a myriad of Data Center Wireless Sensor Network (DCWSN) solutions in literature, their adoption in DCs is scarce due to four challenges: low reliability, short battery lifetime, lack of adaptability, and labour intensive deployment. The main objective of this research is to address these challenges in our specifically designed hierarchical DCWSN, called Low Energy Monitoring Network (LEMoNet). LEMoNet is a two-tier protocol, which features Bluetooth Low Energy (BLE) for sensors communication in the first tier. It leverages multi-gateway packet reception in its second tier to mitigate the unreliability of BLE. The protocol has been experimentally validated in a small DC and evaluated by simulations in a midsize DC. However, since the main application of DCWSNs is in colocation and large DCs, an affordable and fast approach is still required to assess LEMoNet in large scale. As the first contribution, we develop an analytical model to characterize its scalability and energy efficiency in a given network topology. The accuracy of the model is validated through extensive event-driven simulations. Evaluation results show that LEMoNet can achieve high reliability in a network of 4800 nodes at a duty cycle of 15s. To achieve the network adaptability, we introduce and design SoftBLE, a Software-Defined Networking (SDN) based framework that provides controllability to the network. It takes advantages of advanced control knobs recently available in BLE protocol stacks. SoftBLE is complemented by two orchestration algorithms to optimize gateway and sensor parameters based on run-time measurements. Evaluation results from both an experimental testbed and a large-scale simulation study show that using SoftBLE, sensors consume 70% less power in data collection compared to those in baseline approaches while achieving the Packet Reception Rate (PRR) no less than 99.9%. One of its main steps of DCWSN commissioning is sensor localization, which is labour-intensive if is driven manually. To streamline the process, we devise a novel approach for automated sensor mapping. Since Radio Frequency (RF) alone is not a reliable data source for sensor localization in harsh and multi-path rich environments such as a DCs, we investigate using non-RF alternatives. Thermal Piloting is a classification model to correlate temperature sensor measurements with the expected thermal values at their locations. It achieves an average localization error of 0.64 meters in a modular DC testbed. The idea is further improved by a multimodal approach that incorporates pairwise Received Signal Strength (RSS) measurements of RF signals. The problem is formulated as Weighted Graph Matching (WGM) between an analytical graph and an experimental graph. A parallel algorithm is proposed to find heuristic solutions to this NP-hard problem, which is 30% more accurate than the baselines. The evaluation in a modular DC testbed shows that the localization errors using multi-modality are less than one-third of that of using thermal data alone. / Thesis / Candidate in Philosophy

Projektet riktar sig mot ett företag som är verksamma inom förpackningsindustrin med behandling och hållbarhet av papper. Operatörerna på anläggningen arbetar med att applicera plastbeläggning på papper för att göra det mer resistent mot vätska. När pappersrullar erhåller ett mindre vanligt produktionsfel utan tydlig åtgärd, finns en felhantering som kallas för ''tveksam bedömning'' (TVB). Hanteringen av TVB:er har tidigare bestått av mycket manuell hantering, vilket är ineffektivt, tidskrävande och otydligt som i sin tur kan leda till missförstånd. För att förbättra felhanteringen har ett befintligt program, som företaget använder sedan tidigare, vidareutvecklats för att kunna rapportera TVB:er och även kunna spåra tidigare TVB:er. Syftet med projektet är att förenkla processen för hanteringen av TVB:er samt minimera och underlätta arbetet för operatörerna. Genom att automatisera hur och var information sparas, samt koppla det till ett lättanvänt användargränssnitt, förenklas proceduren och minimerar arbetet för operatörerna. / This project targets a company that are operative within treatment and durability of paper in the packaging industry. At their facility, the production consists of applying plastic coating on the paper to make it more resistant to liquids, as the final product is intended as a container for liquid, e.g. milk. When paper rolls have a less common or more difficult error, there is an error handling procedure called ''questionable assessment''. This procedure consists of much manual handling which is ineffective, time consuming and inexplicit, which can lead to misunderstanding. To improve the error handling, an existing program that the company uses, will be further developed to manage error reports and to track earlier error reports. The purpose of the project is to simplify the process and minimize the workload for the staff. By automating how and where the information is stored as well as connecting it to a user-friendly interface, it will improve the procedure and minimize the work needed by the staff.

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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.

No / This paper addresses interoperability issues in an IoT-based cloud environment consisting of multiple WSN clusters made up of connected objects embedded with smart devices which are fully integrated to the Web, forming the Web-ofThings (WoT). Two levels of interoperability are considered: Device-level interoperability and semantic-level interoperability. Eminent issues relating to device heterogeneity and platform dependencies are resolved by using an OSGi (Open Service Gateway initiative) framework as the software fabric for IoT deployment. However, OSGi alone is not enough to resolve data heterogeneity issues, and even less in providing a semantic mapping of devices and their data streams in a generic deployment. To enable this level of interoperability, a novel system that envisages an all-purpose collaboration framework for the WoT to deliver Sensing and Collaboration as a Service (SeaaS/CaaS) is presented.

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.

Edge computing is an emerging technology that can overcome the limitations of centralized cloud computing by enabling distributed, low-latency computation at a network edge. Particularly, in edge computing, some of the cloud's functionalities such as storage, processing, and computing are migrated to end-user devices called edge nodes so as to reduce the round-trip delay needed to reach the cloud data center. Despite the major benefits and practical applications of using edge computing, one must address many technical challenges that include edge network formation, computational task allocation, and radio resource allocation, while considering the uncertainties innate in edge nodes, such as incomplete future information on their wireless channel gains and computing capabilities. The goal of this dissertation is to develop foundational science for the deployment, performance analysis, and low-complexity optimization of edge computing under the aforementioned uncertainties. First, the problems of edge network formation and task distribution are jointly investigated while considering a hybrid edge-cloud architecture under uncertainty on the arrivals of computing tasks. In particular, a novel online framework is proposed to form an edge network, distribute the computational tasks, and update a target competitive ratio defined as the ratio between the latency achieved by the proposed online algorithm and the optimal latency. The results show that the proposed framework achieves the target competitive ratio that is affected by the wireless data rate and computing speeds of edge nodes. Next, a new notion of ephemeral edge computing is proposed in which edge computing must occur under a stringent requirement on the total computing time period available for the computing process. To maximize the number of computed tasks in ephemeral edge networks under the uncertainty on future task arrivals, a novel online framework is proposed to enable a source edge node to offload computing tasks from sensors and allocate them to neighboring edge nodes for distributed task computing, within the limited total time period. Then, edge computing is applied for mobile blockchain and online caching systems, respectively. First, a mobile blockchain framework is designed to use edge devices as mobile miners, and the performance is analyzed in terms of the probability of forking event and energy consumption. Second, an online computational caching framework is designed to minimize the edge network latency. The proposed caching framework enables each edge node to store intermediate computation results (IRs) from previous computations and download IRs from neighboring nodes under uncertainty on future computation. Subsequently, online optimization is extended to investigate other edge networking applications. In particular, the problem of online ON/OFF scheduling of self-powered small cell base stations is studied, in the presence of energy harvesting uncertainty with the goal of minimizing the operational costs that consist of energy consumption and transmission delay of a network. Such a framework can enable the self-powered base stations to be functioned as energy-efficient edge nodes. Also, the problem of radio resource allocation is studied when a base station is assisted by self-powered reconfigurable intelligent surfaces (RIS). To this end, a deep reinforcement learning approach is proposed to jointly optimize the transmit power, phase shifting, and RIS reflector's ON/OFF states under the uncertainties on the downlink wireless channel information and the harvested energy at the RIS. Finally, the online problem of dynamic channel allocation is studied for full-duplex device-to-device (D2D) networks so that D2D users can share their data with a low communication latency when users dynamically arrive on the network. In conclusion, the analytical foundations and frameworks presented in this dissertation will provide key guidelines for effective design of edge computing in wireless networks. / Doctor of Philosophy / Smart cities will rely on an Internet of Things (IoT) system that interconnects cars, drones, sensors, home appliances, and other digital devices. Modern IoT systems are inherently designed to process real-time information such as temperature, humidity, or even car navigational data, at any time and location. A unique challenge in the design of such an IoT is the need to process large volumes of data over a wireless network that consists of heterogeneous IoT devices such as smartphones, vehicles, home access points, robots, and drones. These devices must perform local (on-device or so-called edge) processing of their data without relying on a remote cloud. This vision of a smart city seen as a mobile computing platform gives rise to the emerging concept of edge computing using which smartphones, sensors, vehicles, and drones can exchange and process data locally on their own devices. Edge computing allows overcoming the limitations of centralized cloud computation by enabling distributed, low-latency computation at the network edge. Despite the promising opportunities of edge computing as an enabler for smart city services such as autonomous vehicles, drones, or smart homes, one must address many challenges related to managing time-varying resources such as energy and storage, in a dynamic way. For instance, managing communication, energy, and computing resources in an IoT requires handling many uncertain factors such as the intermittent availability of wireless connectivity and the fact that the devices do not know a priori what type of tasks they need to process. The goal of this dissertation is to address the fundamental challenges in edge computing under uncertainty in an IoT. In particular, this dissertation introduces novel mathematical algorithms and frameworks that exploit ideas from the fields of online optimization, machine learning, and wireless communication to enable future IoT services such as smart factories, virtual reality, and autonomous systems. In this dissertation, holistic frameworks are developed by designing, analyzing, and optimizing wireless communications systems with an emphasize on emerging IoT applications. To this end, various mathematical frameworks and efficient algorithms are proposed by drawing on tools from wireless communications, online optimization, and machine learning to yield key innovations. The results show that the developed solutions can enable an IoT to operate efficiently in presence of uncertainty stemming from time-varying dynamics such as mobility of vehicles or changes in the wireless networking environment. As such, the outcomes of this research can be used as a building block for the large deployment of smart city technologies that heavily rely on the IoT.