The IoT offers enormous potentials thanks to its Widespread adoption by many industries, individuals, and governments, leading explosive growth and remarkable breakthroughs that have made it a technology with seemingly boundless applications. However, the far-reaching IoT applications cum its characteristic heterogeneity and ubiquity come with a huge price for more security vulnerabilities, making the deployed IoT systems increasingly susceptible to, and prime targets of many different physical and cyber-attacks including insider attacks, thereby growing the overall security risks to the systems. This research, which focuses on addressing insider attacks on IoT, studies the likelihood of malicious insiders' activities compromising some of the security triad of Confidentiality, Integrity and Availability (CIA) of a supposedly secure IoT system with implemented security mechanisms. To further establish the vulnerability of the IoT systems to the insider attack being investigated in our research, we first produced a research output that emphasized the need for multi-layer security of the overall system and proposed the implementation of security mechanisms on components at all layers of the IoT system to safeguard the system and ensure its CIA. Those conventional measures however do not safeguard against insider attacks, as found by our experimental investigation of a working IoT system prototype. The outcome of the investigation therefore necessitates our proposed solution to the problem, which leverages the integration of distributed edge computing with decentralized Ethereum blockchain technology to provide countermeasures that preserve the Integrity of the IoT system data and improve effectiveness of the system. We employed the power of Ethereum smart contracts to perform integrity checks on the system data logically and take risk management decisions. We considered the industry use case of Downstream Petroleum sector for application of our solution. The solution was evaluated using datasets from different experimental settings and showed up to 86% accuracy rate. / Government of the Federal Republic of Nigeria through the Petroleum Technology Development Fund (PTDF) Overseas Scholarship Scheme (OSS)

[pt] Os desafios acerca do desenvolvimento sustentável impulsionam medidas governamentais e incentivos a empresas que investem em soluções nessa direção. Alinhado a isso, no que se refere ao setor do transporte rodoviário de cargas, os avanços em tecnologia permitem a gestão de frotas eficiente e a maximização dos lucros de empreendedores do setor. A Internet das Coisas é um meio pelo qual é possível se definir indicadores que auxiliam a operação eficiente das empresas e que possibilitam a análise mais precisa de pegada ambiental em diferentes níveis: desde a escala micro, que compreende o veículo e a empresa, até a escala macro, que compreende o setor de transporte de cargas do país. Esse estudo busca levantar indicadores operacionais e ambientais possíveis de serem desenvolvidos a partir de um estudo de caso em que são fornecidos dados enviados de um veículo e seus componentes conectados à internet. / [en] The challenges upon sustainable development drive government actions and incentives to companies that invest in solutions in this direction. In line with this, with regard to the road freight transport sector, advances in technology allow the efficient fleet management and the maximization of entrepreneurs profit. The Internet of Things is a modern mean by which it is possible to define indicators that help the efficient operation of companies and that allow the more accurate analysis of the environmental footprint at different levels: from the micro scale, which comprises the vehicle and the company, to the macro scale, which comprises the country s freight sector. The environment conservation is one of the pillars of sustainability s concept. A development is considered sustainable when it takes into account social, ecological and economic factors (IUCN, 1980). The warranty of economic interest and reduction of environmental impacts caused by the freight transport sector is relevant to the evolution of the logistics in this direction. The concept of logistics is the activity that manages materials and products evolving, beyond other activities, purchasing, transport, distribution, movement, storage and packing. The part of logistics that consider the aspects and impacts caused by its activities is called Green Logistics or Ecologistics (Donato, 2008). The freight transport is one of the most fast-growing sectors in terms of energy consumption and emissions in Brazil (World Bank, 2011a). According to estimations this sector can be emitting 60 percent more CO2 in 2020 than it had in 2009, of which 36 percent from trucking, 13 percent from buses, 40 percent from passenger cars and 3 percent from motocycles (MMA, 2011). There s an interest towards the carbon emission s growing rates from freight transport. Reducing unnecessary travels without impacting the economic growth is one of the fundamental ways to reduce the intensity of emissions (World Bank, 2011b).

Yes / Internet of Things (IoT) platforms are responsible for overall data processing in the IoT System. This ranges from analytics and big data processing to gathering all sensor data over time to analyze and produce long-term trends. However, this comes with prohibitively high demand for resources such as memory, computing power and bandwidth, which the highly resource constrained IoT devices lack to send data to the platforms to achieve efficient operations. This results in poor availability and risk of data loss due to single point of failure should the cloud platforms suffer attacks. The integrity of the data can also be compromised by an insider, such as a malicious system administrator, without leaving traces of their actions. To address these issues, we propose in this work an edge-based blockchain enabled anomaly detection technique to prevent insider attacks in IoT. The technique first employs the power of edge computing to reduce the latency and bandwidth requirements by taking processing closer to the IoT nodes, hence improving availability, and avoiding single point of failure. It then leverages some aspect of sequence-based anomaly detection, while integrating distributed edge with blockchain that offers smart contracts to perform detection and correction of abnormalities in incoming sensor data. Evaluation of our technique using real IoT system datasets showed that the technique remarkably achieved the intended purpose, while ensuring integrity and availability of the data which is critical to IoT success. / Petroleum Technology Development Fund(PTDF) Nigeria, Grant/Award Number:PTDF/ED/PHD/TYM/858/16

<p dir="ltr">Mobile vision is becoming the norm, transforming our daily lives. It powers numerous applications, enabling seamless interactions between the digital and physical worlds, such as augmented reality, real-time object detection, and many others. The popularity of mobile vision has spurred advancements from both computer vision (CV) and mobile edge computing (MEC) communities. The former focuses on improving analytics accuracy through the use of proper deep neural networks (DNNs), while the latter addresses the resource limitations of mobile environments by coordinating tasks between mobile and edge devices, determining which data to transmit and process to enable real-time performance. </p><p dir="ltr"> Despite recent advancements, existing approaches typically integrate the functionalities of the two camps at a basic task level. They rely on a uniform on-device processing scheme that streams the same type of data and uses the same DNN model for identical CV tasks, regardless of the analytical complexity of the current input, input size, or latency requirements. This lack of adaptability to dynamic contexts limits their ability to achieve optimal efficiency in scenarios involving diverse source data, varying computational resources, and differing application requirements. </p><p dir="ltr">Our approach seeks to move beyond task-level adaptation by emphasizing customized optimizations tailored to dynamic use scenarios. This involves three key adaptive strategies: dynamically compressing source data based on contextual information, selecting the appropriate computing model (e.g., DNN or sub-DNN) for the vision task, and establishing a feedback mechanism for context-aware runtime tuning. Additionally, for scenarios involving movable cameras, the feedback mechanism guides the data capture process to further enhance performance. These innovations are explored across three use cases categorized by the capture device: one stationary camera, one moving camera, and cross-camera analytics. </p><p dir="ltr">My dissertation begins with a stationary camera scenario, where we improve efficiency by adapting to the use context on both the device and edge sides. On the device side, we explore a broader compression space and implement adaptive compression based on data context. Specifically, we leverage changes in confidence scores as feedback to guide on-device compression, progressively reducing data volume while preserving the accuracy of visual analytics. On the edge side, instead of training a specialized DNN for each deployment scenario, we adaptively select the best-fit sub-network for the given context. A shallow sub-network is used to “test the waters”, accelerating the search for a deep sub-network that maximizes analytical accuracy while meeting latency requirements.</p><p dir="ltr"> Next, we explore scenarios involving a moving camera, such as those mounted on drones. These introduce new challenges, including increased data encoding demands due to camera movement and degraded analytics performance (e.g., tracking) caused by changing perspectives. To address these issues, we leverage drone-specific domain knowledge to optimize compression for object detection by applying global motion compensation and assigning different resolutions at a tile-granularity level based on the far-near effect. Furthermore, we tackle the more complex task of object tracking and following, where the analytics results directly influence the drone’s navigation. To enable effective target following with minimal processing overhead, we design an adaptive frame rate tracking mechanism that dynamically adjusts based on changing contexts.</p><p dir="ltr"> Last but not least, we extend the work to cross-camera analytics, focusing on coordination between one stationary ground-based camera and one moving aerial camera. The primary challenge lies in addressing significant misalignments (e.g., scale, rotation, and lighting variations) between the two perspectives. To overcome these issues, we propose a multi-exit matching mechanism that prioritizes local feature matching while incorporating global features and additional cues, such as color and location, to refine matches as needed. This approach ensures accurate identification of the same target across viewpoints while minimizing computational overhead by dynamically adapting to the complexity of the matching task. </p><p dir="ltr">While the current work primarily addresses ideal conditions, assuming favorable weather, optimal lighting, and reliable network performance, it establishes a solid foundation for future innovations in adaptive video processing under more challenging conditions. Future efforts will focus on enhancing robustness against adversarial factors, such as sensing data drift and transmission losses. Additionally, we plan to explore multi-camera coordination and multimodal data integration, leveraging the growing potential of large language models to further advance this field.</p>

Internet of Things (IoT) is a promising technology with great potential for individuals, society, governments, and the economy. IoT is expected to become ubiquitous and influence every aspect of everyday experience. Thus, IoT represents an important phenomena for both organizational and behavioral information system (IS) researchers. This dissertation seeks to contribute to IS research by studying the aspects that influence IoT adoption and use at both consumer and organizational levels. This dissertation achieves this purpose in a series of three essays. The first essay focuses on IoT acceptance in the context of smart home. The second essay focuses on examining the effect of artificial intelligence (AI) capabilities on consumers' IoT perceptions and intentions. Finally, the third essay focuses on the organizational investment and adoption of IoT technologies.

The Internet of Things (IoT) concept has been realised with the advent of Machineto-Machine (M2M) communication through which the vision of future Internet has been revolutionised. IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) provides feasible IPv6 connectivity to previously isolated environments, e.g. wireless M2M sensors and actuator networks. This thesis's contributions include a novel mathematical model, energy-efficient algorithms, and a centralised software controller for dynamic consolidation of programmability features in cloud-based M2M networks. A new generalised joint mathematical model has been proposed for performance analysis of the 6LoWPAN MAC and PHY layers. The proposed model differs from existing analytical models as it precisely adopts the 6LoWPAN specifications introduced by the Internet Engineering Task Force (IETF) working group. The proposed approach is based on Markov chain modelling and validated through Monte-Carlo simulation. In addition, an intelligent mechanism has been proposed for optimal 6LoWPAN MAC layer parameters set selection. The proposed mechanism depends on Artificial Neural Network (ANN), Genetic Algorithm (GA), and Particles Swarm Optimisation (PSO). Simulation results show that utilising the optimal MAC parameters improve the 6LoWPAN network throughput by 52-63% and reduce end-to-end delay by 54-65%. This thesis focuses on energy-efficient data extraction and dissemination in a wireless M2M sensor network based on 6LoWPAN. A new scalable and self-organised clustering technique with a smart sleep scheduler has been proposed for prolonging M2M network's lifetime and enhancing network connectivity. These solutions succeed in overcoming performance degradation and unbalanced energy consumption problems in homogeneous and heterogeneous sensor networks. Simulation results show that by adopting the proposed schemes in multiple mobile sink sensory field will improve the total aggregated packets by 38-167% and extend network lifetime by 30-78%. Proof-of-concept real-time hardware testbed experiments are used to verify the effectiveness of Software-Defined Networking (SDN), Network Function Virtualisation (NFV) and cloud computing on a 6LoWPAN network. The implemented testbed is based on open standards development boards (i.e. Arduino), with one sink, which is the M2M 6LoWPAN gateway, where the network coordinator and the customised SDN controller operated. Experimental results indicate that the proposed approach reduces network discovery time by 60% and extends the node lifetime by 65% in comparison with the traditional 6LoWPAN network. Finally, the thesis is concluded with an overall picture of the research conducted and some suggestions for future work.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-04-20T13:56:57Z No. of bitstreams: 1 Armando Leopoldo Keller_.pdf: 2124143 bytes, checksum: ba23113da63873463958e38c05ddbd88 (MD5) / Made available in DSpace on 2017-04-20T13:56:57Z (GMT). No. of bitstreams: 1 Armando Leopoldo Keller_.pdf: 2124143 bytes, checksum: ba23113da63873463958e38c05ddbd88 (MD5) Previous issue date: 2017-01-13 / Nenhuma / O objetivo deste trabalho, é realizar um estudo de forma mais abrangente sobre o conceito de Internet das Coisas e seus principais protocolos. Explora-se especificamente o conceito de IoT (Internet of Things) aplicado em sistemas de automação. Para tanto é apresentada uma revisão bibliográfica sobre o assunto, explorando os diversos protocolos desenvolvidos para aplicações de IoT, caracterizando-os quanto a taxa de transmissão, eficiência, segurança e confiabilidade. Também é realizado um levantamento do cenário atual, quanto a aplicação de protocolos de IoT em sistemas de automação, sempre tendo em mente a confiabilidade do sistema. Percebe-se que um grande dificultador do uso destes tipos de protocolo em ambientes industriais é justamente a heterogeneidade das redes existentes. Diante deste problema, a proposta do trabalho é desenvolver um dispositivo que atue como middleware para a interligação de redes de automação distribuídas, no caso especificamente a rede Modbus RTU, fazendo com que esta interligação seja de forma transparente utilizando o protocolo de Internet das Coisas MQTT (Message Queuing Telemetry Transport). Este dispositivo é testado com equipamentos em um cenário real através de um estudo de caso, onde duas redes Modbus RTU de um sistema geograficamente distribuído de geração de energia solar fotovoltaica, são interligadas, permitindo a criação de uma planta virtual de geração de energia do inglês virtual power plant (VPP). Com isso é possível tratar e gerenciar os sistemas distribuídos de geração como sendo uma única unidade geradora, facilitando o despacho. Para comprovar a eficiência e a confiabilidade do sistema, foram realizados testes onde o tempo entre as requisições e respostas foi medido, e através da sua distribuição foi obtido um tempo de 2,5 segundos para obter uma comunicação com baixa taxa de perda de mensagens. Estes testes comprovam o correto funcionamento do sistema proposto. / The objective of this work is to develop a more comprehensive study on the concept of Internet of Things (IoT) and its main protocols, specifically exploring the concept of IoT applied in automation systems. A bibliographic review explores the diverse protocols developed for IoT applications, characterizing them as transmission rate, efficiency, safety and confiability. A survey of the current scenario about the application of IoT protocols in automation systems is presented, always having the system confiability in mind. The heterogenity of the existent networks makes the use of this protocols a harder task. The proposal of this work is develop a device that acts as middleware for interlink distributed automation networks, in this case the Modbus RTU networks, in a transparent way using the internet of things procol MQTT (Message Queuing Telemetry Transport). This device is tested with equipments in a real scenario trough a case study, where two Modbus RTU networks of a geographically distributed solar photovoltaic power plant, is interlinked, allowing the criation of a VPP (Virtual Power Plant). This makes possible to manage the distributed power generator systems as a single generator unit, improving the electric energy dispatch. To prove the efficiency and confiability of the system, tests were made where the time between request and response was mensured, and based on his distribution the time of 2.5 seconds was determined to have a low message loss communication. Those tests validate the proposed system and the achievement of the goals of the present work.

[pt] O crescimento da Internet das Coisas (IoT) nos trouxe a oportunidade de criar aplicações em diversas áreas com o uso de sensores e atuadores. Um dos problemas encontrados em sistemas de IoT é a dificuldade de adicionar relações semânticas aos dados brutos produzidos por estes sensores e conseguir inferir novos fatos a partir destas relações. Além disso, devido à natureza destes sistemas, os dados produzidos por eles, conhecidos como streams, precisam ser analisados em tempo real. Streams são uma sequência de elementos de dados com variação de tempo e que não devem ser tratados como dados a serem armazenados para sempre e consultados sob demanda. Os dados em streaming precisam ser consumidos rapidamente por meio de consultas contínuas que analisam e produzem novos dados relevantes. A capacidade de inferir novas relações semânticas sobre dados em streaming é chamada de inferência sobre streams. Nesta pesquisa, propomos um modo semântico e um mecanismo para processamento e inferência sobre streams em tempo real baseados em Processamento de Eventos Complexos (CEP), RDF (Resource Description Framework) e OWL (Web Ontology Language). Apresentamos um middleware que suporta uma inferência contínua sobre dados produzidores por sensores. As principais vantagens de nossa abodagem são: (a) considerar o tempo como uma relação-chave entre a informação; (b) processamento de fluxo por ser implementado usando o CEP; (c) é geral o suficiente para ser aplicado a qualquer sistema de gerenciamento de fluxo de dados (DSMS). Foi desenvolvido no Laboratório de Colaboração Avançada (LAC) utlizando e um estudo de caso no domínio da detecção de incêndio é conduzido e implementado, elucidando o uso de inferência em tempo real sobre streams. / [en] The growth of the Internet of Things (IoT) has brought the opportunity to create applications in several areas, with the use of sensors and actuators. One of the problems encountered in IoT systems is the difficulty of adding semantic relations to the raw data produced by the sensors and being able to infer new facts from these relations. Moreover, due to the fact that many IoT applications are online and need to react instantly on sensor data collected by them, they need to be analyzed in real-time. Streams are a sequence of time-varying data elements that should not be stored forever and queried on demand. Streaming data needs to be consumed quickly through ongoing queries that continue to analyze and produce new relevant data, i.e. stream of output/result events. The ability to infer new semantic relationships over streaming data is called Stream Reasoning. We propose a semantic model and a mechanism for real-time data stream processing and reasoning based on Complex Event Processing (CEP), RDF (resource description structure) and OWL (Web Ontology Language). This work presents a middleware service that supports continuous reasoning on data produced by sensors. The main advantages of our approach are: (a) to consider time as a key relationship between information; (b) flow processing can be implemented using CEP; (c) is general enough to be applied to any data flow management system (DSMS). It was developed in the Advanced Collaboration Laboratory (LAC) and a case study in the field of fire detection is conducted and implemented, elucidating the use of real-time inference on streams.

Background – Through the progress in information and communication technology (ICT) new possibilities to connect smart objects via the internet arose. The number of connected devices had a strong growth in the past years and will continue rising fast in the next years as well. This new kind of smart and connected products (SCP) enables a lot of new product capabilities which have an impact on the creation of new customer value and competition on the market. Related to that, companies have to deal with digitisation and the affects for their products and manufacturing system. Purpose – The purpose of this thesis is to investigate changes in the manufacturing system when the products are getting smarter and connected. A special focus lays on the well-established Lean thinking approach. The results shall help to understand what new circumstances the decision to make the products smarter and connected will bring for a manufacturing department. Methodology – Relevant literature was reviewed to gain a theoretical framework. For gathering primary data, a qualitative case study was applied. Meetings with members of the case company’s management were arranged to conduct interviews. Additionally, observations were made during a guided tour through the production shop-floor and at a company presentation. The interview was recorded, transcribed and evaluated. Afterwards, the results from the case study were analysed and compared with theory based on the theoretical framework. Conclusions were made. Findings – The differences or changes in manufacturing because the products are getting smarter and connected are much more electronic components and software. Furthermore, new operating equipment is needed. The new circumstances require new knowledge and skills. Therefore, people have to be trained. New problems occur e.g. software problems. The use of Lean tools can be more difficult and time-consuming because of missing know-how and improvements itself are becoming more digital. Contribution – This thesis investigated the effects on the manufacturing system when the products get smarter and connected, which nobody did before. A practical case study with interviews, observations and secondary data from the company was applied. Limitations – The findings match reality based on data from the case company. Available time and access to data from the company’s side were limited. This means that the generalisation must be done with caution. However, it can be said that the findings may apply to many other industrial companies of similar size and similar products.

Every day, a new publication on information systems highlights about Cloud of Things (CoT) vulnerabilities; in most of these publications, vulnerability is quoted as the most substantial barrier for CoT realization. However, formulating a justifiable appraisement of the actual vulnerability impact on CoT is difficult because in many of these publications, the term security “vulnerability” is stated incorrectly as a threat or the publication does not discuss CoT-specific vulnerabilities. To achieve a well-founded understanding of CoT vulnerabilities, this literature review identifies the major vulnerabilities and their security controls and to identify any gaps for future research. A systematic literature review (SLR) approach using 58 articles is considered for this review. Based on this review, a taxonomy is created to classify the existing CoT vulnerabilities and security controls. This literature review identifies and discusses similarities and differences among various vulnerability issues and solutions. Most reviews previously performed were limited to the threats to the application interface and virtualization level, whereas this SLR thesis expand to the vulnerabilities in connectivity and things level of CoT. This study emphasize the importance of a clear definition of cloud of things vulnerabilities and to facilitate better understanding and assessment of CoT vulnerabilities to build more secure systems.