<p>Developing resiliency in supply chains became vital in the recent years due to global diversification and vulnerability to risks. Firms need to identify, evaluate, and mitigate risks in supply chain to maintain continuity and create competitive advantage. Although the problem of supply chain disruptions has existed for a long time, less attention has been given by researchers in exploring the adoption of advanced technologies to build resilient supply chains. This study explored the potential of mitigating supply chain disruptions with the use of Industry 4.0 technologies such as Internet of Things (IoT) and Supply chain data analytics platform which develops digital twin environment for supply chains. </p> <p><br></p> <p>This research gathered expert’s opinion on the resilience capabilities developed in supply chain by digital twin adoption, stages and practices involved in digital twin assimilation through Delphi survey with subject matter experts and supply chain practitioners. Semi-structured interviews were conducted with participants to attain deep understanding on the resilience capabilities gained by digital twin and stages in digital twin adoption. Comparison of the results from Delphi survey and interviews was carried out to synthesize the results to yield a comprehensive understanding of resilience capabilities gained through digital twin and adoption stages of supply chain digital twin. This research has conducted interviews with 21 subject matter experts and completed three rounds of Delphi survey (with participants n = 15, 11, 11 in three rounds respectively) to develop a framework for digital twin adoption to enhance supply chain resilience. </p> <p><br></p> <p>This research determined that digital twin develops real-time monitoring and sensing capabilities, planning and decision support system, and automating decisions and action execution capabilities in supply chain. In addition, digital twin positively impacts resilience elements such as agility, supply chain reconfiguration, robustness, and collaboration in supply chain, which improves the supply chain performance. The results from this study were utilized to develop a framework for enabling supply chain resilience through digital twin. The framework included antecedents, consequences, and various moderators that impact digital twin adoption and diffusion in supply chains. Finally, this research developed a five-stage roadmap for adopting digital twin capabilities in supply chain. </p>

Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

Internet of Things (IoT) are becoming increasingly popular replacements for their analogue counterparts. However, there is still demand to keep analogue equipment that is already installed, while also having automated monitoring of the equipment, such as analogue water meters. A proposed solution for this problem is to install a battery powered add-on component that can optically read meter values using Optical Character Recognition (OCR) and transmit the readings wirelessly. Two ways to do this could be to either offload the OCR process to a server, or to do the OCR processing locally on the add-on component. Since water meters are often located where reception is weak and the add-on component is battery powered, a suitable technology for data transmission could be Long Range (LoRa) because of its low-power and long-range capabilities. Since LoRa has low transfer rate there is a need to keep data transfers small in size, which could make offloading a less favorable alternative compared to local OCR processing. The purpose of this thesis is therefore to research the feasibility, in terms of energy efficiency, of doing local OCR processing on the add-on component. The feasibility condition of this study is defined as being able to continually read an analogue meter for a 10-year lifespan, while consuming under 2600 milliampere hours (mAh) of energy. The two OCR algorithms developed for this study are a specialized OCR algorithm that utilizes pattern matching principles, and a Sum of Absolute Differences (SAD) OCR algorithm. These two algorithms have been compared against each other, to determine which one is more suitable for the system. This comparison yielded that the SAD algorithm was more suitable, and was then studied further by using different image resolutions and settings to determine if it was possible to further reduce energy consumption. The results showed that it was possible to significantly reduce energy consumption by reducing the image resolution. The study also researched the possibility of reducing energy consumption further by not reading all digits on the tested water meter, depending on the measuring frequency and water flow. The study concluded that OCR processing is feasible on an energy critical driven system when reading analouge meters, depending on the measuring frequency.

The original FlexiChain and its descendants are a revolutionary distributed ledger technology (DLT) for cyber-physical systems (CPS) and their embedded systems (ES). FlexiChain, a DLT implementation, uses cryptography, distributed ledgers, peer-to-peer communications, scalable networks, and consensus. FlexiChain facilitates data structure agreements. This thesis offers a Block Directed Acyclic Graph (BDAG) architecture to link blocks to their forerunners to speed up validation. These data blocks are securely linked. This dissertation introduces Proof of Rapid Authentication, a novel consensus algorithm. This innovative method uses a distributed file to safely store a unique identifier (UID) based on node attributes to verify two blocks faster. This study also addresses CPS hardware security. A system of interconnected, user-unique identifiers allows each block's history to be monitored. This maintains each transaction and the validators who checked the block to ensure trustworthiness and honesty. We constructed a digital version that stays in sync with the distributed ledger as all nodes are linked by a NodeChain. The ledger is distributed without compromising node autonomy. Moreover, FlexiChain Layer 0 distributed ledger is also introduced and can connect and validate Layer 1 blockchains. This project produced a DAG-based blockchain integration platform with hardware security. The results illustrate a practical technique for creating a system depending on diverse applications' needs. This research's design and execution showed faster authentication, less cost, less complexity, greater scalability, higher interoperability, and reduced power consumption.

Modern smartphone technology has created a myriad of opportunities in the field of RF and Microwave. Specifically, Chipless RFID sensor, compact microwave filter, antenna based on a microstrip structure, and many more. In this thesis, innovative ideas for the industrial and biomedical device has been explored. The work presents the reconfigurable filter design, Switch-beam antenna, Microwave interferometer, X-band Rotman Lens antenna, Ultra-wideband antenna based on SIW resonator, L-band Stepped Frequency Continuous Wave antenna, development of a wireless sensor system for environmental monitoring, Indoor Air Quality monitoring, and Wildfire Monitoring based on the modulated scattering technique (MST). The MST sensor probes are based on the scattering properties of small passive antennas and radiate part of the impinging electromagnetic field generated by an interrogating antenna, which also acquires the backscattered signal as information. The MST probes are able to deliver data without a radio frequency front end. They use a simple circuit that alternatively terminates the antenna probe on suitable loads to generate a low modulation signal on the backscattered electromagnetic wave. The antenna presented in this work has been designed in ADS Software by Keysight Technologies. The designed antenna has been assessed numerically and experimentally. The experimental measurement data demonstrate the effectiveness of the individual system. Simultaneously, the MST sensor system has been proposed to obtain the best performance in communication range, load efficiency, and power harvesting. The MST sensor has been fabricated and assessed in practical scenarios. The proposed prototype, able to provide a communication range of about 15 m, serves as a proof-of-concept. The acquired measurements of MST demonstrate the accuracy of the data without radio frequency front end or bulky wired connection with the same efficiency of standard wireless sensors such as radio frequency identifier (RFID) or wireless sensor networks (WSN).

[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).

<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>

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.