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Microservices in context: Internet of Things : Infrastructure and ArchitectureKhan, Asghar January 2017 (has links)
The enterprise systems are built with a monolithic way of software architecture which may not be the very suitable and realistic solutions, due to the quantity and diversity of Internet of Things (IOT) devices connected to the internet. The large code is based as a single deployable unit; making it cumbersome and difficult to change, test, and deploy – typically in the monthly deployment cycles. Which could potentially be a large impact and high-risk for updates and deployment build up between releases. The thesis aims to exploit Microservices Architecture in context of the Internet of Things. Because of scalability and rapid deployment, Microservices Architecture (MSA) naturally fits for M2M and IOT developments. Therefore, it has become increasingly popular and highly suited as a design choice in the context of developing IOT applications. We have proposed various ontologies for conceptual architecture designs. Our conceptual model encompasses all the essential elements and entities that constitute IOT; describing various characteristics of microservices and consideration to be used as a basis for developing a Reference Architecture model. The reference architecture is effectively exploiting Microservices Architectural Style for a diverse set of things composition like sensor devices, connectivity and transportation protocols for IP enabled IOT smart objects. The thesis will contribute to the conceptual space of more successful reference architectures. The goal is to provide features and deployment information with instantiation guidelines to derive the software architecture instances in a given IOT application domain. Thus, it promises to become a valuable framework in the derivation of the Microservices IOT architectures.
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A Proposed IoT Architecture for Effective Energy Management in Smart MicrogridsNumair, M., Mansour, D-EA, Mokryani, Geev 11 May 2021 (has links)
yes / The current electricity grid suffers from numerous challenges due to the lack of an effective energy management strategy that is able to match the generated power to the load demand. This problem becomes more pronounced with microgrids, where the variability of the load is obvious and the generation is mostly coming from renewables, as it depends on the usage of distributed energy sources. Building a smart microgrid would be much more economically feasible than converting the large electricity grid into a smart grid, as it would require huge investments in replacing legacy equipment with smart equipment. In this paper, application of Internet of Things (IoT) technology in different parts of the microgrid is carried out to achieve an effective IoT architecture in addition to proposing the Internet-of-Asset (IoA) concept that will be able to convert any legacy asset into a smart IoT-ready one. This will allow the effective connection of all assets to a cloud-based IoT. The role of which is to perform computations and big data analysis on the collected data from across the smart microgrid to send effective energy management and control commands to different controllers. Then the IoT cloud will send control actions to solve microgrid's technical issues such as solving energy mismatch problem by setting prediction models, increasing power quality by the effective commitment of DERs and eliminating load shedding by turning off only unnecessary loads so consumers won't suffer from power outages. The benefits of using IoT on various parts within the microgrid are also addressed.
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The Role of AI in IoT Systems : A Semi-Systematic Literature ReviewAnyonyi, Yvonne Ivakale, Katambi, Joan January 2023 (has links)
The Internet of Things (IoT) is a network of interconnected devices and objects that have various functions,such as sensing, identifying, computing, providing services and communicating. It is estimated that by the year 2030, there will be approximately 29.42 billion IoT devices globally, facilitating extensive data exchange among them. In response to this rapid growth of IoT, Artificial Intelligence (AI) has become a pivotal technology in automating key aspects of IoT systems, including decision-making, predictive data analysis among others. The widespread use of AI across various industries has brought about significant transformations in business ecosystems. Despite its immense potential, IoT systems still face several challenges. These challenges encompass concerns related to privacy and security, data management, standardization issues, trust among others. Looking at these challenges, AI emerges as an essential enabler, enhancing the intelligence and sophistication of IoT systems. Its diverse applications offer effective solutions to address the inherent challenges within IoT systems. This, in turn, leads to the optimization of processes and the development of more intelligent and smart IoT systems.This thesis presents a semi-systematic literature review (SSLR) that aims to explore the role of AI in IoT systems. A systematic search was performed on three (3) databases (Scopus, IEEE-Xplore and the ACM digital library), 29 scientific and peer reviewed studies published between 2018-2022 were selected and examined to provide answers to the research questions. This study also encompasses an additional study within the context of AI and trustworthiness in IoT systems, user acceptance within IoT systems and AIoT's impact on sustainable economic growth across industries. This thesis also presents the DIMACERI Framework which encompasses eight dimensions of IoT challenges and concludes with recommendations for stakeholders in AIoT systems. AI is strategically integrated across the DIMACERI dimensions to create reliable, secure and efficient IoT systems.
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AI-Based Intrusion Detection Systems to Secure Internet of Things (IoT)Otoum, Yazan 20 September 2022 (has links)
The Internet of Things (IoT) is comprised of numerous devices that are connected through wired or wireless networks, including sensors and actuators. The number of IoT applications has recently increased dramatically, including Smart Homes, Internet of Vehicles (IoV), Internet of Medical Things (IoMT), Smart Cities, and Wearables. IoT Analytics has reported that the number of connected devices is expected to grow 18% to 14.4 billion in 2022 and will be 27 billion by 2025. Security is a critical issue in today's IoT, due to the nature of the architecture, the types of devices, the different methods of communication (mainly wireless), and the volume of data being transmitted over the network. Furthermore, security will become even more important as the number of devices connected to the IoT increases. However, devices can protect themselves and detect threats with the Intrusion Detection System (IDS). IDS typically use one of two approaches: anomaly-based or signature-based. In this thesis, we define the problems and the particular requirements of securing the IoT environments, and we have proposed a Deep Learning (DL) anomaly-based model with optimal features selection to detect the different potential attacks in IoT environments. We then compare the performance results with other works that have been used for similar tasks. We also employ the idea of reinforcement learning to combine the two different IDS approaches (i.e., anomaly-based and signature-based) to enable the model to detect known and unknown IoT attacks and classify the recognized attacked into five classes: Denial of Service (DDoS), Probe, User-to-Root (U2R), Remote-to-Local (R2L), and Normal traffic. We have also shown the effectiveness of two trending machine-learning techniques, Federated and Transfer learning (FL/TL), over using the traditional centralized Machine and Deep Learning (ML/DL) algorithms. Our proposed models improve the model's performance, increase the learning speed, reduce the amount of data that needs to be trained, and reserve user data privacy when compared with the traditional learning approaches. The proposed models are implemented using the three benchmark datasets generated by the Canadian Institute for Cybersecurity (CIC), NSL-KDD, CICIDS2017, and the CSE-CIC-IDS2018. The performance results were evaluated in different metrics, including Accuracy, Detection Rate (DR), False Alarm Rate (FAR), Sensitivity, Specificity, F-measure, and training and fine-tuning times.
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A secure IoT-based modern healthcare system with fault-tolerant decision making processGope, P., Gheraibia, Y., Kabir, Sohag, Sikdar, B. 11 October 2020 (has links)
Yes / The advent of Internet of Things (IoT) has escalated the information sharing among various smart devices by many folds, irrespective of their geographical locations. Recently, applications like e-healthcare monitoring has attracted wide attention from the research community, where both the security and the effectiveness of the system are greatly imperative. However, to the best of our knowledge none of the existing literature can accomplish both these objectives (e.g., existing systems are not secure against physical attacks). This paper addresses the shortcomings in existing IoT-based healthcare system. We propose an enhanced system by introducing a Physical Unclonable Function (PUF)-based authentication scheme and a data driven fault-tolerant decision-making scheme for designing an IoT-based modern healthcare system. Analyses show that our proposed scheme is more secure and efficient than existing systems. Hence, it will be useful in designing an advanced IoT-based healthcare system. / Supported in part by Singapore Ministry of Education Academic Research Fund Tier 1 (R-263-000- D63-114). / Research Development Fund Publication Prize Award winner, July 2020.
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Building occupancy analytics based on deep learning through the use of environmental sensor dataZhang, Zheyu 24 May 2023 (has links)
Balancing indoor comfort and energy consumption is crucial to building energy efficiency.
Occupancy information is a vital aspect in this process, as it determines the energy demand.
Although there are various sensors used to gather occupancy information, environmental sensors stand out due to their low cost and privacy benefits. Machine learning algorithms play a critical role in estimating the relationship between occupancy levels and environmental data. To improve performance, more complex models such as deep learning algorithms are necessary. Long Short-Term Memory (LSTM) is a powerful deep learning algorithm that has been utilized in occupancy estimation. However, recently, an algorithm named Attention has emerged with improved performance. The study proposes a more effective model for occupancy level estimation by incorporating Attention into the existing Long Short-Term Memory algorithm. The results show that the proposed model is more accurate than using a single algorithm and has the potential to be integrated into building energy control systems to conserve even more energy. / Master of Science / The motivation for energy conservation and sustainable development is rapidly increasing, and building energy consumption is a significant part of overall energy use. In order to make buildings more energy efficient, it is necessary to obtain information on the occupancy level of rooms in the building. Environmental sensors are used to measure factors such as humidity and sound to determine occupancy information. However, the relationship between sensor readings and occupancy levels is complex, making it necessary to use machine learning algorithms to establish a connection. As a subfield of machine learning, deep learning is capable of processing complex data. This research aims to utilize advanced deep learning algorithms to estimate building occupancy levels based on environmental sensor data.
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Adaptation of Model Transformation for Safety Analysis of IoT-based ApplicationsAbdulhamid, Alhassan, Kabir, Sohag, Ghafir, Ibrahim, Lei, Ci 05 September 2023 (has links)
Yes / The Internet of Things (IoT) paradigm has continued to provide valuable services across various domains. However, guaranteeing the safety assurance of the IoT system is increasingly becoming a concern. While the growing complexity of IoT design has brought additional safety requirements, developing safe systems remains a critical design objective. In earlier studies, a limited number of approaches have been proposed to evaluate the safety requirements of IoT systems through the generation of static safety artefacts based on manual processes. This paper proposes a model-based approach to the safety analysis of the IoT system. The proposed framework explores the expressiveness of UML/SysML graphical modelling languages to develop a dynamic fault tree (DFT) as an analysis artefact of the IoT system. The framework was validated using a hypothetical IoT-enabled Smart Fire Detection and Prevention System (SFDS). The novel framework can capture dynamic failure behaviour, often ignored in most model-based approaches. This effort complements the inherent limitations of existing manual static failure analysis of the IoT systems and, consequently, facilitates a viable safety analysis that increases public assurance in the IoT systems. / The full text of this accepted manuscript will be available at the end of the publisher's embargo: 11th Feb 2025
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Trust-based Service Management of Internet of Things Systems and Its ApplicationsGuo, Jia 18 April 2018 (has links)
A future Internet of Things (IoT) system will consist of a huge quantity of heterogeneous IoT devices, each capable of providing services upon request. It is of utmost importance for an IoT device to know if another IoT service is trustworthy when requesting it to provide a service. In this dissertation research, we develop trust-based service management techniques applicable to distributed, centralized, and hybrid IoT environments.
For distributed IoT systems, we develop a trust protocol called Adaptive IoT Trust. The novelty lies in the use of distributed collaborating filtering to select trust feedback from owners of IoT nodes sharing similar social interests. We develop a novel adaptive filtering technique to adjust trust protocol parameters dynamically to minimize trust estimation bias and maximize application performance. Our adaptive IoT trust protocol is scalable to large IoT systems in terms of storage and computational costs. We perform a comparative analysis of our adaptive IoT trust protocol against contemporary IoT trust protocols to demonstrate the effectiveness of our adaptive IoT trust protocol. For centralized or hybrid cloud-based IoT systems, we propose the notion of Trust as a Service (TaaS), allowing an IoT device to query the service trustworthiness of another IoT device and also report its service experiences to the cloud. TaaS preserves the notion that trust is subjective despite the fact that trust computation is performed by the cloud. We use social similarity for filtering recommendations and dynamic weighted sum to combine self-observations and recommendations to minimize trust bias and convergence time against opportunistic service and false recommendation attacks. For large-scale IoT cloud systems, we develop a scalable trust management protocol called IoT-TaaS to realize TaaS. For hybrid IoT systems, we develop a new 3-layer hierarchical cloud structure for integrated mobility, service, and trust management. This architecture supports scalability, reconfigurability, fault tolerance, and resiliency against cloud node failure and network disconnection. We develop a trust protocol called IoT-HiTrust leveraging this 3-layer hierarchical structure to realize TaaS.
We validate our trust-based IoT service management techniques developed with real-world IoT applications, including smart city air pollution detection, augmented map travel assistance, and travel planning, and demonstrate that our trust-based IoT service management techniques outperform contemporary non-trusted and trust-based IoT service management solutions. / Ph. D.
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Study of Linkage between Indoor Air Quality along with Indoor Activities and the Severity of Asthma Symptoms in Asthma PatientsJohn, Reena January 2023 (has links)
Asthma, a chronic respiratory disease affecting millions of people worldwide, can vary in severity depending on individual triggers such as Carbon Dioxide, Particulate Matter, dust mites, tobacco smoke, and indoor household activities such as cooking, cleaning, use of heating, and window opening, which can have a negative impact on indoor air quality (IAQ) and exacerbate asthma symptoms. Investigating the relationship between IAQ and asthma severity, a case study was conducted on five asthmatic participants from Bradford, UK. IAQ was measured using IoT indoor air quality monitoring devices. Indoor activities were recorded using a daily household activities questionnaire, and asthma severity was assessed using the Asthma Control Questionnaire (ACQ). Machine learning prediction models were used to analyse various IAQ parameters, such as particulate matter, carbon dioxide, and humidity levels, to identify the most significant predictors of asthma severity with IAQ. The study aimed to develop targeted interventions to improve IAQ and reduce the burden of asthma. Results showed that higher asthma severity scores were associated with increased indoor activity and higher levels of indoor air pollution. Some interventions were implemented to improve ventilation hours, significantly improving IAQ and reducing asthma symptoms, particularly those with more severe asthma. The findings indicate that interventions targeting IAQ, and indoor activities can effectively reduce asthma severity, with up to a 60% reduction in symptoms for asthma patients.
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Perspectives on the future of manufacturing within the Industry 4.0 eraHughes, L., Dwivedi, Y.K., Rana, Nripendra P., Williams, M.D., Raghaven, V. 06 December 2019 (has links)
Yes / The technological choices facing the manufacturing industry are vast and complex as the industry contemplates the increasing levels of digitization and automation in readiness for the modern competitive age. These changes broadly categorized as Industry 4.0, offer significant transformation challenges and opportunities, impacting a multitude of operational aspects of manufacturing organizations. As manufacturers seek to deliver increased levels of productivity and adaptation by innovating many aspects of their business and operational processes, significant challenges and barriers remain. The roadmap toward Industry 4.0 is complex and multifaceted, as manufacturers seek to transition toward new and emerging technologies, whilst retaining operational effectiveness and a sustainability focus. This study approaches many of these significant themes by presenting a critical evaluation of the core topics impacting the next generation of manufacturers, challenges and key barriers to implementation. These factors are further evaluated via the presentation of a new Industry 4.0 framework and alignment of I4.0 themes with the UN Sustainability Goals.
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