1,723 research outputs found
On the real world practice of Behaviour Driven Development
Surveys of industry practice over the last decade suggest that Behaviour Driven Development is a popular Agile practice. For example, 19% of respondents to the 14th State of Agile annual survey reported using BDD, placing it in the top 13 practices reported. As well as potential benefits, the adoption of BDD necessarily involves an additional cost of writing and maintaining Gherkin features and scenarios, and (if used for acceptance testing,) the associated step functions. Yet there is a lack of published literature exploring how BDD is used in practice and the challenges experienced by real world software development efforts. This gap is significant because without understanding current real world practice, it is hard to identify opportunities to address and mitigate challenges. In order to address this research gap concerning the challenges of using BDD, this thesis reports on a research project which explored: (a) the challenges of applying agile and undertaking requirements engineering in a real world context; (b) the challenges of applying BDD specifically and (c) the application of BDD in open-source projects to understand challenges in this different context.
For this purpose, we progressively conducted two case studies, two series of interviews, four iterations of action research, and an empirical study. The first case study was conducted in an avionics company to discover the challenges of using an agile process in a large scale safety critical project environment. Since requirements management was found to be one of the biggest challenges during the case study, we decided to investigate BDD because of its reputation for requirements management. The second case study was conducted in the company with an aim to discover the challenges of using BDD in real life. The case study was complemented with an empirical study of the practice of BDD in open source projects, taking a study sample from the GitHub open source collaboration site.
As a result of this Ph.D research, we were able to discover: (i) challenges of using an agile process in a large scale safety-critical organisation, (ii) current state of BDD in practice, (iii) technical limitations of Gherkin (i.e., the language for writing requirements in BDD), (iv) challenges of using BDD in a real project, (v) bad smells in the Gherkin specifications of open source projects on GitHub. We also presented a brief comparison between the theoretical description of BDD and BDD in practice. This research, therefore, presents the results of lessons learned from BDD in practice, and serves as a guide for software practitioners planning on using BDD in their projects
Pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inΕΎenjerstvu voΔenom modelima
In this thesis, we present an approach to the production process specification and generation based on the model-driven paradigm, with the goal to increase the flexibility of factories and respond to the challenges that emerged in the era of Industry 4.0 more efficiently. To formally specify production processes and their variations in the Industry 4.0 environment, we created a novel domain-specific modeling language, whose models are machine-readable. The created language can be used to model production processes that can be independent of any production system, enabling process models to be used in different production systems, and process models used for the specific production system. To automatically transform production process models dependent on the specific production system into instructions that are to be executed by production system resources, we created an instruction generator. Also, we created generators for different manufacturing documentation, which automatically transform production process models into manufacturing documents of different types. The proposed approach, domain-specific modeling language, and software solution contribute to introducing factories into the digital transformation process. As factories must rapidly adapt to new products and their variations in the era of Industry 4.0, production must be dynamically led and instructions must be automatically sent to factory resources, depending on products that are to be created on the shop floor. The proposed approach contributes to the creation of such a dynamic environment in contemporary factories, as it allows to automatically generate instructions from process models and send them to resources for execution. Additionally, as there are numerous different products and their variations, keeping the required manufacturing documentation up to date becomes challenging, which can be done automatically by using the proposed approach and thus significantly lower process designers' time.Π£ ΠΎΠ²ΠΎΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ΅Π½ ΡΠ΅ ΠΏΡΠΈΡΡΡΠΏ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠΈ ΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° Π·Π°ΡΠ½ΠΎΠ²Π°Π½ Π½Π° ΠΈΠ½ΠΆΠ΅ΡΠ΅ΡΡΡΠ²Ρ Π²ΠΎΡΠ΅Π½ΠΎΠΌ ΠΌΠΎΠ΄Π΅Π»ΠΈΠΌΠ°, Ρ ΡΠΈΡΡ ΠΏΠΎΠ²Π΅ΡΠ°ΡΠ° ΡΠ»Π΅ΠΊΡΠΈΠ±ΠΈΠ»Π½ΠΎΡΡΠΈ ΠΏΠΎΡΡΡΠΎΡΠ΅ΡΠ° Ρ ΡΠ°Π±ΡΠΈΠΊΠ°ΠΌΠ° ΠΈ Π΅ΡΠΈΠΊΠ°ΡΠ½ΠΈΡΠ΅Π³ ΡΠ°Π·ΡΠ΅ΡΠ°Π²Π°ΡΠ° ΠΈΠ·Π°Π·ΠΎΠ²Π° ΠΊΠΎΡΠΈ ΡΠ΅ ΠΏΠΎΡΠ°Π²ΡΡΡΡ Ρ Π΅ΡΠΈ ΠΠ½Π΄ΡΡΡΡΠΈΡΠ΅ 4.0. ΠΠ° ΠΏΠΎΡΡΠ΅Π±Π΅ ΡΠΎΡΠΌΠ°Π»Π½Π΅ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° ΠΈ ΡΠΈΡ
ΠΎΠ²ΠΈΡ
Π²Π°ΡΠΈΡΠ°ΡΠΈΡΠ° Ρ Π°ΠΌΠ±ΠΈΡΠ΅Π½ΡΡ ΠΠ½Π΄ΡΡΡΡΠΈΡΠ΅ 4.0, ΠΊΡΠ΅ΠΈΡΠ°Π½ ΡΠ΅ Π½ΠΎΠ²ΠΈ Π½Π°ΠΌΠ΅Π½ΡΠΊΠΈ ΡΠ΅Π·ΠΈΠΊ, ΡΠΈΡΠ΅ ΠΌΠΎΠ΄Π΅Π»Π΅ ΡΠ°ΡΡΠ½Π°Ρ ΠΌΠΎΠΆΠ΅ Π΄Π° ΠΎΠ±ΡΠ°Π΄ΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½. ΠΡΠ΅ΠΈΡΠ°Π½ΠΈ ΡΠ΅Π·ΠΈΠΊ ΠΈΠΌΠ° ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΎΠ²Π°ΡΠ° ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° ΠΊΠΎΡΠΈ ΠΌΠΎΠ³Ρ Π±ΠΈΡΠΈ Π½Π΅Π·Π°Π²ΠΈΡΠ½ΠΈ ΠΎΠ΄ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ° ΠΈ ΡΠΈΠΌΠ΅ ΡΠΏΠΎΡΡΠ΅Π±ΡΠ΅Π½ΠΈ Ρ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΠΌ ΠΏΠΎΡΡΡΠΎΡΠ΅ΡΠΈΠΌΠ° ΠΈΠ»ΠΈ ΡΠ°Π±ΡΠΈΠΊΠ°ΠΌΠ°, Π°Π»ΠΈ ΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° ΠΊΠΎΡΠΈ ΡΡ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΈ Π·Π° ΠΎΠ΄ΡΠ΅ΡΠ΅Π½ΠΈ ΡΠΈΡΡΠ΅ΠΌ. ΠΠ°ΠΊΠΎ Π±ΠΈ ΠΌΠΎΠ΄Π΅Π»Π΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° Π·Π°Π²ΠΈΡΠ½ΠΈΡ
ΠΎΠ΄ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ° Π±ΠΈΠ»ΠΎ ΠΌΠΎΠ³ΡΡΠ΅ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΠ°ΡΠΈ Ρ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ΅ ΠΊΠΎΡΠ΅ ΡΠ΅ΡΡΡΡΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ° ΠΈΠ·Π²ΡΡΠ°Π²Π°ΡΡ, ΠΊΡΠ΅ΠΈΡΠ°Π½ ΡΠ΅ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ°. Π’Π°ΠΊΠΎΡΠ΅ ΡΡ ΠΊΡΠ΅ΠΈΡΠ°Π½ΠΈ ΠΈ Π³Π΅Π½Π΅ΡΠ°ΡΠΎΡΠΈ ΡΠ΅Ρ
Π½ΠΈΡΠΊΠ΅ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ΅, ΠΊΠΎΡΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΡ ΠΌΠΎΠ΄Π΅Π»Π΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π½ΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΠ° Ρ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΡΠΈΠΏΠΎΠ²Π°. Π£ΠΏΠΎΡΡΠ΅Π±ΠΎΠΌ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠ³ ΠΏΡΠΈΡΡΡΠΏΠ°, Π½Π°ΠΌΠ΅Π½ΡΠΊΠΎΠ³ ΡΠ΅Π·ΠΈΠΊΠ° ΠΈ ΡΠΎΡΡΠ²Π΅ΡΡΠΊΠΎΠ³ ΡΠ΅ΡΠ΅ΡΠ° Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠΈ ΡΠ΅ ΡΠ²ΠΎΡΠ΅ΡΡ ΡΠ°Π±ΡΠΈΠΊΠ° Ρ ΠΏΡΠΎΡΠ΅Ρ Π΄ΠΈΠ³ΠΈΡΠ°Π»Π½Π΅ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΡΠ΅. ΠΠ°ΠΊΠΎ ΡΠ°Π±ΡΠΈΠΊΠ΅ Ρ Π΅ΡΠΈ ΠΠ½Π΄ΡΡΡΡΠΈΡΠ΅ 4.0 ΠΌΠΎΡΠ°ΡΡ Π±ΡΠ·ΠΎ Π΄Π° ΡΠ΅ ΠΏΡΠΈΠ»Π°Π³ΠΎΠ΄Π΅ Π½ΠΎΠ²ΠΈΠΌ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠΌΠ° ΠΈ ΡΠΈΡ
ΠΎΠ²ΠΈΠΌ Π²Π°ΡΠΈΡΠ°ΡΠΈΡΠ°ΠΌΠ°, Π½Π΅ΠΎΠΏΡ
ΠΎΠ΄Π½ΠΎ ΡΠ΅ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠΊΠΈ Π²ΠΎΠ΄ΠΈΡΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡ ΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΡΠ»Π°ΡΠΈ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ΅ ΡΠ΅ΡΡΡΡΠΈΠΌΠ° Ρ ΡΠ°Π±ΡΠΈΡΠΈ, Ρ Π·Π°Π²ΠΈΡΠ½ΠΎΡΡΠΈ ΠΎΠ΄ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π° ΠΊΠΎΡΠΈ ΡΠ΅ ΠΊΡΠ΅ΠΈΡΠ°ΡΡ Ρ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠΌ ΠΏΠΎΡΡΡΠΎΡΠ΅ΡΡ. Π’ΠΈΠΌΠ΅ ΡΡΠΎ ΡΠ΅ Ρ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠΌ ΠΏΡΠΈΡΡΡΠΏΡ ΠΌΠΎΠ³ΡΡΠ΅ ΠΈΠ· ΠΌΠΎΠ΄Π΅Π»Π° ΠΏΡΠΎΡΠ΅ΡΠ° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ΠΎ Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΠΈ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡΠ΅ ΠΈ ΠΏΠΎΡΠ»Π°ΡΠΈ ΠΈΡ
ΡΠ΅ΡΡΡΡΠΈΠΌΠ°, Π΄ΠΎΠΏΡΠΈΠ½ΠΎΡΠΈ ΡΠ΅ ΠΊΡΠ΅ΠΈΡΠ°ΡΡ ΡΠ΅Π΄Π½ΠΎΠ³ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠΊΠΎΠ³ ΠΎΠΊΡΡΠΆΠ΅ΡΠ° Ρ ΡΠ°Π²ΡΠ΅ΠΌΠ΅Π½ΠΈΠΌ ΡΠ°Π±ΡΠΈΠΊΠ°ΠΌΠ°. ΠΠΎΠ΄Π°ΡΠ½ΠΎ, ΡΡΠ»Π΅Π΄ Π²Π΅Π»ΠΈΠΊΠΎΠ³ Π±ΡΠΎΡΠ° ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄Π° ΠΈ ΡΠΈΡ
ΠΎΠ²ΠΈΡ
Π²Π°ΡΠΈΡΠ°ΡΠΈΡΠ°, ΠΏΠΎΡΡΠ°ΡΠ΅ ΠΈΠ·Π°Π·ΠΎΠ²Π½ΠΎ ΠΎΠ΄ΡΠΆΠ°Π²Π°ΡΠΈ Π½Π΅ΠΎΠΏΡ
ΠΎΠ΄Π½Ρ ΡΠ΅Ρ
Π½ΠΈΡΠΊΡ Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠ°ΡΠΈΡΡ, ΡΡΠΎ ΡΠ΅ Ρ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠΌ ΠΏΡΠΈΡΡΡΠΏΡ ΠΌΠΎΠ³ΡΡΠ΅ ΡΡΠ°Π΄ΠΈΡΠΈ Π½Π° Π°ΡΡΠΎΠΌΠ°ΡΠΈΠ·ΠΎΠ²Π°Π½ Π½Π°ΡΠΈΠ½ ΠΈ ΡΠΈΠΌΠ΅ Π·Π½Π°ΡΠ°ΡΠ½ΠΎ ΡΡΡΠ΅Π΄Π΅ΡΠΈ Π²ΡΠ΅ΠΌΠ΅ ΠΏΡΠΎΡΠ΅ΠΊΡΠ°Π½Π°ΡΠ° ΠΏΡΠΎΡΠ΅ΡΠ°.U ovoj disertaciji predstavljen je pristup specifikaciji i generisanju proizvodnih procesa zasnovan na inΕΎenjerstvu voΔenom modelima, u cilju poveΔanja fleksibilnosti postrojenja u fabrikama i efikasnijeg razreΕ‘avanja izazova koji se pojavljuju u eri Industrije 4.0. Za potrebe formalne specifikacije proizvodnih procesa i njihovih varijacija u ambijentu Industrije 4.0, kreiran je novi namenski jezik, Δije modele raΔunar moΕΎe da obradi na automatizovan naΔin. Kreirani jezik ima moguΔnost modelovanja proizvodnih procesa koji mogu biti nezavisni od proizvodnih sistema i time upotrebljeni u razliΔitim postrojenjima ili fabrikama, ali i proizvodnih procesa koji su specifiΔni za odreΔeni sistem. Kako bi modele proizvodnih procesa zavisnih od konkretnog proizvodnog sistema bilo moguΔe na automatizovan naΔin transformisati u instrukcije koje resursi proizvodnog sistema izvrΕ‘avaju, kreiran je generator instrukcija. TakoΔe su kreirani i generatori tehniΔke dokumentacije, koji na automatizovan naΔin transformiΕ‘u modele proizvodnih procesa u dokumente razliΔitih tipova. Upotrebom predloΕΎenog pristupa, namenskog jezika i softverskog reΕ‘enja doprinosi se uvoΔenju fabrika u proces digitalne transformacije. Kako fabrike u eri Industrije 4.0 moraju brzo da se prilagode novim proizvodima i njihovim varijacijama, neophodno je dinamiΔki voditi proizvodnju i na automatizovan naΔin slati instrukcije resursima u fabrici, u zavisnosti od proizvoda koji se kreiraju u konkretnom postrojenju. Time Ε‘to je u predloΕΎenom pristupu moguΔe iz modela procesa automatizovano generisati instrukcije i poslati ih resursima, doprinosi se kreiranju jednog dinamiΔkog okruΕΎenja u savremenim fabrikama. Dodatno, usled velikog broja razliΔitih proizvoda i njihovih varijacija, postaje izazovno odrΕΎavati neophodnu tehniΔku dokumentaciju, Ε‘to je u predloΕΎenom pristupu moguΔe uraditi na automatizovan naΔin i time znaΔajno uΕ‘tedeti vreme projektanata procesa
Air Quality Research Using Remote Sensing
Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the worldβs population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earthβs energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Design and Implementation of a Portable Framework for Application Decomposition and Deployment in Edge-Cloud Systems
The emergence of cyber-physical systems has brought about a significant increase in complexity and heterogeneity in the infrastructure on which these systems are deployed. One particular example of this complexity is the interplay between cloud, fog, and edge computing. However, the complexity of these systems can pose challenges when it comes to implementing self-organizing mechanisms, which are often designed to work on flat networks. Therefore, it is essential to separate the application logic from the specific deployment aspects to promote reusability and flexibility in infrastructure exploitation.
To address this issue, a novel approach called "pulverization" has been proposed. This approach involves breaking down the system into smaller computational units, which can then be deployed on the available infrastructure.
In this thesis, the design and implementation of a portable framework that enables the "pulverization" of cyber-physical systems are presented.
The main objective of the framework is to pave the way for the deployment of cyber-physical systems in the edge-cloud continuum by reducing the complexity of the infrastructure and exploit opportunistically the heterogeneous resources available on it. Different scenarios are presented to highlight the effectiveness of the framework in different heterogeneous infrastructures and devices.
Current limitations and future work are examined to identify improvement areas for the framework
Adaptive Data-driven Optimization using Transfer Learning for Resilient, Energy-efficient, Resource-aware, and Secure Network Slicing in 5G-Advanced and 6G Wireless Systems
Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 134-141)Dissertation (Ph.D)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 20225GβAdvanced is the next step in the evolution of the fifthβgeneration (5G) technology. It will introduce a new level of expanded capabilities beyond connections and enables a broader range of advanced applications and use cases. 5GβAdvanced will support modern applications with greater mobility and high dependability. Artificial intelligence and Machine Learning will enhance network performance with spectral efficiency and energy savings enhancements.
This research established a framework to optimally control and manage an appropriate selection of network slices for incoming requests from diverse applications and services in Beyond 5G networks. The developed DeepSlice model is used to optimize the network and individual slice load efficiency across isolated slices and manage slice lifecycle in case of failure. The DeepSlice framework can predict the unknown connections by utilizing the learning from a developed deep-learning neural network model.
The research also addresses threats to the performance, availability, and robustness of B5G networks by proactively preventing and resolving threats. The study proposed a Secure5G framework for authentication, authorization, trust, and control for a network slicing architecture in 5G systems. The developed model prevents the 5G infrastructure from Distributed Denial of Service by analyzing incoming connections and learning from the developed model. The research demonstrates the preventive measure against volume attacks, flooding attacks, and masking (spoofing) attacks. This research builds the framework towards the zero trust objective (never trust, always verify, and verify continuously) that improves resilience.
Another fundamental difficulty for wireless network systems is providing a desirable user experience in various network conditions, such as those with varying network loads and bandwidth fluctuations. Mobile Network Operators have long battled unforeseen network traffic events. This research proposed ADAPTIVE6G to tackle the network load estimation problem using knowledge-inspired Transfer Learning by utilizing radio network Key Performance Indicators from network slices to understand and learn network load estimation problems. These algorithms enable Mobile Network Operators to optimally coordinate their computational tasks in stochastic and time-varying network states.
Energy efficiency is another significant KPI in tracking the sustainability of network slicing. Increasing traffic demands in 5G dramatically increase the energy consumption of mobile networks. This increase is unsustainable in terms of dollar cost and environmental impact. This research proposed an innovative ECO6G model to attain sustainability and energy efficiency. Research findings suggested that the developed model can reduce network energy costs without negatively impacting performance or end customer experience against the classical Machine Learning and Statistical driven models. The proposed model is validated against the industry-standardized energy efficiency definition, and operational expenditure savings are derived, showing significant cost savings to MNOs.Introduction -- A deep neural network framework towards a resilient, efficient, and secure network slicing in Beyond 5G Networks -- Adaptive resource management techniques for network slicing in Beyond 5G networks using transfer learning -- Energy and cost analysis for network slicing deployment in Beyond 5G networks -- Conclusion and future scop
SCALING UP TASK EXECUTION ON RESOURCE-CONSTRAINED SYSTEMS
The ubiquity of executing machine learning tasks on embedded systems with constrained resources has made efficient execution of neural networks on these systems under the CPU, memory, and energy constraints increasingly important. Different from high-end computing systems where resources are abundant and reliable, resource-constrained systems only have limited computational capability, limited memory, and limited energy supply. This dissertation focuses on how to take full advantage of the limited resources of these systems in order to improve task execution efficiency from different aspects of the execution pipeline. While the existing literature primarily aims at solving the problem by shrinking the model size according to the resource constraints, this dissertation aims to improve the execution efficiency for a given set of tasks from the following two aspects. Firstly, we propose SmartON, which is the first batteryless active event detection system that considers both the event arrival pattern as well as the harvested energy to determine when the system should wake up and what the duty cycle should be. Secondly, we propose Antler, which exploits the affinity between all pairs of tasks in a multitask inference system to construct a compact graph representation of the task set for a given overall size budget. To achieve the aforementioned algorithmic proposals, we propose the following hardware solutions. One is a controllable capacitor array that can expand the systemβs energy storage on-the-fly. The other is a FRAM array that can accommodate multiple neural networks running on one system.Doctor of Philosoph
Challenges in the Design and Implementation of IoT Testbeds in Smart-Cities : A Systematic Review
Advancements in wireless communication and the increased accessibility to low-cost sensing and data processing IoT technologies have increased the research and development of urban monitoring systems. Most smart city research projects rely on deploying proprietary IoT testbeds for indoor and outdoor data collection. Such testbeds typically rely on a three-tier architecture composed of the Endpoint, the Edge, and the Cloud. Managing the system's operation whilst considering the security and privacy challenges that emerge, such as data privacy controls, network security, and security updates on the devices, is challenging. This work presents a systematic study of the challenges of developing, deploying and managing urban monitoring testbeds, as experienced in a series of urban monitoring research projects, followed by an analysis of the relevant literature. By identifying the challenges in the various projects and organising them under the V-model development lifecycle levels, we provide a reference guide for future projects. Understanding the challenges early on will facilitate current and future smart-cities IoT research projects to reduce implementation time and deliver secure and resilient testbeds
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