| 1. |
- Ashouri, Majid
(författare)
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Towards Supporting IoT System Designers in Edge Computing Deployment Decisions
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various application components is not a straightforward task, and IoT system designer should be supported for the decision.To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation.
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| 2. |
- Issa Mattos, David, 1990, et al.
(författare)
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Automated optimization of software parameters in a long term evolution radio base station
- 2019
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Ingår i: SysCon 2019 - 13th Annual IEEE International Systems Conference, Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Radio network optimization is concerned with the configuration of radio base station parameters in order to achieve the desired level of service quality in addition to many other differentiating technical factors. Mobile network operators have different physical locations, levels of traffic profiles, number of connected devices, and the desired quality of service. All of these conditions make the problem of optimizing the parameters of a radio base station specific to the operator's business goals. The high number of calibration parameters and the complex interaction between them make the system behave as a black-box model for any practical purpose. The computation of relevant operator metrics is often stochastic, and it can take several minutes to compute the effect of changing a single, making it impractical to optimize systems with approaches that require a large number of iterations. Operators want to optimize their already deployed system in online scenarios while minimizing the exposure of the system to a negative set of parameters during the optimization procedure. {This paper presents a novel approach to the optimization of a Long Term Evolution (LTE) radio base station in a large search space with an expensive stochastic objective and a limited regret bounds scenario. We show the feasibility of this approach by implementing it in an industrial testing bed radio base station connected to real User Equipment (UE) in collaboration with Ericsson. Two optimization processes in this experimental setup are executed to show the feasibility of the approach in real-world scenarios.
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| 3. |
- Zhang, Hongyi, 1996, et al.
(författare)
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Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning
- 2022
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Ingår i: Proceedings - 2022 IEEE Future Networks World Forum, FNWF 2022. - : IEEE. ; , s. 184-189, s. 184-189
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Konferensbidrag (refereegranskat)abstract
- Fast and reliable connectivity is essential to enhance situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.
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| 4. |
- Ashouri, Majid, et al.
(författare)
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Edge Computing Simulators for IoT System Design: An Analysis of Qualities and Metrics
- 2019
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Ingår i: Future Internet. - : MDPI. - 1999-5903. ; 11:11, s. 235-246
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Tidskriftsartikel (refereegranskat)abstract
- The deployment of Internet of Things (IoT) applications is complex since many quality characteristics should be taken into account, for example, performance, reliability, and security. In this study, we investigate to what extent the current edge computing simulators support the analysis of qualities that are relevant to IoT architects who are designing an IoT system. We first identify the quality characteristics and metrics that can be evaluated through simulation. Then, we study the available simulators in order to assess which of the identified qualities they support. The results show that while several simulation tools for edge computing have been proposed, they focus on a few qualities, such as time behavior and resource utilization. Most of the identified qualities are not considered and we suggest future directions for further investigation to provide appropriate support for IoT architects.
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| 5. |
- Ashouri, Majid, et al.
(författare)
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Quality attributes in edge computing for the Internet of Things : A systematic mapping study
- 2021
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Ingår i: Internet of Things. - : Elsevier. - 2542-6605. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Many Internet of Things (IoT) systems generate a massive amount of data needing to be processed and stored efficiently. Cloud computing solutions are often used to handle these tasks. However, the increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage. Edge computing may help to improve IoT systems regarding important quality attributes like latency, energy consumption, privacy, and bandwidth utilization. However, deciding where to deploy the various application components is not a straightforward task. This is largely due to the trade-offs between the quality attributes relevant for the application. We have performed a systematic mapping study of 98 articles to investigate which quality attributes have been used in the literature for assessing IoT systems using edge computing. The analysis shows that time behavior and resource utilization are the most frequently used quality attributes; further, response time, turnaround time, and energy consumption are the most used metrics for quantifying these quality attributes. Moreover, simulation is the main tool used for the assessments, and the studied trade-offs are mainly between only two qualities. Finally, we identified a number of research gaps that need further study.
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| 6. |
- Ouhaichi, Hamza, et al.
(författare)
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Dynamic data management for machine learning in embedded systems: A case study
- 2019
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Ingår i: Lecture Notes in Business Information Processing. - Cham : Springer International Publishing. - 1865-1356 .- 1865-1348. ; 370 LNBIP, s. 145-154
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Konferensbidrag (refereegranskat)abstract
- Dynamic data and continuously evolving sets of records are essential for a wide variety of today’s data management applications. Such applications range from large, social, content-driven Internet applications, to highly focused data processing verticals like data intensive science, telecommunications and intelligence applications. However, the dynamic and multimodal nature of data makes it challenging to transform it into machine-readable and machine-interpretable forms. In this paper, we report on an action research study that we conducted in collaboration with a multinational company in the embedded systems domain. In our study, and in the context of a real-world industrial application of dynamic data management, we provide insights to data science community and research to guide discussions and future research into dynamic data management in embedded systems. Our study identifies the key challenges in the phases of data collection, data storage and data cleaning that can significantly impact the overall performance of the system.
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| 7. |
- Khoshkangini, Reza, 1984-, et al.
(författare)
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Optimal Task Grouping Approach in Multitask Learning
- 2024
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Ingår i: Neural Information Processing. ICONIP 2023. - Heidelberg : Springer Nature. - 9789819980758 - 9789819980765 ; , s. 206-225
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Konferensbidrag (refereegranskat)abstract
- Multi-task learning has become a powerful solution in which multiple tasks are trained together to leverage the knowledge learned from one task to improve the performance of the other tasks. However, the tasks are not always constructive on each other in the multi-task formulation and might play negatively during the training process leading to poor results. Thus, this study focuses on finding the optimal group of tasks that should be trained together for multi-task learning in an automotive context. We proposed a multi-task learning approach to model multiple vehicle long-term behaviors using low-resolution data and utilized gradient descent to efficiently discover the optimal group of tasks/vehicle behaviors that can increase the performance of the predictive models in a single training process. In this study, we also quantified the contribution of individual tasks in their groups and to the other groups’ performance. The experimental evaluation of the data collected from thousands of heavy-duty trucks shows that the proposed approach is promising. © 2024 Springer Nature
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| 8. |
- Ashouri, Majid, et al.
(författare)
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Analyzing Distributed Deep Neural Network Deployment on Edge and Cloud Nodes in IoT Systems
- 2020
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Ingår i: IEEE International Conference on Edge Computing (EDGE), Virtual conference, October 18–24, 2020.. - 9781728182544 - 9781728182551 ; , s. 59-66
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Konferensbidrag (refereegranskat)abstract
- For the efficient execution of Deep Neural Networks (DNN) in the Internet of Things, computation tasks can be distributed and deployed on edge nodes. In contrast to deploying all computation to the cloud, the use of Distributed DNN (DDNN) often results in a reduced amount of data that is sent through the network and thus might increase the overall performance of the system. However, finding an appropriate deployment scenario is often a complex task and requires considering several criteria. In this paper, we introduce a multi-criteria decision-making method based on the Analytical Hierarchy Process for the comparison and selection of deployment alternatives. We use the RECAP simulation framework to model and simulate DDNN deployments on different scales to provide a comprehensive assessment of deployments to system designers. In a case study, we apply the method to a smart city scenario where different distributions and deployments of a DNN are analyzed and compared.
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| 9. |
- Issa Mattos, David, 1990, et al.
(författare)
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Optimization experiments in the continuous space: The limited growth optimistic optimization algorithm
- 2018
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 11036 LNCS, s. 293-308, s. 293-308
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Konferensbidrag (refereegranskat)abstract
- Online controlled experiments are extensively used by web-facing companies to validate and optimize their systems, providing a competitive advantage in their business. As the number of experiments scale, companies aim to invest their experimentation resources in larger feature changes and leave the automated techniques to optimize smaller features. Optimization experiments in the continuous space are encompassed in the many-armed bandits class of problems. Although previous research provides algorithms for solving this class of problems, these algorithms were not implemented in real-world online experimentation problems and do not consider the application constraints, such as time to compute a solution, selection of a best arm and the estimation of the mean-reward function. This work discusses the online experiments in context of the many-armed bandits class of problems and provides three main contributions: (1) an algorithm modification to include online experiments constraints, (2) implementation of this algorithm in an industrial setting in collaboration with Sony Mobile, and (3) statistical evidence that supports the modification of the algorithm for online experiments scenarios. These contributions support the relevance of the LG-HOO algorithm in the context of optimization experiments and show how the algorithm can be used to support continuous optimization of online systems in stochastic scenarios.
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| 10. |
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