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| 2. |
- Campadello, Stefano, et al.
(författare)
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Privacy, Trust and Group Communications
- 2007
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Ingår i: Enabling Technologies for Mobile Services: The MobiLife Book. - : John Wiley & Sons. - 9780470512906
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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- Dobslaw, Felix, 1983-, et al.
(författare)
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An Object-Oriented Model in Support of Context Aware Mobile Applications
- 2010
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Ingår i: Mobile Wireless Middleware, Operating Systems, and Applications. - Berlin Hedielberg : Springer Verlag. - 9783642177576
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Konferensbidrag (refereegranskat)abstract
- Intelligent and context-aware mobile services require users and applications to share information and utilize services from remote locations. Thus, context information from the users must be structured and be accessible to applications running in end-devices. In response to this challenge, we present a shared object-oriented meta model for a persistent agent environment. The approach enables agents to be context aware facilitating the creation of ambient intelligence demonstrated by a sensor-based scenario. The agents are context-aware as agent actions are based upon sensor information, social information, and the behavior of co-agents.
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| 7. |
- Dobslaw, Felix, 1983-
(författare)
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Automatic Instance-based Tailoring of Parameter Settings for Metaheuristics
- 2011
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Many industrial problems in various fields, such as logistics, process management, orproduct design, can be formalized and expressed as optimization problems in order tomake them solvable by optimization algorithms. However, solvers that guarantee thefinding of optimal solutions (complete) can in practice be unacceptably slow. Thisis one of the reasons why approximative (incomplete) algorithms, producing near-optimal solutions under restrictions (most dominant time), are of vital importance.Those approximative algorithms go under the umbrella term metaheuristics, each of which is more or less suitable for particular optimization problems. These algorithmsare flexible solvers that only require a representation for solutions and an evaluation function when searching the solution space for optimality.What all metaheuristics have in common is that their search is guided by certain control parameters. These parameters have to be manually set by the user andare generally problem and interdependent: A setting producing near-optimal resultsfor one problem is likely to perform worse for another. Automating the parameter setting process in a sophisticated, computationally cheap, and statistically reliable way is challenging and a significant amount of attention in the artificial intelligence and operational research communities. This activity has not yet produced any major breakthroughs concerning the utilization of problem instance knowledge or the employment of dynamic algorithm configuration.The thesis promotes automated parameter optimization with reference to the inverse impact of problem instance diversity on the quality of parameter settings with respect to instance-algorithm pairs. It further emphasizes the similarities between static and dynamic algorithm configuration and related problems in order to show how they relate to each other. It further proposes two frameworks for instance-based algorithm configuration and evaluates the experimental results. The first is a recommender system for static configurations, combining experimental design and machine learning. The second framework can be used for static or dynamic configuration,taking advantage of the iterative nature of population-based algorithms, which is a very important sub-class of metaheuristics.A straightforward implementation of framework one did not result in the expected improvements, supposedly because of pre-stabilization issues. The second approach shows competitive results in the scenario when compared to a state-of-the-art model-free configurator, reducing the training time by in excess of two orders of magnitude.
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| 8. |
- Firouzi, Ramin, et al.
(författare)
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An Autonomic IoT Gateway for Smart Home Using Fuzzy Logic Reasoner
- 2020
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Ingår i: The 10th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2020). - : Elsevier BV. ; , s. 102-111
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Konferensbidrag (refereegranskat)abstract
- With recent advancements in communications and sensor technologies, the Internet of Things (IoT) has been experiencing rapid growth. It is estimated that billions of objects will be connected, which would create a vast amount of data. Cloud computing has been the predominant choice for monitoring connected objects and delivering data-based intelligence, but high response time and network load of cloud-based solutions are limiting factors for IoT deployment. In order to cope with this challenge, this paper proposes a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge
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| 9. |
- Firouzi, Ramin, et al.
(författare)
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Context-based Reasoning through Fuzzy Logic for Edge Intelligence
- 2021
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Ingår i: International Journal of Ubiquitous Systems and Pervasive Networks (JUSPN). - 1923-7324 .- 1923-7332. ; 15:1, s. 17-25
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of edge computing, the Internet of Things (IoT) environment has the ability to process data locally. The complexity of the context reasoning process can be scattered across several edge nodes that physically placed at the source of the qualitative information by moving the processing and knowledge inference to the edge of the IoT network. This facilitates the real-time processing of a large range of rich data sources that would be less complex and expensive compare to the traditional centralized cloud system. In this paper, we propose a novel approach to provide low-level intelligence for IoT applications through an IoT edge controller that is leveraging the Fuzzy Logic Controller along with edge computing. This low-level intelligence, together with cloud-based intelligence, forms the distributed IoT intelligence. The proposed controller allows distributed IoT gateway to manage input uncertainties; besides, by interacting with its environment, the learning system can enhance its performance over time, which leads to improving the reliability of the IoT gateway. Therefore, such a controller is able to offer different context-aware reasoning to alleviate the distributed IoT. A simulated smart home scenario has been done to prove the plausibility of the low-level intelligence concerning reducing latency and more accurate prediction through learning experiences at the edge.
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| 10. |
- Firouzi, Ramin, et al.
(författare)
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Distributed-Reasoning for Task Scheduling through Distributed Internet of Things Controller
- 2021
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Ingår i: Procedia Computer Science. - : Elsevier. ; , s. 24-32
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Konferensbidrag (refereegranskat)abstract
- The introduction of distributed-reasoning through ubiquitous instrumentation within the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault-tolerance, traffic, healthcare, so on. Using a ubiquitous controller to interconnect devices in the IoT, however monumental, is still in its embryonic stage, it has the potential to create distributed-intelligent IoT solutions that are more eclient and safer than centric intelligence. It is essential to step in a new direction for designing a distributed intelligent controller for task scheduling as a means to, first, dynamically interact with a smart environment in eclient real-time data processing and, second, react to flexible changes. To cope with these issues, we outline a two-level intelligence schema, using edge computing to enhance distributed IoT. The edge schema pushes the streaming processing capability from cloud to edge devices to better support timely and reliable streaming analytics to improve the performance of smart IoT applications. In this paper, in order to provide better, reliable, and flexible streaming analytics and overcome the data uncertainties, we proposed an IoT gateway controller to provide low-level intelligence by employing a fuzzy abductive reasoner. Numerical simulations support the feasibility of our proposed approaches.
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