| 1. |
- Alinia, Bahram, et al.
(author)
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Competitive Online Scheduling Algorithms with Applications in Deadline-Constrained EV Charging
- 2018
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In: 2018 IEEE/ACM 26th International Symposium on Quality of Service, IWQoS 2018. - : IEEE. - 9781538625422
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Conference paper (peer-reviewed)abstract
- This paper studies the classical problem of online scheduling of deadline-sensitive jobs with partial values and investigates its extension to Electric Vehicle (EV) charging scheduling by taking into account the processing rate limit of jobs and charging station capacity constraint. The problem lies in the category of time-coupled online scheduling problems without availability of future information. This paper proposes two online algorithms, both of which are shown to be (2-\frac{1}{U})-competitive, where U is the maximum scarcity level, a parameter that indicates demand-to-supply ratio. The first proposed algorithm is deterministic, whereas the second is randomized and enjoys a lower computational complexity. When U grows large, the performance of both algorithms approaches that of the state-of-the-art for the case where there is processing rate limits on the jobs. Nonetheless in realistic cases, where U is typically small, the proposed algorithms enjoy a much lower competitive ratio. To carry out the competitive analysis of our algorithms, we present a proof technique, which is novel to the best of our knowledge. This technique could also be used to simplify the competitive analysis of some existing algorithms, and thus could be of independent interest.
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| 2. |
- Roelands, Marc, et al.
(author)
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The diy smart experiences project
- 2011
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In: Architecting the Internet of Things. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642191572 ; , s. 279-315
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Book chapter (other academic/artistic)
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| 3. |
- Saleem, Yasir, et al.
(author)
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IoTRec: The IoT Recommender for Smart Parking System
- 2022
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In: IEEE Transactions on Emerging Topics in Computing. - : IEEE Computer Society. - 2168-6750. ; 10:1, s. 280-296
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Journal article (peer-reviewed)abstract
- This paper proposes a General Data Protection Regulation (GDPR)-compliant Internet of Things (IoT) Recommender (IoTRec) system, developed in the framework of H2020 EU-KR WISE-IoT (Worldwide Interoperability for Semantic IoT) project, which provides the recommendations of parking spots and routes while protecting users' privacy. It provides recommendations by exploiting the IoT technology (parking and traffic sensors). The IoTRec provides four-fold functions. Firstly, it helps the user to find a free parking spot based on different metrics (such as the nearest or nearest trusted parking spot). Secondly, it recommends a route (the least crowded or the shortest route) leading to the recommended parking spot from the user's current location. Thirdly, it provides the real-time provision of expected availability of parking areas (comprised of parking spots organized into groups) in a user-friendly manner. Finally, it provides a GDPR-compliant implementation for operating in a privacy-aware environment. The IoTRec is integrated into the smart parking use case of the WISE-IoT project and is evaluated by the citizens of Santander, Spain through a prototype, but it can be applied to any IoT-enabled locality. The evaluation results show the citizen's satisfaction with the quality, functionalities, ease of use and reliability of the recommendations/services offered by the IoTRec.
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