Special issue on advances in ambient intelligence and pervasive computing
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EDITORIAL
Special issue on advances in ambient intelligence and pervasive computing Elhadi M. Shakshuki1 · Ansar‑Ul‑Haque Yasar2 · Haroon Malik3
© Springer-Verlag GmbH Germany, part of Springer Nature 2019
This special issue is based on the best papers selected from the 10th International Conference on Ambient Systems, Networks and Technologies (ANT-2019), which was held on April 29–May 2, 2019, in Leuven, Belgium. The conference attracted a large number of scientific papers that contributed to the state-of-the-art in the ambient systems, networks and technologies. All the papers selected for this special issue have been extended significantly from their original versions and underwent two rounds of rigorous peer-review process. Based on the reviewers’ feedback, as well as the evaluations of the Guest Editors, eight papers were selected for this special issue from 11 invited submissions. The accepted papers augment ambient systems by uncovering interesting methodologies related to bid data management, indoor localization, recommendation system with reinforcement learning, interference in wireless channels, and scheduling in cloud computing. The first paper by Maguerra et al. is entitled “Towards a Reactive System for Managing Big Trajectory Data”. The authors of this paper discussed the importance of extracting knowledge from heterogeneous, massive data generated from indoor and outdoor tracking devices. The main aim of this paper is to incorporate a fully-fledged, reactive system for big trajectory data management. The authors claim that this system is unique of its kind because it is actor-based * Elhadi M. Shakshuki [email protected] Ansar‑Ul‑Haque Yasar [email protected] Haroon Malik [email protected] 1
Jodrey School of Computer Science Acadia University, Wolfville, Canada
2
Transportation Research Institute Hasselt University, Hasselt, Belgium
3
Weisberg Division of Computer Science, Marshall University, Huntington, WV, USA
and features responsiveness, resiliency, and elasticity. The system is implemented using Scala that allowed the authors to reach a higher level of abstraction to be able to process any trajectory type. In this work, Geolife project’s GPS trajectory dataset is utilized. The second paper by Martins et al. is entitled “Improving Bluetooth Beacon-based Indoor Location and Fingerprinting”. This paper investigated how Bluetooth Low Energy (BLE) beacons radio signals can be used for indoor location scenarios, as well as their precision. In this paper, a method for beacon-based positioning is introduced, which is based on signal strength measurements at key distances for each beacon. This method allowed the authors to use different beacon types, brands, and location conditions/constraints. Depending on each situation (i.e., hardware and location) it was possible to adapt the distance measuring curve to minimize errors and support higher distances, while at the same time keeping good precision. This paper also presented a comparison of their proposed approach with
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