We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set of features defined on the bases of the image metadata to determine the relevance and composability of services. Novel heuristics are developed to filter out non-relevant services. Multiple machine learning strategies are employed to produce smooth service composition resulting in a mosaic of relevant images indexed by geolocation and time. The preliminary analytical results prove the feasibility of the proposed composition model.
@article{arxiv.2009.11663,
title = {Heuristics based Mosaic of Social-Sensor Services for Scene Reconstruction},
author = {Tooba Aamir and Hai Dong and Athman Bouguettaya},
journal= {arXiv preprint arXiv:2009.11663},
year = {2020}
}
Comments
13 pages, 8 figures and 1 table. This is an accepted paper and it is going to appear in the Proceedings of the 2020 21st International Conference on Web Information Systems Engineering (WISE 2020), Amsterdam and Leiden, Netherlands. The proceedings of WISE 2020 will be published by Springer in its Lecture Notes in Computer Science series