Heuristics based Mosaic of Social-Sensor Services for Scene Reconstruction
This addresses scene reconstruction for applications like urban planning or event monitoring, but it appears incremental as it builds on existing social-sensor and service composition methods.
The paper tackled the problem of reconstructing scenes using crowdsourced social media images by proposing a heuristics-based service selection and composition model, with preliminary analytical results proving its feasibility.
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.