PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data

Emergence of smartphone and the participatory sensing (PS) paradigm have paved the way for a new variant of pervasive computing. In PS, human user performs sensing tasks and generates notifications, typically in lieu of incentives. These notifications are real-time, large-volume, and multi-modal, which are eventually fused by the PS platform to generate a summary. One major limitation with PS is the sparsity of notifications owing to lack of active participation, thus inhibiting large scale real-life experiments for the research community. On the flip side, research community always needs ground truth to validate the efficacy of the proposed models and algorithms. Most of the PS applications involve human mobility and report generation following sensing of any event of interest in the adjacent environment. This work is an attempt to study and empirically model human participation behavior and event occurrence distributions through development of a location-sensitive data simulation framework, called PS-Sim. From extensive experiments it has been observed that the synthetic data generated by PS-Sim replicates real participation and event occurrence behaviors in PS applications, which may be considered for validation purpose in absence of the groundtruth. As a proof-of-concept, we have used real-life dataset from a vehicular traffic management application to train the models in PS-Sim and cross-validated the simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International Conference on Smart Computing (SMARTCOMP-2018

eDWaaS: A Scalable Educational Data Warehouse as a Service

The university management is perpetually in the process of innovating policies to improve the quality of service. Intellectual growth of the students, the popularity of university are some of the major areas that management strives to improve upon. Relevant historical data is needed in support of taking any decision. Furthermore, providing data to various university ranking frameworks is a frequent activity in recent years. The format of such requirement changes frequently which requires efficient manual effort. Maintaining a data warehouse can be a solution to this problem. However, both in-house and outsourced implementation of a dedicated data warehouse may not be a cost-effective and smart solution. This work proposes an educational data warehouse as a service (eDWaaS) model to store historical data for multiple universities. The proposed multi-tenant schema facilitates the universities to maintain their data warehouse in a cost-effective solution. It also addresses the scalability issues in implementing such data warehouse as a service model.Comment: 17th International Conference on Intelligent Systems Design and Applications (ISDA 2017). Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. 7th World Congress on Information and Communication Technologies (WICT 2017). December 14-16, 2017. \copyright 2018 Springer International Publishing AG, part of Springer Natur

Volunteer Selection in Collaborative Crowdsourcing with Adaptive Common Working Time Slots

Skill-based volunteering is an expanding branch of crowdsourcing where one may acquire sustainable services, solutions, and ideas from the crowd by connecting with them online. The optimal mapping between volunteers and tasks with collaboration becomes challenging for complex tasks demanding greater skills and cognitive ability. Unlike traditional crowdsourcing, volunteers like to work on their own schedule and locations. To address this problem, we propose a novel two-phase framework consisting of Initial Volunteer-Task Mapping (i-VTM) and Adaptive Common Slot Finding (a-CSF) algorithms. The i-VTM algorithm assigns volunteers to the tasks based on their skills and spatial proximity, whereas the a-CSF algorithm recommends appropriate common working time slots for successful volunteer collaboration. Both the algorithms aim to maximize the overall utility of the crowdsourcing platform. Experimenting with the UpWork dataset demonstrates the efficacy of our framework over existing state-of-the-art methods