Abstract
In mobile crowd computing (MCC), people’s smart mobile devices (SMDs) are utilized as computing resources. Considering the ever-growing computing capabilities of today’s SMDs, a collection of them can offer significantly high-performance computing services. In a local MCC, the SMDs are typically connected to a local Wi-Fi network. Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden. Though it offers an economical and sustainable computing solution, users’ mobility poses a serious issue in the QoS of MCC. To address this, before submitting a job to an SMD, we suggest estimating that particular SMD’s availability in the network until the job is finished. For this, we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of time. For experimental purposes, we collected real users’ mobility data (in-time and out-time) with respect to a Wi-Fi access point. To build the prediction model, we presented a novel feature extraction method to be applied to the time-series data. The experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.
Highlights
The continuous growth of IT infrastructure has led to severe environmental concerns [1]
Since in mobile crowd computing (MCC), the computing resources are mobile, there is no guarantee of their availability in a local MCC, where the devices are connected to a local network for contributing their resources to an organizational crowd computing application
We presented a novel dynamic feature extraction method where the features are unknown
Summary
The continuous growth of IT (information technology) infrastructure has led to severe environmental concerns [1]. Since in MCC, the computing resources (contributing SMDs) are mobile, there is no guarantee of their availability in a local MCC, where the devices are connected to a local network (typically Wi-Fi access point) for contributing their resources to an organizational crowd computing application This uncertainty affects the QoS (quality of service) of MCC significantly because if an SMD leaves the network before completing the assigned job, it has to be reassigned to another SMD, which introduces a significant delay or, in the worst case, would result in job loss [5]. The user mobility data change with respect to the users’ behavior in a long duration This change needs to be captured by the prediction models to provide expected prediction results.
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