Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

• Published in: Computers, materials & continua materials & continua, 2022, Vol.70 (3), p.5199-5212
• Main Authors: Kanti Dutta Pramanik, Pijush, Sinhababu, Nilanjan, Nayyar, Anand, Masud, Mehedi, Choudhury, Prasenjit
• Format: Article
• Language: English
• Subjects: Accuracy; Availability; Computer science; Deep learning; Electronic devices; Feature extraction; Information technology; Mobile computing; Prediction models; Quality of service
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2022.019630

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.