A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

Said, Ahmed Ben; Erradi, Abdelkarim; Neiat, Azadeh Ghari; Bouguettaya, Athman

doi:10.1007/s11036-018-1105-0

Cited by 11 publications

(4 citation statements)

References 33 publications

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“…There are a lot of interpretations of the concept of "mobile learning", but the most common is that such learning doesn't need connection to the cable network. Mobile learning involves availability of mobile tools regardless of time and place using special software [2,16,18]. Thus, mobile learning is a form of learning organization based on the use of mobile ICT and wireless communications.…”

Section: Methodsmentioning

confidence: 99%

Application of Mobile Technologies for the Formation of Analytical Competence of Future Specialists

Galustyan

Smetannikov

Kolbaya

et al. 2020

Int. J. Interact. Mob. Technol.

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The article deals with application of mobile technologies for the formation of analytical competence of future specialists. It discusses the concepts of mobile learning and mobile technologies. Advantages of using mobile technologies within the learning process are presented. The use of mobile learning is one of the most effective and affordable ways of formation of students' analytical competence. Managing, strategic, informational, reflexive components within the structure of analytical competence are revealed in the article. The results of experimental work of formation of analytical competence of future specialists by means of mobile learning technologies are presented. The authors conclude that formation analytical competence of future specialists using mobile devices expands the conventional framework by using the capabilities of mobile platforms and expands the learning environment.

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Section: Methodsmentioning

confidence: 99%

Application of Mobile Technologies for the Formation of Analytical Competence of Future Specialists

Galustyan

Smetannikov

Kolbaya

et al. 2020

Int. J. Interact. Mob. Technol.

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“…Sarma et al [14] considered compressive sensing to address scalability-accuracy trade-off. Lastly, Ben Said et al [41] developed a deep learning framework that predicts the availability of crowdsourced service in a specific region based on historical spatio-temporal presence of mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Efficient Mobile Crowdsourcing for Environmental Noise Monitoring

Alashaikh

Al-Hazemi

2022

IEEE Access

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Governments and commissions all around the globe have worked on developing policies and rules as well as actions and measures for continuous assessment and remedies for environmental noise pollution. All of which requires continuous and accurate measurements of noise levels. The advent of smart city and IoT-based monitoring makes it possible for different technologies to cooperate in collecting and reporting environmental noise levels for longer duration and wider geographical region. Among others, mobile crowdsourcing (MCS) appears to be a promising technique for environmental noise monitoring with minimal upfront cost and almost no recurrent cost for regulators. This is due to the assumption of voluntary participation from mobile device owners who may be reluctant to participate if the recurrent cost of mobile resource consumption is high. In this work, an approach to improve mobile device resources efficiency for mobile crowdsourcing application in environmental noise monitoring is proposed. That is, noise samples from a device are collected only if it is significant to the accuracy of the measurements. Also this paper defines and mathematically formalizes the problem under study and develops two algorithms to optimize both resource and measurements efficiency. The results demonstrate the efficiency of the proposed solution. INDEX TERMS Environmental Noise monitoring, Mobile crowdsourcing (MCS), measurement samplingThis article has been accepted for publication in IEEE Access.

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“…In our previous work [9], we focused on undeterministic crowdsourced WiFi services. Undeterministic refers to the lack of a priori knowledge on the availability of a crowdsourced service at a certain location for a certain period.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Approach for Maximizing Travel Journey WiFi Coverage Using Mobile Crowdsourced Services

Said

Erradi

2019

IEEE Access

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A public transport journey planning service often yields multiple alternative journeys plans to get from a source to a destination. In addition to journey preferences, such as connecting time and walking distance, passengers can select the optimal plan based on mobile crowdsourced WiFi coverage available along the journey. This requires discovering mobile crowdsourced WiFi services available along the journey path. However, this task is challenging due to the uncertain availability of discovered services. To enhance the availability of WiFi coverage, we propose a probabilistic approach to discover groups of available crowdsourced WiFi services along with the journey segments. We first analyze the log of their trajectories and use a density estimation technique to discover reference spots representing the frequently visited locations. Then, a joint discrete Fourier transform and autocorrelation analysis are applied to mine the periods of the presence of moving crowdsourced services with respect to each reference spot. A low-complexity cluster analysis based on Jensen-Shannon divergence is then used to mine the periodic movement behaviors of services during the identified periods. Finally, mobile crowdsourced WiFi services that are simultaneously available at intersecting reference spots are grouped. The QoS of discovered groups is computed in terms of availability confidence, failover capacity, aggregated bandwidth capacity, and coverage. Additionally, we propose an algorithm to determine the best public transport journey plan offering based on the QoS of available WiFi service groups along the journey path. We conduct a comprehensive comparative study to validate the effectiveness of the proposed framework.

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A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services

Cited by 11 publications

References 33 publications

Application of Mobile Technologies for the Formation of Analytical Competence of Future Specialists

Application of Mobile Technologies for the Formation of Analytical Competence of Future Specialists

Efficient Mobile Crowdsourcing for Environmental Noise Monitoring

A Probabilistic Approach for Maximizing Travel Journey WiFi Coverage Using Mobile Crowdsourced Services

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