Comprehensive Review: Privacy Protection of User in Location-Aware Services of Mobile Cloud Computing

Almusaylim, Zahrah A.; Jhanjhi, N. Z.

doi:10.1007/s11277-019-06872-3

Cited by 101 publications

(31 citation statements)

References 57 publications

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“…3) On top of all these functionalities which needs to be implemented, the introduction of a cloud-based storage system cannot be ignored. With the flow of information at different nodes from the AMBLE prototype, it is inevitably true that a robust storage service will provide possible solutions to managing the dynamic spread of context data and offloading of adapted learning contents [21]. Finally, an in-depth evaluation needs to be performed for the instructional representation of contents while combining both the user and physical models.…”

Section: Resultsmentioning

confidence: 99%

AMBLE: A Context-Aware Mobile Learning Framework

Curum¹,

Khedo²

2019

EAI Endorsed Transactions on Context-aware Systems and Applicat

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INTRODUCTION: Mobile Learning is a new pivotal learning trend nowadays. With the increasing use of sophisticated smartphones equipped with augmented reality supporting tools and sensors, mobile learning platforms are expected to deliver tailor-made and customized learning elements to learners. Context-awareness is regarded as the fundamental approach or workaround to lift this learning style to distribute adaptive and personalized learning elements in mobile devices. OBJECTIVES: The main priority in mobile learning is to make learning elements as flexible as possible using different forms of context data to extend the natural adaptation capabilities in mobile devices in order to engage learners in extremely rich environments. METHODS: In this paper, A context-aware MoBile LEarning framework is proposed, namely the AMBLE framework. It processes contextual data at four distinct levels namely: Sensing Layer, Adaptation Layer, Context Processing Layer, and Application Layer to perform adaptation of learning contents based on the actual environment and conditions of the learner. RESULTS: Partial implementation of the proposed framework has the potential to capture and represent the physical context information that may be used to perform a dynamic adaptation of learning contents and thus significantly improve the mobile learning experiences. Extra work is expected regarding the implementation of the other layers and components of the framework including the user model, context manager, and the adaptation engine. CONCLUSION: The AMBLE framework proposes some relevant content adaptations with some positive results. As future works, new forms of user-context adaptation synthesized with other extracted data sets of contextual information will be used to establish and align relevant dynamic adaptation and personalization of learning contents.

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Section: Resultsmentioning

confidence: 99%

AMBLE: A Context-Aware Mobile Learning Framework

Curum¹,

Khedo²

2019

EAI Endorsed Transactions on Context-aware Systems and Applicat

View full text Add to dashboard Cite

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“…On the other hand, energy optimization is another problem in 5G that need to be addressed, but there exist only a few studies that discuss energy problem in IIoT and 5G realm, and their main focus is on energy efficiency rather than optimization [39][40]. At the same time, a few more recent studies are focusing to the user privacy issues linked with their daily lives [41][42][43], while at home, using 5G based gadgets and while travelling. To fill these two gaps; in the upcoming section, we will provide a framework to address the issues of privacy preservation, and energy optimization in 5G aided IIoT.…”

Section: B Energy Optimization In 5gmentioning

confidence: 99%

Privacy Protection and Energy Optimization for 5G-Aided Industrial Internet of Things

et al. 2020

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The 5G is expected to revolutionize every sector of life by providing interconnectivity of everything everywhere at high speed. However, massively interconnected devices and fast data transmission will bring the challenge of privacy as well as energy deficiency. In today's fast-paced economy, almost every sector of the economy is dependent on energy resources. On the other hand, the energy sector is mainly dependent on fossil fuels and is constituting about 80% of energy globally. This massive extraction and combustion of fossil fuels lead to a lot of adverse impacts on health, environment, and economy. The newly emerging 5G technology has changed the existing phenomenon of life by connecting everything everywhere using IoT devices. 5G enabled IIoT devices has transformed everything from traditional to smart, e.g. smart city, smart healthcare, smart industry, smart manufacturing etc. However, massive I/O technologies for providing D2D connection has also created the issue of privacy that need to be addressed. Privacy is the fundamental right of every individual. 5G industries and organizations need to preserve it for their stability and competency. Therefore, privacy at all three levels (data, identity and location) need to be maintained. Further, energy optimization is a big challenge that needs to be addressed for leveraging the potential benefits of 5G and 5G aided IIoT. Billions of IIoT devices that are expected to communicate using the 5G network will consume a considerable amount of energy while energy resources are limited. Therefore, energy optimization is a future challenge faced by 5G industries that need to be addressed. To fill these gaps, we have provided a comprehensive framework that will help energy researchers and practitioners in better understanding of 5G aided industry 4.0 infrastructure and energy resource optimization by improving privacy. The proposed framework is evaluated using case studies and mathematical modelling.

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“…Moreover, cloud efficiency security, optimization including load balancing [55][56][57][58] and cloud location services have also have impact on cloud based health cares. In addition, cloud design, authentication mechanism such as using blockchain and wireless sensor network [59][60][61][62][63][64][65][66], security and privacy in remote health care also consider a high impact on this.…”

Section: Threats To Validity (Tv)mentioning

confidence: 99%

Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based

et al. 2020

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To survive in the competitive environment, most organizations have adopted componentbased software development strategies in the rapid technology advancement era and the proper utilization of cloud-based services. To facilitate the continuous configuration, reduce complexity, and faster system delivery for higher user satisfaction in dynamic scenarios. In cloud services, customers select services from web applications dynamically. Healthcare body sensors are commonly used for diagnosis and monitoring patients continuously for their emergency treatment. The healthcare devices are connected with mobile or laptop etc. on cloud environment with network and frequently change applications. Thus, organizations rely on regression testing during changes and implementation to validate the quality and reliability of the system after the alteration. However, for a large application with limited resources and frequently change component management activities in the cloud computing environment, component-based system verification is difficult and challenging due to irrelevant and redundant test cases and faults. In this study, proposed a test case selection and prioritization framework using a design pattern to increase the faults detection rate. First, we select test cases on frequently accessed components using observer patterns and, secondly, prioritize test cases on adopting some strategies. The proposed framework was validated by an experiment and compared with other techniques (previous faults based and random priority). Hence, experimental results show that the proposed framework successfully verified changes. Subsequently, the proposed framework increases the fault detection rate (i.e., more than 90%) than previous faults based and random priority (i.e., more than 80% respectively).

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Comprehensive Review: Privacy Protection of User in Location-Aware Services of Mobile Cloud Computing

Cited by 101 publications

References 57 publications

AMBLE: A Context-Aware Mobile Learning Framework

AMBLE: A Context-Aware Mobile Learning Framework

Privacy Protection and Energy Optimization for 5G-Aided Industrial Internet of Things

Towards Pattern-Based Change Verification Framework for Cloud-Enabled Healthcare Component-Based

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