Enabling Smarter Societies through Mobile Big Data Fogs and Clouds

Arfat, Yasir; Aqib, Muhammad; Mehmood, Rashid; Albeshri, Aiiad; Katib, Iyad; Albogami, Nasser N.; Alzahrani, Ahmed

doi:10.1016/j.procs.2017.05.439

Cited by 49 publications

(21 citation statements)

References 19 publications

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“…A range of technologies is contributing to the development of these smart systems. These include the Internet of Things (IoT) [32][33][34][35][36], social media [21][22][23]37,38], big data [39][40][41][42][43][44], high performance computing (HPC) [45][46][47][48], cloud, fog, and edge computing [34,[49][50][51][52], and machine learning [36,53]. The applications include healthcare [34,39,[54][55][56], transportation [57,58], and others [59,60].…”

Section: Literature Reviewmentioning

confidence: 99%

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

et al. 2020

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Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual afflicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics.

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Section: Literature Reviewmentioning

confidence: 99%

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

et al. 2020

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“…Akrivopoulos et al [28] presented a technology to respond to the combination of IoT applications and the fog computing and introduced the use of fog computing in an automated medical monitoring platform to improve the medical work for the patients. Arfat et al [29] not only unprecedentedly proposed the integration of mobile applications, big data analysis, and fog computing, but also introduced Google Maps as an example, showing the system of information feedback diversification. Taneja and Davy [30] studied the computational efficiency in the fog environment and constructed a resource-aware placement.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Resource Allocation for Load Balancing in Fog Environment

Qing

et al. 2018

Wireless Communications and Mobile Computing

155

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Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load-balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM.

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“…Importantly to underline is that these processing activities happen not only on fixed platforms, but together with mobile platforms which involve smart mobile devices, fogs and clouds (Arfat et al, 2017). To assure interoperability and hide the hardware complexity of many public and private devices operating within a city, more abstraction layers are usually required for the development of applications.…”

Section: Fig 2: Estimation Of Data Explosionmentioning

confidence: 99%

Questioning if Macro-Systemic Top-Down Approaches Should Be Used to Develop Smart Cities of the Future

Vasile¹,

Mocan²

2019

JAST

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Cities differ greatly from one to another, even if they serve the same essential purposes. Moreover, they should collaborate at all levels for producing synergistic effects, therefore, their major smart solutions should be standardized or at least compatible, whilst adapted to the local contexts. Cities are systems of systems and contain a large variety of components, each of them presenting different characteristics. Given the vast range of technological choices and the unforgiving overall pressure, governors struggle to implement best specific smart solutions for their cities, by usually adopting bottom-up methods, thus, endangering future possible compatibilities with other local or international systems. This research stresses the importance of top-down smart city deployments, by acknowledging also the local specificities. It investigates the status of relevant international contributions, questioning what kind of approaches should be applied by public administrations. The results point out to complex challenges that exist by revealing the status of standardization proceedings, the authors' perspective on the enabling technologies and their dimensions, as well as a related case study concerning a local community of Romania so as to bring the beforementioned issues to light. The main objective of this research is to highlight the associated problematics and draw attention to the urgent need of a new macro-systemic top-down attitude for developing smart cities.

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Enabling Smarter Societies through Mobile Big Data Fogs and Clouds

Cited by 49 publications

References 19 publications

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

Sehaa: A Big Data Analytics Tool for Healthcare Symptoms and Diseases Detection Using Twitter, Apache Spark, and Machine Learning

Dynamic Resource Allocation for Load Balancing in Fog Environment

Questioning if Macro-Systemic Top-Down Approaches Should Be Used to Develop Smart Cities of the Future

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