Enabling Reliable and Resilient IoT Based Smart City Applications

Muhammed, Thaha; Mehmood, Rashid; Albeshri, Aiiad

doi:10.1007/978-3-319-94180-6_17

Cited by 13 publications

(5 citation statements)

References 30 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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“…Irrespective of these methods, there exist some unique challenges for the deployment of the WSN for UEMS. Another important observation is that in most of the deployment, the base station's position is always fixed, which does not provide an optimal network performance [13].…”

Section: Intelligent Systemmentioning

confidence: 99%

“…There are two scenarios to validate the model, and these scenarios are 1) Traditional [6]- [13] and proposed approach of data communication without the data aggregation process and 2) Traditional and proposed approach of data communication with data aggregation process. Fig.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Secure Communication Process of Wireless Sensor Network Architecture for Smart Urban Environment Monitoring Applications

Bhaskar¹,

Chakravarthi²

2021

IJACSA

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Wireless Sensor Network has been increasingly used for remote monitoring system and its adoption in increasing exponentially for larger application too. However, there are various challenges associated with both resource management and security that roots up when the deployment scale goes massive and distributed in order. The proposed system considers a case study of smart city management where the problems associated with data transmission and security has been addressed. This is carried out using the provisioning of urban environment monitoring system that is an essential system for smart city projects to assure the citizens' better-quality wellbeing. The scalable and effective urban environment monitoring system requires a seamless transmission of the data from the sensor nodes to the analytics engine. The existing architectures are more designed to suit very specifically the use-cases. As a contribution, the proposed system introduces a cost-effective architecture for environmental monitoring in urban zones of smart city named as a Smart Sensor Surveillance System (4S-UEM). The core idea of the proposed system is to offer a balance between resource efficiency and resilience secure communication in large scale deployment of WSN considering smart city as deployment and assessment area. The proposed system makes use of urban geographical clustering process in order to develop an organized structure of sensor nodes. Different from any existing studies, the proposed system introduces data analytical engine followed by secure routing using gateway. The design of the proposed system is carried out using layered architecture of the communication model targeting towards a cost-effective, energy optimal, and secure data transmission to the analytics engine.

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“…Various failures can occur including face recognition, community infrastructure management, and [95] is developed to capture the localized nature of community IoT deployments in the context of small failures. Research in [96] proposed a new fault-tolerant routing technique for hierarchical sensor networks. Another challenge for constant running and managing these IoT devices is costs related to energy , communication, computation, infrastructure, and operation.…”

Section: Implementation Challengesmentioning

confidence: 99%

Future Smart Connected Communities to Fight COVID-19 Outbreak

Gupta¹,

Bhatt²,

Gupta³

et al. 2020

Preprint

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Internet of Things (IoT) has grown rapidly in the last decade and continue to develop in terms of dimension and complexity offering wide range of devices to support diverse set of applications. With ubiquitous Internet, connected sensors and actuators, networking and communication technology, and artificial intelligence (AI), smart cyber-physical systems (CPS) provide services rendering assistance to humans in their daily lives. However, the recent outbreak of COVID-19 (also known as coronavirus) pandemic has exposed and highlighted the limitations of current technological deployments to curtail this disease. IoT and smart connected technologies together with data-driven applications can play a crucial role not only in prevention, continuous monitoring, and mitigation of the disease, but also enable prompt enforcement of guidelines, rules and government orders to contain such future outbreaks. In this paper, we envision an IoT-enabled ecosystem for intelligent monitoring, proactive prevention and control, and mitigation of COVID-19. We propose different architectures, applications and technology systems for various smart infrastructures including E-health, smart home, smart supply chain management, smart locality, and smart city, to develop future connected communities to manage and mitigate similar outbreaks. Furthermore, we present research challenges together with future directions to enable and develop these smart communities and infrastructures to fight and prepare against such outbreaks.

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Enabling Reliable and Resilient IoT Based Smart City Applications

Cited by 13 publications

References 30 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

A Secure Communication Process of Wireless Sensor Network Architecture for Smart Urban Environment Monitoring Applications

Future Smart Connected Communities to Fight COVID-19 Outbreak

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