A Review on Fog Computing: Architecture, Fog with IoT, Algorithms and Research Challenges

Sabireen, H.; Neelanarayanan, V.

doi:10.1016/j.icte.2021.05.004

Cited by 163 publications

(28 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Fog computing is an extension of cloud computing, which takes cloud computing services closer to IoT devices. Advancements in applications of IoT, and integrated cloud computing such as real-time monitoring of patient vital signs, physical activities have increased threats like security, performance, latency, and network breakdown to cloud computing (Sabireen & Venkataraman, 2021). Fog computing is a distributed or decentralized virtual network to act as a medium between IoT devices and the cloud (Alwakeel, 2021).…”

Section: Fog and Edge Computingmentioning

confidence: 99%

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

139

View full text Add to dashboard Cite

The adoption of artificial intelligence (AI) in healthcare is growing rapidly.Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI-enabled RPM. This review explores the benefits and challenges of patient-centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI-enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends.

show abstract

Section: Fog and Edge Computingmentioning

confidence: 99%

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Shaik

Tao

Higgins

et al. 2023

WIREs Data Min & Knowl

139

View full text Add to dashboard Cite

show abstract

“…It outlines the difficulties in integrating blockchain and IoT as well as potential consensus mechanisms and platforms for putting any applications based on smart cities into use [22]. Sabireen and Neelanarayanan [23] give a certificate-based security solution among different layers such as IoT devices, edged devices, fog nodes, and cloud service providers. Moreover, it preserves the users' data privacy when information floats from one layer to another.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Internet of things-blockchain lightweight cryptography to data security and integrity for intelligent application

Parmar

Shah

2023

IJECE

View full text Add to dashboard Cite

The industrial internet of things (IoT) plays a major role in the growth of automation and increasing digital connectivity for machine-to-machine communication. The research community has extensively investigated the possibility of IoT and blockchain integration for the last couple of years. The major research is focused on the benefits of integrating blockchain with IoT. In this work, we first focus on the issue of integrating IoT nodes with blockchain networks, especially for non-real-time IoT nodes that do not have an in-built clock mechanism. As a result, they cannot establish communication with real-time blockchain networks. Another critical security issue is protecting data coming from IoT devices to blockchain networks. Blockchain is enough mature to protect the data in its ecosystem. However, information coming from outside of the world does not have any guarantee of data integrity and security. This paper first addresses the clock synchronization issue of IoT nodes with blockchain using a network time protocol and then proposes an IoT-blockchain light-weight cryptographic (IBLWC) approach to secure the entire IoT-blockchain ecosystem. This paper also presents the performance analysis of IBLWC as a suitable and cost-effective solution that incurs less processing overhead for IoT-blockchain-based applications.

show abstract

“…At the same time, a series of smart vehicular user (SVU) devices and applications installed on autonomous vehicles have emerged. Communication among these SVU devices and applications is extremely popular [ 1 , 2 , 3 , 4 , 5 ]. Therefore, to reduce the burden of SVUs, vehicular edge computing (VEC) has been introduced to process tasks offloaded by SVUs, while ensuring low processing latency for these tasks [ 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

A Power Allocation Scheme for MIMO-NOMA and D2D Vehicular Edge Computing Based on Decentralized DRL

Long

Fan

et al. 2023

Sensors

View full text Add to dashboard Cite

In vehicular edge computing (VEC), some tasks can be processed either locally or on the mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact, tasks are offloaded or not, based on the status of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. In this paper, device-to-device (D2D)-based V2V communication and multiple-input multiple-output and nonorthogonal multiple access (MIMO-NOMA)-based V2I communication are considered. In actual communication scenarios, the channel conditions for MIMO-NOMA-based V2I communication are uncertain, and the task arrival is random, leading to a highly complex environment for VEC systems. To solve this problem, we propose a power allocation scheme based on decentralized deep reinforcement learning (DRL). Since the action space is continuous, we employ the deep deterministic policy gradient (DDPG) algorithm to obtain the optimal policy. Extensive experiments demonstrate that our proposed approach with DRL and DDPG outperforms existing greedy strategies in terms of power consumption and reward.

show abstract

A Review on Fog Computing: Architecture, Fog with IoT, Algorithms and Research Challenges

Cited by 163 publications

References 61 publications

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

Internet of things-blockchain lightweight cryptography to data security and integrity for intelligent application

A Power Allocation Scheme for MIMO-NOMA and D2D Vehicular Edge Computing Based on Decentralized DRL

Contact Info

Product

Resources

About