Integrating mobile internet of things and cloud computing towards scalability: lessons learned from existing fog computing architectures and solutions

2017 ◽  
Vol 6 (4) ◽  
pp. 393 ◽  
Author(s):  
Paolo Bellavista ◽  
Antonio Corradi ◽  
Alessandro Zanni
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Fog Computing ◽  
Lessons Learned ◽  
Mobile Internet

Proposed Back Propagation Deep Neural Network for Intrusion Detection in Internet of Things Fog Computing

2021 ◽  
Vol 9 (4) ◽  
pp. 464-469
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Fog Computing ◽  
Denial Of Service ◽  
Back Propagation ◽  
Security And Privacy ◽  
Brute Force ◽  
Iot Platform ◽  
Proposed Model

Internet of things (IoT) is an emerging concept which aims to connect billions of devices with each other anytime regardless of their location. Sadly, these IoT devices do not have enough computing resources to process huge amount of data. Therefore, Cloud computing is relied on to provide these resources. However, cloud computing based architecture fails in applications that demand very low and predictable latency, therefore the need for fog computing which is a new paradigm that is regarded as an extension of cloud computing to provide services between end users and the cloud user. Unfortunately, Fog-IoT is confronted with various security and privacy risks and prone to several cyberattacks which is a serious challenge. The purpose of this work is to present security and privacy threats towards Fog-IoT platform and discuss the security and privacy requirements in fog computing. We then proceed to propose an Intrusion Detection System (IDS) model using Standard Deep Neural Network's Back Propagation algorithm (BPDNN) to mitigate intrusions that attack Fog-IoT platform. The experimental Dataset for the proposed model is obtained from the Canadian Institute for Cybersecurity 2017 Dataset. Each instance of the attack in the dataset is separated into separate files, which are DoS (Denial of Service), DDoS (Distributed Denial of Service), Web Attack, Brute Force FTP, Brute Force SSH, Heartbleed, Infiltration and Botnet (Bot Network) Attack. The proposed model is trained using a 3-layer BP-DNN

scholarly journals IoT-Fog A Communication Framework using Blockchain in the Internet of Things

2020 ◽  
Author(s):  
Tanweer Alam
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Fog Computing ◽  
Emerging Technology ◽  
Smart Devices ◽  
The Internet ◽  
Next Generation ◽  
Communication Framework ◽  
Generation Computing ◽  
The Internet Of Things

<p>The fog computing is the emerging technology to compute, store, control and connecting smart devices with each other using cloud computing. The Internet of Things (IoT) is an architecture of uniquely identified interrelated physical things, these physical things are able to communicate with each other and can transmit and receive information. <a>This research presents a framework of the combination of the Internet of Things (IoT) and Fog computing. The blockchain is also the emerging technology that provides a hyper, distributed, public, authentic ledger to record the transactions. Blockchains technology is a secured technology that can be a boon for the next generation computing. The combination of fog, blockchains, and IoT creates a new opportunity in this area. In this research, the author presents a middleware framework based on the blockchain, fog, and IoT. The framework is implemented and tested. The results are found positive. </a></p>

Security and Privacy Issues in IoT

2019 ◽  
pp. 129-156
Author(s):  
S. R. Mani Sekhar ◽  
Sharmitha S. Bysani ◽  
Vasireddy Prabha Kiranmai
Keyword(s):  
Cloud Computing ◽  
Information Systems ◽  
Internet Of Things ◽  
Case Studies ◽  
Communication Systems ◽  
Fog Computing ◽  
Security And Privacy ◽  
Privacy Issues

Security and privacy issues are the challenging areas in the field of internet of things (IoT) and fog computing. IoT and fog has become an involving technology allowing major changes in the field of information systems and communication systems. This chapter provides the introduction of IoT and fog technology with a brief explanation of how fog is overcoming the challenges of cloud computing. Thereafter, the authors discuss the different security and privacy issues and its related solutions. Furthermore, they present six different case studies which will help the reader to understand the platform of IoT in fog.

scholarly journals A Novel Fog-IoT based Framework for Improving Performance of Cloud Computing

2019 ◽  
Vol 8 (12S) ◽  
pp. 889-895
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Secure Communication ◽  
Fog Computing ◽  
Cloud Service ◽  
Raspberry Pi ◽  
New Paradigm ◽  
Privacy And Security ◽  
Security Issues ◽  
Iot Devices

Internet-of-Things (IoT) has been considered as a fundamental part of our day by day existence with billions of IoT devices gathering information remotely and can interoperate within the current Internet framework. Fog computing is nothing but cloud computing to the extreme of network security. It provides computation and storage services via CSP (Cloud Service Provider) to end devices in the Internet of Things (IoT). Fog computing allows the data storing and processing any nearby network devices or nearby cloud endpoint continuum. Using fog computing, the designer can reduce the computation architecture of the IoT devices. Unfortunitily, this new paradigm IoT-Fog faces numerous new privacy and security issues, like authentication and authorization, secure communication, information confidentiality. Despite the fact that the customary cloud-based platform can even utilize heavyweight cryptosystem to upgrade security, it can't be performed on fog devices drectly due to reseource constraints. Additionally, a huge number of smart fog devices are fiercely disseminated and situated in various zones, which expands the danger of being undermined by some pernicious gatherings. Trait Based Encryption (ABE) is an open key encryption conspire that enables clients to scramble and unscramble messages dependent on client qualities, which ensures information classification and hearty information get to control. Be that as it may, its computational expense for encryption and unscrambling stage is straightforwardly corresponding to the multifaceted nature of the arrangements utilized. The points is to assess the planning, CPU burden, and memory burden, and system estimations all through each phase of the cloud-to-things continuum amid an analysis for deciding highlights from a finger tapping exercise for Parkinson's Disease patients. It will be appeared there are confinements to the proposed testbeds when endeavoring to deal with upwards of 35 customers at the same time. These discoveries lead us to a proper conveyance of handling the leaves the Intel NUC as the most suitable fog gadget. While the Intel Edison and Raspberry Pi locate a superior balance at in the edge layer, crossing over correspondence conventions and keeping up a self-mending network topology for "thing" devices in the individual territory organize.

scholarly journals Research on Internet of Things security architecture based on fog computing

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988816 ◽  
Author(s):  
Trung Dong Mai
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Transmission ◽  
Fog Computing ◽  
Security Architecture ◽  
Time Data ◽  
Local Data ◽  
Computing Device ◽  
Network Bandwidth ◽  
Processing Power

The traditional data processing of the Internet of Things is concentrated in cloud computing, and its huge number of devices and massive real-time data transmission are extremely stressful on network bandwidth and cloud computing data centers. Fog computing is the infrastructure that can use processing power anywhere in the cloud. Virtual computing extends the power of cloud computing to the edge of the network, enabling any computing device to host and process software services, analyzing and storing data closer to where data are generated. The architecture of the fog computing brings enormous processing power. Since its processing power is often located near the required equipment, the distance of data transmission is reduced and the delay is reduced. This article explores how to use the fog computing layer between the cloud data center and the end node layer to store and process large amounts of local data in a timely manner, speeding decision making and enabling Internet of Things manufacturers and software developers to limit their ability to send data. They reduced cloud computing costs and built a reasonable security architecture.

A Hybrid Fog Architecture: Improving the Efficiency in IoT-based Smart Parking Systems

2019 ◽  
Vol 12 ◽  
Author(s):  
Bhawna Suri ◽  
Pijush Kanti Dutta Pramanik ◽  
Shweta Taneja
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Real World ◽  
Fog Computing ◽  
Shopping Malls ◽  
Communication Latency ◽  
Cloud Architecture ◽  
Smart Parking ◽  
Real Time Applications ◽  
Simulation Results

Background: The abundant use of personal vehicles has raised the challenge of parking the vehicle in a crowded place such as shopping malls. To help the driver with efficient and trouble-free parking, a smart and innovative parking assistance system is required. In addition to discussing the basics of smart parking, Internet of Things (IoT), Cloud computing, and Fog computing, this chapter proposes an IoT-based smart parking system for shopping malls. Methods: To process the IoT data, a hybrid Fog architecture is adopted, to reduce the latency, where the Fog nodes are connected across the hierarchy. The advantages of this auxiliary connection are discussed critically by comparing with other Fog architectures (hierarchical and P2P). An algorithm is defined to support the proposed architecture and is implemented on two real-world use-cases having requirements of identifying the nearest free car parking slot. The implementation is simulated for a single mall scenario as well as for a campus with multiple malls with parking areas spread across them. Results: The simulation results have proved that our proposed architecture shows lower latency as compared to the traditional smart parking systems that use Cloud architecture. Conclusion: The hybrid Fog architecture minimizes communication latency significantly. Hence, the proposed architecture can be suitably applied for other IoT-based real-time applications.

Cyber Resilience for the Internet of Things

2019 ◽  
pp. 1018-1049
Author(s):  
Marcus Tanque ◽  
Harry J. Foxwell
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Cyber Security ◽  
Fog Computing ◽  
The Internet ◽  
Management Processes ◽  
Business Resilience ◽  
Core Capabilities ◽  
Strategic Policies ◽  
The Internet Of Things

This chapter examines and explains cyber resilience, internet of things, software-defined networking, fog computing, cloud computing, and related areas. Organizations develop these technologies in tandem with cyber resilience best practices, such as processes and standards. Cyber resilience is at the intersection of cyber security and business resilience. Its core capabilities encompass integrated strategic policies, processes, architectures, and frameworks. Governments and industries often align defensive and resilient capabilities, to address security and network vulnerability breaches through strategic management processes.

Processing IoT Data

2021 ◽  
pp. 920-943
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Aditya Brahmachari ◽  
Prasenjit Choudhury
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Fog Computing ◽  
Batch Processing ◽  
Time Response ◽  
Iot Applications

This chapter describes how traditionally, Cloud Computing has been used for processing Internet of Things (IoT) data. This works fine for the analytical and batch processing jobs. But most of the IoT applications demand real-time response which cannot be achieved through Cloud Computing mainly because of inherent latency. Fog Computing solves this problem by offering cloud-like services at the edge of the network. The computationally powerful edge devices have enabled realising this idea. Witnessing the exponential rise of IoT applications, Fog Computing deserves an in-depth exploration. This chapter establishes the need for Fog Computing for processing IoT data. Readers will be able to gain a fair comprehension of the various aspects of Fog Computing. The benefits, challenges and applications of Fog Computing with respect to IoT have been mentioned elaboratively. An architecture for IoT data processing is presented. A thorough comparison between Cloud and Fog has been portrayed. Also, a detailed discussion has been depicted on how the IoT, Fog, and Cloud interact among them.

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