Comparing User Authentication Techniques for Fog Computing

2019 ◽  
pp. 111-125 ◽  
Author(s):  
Kashif Munir ◽  
Lawan A. Mohammed
Keyword(s):  
Internet Of Things ◽  
User Authentication ◽  
Fog Computing ◽  
Security And Privacy ◽  
Password Authentication ◽  
Industrial Internet ◽  
Massive Scale ◽  
Computing Environments ◽  
Password Based Authentication ◽  
Gain Access

In the IoT scenario, things at the edge can create significantly large amounts of data. Fog computing has recently emerged as the paradigm to address the needs of edge computing in internet of things (IoT) and industrial internet of things (IIoT) applications. Authentication is an important issue for the security of fog computing since services are offered to massive-scale end users by front fog nodes. Fog computing faces new security and privacy challenges besides those inherited from cloud computing. Authentication helps to ensure and confirms a user's identity. The existing traditional password authentication does not provide enough security for the data, and there have been instances when the password-based authentication has been manipulated to gain access to the data. Since the conventional methods such as passwords do not serve the purpose of data security, this chapter focuses on biometric user authentication in fog computing environments. In this chapter, the authors present biometric smartcard authentication to protect the fog computing environment.

scholarly journals Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling

2021 ◽  
Vol 11 (22) ◽  
pp. 10996
Author(s):  
Jongbeom Lim
Keyword(s):  
Internet Of Things ◽  
Task Scheduling ◽  
Industrial Revolution ◽  
Virtual Machines ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Virtualization Technology ◽  
Computing Environments ◽  
One Machine

As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

Towards the Protection and Security in Fog Computing for Industrial Internet of Things

2021 ◽  
pp. 17-32
Author(s):  
G. Rama Subba Reddy ◽  
K. Rangaswamy ◽  
Malla Sudhakara ◽  
Pole Anjaiah ◽  
K. Reddy Madhavi
Keyword(s):  
Internet Of Things ◽  
Distributed Computing ◽  
Fog Computing ◽  
Security And Privacy ◽  
Safety Measures ◽  
Iot Applications ◽  
Industrial Internet ◽  
Sensor Devices ◽  
Key Enabling Technologies ◽  
Privacy Issues

Internet of things (IoT) has given a promising chance to construct amazing industrial frameworks and applications by utilizing wireless and sensor devices. To support IIoT benefits efficiently, fog computing is typically considered as one of the potential solutions. Be that as it may, IIoT services still experience issues such as high-latency and unreliable connections between cloud and terminals of IIoT. In addition to this, numerous security and privacy issues are raised and affect the users of the distributed computing environment. With an end goal to understand the improvement of IoT in industries, this chapter presents the current research of IoT along with the key enabling technologies. Further, the architecture and features of fog computing towards the fog-assisted IoT applications are presented. In addition to this, security and protection threats along with safety measures towards the IIoT applications are discussed.

scholarly journals Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3715
Author(s):  
Ioan Ungurean ◽  
Nicoleta Cristina Gaitan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Development Process ◽  
Fog Computing ◽  
Low Latency ◽  
Practical Implementation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Starting Point ◽  
Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

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

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 Industrial Internet of Things and Fog Computing to Reduce Energy Consumption in Drinking Water Facilities

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 282 ◽  
Author(s):  
Adrian Korodi ◽  
Ruben Crisan ◽  
Andrei Nicolae ◽  
Ioan Silea
Keyword(s):  
Drinking Water ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Fog Computing ◽  
Industrial Internet Of Things ◽  
Non Invasive ◽  
Data Accumulation ◽  
Industrial Internet ◽  
Reduce Energy Consumption ◽  
And Control

The industry is generally preoccupied with the evolution towards Industry 4.0 principles and the associated advantages as cost reduction, respectively safety, availability, and productivity increase. So far, it is not completely clear how to reach these advantages and what their exact representation or impact is. It is necessary for industrial systems, even legacy ones, to assure interoperability in the context of chronologically dispersed and currently functional solutions, respectively; the Open Platform Communications Unified Architecture (OPC UA) protocol is an essential requirement. Then, following data accumulation, the resulting process-aware strategies have to present learning capabilities, pattern identification, and conclusions to increase efficiency or safety. Finally, model-based analysis and decision and control procedures applied in a non-invasive manner over functioning systems close the optimizing loop. Drinking water facilities, as generally the entire water sector, are confronted with several issues in their functioning, with a high variety of implemented technologies. The solution to these problems is expected to create a more extensive connection between the physical and the digital worlds. Following previous research focused on data accumulation and data dependency analysis, the current paper aims to provide the next step in obtaining a proactive historian application and proposes a non-invasive decision and control solution in the context of the Industrial Internet of Things, meant to reduce energy consumption in a water treatment and distribution process. The solution is conceived for the fog computing concept to be close to local automation, and it is automatically adaptable to changes in the process’s main characteristics caused by various factors. The developments were applied to a water facility model realized for this purpose and on a real system. The results prove the efficiency of the concept.

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