The Challenges, Technologies, and Role of Fog Computing in the Context of Industrial Internet of Things

2021 ◽  
pp. 1-16
Author(s):  
Sasikala Chinthakunta ◽  
Shoba Bindu Chigarapalle ◽  
Sudheer Kumar E.
Keyword(s):  
Data Storage ◽  
Smart Grids ◽  
Traffic Management ◽  
Fog Computing ◽  
Poor Quality ◽  
Data Traffic ◽  
Cloud Data ◽  
Security Issues ◽  
Huge Data ◽  
Iot Devices

Typically, the analysis of the industrial big data is done at the cloud. If the technology of IIoT is relying on cloud, data from the billions of internet-connected devices are voluminous and demand to be processed within the cloud DCs. Most of the IoT infrastructures—smart driving and car parking systems, smart vehicular traffic management systems, and smart grids—are observed to demand low-latency, real-time services from the service providers. Since cloud includes data storage, processing, and computation only within DCs, huge data traffic generated from the IoT devices probably experience a network bottleneck, high service latency, and poor quality of service (QoS). Hence, the placement of an intermediary node that can perform tasks efficiently and effectively is an unavoidable requirement of IIoT. Fog can be such an intermediary node because of its ability and location to perform tasks at the premise of an industry in a timely manner. This chapter discusses challenges, need, and framework of fog computing, security issues, and solutions of fog computing for IIoT.

Large-Scale Data Streaming in Fog Computing and Its Applications

2021 ◽  
pp. 50-65
Author(s):  
Oshin Sharma ◽  
Anusha S.
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Fog Computing ◽  
Data Streaming ◽  
Cloud Data ◽  
Large Scale Data ◽  
Emerging Trends ◽  
Iot Devices ◽  
Data Centres ◽  
Scale Data

The emerging trends in fog computing have increased the interests and focus in both industry and academia. Fog computing extends cloud computing facilities like the storage, networking, and computation towards the edge of networks wherein it offloads the cloud data centres and reduces the latency of providing services to the users. This paradigm is like cloud in terms of data, storage, application, and computation services, except with a fundamental difference: it is decentralized. Furthermore, these fog systems can process huge amounts of data locally and can be installed on hardware of different types. These characteristics make fog suitable for time- and location-based applications like internet of things (IoT) devices which can process large amounts of data. In this chapter, the authors present fog data streaming, its architecture, and various applications.

scholarly journals An enhanced security tree to secure cloud data

2017 ◽  
Vol 7 (1.1) ◽  
pp. 64 ◽  
Author(s):  
S. Renu ◽  
S.H. Krishna Veni
Keyword(s):  
Data Storage ◽  
Binary Tree ◽  
Data Encryption ◽  
Third Party ◽  
Data Outsourcing ◽  
Cloud Data ◽  
Security Issues ◽  
Security Services ◽  
Computing Services ◽  
User Data

The Cloud computing services and security issues are growing exponentially with time. All the CSPs provide utmost security but the issues still exist. Number of technologies and methods are emerged and futile day by day. In order to overcome this situation, we have also proposed a data storage security system using a binary tree approach. Entire services of the binary tree are provided by a Trusted Third Party (TTP) .TTP is a government or reputed organization which facilitates to protect user data from unauthorized access and disclosure. The security services are designed and implemented by the TTP and are executed at the user side. Data classification, Data Encryption and Data Storage are the three vital stages of the security services. An automated file classifier classify unorganized files into four different categories such as Sensitive, Private, Protected and Public. Applied cryptographic techniques are used for data encryption. File splitting and multiple cloud storage techniques are used for data outsourcing which reduces security risks considerably. This technique offers  file protection even when the CSPs compromise. 

scholarly journals A New Secure Model for Data Protection over Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Amr M. Sauber ◽  
Passent M. El-Kafrawy ◽  
Amr F. Shawish ◽  
Mohamed A. Amin ◽  
Ismail M. Hagag
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Protection ◽  
Cloud Services ◽  
Security And Privacy ◽  
Storage Model ◽  
Cloud Data ◽  
Security Issues ◽  
Data Owner ◽  
Proposed Model

The main goal of any data storage model on the cloud is accessing data in an easy way without risking its security. A security consideration is a major aspect in any cloud data storage model to provide safety and efficiency. In this paper, we propose a secure data protection model over the cloud. The proposed model presents a solution to some security issues of cloud such as data protection from any violations and protection from a fake authorized identity user, which adversely affects the security of the cloud. This paper includes multiple issues and challenges with cloud computing that impairs security and privacy of data. It presents the threats and attacks that affect data residing in the cloud. Our proposed model provides the benefits and effectiveness of security in cloud computing such as enhancement of the encryption of data in the cloud. It provides security and scalability of data sharing for users on the cloud computing. Our model achieves the security functions over cloud computing such as identification and authentication, authorization, and encryption. Also, this model protects the system from any fake data owner who enters malicious information that may destroy the main goal of cloud services. We develop the one-time password (OTP) as a logging technique and uploading technique to protect users and data owners from any fake unauthorized access to the cloud. We implement our model using a simulation of the model called Next Generation Secure Cloud Server (NG-Cloud). These results increase the security protection techniques for end user and data owner from fake user and fake data owner in the cloud.

Social Network Analysis

2020 ◽  
pp. 107-117
Author(s):  
Sheik Abdullah A. ◽  
Abiramie Shree T. G. R.
Keyword(s):  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Storage ◽  
Social Data ◽  
Cloud Data ◽  
Huge Data ◽  
Cloud Data Storage ◽  
Social Data Analysis

Each day, 2.5 quintillion bytes of data are generated due to our daily activity. It is due to the vast amount of use of the smart mobiles, Cloud data storage, and the Internet of Things. In earlier days, these technologies were utilized by large IT companies and the private sector, but now each person has a high-end smartphone along with the cloud and IoT for the easy storage of data and backup. The analysis of the data generated by social media is a tedious process and involves a lot of techniques. Some tools for social network analysis are: Gephi, Networkx, IGraph, Pajek, Node XL, and cytoscope. Apart from these tools there are various efficient social data analysis algorithms that are far more helpful in doing analytics. The need for and use of social network analysis is very helpful in our current problem of huge data generation. In this chapter, the need for the analysis of social data along with the tools that are needed for the analysis and the techniques that are to be implemented in the field of social data analysis are covered.

Healthcare-Internet of Things and Its Components

2021 ◽  
pp. 258-277
Author(s):  
Aman Tyagi
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Communication Technologies ◽  
The Internet ◽  
Healthcare Resources ◽  
Iot Security ◽  
Global Epidemic ◽  
Security Issues ◽  
Analyse Data ◽  
Iot Devices

Elderly population in the Asian countries is increasing at a very fast rate. Lack of healthcare resources and infrastructure in many countries makes the task of provding proper healthcare difficult. Internet of things (IoT) in healthcare can address the problem effectively. Patient care is possible at home using IoT devices. IoT devices are used to collect different types of data. Various algorithms may be used to analyse data. IoT devices are connected to the internet and all the data of the patients with various health reports are available online and hence security issues arise. IoT sensors, IoT communication technologies, IoT gadgets, components of IoT, IoT layers, cloud and fog computing, benefits of IoT, IoT-based algorithms, IoT security issues, and IoT challenges are discussed in the chapter. Nowadays global epidemic COVID19 has demolished the economy and health services of all the countries worldwide. Usefulness of IoT in COVID19-related issues is explained here.

Security and Privacy Issues in Wireless Networks

2020 ◽  
Author(s):  
Nurul Fatini Azhar ◽  
Qi Jie Ngoo ◽  
Tae Hyun Kim ◽  
Kohei Dozono ◽  
Fatima tuz Zahra
Keyword(s):  
Wireless Networks ◽  
Fog Computing ◽  
Security And Privacy ◽  
Ddos Attacks ◽  
Vehicle To Grid ◽  
Network Location ◽  
Security Issues ◽  
Privacy Laws ◽  
Iot Devices ◽  
Privacy Issues

Communication between devices has transitioned from wired to unwired. Wireless networks have been in use widely around the globe since the advent of smartphones, IoT devices and other technologies that are compatible with wireless mode of communication. At the same time security issues have also increased in such communication methods. The aim of this paper is to propose security and privacy issues of the wireless networks and present them through comprehensive surveys. In context of security issues, there are 2 typical DDoS attacks - HTTP flood and SYN flood. Other than DDoS attacks, there are several other threats to wireless networks. One of the most prevalent include security issues in Internet of Things. In terms of privacy issues in a wireless network, location-based applications, individual data, cellular network and V2G (Vehicle to Grid) network are surveyed. The survey is hosted using questionnaire and responses of 70 participants is recorded. It is observed from the survey results that many groups of people lack the knowledge of security and privacy of wireless technologies and networks despite their increased use, however, students are relatively more aware and have strong knowledge of those issues. It is concluded from the results that an effective solution to these problems can be hosting campaigns for spreading the security and privacy laws to help the groups of people who are lagging behind in this domain of knowledge become more aware. A unique solution is also presented to overcome the security issues which include implementation of detection and mitigation techniques, implementing Blockchain in the IoT devices and implementing fog computing solutions. The unique solutions to overcome the privacy issues are proposed in the form of a privacy approach from the LBS server between pairs of users to increase the implementation of DSPM and blockchain as a solution.

On Data Security and Encryption Algorithms in Cloud Environment

2014 ◽  
Vol 701-702 ◽  
pp. 1106-1111 ◽  
Author(s):  
Xin Zheng Zhang ◽  
Ya Juan Zhang
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Data Security ◽  
Service Providers ◽  
Cloud Service ◽  
Working Environment ◽  
Cloud Environment ◽  
Cloud Data ◽  
Security Issues ◽  
Encryption Algorithms

As information and processes are migrating to the cloud, Cloud Computing is drastically changing IT professionals’ working environment. Cloud Computing solves many problems of conventional computing. However, the new technology has also created new challenges such as data security, data ownership and trans-code data storage. We discussed about Cloud computing security issues, mechanism, challenges that Cloud service providers and consumers face during Cloud engineering. Based on concerning of security issues and challenges, we proposed several encryption algorithms to make cloud data secure and invulnerable. We made comparisons among DES, AES, RSA and ECC algorithms to find combinatorial optimization solutions, which fit Cloud environment well for making cloud data secure and not to be hacked by attackers.

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.

IoT Big Data Security, Privacy and Challenges Related to Smart Grid

2022 ◽  
pp. 368-379
Author(s):  
Kimmi Kumari ◽  
M. Mrunalini
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Smart Grids ◽  
Type Systems ◽  
Heterogeneous Data ◽  
Security Measures ◽  
Smart Meters ◽  
Efficient Manner ◽  
Security Issues ◽  
Iot Devices

The highly interconnected network of heterogeneous devices which enables all kinds of communications to take place in an efficient manner is referred to as “IOT.” In the current situation, the data are increasing day by day in size as well as in terms of complexities. These are the big data which are in huge demand in the industrial sectors. Various IT sectors are adopting big data present on IOT for the growth of their companies and fulfilling their requirements. But organizations are facing a lot of security issues and challenges while protecting their confidential data. IOT type systems require security while communications which is required currently by configuration levels of security algorithms, but these algorithms give more priority to functionalities of the applications over security. Smart grids have become one of the major subjects of discussions when the demands for IOT devices increases. The requirements arise related to the generation and transmission of electricity, consumption of electricity being monitored, etc. The system which is responsible to collect heterogeneous data are a complicated structure and some of its major subsystems which they require for smooth communications include log servers, smart meters, appliances which are intelligent, different sensors chosen based on their requirements, actuators with proper and efficient infrastructure. Security measures like collection, storage, manipulations and a massive amount of data retention are required as the system is highly diverse in its architecture and even the heterogeneous IOT devices are interacting with each other. In this article, security challenges and concerns of IOT big data associated with smart grid are discussed along with the new security enhancements for identification and authentications of things in IOT big data environments.

Security Issues in Fog Computing and ML-Based Solutions

2021 ◽  
pp. 209-234
Author(s):  
Himanshu Sahu ◽  
Gaytri
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Processing ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Special Focus ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Security Issues ◽  
Learning Techniques

IoT requires data processing, which is provided by the cloud and fog computing. Fog computing shifts centralized data processing from the cloud data center to the edge, thereby supporting faster response due to reduced communication latencies. Its distributed architecture raises security and privacy issues; some are inherited from the cloud, IoT, and network whereas others are unique. Securing fog computing is equally important as securing cloud computing and IoT infrastructure. Security solutions used for cloud computing and IoT are similar but are not directly applicable in fog scenarios. Machine learning techniques are useful in security such as anomaly detection, intrusion detection, etc. So, to provide a systematic study, the chapter will cover fog computing architecture, parallel technologies, security requirements attacks, and security solutions with a special focus on machine learning techniques.

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