scholarly journals A Scalable Architecture for Monitoring IoT Devices Using Ethereum and Fog Computing

2020 ◽  
Author(s):  
Shirin Tahmasebi ◽  
Jafar Habibi ◽  
Abolhassan Shamsaie
Keyword(s):  
Fog Computing ◽  
Scalable Architecture ◽  
Iot Devices

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 61-83 ◽  
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

scholarly journals A Containerized Approach for Allocating Distributed Stream Queries to Fog Nodes

2021 ◽  
Author(s):  
Hamed Hasibi ◽  
Saeed Sedighian Kashi
Keyword(s):  
Fog Computing ◽  
Stream Processing ◽  
Stream Data ◽  
Process Data ◽  
Stream Query Processing ◽  
Tremendous Amount ◽  
Stream Processing Engines ◽  
Iot Devices ◽  
Distributed Stream Processing

Fog computing brings cloud capabilities closer to the Internet of Things (IoT) devices. IoT devices generate a tremendous amount of stream data towards the cloud via hierarchical fog nodes. To process data streams, many Stream Processing Engines (SPEs) have been developed. Without the fog layer, the stream query processing executes on the cloud, which forwards much traffic toward the cloud. When a hierarchical fog layer is available, a complex query can be divided into simple queries to run on fog nodes by using distributed stream processing. In this paper, we propose an approach to assign stream queries to fog nodes using container technology. We name this approach Stream Queries Placement in Fog (SQPF). Our goal is to minimize end-to-end delay to achieve a better quality of service. At first, in the emulation step, we make docker container instances from SPEs and evaluate their processing delay and throughput under different resource configurations and queries with varying input rates. Then in the placement step, we assign queries among fog nodes by using a genetic algorithm. The practical approach used in SQPF achieves a near-the-best assignment based on the lowest application deadline in real scenarios, and evaluation results are evidence of this goal.

scholarly journals Design a blockchain-based middleware layer in the Internet of Things Architecture

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Tanweer Alam
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Smart Devices ◽  
The Internet ◽  
Novel Approach ◽  
Generation Computing ◽  
Critical Issues ◽  
Iot Devices ◽  
The Internet Of Things

In next-generation computing, the role of cloud, internet and smart devices will be capacious. Nowadays we all are familiar with the word smart. This word is used a number of times in our daily life. The Internet of Things (IoT) will produce remarkable different kinds of information from different resources. It can store big data in the cloud. The fog computing acts as an interface between cloud and IoT. The extension of fog in this framework works on physical things under IoT. The IoT devices are called fog nodes, they can have accessed anywhere within the range of the network. The blockchain is a novel approach to record the transactions in a sequence securely. Developing a new blockchains based middleware framework in the architecture of the Internet of Things is one of the critical issues of wireless networking where resolving such an issue would result in constant growth in the use and popularity of IoT. The proposed research creates a framework for providing the middleware framework in the internet of smart devices network for the internet of things using blockchains technology. Our main contribution links a new study that integrates blockchains to the Internet of things and provides communication security to the internet of smart devices.

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.

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.

A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

2020 ◽  
pp. 42-52
Author(s):  
Shanthi Thangam Manukumar ◽  
Vijayalakshmi Muthuswamy
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Mobile Devices ◽  
Service Providers ◽  
Learning Algorithm ◽  
Fog Computing ◽  
Mobile Network ◽  
Resource Provisioning ◽  
Management Framework ◽  
Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

scholarly journals A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing

2019 ◽  
Vol 9 (1) ◽  
pp. 178 ◽  
Author(s):  
Belal Sudqi Khater ◽  
Ainuddin Wahid Bin Abdul Wahab ◽  
Mohd Yamani Idna Bin Idris ◽  
Mohammed Abdulla Hussain ◽  
Ashraf Ahmed Ibrahim
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Fog Computing ◽  
Raspberry Pi ◽  
Space Representation ◽  
Location Awareness ◽  
Resource Limitations ◽  
System Calls ◽  
Iot Devices ◽  
Hidden Layer

Fog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force Academy Windows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi.

scholarly journals IoT Security Configurability with Security-by-Contract

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4121 ◽  
Author(s):  
Alberto Giaretta ◽  
Nicola Dragoni ◽  
Fabio Massacci
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Holistic Approach ◽  
The Internet ◽  
Iot Security ◽  
Iot Devices ◽  
Work First ◽  
The Internet Of Things

Cybersecurity is one of the biggest challenges in the Internet of Things (IoT) domain, as well as one of its most embarrassing failures. As a matter of fact, nowadays IoT devices still exhibit various shortcomings. For example, they lack secure default configurations and sufficient security configurability. They also lack rich behavioural descriptions, failing to list provided and required services. To answer this problem, we envision a future where IoT devices carry behavioural contracts and Fog nodes store network policies. One requirement is that contract consistency must be easy to prove. Moreover, contracts must be easy to verify against network policies. In this paper, we propose to combine the security-by-contract (S × C) paradigm with Fog computing to secure IoT devices. Following our previous work, first we formally define the pillars of our proposal. Then, by means of a running case study, we show that we can model communication flows and prevent information leaks. Last, we show that our contribution enables a holistic approach to IoT security, and that it can also prevent unexpected chains of events.

scholarly journals DNS/DANE Collision-Based Distributed and Dynamic Authentication for Microservices in IoT †

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3292 ◽  
Author(s):  
Daniel Díaz-Sánchez ◽  
Andrés Marín-Lopez ◽  
Florina Almenárez Mendoza ◽  
Patricia Arias Cabarcos
Keyword(s):  
Low Cost ◽  
Fog Computing ◽  
Time Data ◽  
Property A ◽  
Domain Name ◽  
Core Networks ◽  
Secure Information ◽  
Nomadic Users ◽  
Iot Devices ◽  
Trapdoor Function

IoT devices provide real-time data to a rich ecosystem of services and applications. The volume of data and the involved subscribe/notify signaling will likely become a challenge also for access and core networks. To alleviate the core of the network, other technologies like fog computing can be used. On the security side, designers of IoT low-cost devices and applications often reuse old versions of development frameworks and software components that contain vulnerabilities. Many server applications today are designed using microservice architectures where components are easier to update. Thus, IoT can benefit from deploying microservices in the fog as it offers the required flexibility for the main players of ubiquitous computing: nomadic users. In such deployments, IoT devices need the dynamic instantiation of microservices. IoT microservices require certificates so they can be accessed securely. Thus, every microservice instance may require a newly-created domain name and a certificate. The DNS-based Authentication of Named Entities (DANE) extension to Domain Name System Security Extensions (DNSSEC) allows linking a certificate to a given domain name. Thus, the combination of DNSSEC and DANE provides microservices’ clients with secure information regarding the domain name, IP address, and server certificate of a given microservice. However, IoT microservices may be short-lived since devices can move from one local fog to another, forcing DNSSEC servers to sign zones whenever new changes occur. Considering DNSSEC and DANE were designed to cope with static services, coping with IoT dynamic microservice instantiation can throttle the scalability in the fog. To overcome this limitation, this article proposes a solution that modifies the DNSSEC/DANE signature mechanism using chameleon signatures and defining a new soft delegation scheme. Chameleon signatures are signatures computed over a chameleon hash, which have a property: a secret trapdoor function can be used to compute collisions to the hash. Since the hash is maintained, the signature does not have to be computed again. In the soft delegation schema, DNS servers obtain a trapdoor that allows performing changes in a constrained zone without affecting normal DNS operation. In this way, a server can receive this soft delegation and modify the DNS zone to cope with frequent changes such as microservice dynamic instantiation. Changes in the soft delegated zone are much faster and do not require the intervention of the DNS primary servers of the zone.

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