scholarly journals Secure and Reliable IoT Networks Using Fog Computing with Software-Defined Networking and Blockchain

2019 ◽  
Vol 8 (1) ◽  
pp. 15 ◽  
Author(s):  
Ammar Muthanna ◽  
Abdelhamied A. Ateya ◽  
Abdukodir Khakimov ◽  
Irina Gudkova ◽  
Abdelrahman Abuarqoub ◽  
...  
Keyword(s):  
High Reliability ◽  
Fog Computing ◽  
Network Virtualization ◽  
Software Defined Networking ◽  
High Availability ◽  
Edge Computing ◽  
Iot Applications ◽  
Distributed Controllers ◽  
Multi Access ◽  
Reliability And Availability

Designing Internet of Things (IoT) applications faces many challenges including security, massive traffic, high availability, high reliability and energy constraints. Recent distributed computing paradigms, such as Fog and multi-access edge computing (MEC), software-defined networking (SDN), network virtualization and blockchain can be exploited in IoT networks, either combined or individually, to overcome the aforementioned challenges while maintaining system performance. In this paper, we present a framework for IoT that employs an edge computing layer of Fog nodes controlled and managed by an SDN network to achieve high reliability and availability for latency-sensitive IoT applications. The SDN network is equipped with distributed controllers and distributed resource constrained OpenFlow switches. Blockchain is used to ensure decentralization in a trustful manner. Additionally, a data offloading algorithm is developed to allocate various processing and computing tasks to the OpenFlow switches based on their current workload. Moreover, a traffic model is proposed to model and analyze the traffic indifferent parts of the network. The proposed algorithm is evaluated in simulation and in a testbed. Experimental results show that the proposed framework achieves higher efficiency in terms of latency and resource utilization.

scholarly journals Secure IoT Network Structure Based on Distributed Fog Computing, with SDN/Blockchain

2019 ◽  
Author(s):  
Ammar Muthanna ◽  
Abdelhamied A. Ateya ◽  
Abdukodir Khakimov ◽  
Irina Gudkova ◽  
Abdelrahman Abuarqoub ◽  
...  
Keyword(s):  
System Performance ◽  
High Efficiency ◽  
High Reliability ◽  
Fog Computing ◽  
Network Virtualization ◽  
Access Network ◽  
Edge Computing ◽  
System Structure ◽  
Communication Paradigm ◽  
High System

IoT is a new communication paradigm that gains a very high importance in the past few years. This communication paradigm supports various heterogeneous applications in many fields and with the dramatic increase of the number of sensor devices, it becomes a demand. Designing IoT networks faces many challenges that include security, massive traffic, high availability, high reliability and energy constraints. Thus, new communication technologies and paradigms should be deployed for IoT networks to overcome these challenges and achieve high system performance. Distributed computing techniques (e.g. fog and MEC), software defined networking (SDN), network virtualization and blockchain are common recent paradigms that should be deployed for IoT networks, either combined or individually, to achieve the main requirements of the IoT networks at a high system performance. Fog computing is a form of edge computing that has been developed to provide the computing capabilities (e.g. storage and processing) at the edge of the access network. Employing Fog computing in IoT networks, as an intermediate layer between IoT devices and the remote cloud, becomes a demand to make use of the edge computing benefits. In this work, we provide a framework for the IoT system structure that employs an edge computing layer of Fog nodes controlled and managed by SDN network with the blockchain technology to achieve a high level of security for latency sensitive IoT applications. The proposed system employs SDN network with distributed controllers and distributed OpenFlow switches; these switches are enabled with limited computing and processing capabilities.  Furthermore, a data offloading algorithm is developed to allocate different processing and computing tasks to the distributed OpenFlow switches with available resources. Moreover, a traffic model is proposed to model and analyze the traffic among different parts of the network. The proposed work achieves various benefits to the IoT network, such as the latency reduction, security improvement and high efficiency of resources utilization. The proposed algorithm is simulated and also the proposed system is experimentally tested over a developed testbed to validate the proposed structure. Experimental results show that the proposed system achieves higher efficiency in terms of latency, security and resource utilization.

scholarly journals Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

2021 ◽  
Author(s):  
Karan Bajaj ◽  
Bhisham Sharma ◽  
Raman Singh
Keyword(s):  
High Speed ◽  
Smart Cities ◽  
Fog Computing ◽  
Edge Computing ◽  
Computation Offloading ◽  
Performance Parameters ◽  
Iot Applications ◽  
Time Sensitivity ◽  
Implementation Analysis ◽  
Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

scholarly journals Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 51 ◽  
Author(s):  
Federico Tonini ◽  
Bahare Khorsandi ◽  
Elisabetta Amato ◽  
Carla Raffaelli
Keyword(s):  
Vehicular Networks ◽  
Programming Model ◽  
High Reliability ◽  
Cost Optimization ◽  
Access Network ◽  
Computational Effort ◽  
Edge Computing ◽  
Hybrid Strategy ◽  
Radio Access ◽  
Multi Access

The global connected cars market is growing rapidly. Novel services will be offered to vehicles, many of them requiring low-latency and high-reliability networking solutions. The Cloud Radio Access Network (C-RAN) paradigm, thanks to the centralization and virtualization of baseband functions, offers numerous advantages in terms of costs and mobile radio performance. C-RAN can be deployed in conjunction with a Multi-access Edge Computing (MEC) infrastructure, bringing services close to vehicles supporting time-critical applications. However, a massive deployment of computational resources at the edge may be costly, especially when reliability requirements demand deployment of redundant resources. In this context, cost optimization based on integer linear programming may result in being too complex when the number of involved nodes is more than a few tens. This paper proposes a scalable approach for C-RAN and MEC computational resource deployment with protection against single-edge node failure. A two-step hybrid model is proposed to alleviate the computational complexity of the integer programming model when edge computing resources are located in physical nodes. Results show the effectiveness of the proposed hybrid strategy in finding optimal or near-optimal solutions with different network sizes and with affordable computational effort.

5G Virtualized Multi-access Edge Computing Platform for IoT Applications

2018 ◽  
Vol 115 ◽  
pp. 94-102 ◽  
Author(s):  
Han-Chuan Hsieh ◽  
Jiann-Liang Chen ◽  
Abderrahim Benslimane
Keyword(s):  
Edge Computing ◽  
Computing Platform ◽  
Iot Applications ◽  
Multi Access

scholarly journals Evolution of V2X Communication and Integration of Blockchain for Security Enhancements

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1338 ◽  
Author(s):  
Rakesh Shrestha ◽  
Seung Yeob Nam ◽  
Rojeena Bajracharya ◽  
Shiho Kim
Keyword(s):  
Autonomous Vehicles ◽  
Vehicular Networks ◽  
High Reliability ◽  
Rapid Evolution ◽  
Edge Computing ◽  
Cloud Services ◽  
Driving Safety ◽  
Future Research ◽  
Traffic Efficiency ◽  
Multi Access

With the rapid evolution in wireless communications and autonomous vehicles, intelligent and autonomous vehicles will be launched soon. Vehicle to Everything (V2X) communications provides driving safety, traffic efficiency, and road information in real-time in vehicular networks. V2X has evolved by integrating cellular 5G and New Radio (NR) access technology in V2X communications (i.e., 5G NR V2X); it can fulfill the ever-evolving vehicular application, communication, and service demands of connected vehicles, such as ultra-low latency, ultra-high bandwidth, ultra-high reliability, and security. However, with the increasing number of intelligent and autonomous vehicles and their safety requirements, there is a backlash in deployment and management because of scalability, poor security and less flexibility. Multi-access Edge Computing (MEC) plays a significant role in bringing cloud services closer to vehicular nodes, which reduces the scalability and flexibility issues. In addition, blockchain has evolved as an effective technology enabler to solve several security, privacy, and networking issues faced by the current 5G-based MEC systems in vehicular networks. Blockchain can be integrated as a strong security mechanism for securing and managing 5G V2X along with MEC. In this survey, we discuss, in detail, state-of-the-art V2X, its evolution based on cellular 5G technology and non-cellular 802.11bd. We investigate the integration of blockchain in 5G-based MEC vehicular networks for security, privacy protection, and content caching. We present the issues and challenges in existing edge computing and 5G V2X and, then, we shed some light on future research directions in these integrated and emerging technologies.

scholarly journals Smart Privacy Protection for Big Video Data Storage Based on Hierarchical Edge Computing

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1517
Author(s):  
Di Xiao ◽  
Min Li ◽  
Hongying Zheng
Keyword(s):  
Data Storage ◽  
Privacy Protection ◽  
Rapid Development ◽  
Fog Computing ◽  
Low Complexity ◽  
Video Data ◽  
Edge Computing ◽  
Scalar Data ◽  
Key Frames ◽  
Multi Access

Recently, the rapid development of the Internet of Things (IoT) has led to an increasing exponential growth of non-scalar data (e.g., images, videos). Local services are far from satisfying storage requirements, and the cloud computing fails to effectively support heterogeneous distributed IoT environments, such as wireless sensor network. To effectively provide smart privacy protection for video data storage, we take full advantage of three patterns (multi-access edge computing, cloudlets and fog computing) of edge computing to design the hierarchical edge computing architecture, and propose a low-complexity and high-secure scheme based on it. The video is divided into three parts and stored in completely different facilities. Specifically, the most significant bits of key frames are directly stored in local sensor devices while the least significant bits of key frames are encrypted and sent to the semi-trusted cloudlets. The non-key frame is compressed with the two-layer parallel compressive sensing and encrypted by the 2D logistic-skew tent map and then transmitted to the cloud. Simulation experiments and theoretical analysis demonstrate that our proposed scheme can not only provide smart privacy protection for big video data storage based on the hierarchical edge computing, but also avoid increasing additional computation burden and storage pressure.

scholarly journals Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 58 ◽  
Author(s):  
Xuan-Qui Pham ◽  
Tien-Dung Nguyen ◽  
VanDung Nguyen ◽  
Eui-Nam Huh
Keyword(s):  
Resource Allocation ◽  
High Reliability ◽  
Resource Limitation ◽  
Optimization Techniques ◽  
Edge Computing ◽  
Mixed Integer ◽  
Node Selection ◽  
Computing Services ◽  
Iot Devices ◽  
Multi Access

The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.

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