scholarly journals IEEE Access Special Section Editorial: Recent Advances in Computational Intelligence Paradigms for Security and Privacy for Fog and Mobile Edge Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 134063-134070 ◽  
Author(s):  
B. B. Gupta ◽  
Yogachandran Rahulamathavan ◽  
Shingo Yamaguchi ◽  
Tyson Brooks ◽  
Zheng Yan
Keyword(s):  
Computational Intelligence ◽  
Special Section ◽  
Edge Computing ◽  
Security And Privacy ◽  
Mobile Edge Computing ◽  
Recent Advances

scholarly journals IEEE Access Special Section Editorial: Mobile Edge Computing for Wireless Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 11439-11442 ◽  
Author(s):  
Guanding Yu ◽  
Jun Zhang ◽  
Victor C. M. Leung ◽  
Marios Kountouris ◽  
Chonggang Wang
Keyword(s):  
Wireless Networks ◽  
Special Section ◽  
Edge Computing ◽  
Mobile Edge Computing

scholarly journals IEEE Access Special Section Editorial: Mobile Edge Computing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 106071-106074
Author(s):  
Yan Zhang ◽  
Jianhua He ◽  
Javier Lopez ◽  
Min Sheng ◽  
Hassnaa Moustafa ◽  
...  
Keyword(s):  
Special Section ◽  
Edge Computing ◽  
Mobile Edge Computing

scholarly journals Mobile edge computing for internet of things (IoT): security and privacy issues

2020 ◽  
Vol 18 (3) ◽  
pp. 1486
Author(s):  
Atiqur Rahman ◽  
Guangfu Wu ◽  
Ali Md Liton
Keyword(s):  
Internet Of Things ◽  
Edge Computing ◽  
Security And Privacy ◽  
Mobile Edge Computing ◽  
Security Threat ◽  
Protection Mechanism ◽  
Challenges And Opportunities ◽  
Privacy Issues ◽  
Computing Structure

Nowadays, the masonry for environment-friendly and protected network structure designs, for example, the Internet of Things and gigantic data analytics are increasing at a faster pace compared to an earlier state. Mobile edge computing for an Internet of Things widget is information processing that is achieved at or close to the collectors of information in an Internet of Things system. Herein, we are proposing to temporarily evaluation the concepts, features, protection, and privacy applications of Internet of Things authorized mobile edge computing with its data protection view in our data-driven globe. We focus on illuminating one of kind components that need to be taken into consideration whilst creating a scalable, consistent, impenetrable and disseminated mobile edge computing structure. We also sum up the fundamental ideas regarding security threat alleviation strategies. After that, we walk around the existing challenges and opportunities in the area of mobile edge computing. In conclusion, we analyze a case study, in which a security protection mechanism can be hardened to lift out everyday jobs.

Recent advances in fog and mobile edge computing

2018 ◽  
Vol 29 (4) ◽  
pp. e3307 ◽  
Author(s):  
Ejaz Ahmed ◽  
Periklis Chatzimisios ◽  
Brij B. Gupta ◽  
Yaser Jararweh ◽  
Houbing Song
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Recent Advances

scholarly journals Homomorphic Evaluation of the Integer Arithmetic Operations for Mobile Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Changqing Gong ◽  
Mengfei Li ◽  
Liang Zhao ◽  
Zhenzhou Guo ◽  
Guangjie Han
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Edge Computing ◽  
Security And Privacy ◽  
Mobile Edge Computing ◽  
Operation Rules ◽  
Original Scheme ◽  
Time Overhead

With the rapid development of the 5G network and Internet of Things (IoT), lots of mobile and IoT devices generate massive amounts of multisource heterogeneous data. Effective processing of such data becomes an urgent problem. However, traditional centralised models of cloud computing are challenging to process multisource heterogeneous data effectively. Mobile edge computing (MEC) emerges as a new technology to optimise applications or cloud computing systems. However, the features of MEC such as content perception, real-time computing, and parallel processing make the data security and privacy issues that exist in the cloud computing environment more prominent. Protecting sensitive data through traditional encryption is a very secure method, but this will make it impossible for the MEC to calculate the encrypted data. The fully homomorphic encryption (FHE) overcomes this limitation. FHE can be used to compute ciphertext directly. Therefore, we propose a ciphertext arithmetic operation that implements data with integer homomorphic encryption to ensure data privacy and computability. Our scheme refers to the integer operation rules of complement, addition, subtraction, multiplication, and division. First, we use Boolean polynomials (BP) of containing logical AND, XOR operations to represent the rulers. Second, we convert the BP into homomorphic polynomials (HP) to perform ciphertext operations. Then, we optimise our scheme. We divide the ciphertext vector of integer encryption into subvectors of length 2 and increase the length of private key of FHE to support the 3-multiplication level additional. We test our optimised scheme in DGHV and CMNT. In the number of ciphertext refreshes, the optimised scheme is reduced by 2/3 compared to the original scheme, and the time overhead of our scheme is reduced by 1/3. We also examine our scheme in CNT of without bootstrapping. The time overhead of optimised scheme over DGHV and CMNT is close to the original scheme over CNT.

scholarly journals When Sensor-Cloud Meets Mobile Edge Computing

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5324 ◽  
Author(s):  
Tian Wang ◽  
Yucheng Lu ◽  
Zhihan Cao ◽  
Lei Shu ◽  
Xi Zheng ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Edge Computing ◽  
Security And Privacy ◽  
Future Research ◽  
Mobile Edge Computing ◽  
Control Center ◽  
Computing Center ◽  
Sensor Cloud ◽  
Evaluation Mechanism ◽  
And Control

Sensor-clouds are a combination of wireless sensor networks (WSNs) and cloud computing. The emergence of sensor-clouds has greatly enhanced the computing power and storage capacity of traditional WSNs via exploiting the advantages of cloud computing in resource utilization. However, there are still many problems to be solved in sensor-clouds, such as the limitations of WSNs in terms of communication and energy, the high latency, and the security and privacy issues due to applying a cloud platform as the data processing and control center. In recent years, mobile edge computing has received increasing attention from industry and academia. The core of mobile edge computing is to migrate some or all of the computing tasks of the original cloud computing center to the vicinity of the data source, which gives mobile edge computing great potential in solving the shortcomings of sensor-clouds. In this paper, the latest research status of sensor-clouds is briefly analyzed and the characteristics of the existing sensor-clouds are summarized. After that we discuss the issues of sensor-clouds and propose some applications, especially a trust evaluation mechanism and trustworthy data collection which use mobile edge computing to solve the problems in sensor-clouds. Finally, we discuss research challenges and future research directions in leveraging mobile edge computing for sensor-clouds.

scholarly journals Efficient of Floading Approach for Resource Allocation and Optimization in Mobile Edge Computing Environment

2021 ◽  
Vol 12 (6) ◽  
pp. 2581-2593
Author(s):  
C. Anuradha, M. Ponnavaikko
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Smart Cities ◽  
Edge Computing ◽  
Cloud Services ◽  
Security And Privacy ◽  
The Internet ◽  
Mobile Edge Computing ◽  
Major Barrier

Cloud computing provides a platform for services and resources over the internet for users. The large pool of data resources and services has enabled the emergence of several novel applications such as smart grids, smart environments, and virtual reality. However, the state-of-the-art of cloud computing faces a delay constraint, which becomes a major barrier for reliable cloud services. This constraint is mostly highlighted in the case of smart cities (SC) and the Internet of Things (IoT). Therefore, the recent cloud computing paradigm has poor performance and cannot meet the low delay, navigation, and mobility support requirements.Machine-to-machine (M2M) connectivity has drawn considerable interest from both academia and industry with a growing number of machine-type communication devices (MTCDs). The data links with M2M communications are usually small but high bandwidth, unlike conventional networking networks, demanding performance management of both energy consumption and computing. The main challenges faced in mobile edge computing are task offloading, congestion control, Resource allocation, security and privacy issue, mobility and standardization .Our work mainly focus on offloading based resource allocation and security issues by analyzing the network parameters like reduction of latency and improvisation of bandwidth involved in cloud environment. The cloudsim simulation tool has been utilized to implement the offload balancing mechanism to decrease the energy consumption and optimize the computing resource allocation as well as improve computing capability.

scholarly journals The Future of Mobile Edge Computing

2021 ◽  
pp. 81-105
Author(s):  
Yan Zhang
Keyword(s):  
Artificial Intelligence ◽  
Edge Computing ◽  
Security And Privacy ◽  
Mobile Edge Computing ◽  
Distributed Ledger ◽  
Distributed Ledger Technology ◽  
Service Efficiency ◽  
The Future

AbstractThis chapter first introduces the fundamental principles of blockchain and the integration of blockchain and mobile edge computing (MEC). Blockchain is a distributed ledger technology with a few desirable security characteristics. The integration of blockchain and MEC can improve the security of current MEC systems and provide greater performance benefits in terms of better decentralization, security, privacy, and service efficiency. Then, the convergence of artificial intelligence (AI) and MEC is presented. A federated learning–empowered MEC architecture is introduced. To improve the performance of the proposed scheme, asynchronous federated learning is proposed. The integration of blockchain and federated learning is also presented to enhance the security and privacy of the federated learning–empowered MEC scheme. Finally, more MEC enabled applications are discussed.

scholarly journals Recent advances in mobile edge computing and content caching

2020 ◽  
Vol 6 (2) ◽  
pp. 189-194 ◽  
Author(s):  
Sunitha Safavat ◽  
Naveen Naik Sapavath ◽  
Danda B. Rawat
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Content Caching ◽  
Recent Advances
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