Enabling Real-Time Context-Aware Collaboration through 5G and Mobile Edge Computing

2015 ◽  
Author(s):  
Swaroop Nunna ◽  
Apostolos Kousaridas ◽  
Mohamed Ibrahim ◽  
Markus Dillinger ◽  
Christoph Thuemmler ◽  
...  
Keyword(s):  
Real Time ◽  
Edge Computing ◽  
Context Aware ◽  
Mobile Edge Computing

scholarly journals An edge based hybrid intrusion detection framework for mobile edge computing

2021 ◽  
Author(s):  
Ashish Singh ◽  
Kakali Chatterjee ◽  
Suresh Chandra Satapathy
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Network Architecture ◽  
Denial Of Service ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Hybrid Detection ◽  
Game Theoretical Approach ◽  
Improved Performance ◽  
Edge Based

AbstractThe Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

Context-aware and Adaptive QoS Prediction for Mobile Edge Computing Services

2020 ◽  
pp. 1-1
Author(s):  
Zhi-Zhong Liu ◽  
Quan Z. Sheng ◽  
Xiaofei Xu ◽  
DianHui Chu ◽  
Wei Emma Zhang
Keyword(s):  
Edge Computing ◽  
Context Aware ◽  
Mobile Edge Computing ◽  
Qos Prediction ◽  
Computing Services ◽  
Adaptive Qos

Context-Aware TDD Configuration and Resource Allocation for Mobile Edge Computing

2020 ◽  
Vol 68 (2) ◽  
pp. 1118-1131 ◽  
Author(s):  
Pengtao Zhao ◽  
Hui Tian ◽  
Kwang-Cheng Chen ◽  
Shaoshuai Fan ◽  
Gaofeng Nie
Keyword(s):  
Resource Allocation ◽  
Edge Computing ◽  
Context Aware ◽  
Mobile Edge Computing

scholarly journals Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

2021 ◽  
Vol 11 (23) ◽  
pp. 11530
Author(s):  
Pangwei Wang ◽  
Xiao Liu ◽  
Yunfeng Wang ◽  
Tianren Wang ◽  
Juan Zhang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Short Term ◽  
Spatiotemporal Features ◽  
Traffic State ◽  
State Prediction ◽  
Comparison Results ◽  
Perception System

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 minutes respectively.

Collaborative Task Offloading with Computation Result Reusing for Mobile Edge Computing

2019 ◽  
Vol 62 (10) ◽  
pp. 1450-1462 ◽  
Author(s):  
Zikai Zhang ◽  
Jigang Wu ◽  
Long Chen ◽  
Guiyuan Jiang ◽  
Siew-Kei Lam
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Online Algorithm ◽  
Computation Time ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Delay Constraint ◽  
Programming Technique ◽  
System Environment ◽  
Task Offloading

AbstractThe task offloading problem, which aims to balance the energy consumption and latency for Mobile Edge Computing (MEC), is still a challenging problem due to the dynamic changing system environment. To reduce energy while guaranteeing delay constraint for mobile applications, we propose an access control management architecture for 5G heterogeneous network by making full use of Base Station’s storage capability and reusing repetitive computational resource for tasks. For applications that rely on real-time information, we propose two algorithms to offload tasks with consideration of both energy efficiency and computation time constraint. For the first scenario, i.e. the rarely changing system environment, an optimal static algorithm is proposed based on dynamic programming technique to get the exact solution. For the second scenario, i.e. the frequently changing system environment, a two-stage online algorithm is proposed to adaptively obtain the current optimal solution in real time. Simulation results demonstrate that the exact algorithm in the first scenario runs 4 times faster than the enumeration method. In the second scenario, the proposed online algorithm can reduce the energy consumption and computation time violation rate by 16.3% and 25% in comparison with existing methods.

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