Task Allocation Mechanism for Cable Real-Time Online Monitoring Business Based on Edge Computing

2020 ◽  
pp. 1-12
Author(s):  
Sujie Shao ◽  
Qinghang Zhang ◽  
Shaoyong Guo ◽  
Feng Qi
Keyword(s):  
Real Time ◽  
Task Allocation ◽  
Online Monitoring ◽  
Edge Computing ◽  
Allocation Mechanism

scholarly journals A new load balancing strategy by task allocation in edge computing based on intermediary nodes

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Guangshun Li ◽  
Yonghui Yao ◽  
Junhua Wu ◽  
Xiaoxiao Liu ◽  
Xiaofei Sheng ◽  
...  
Keyword(s):  
Load Balancing ◽  
Real Time ◽  
Task Allocation ◽  
Normal Load ◽  
Task Assignment ◽  
Edge Computing ◽  
Heavy Load ◽  
Light Load ◽  
Assignment Model ◽  
Classification Evaluation

AbstractThe latency of cloud computing is high for the reason that it is far from terminal users. Edge computing can transfer computing from the center to the network edge. However, the problem of load balancing among different edge nodes still needs to be solved. In this paper, we propose a load balancing strategy by task allocation in edge computing based on intermediary nodes. The intermediary node is used to monitor the global information to obtain the real-time attributes of the edge nodes and complete the classification evaluation. First, edge nodes can be classified to three categories (light-load, normal-load, and heavy-load), according to their inherent attributes and real-time attributes. Then, we propose a task assignment model and allocate new tasks to the relatively lightest load node. Experiments show that our method can balance load among edge nodes and reduce the completion time of tasks.

scholarly journals UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2307 ◽  
Author(s):  
Yaozhong Zhang ◽  
Wencheng Feng ◽  
Guoqing Shi ◽  
Frank Jiang ◽  
Morshed Chowdhury ◽  
...  
Keyword(s):  
Real Time ◽  
Task Allocation ◽  
Dynamic Environment ◽  
Mission Planning ◽  
Planning Problem ◽  
Allocation Mechanism ◽  
The Real ◽  
Dynamic Task ◽  
Time Performance ◽  
Communication Time

To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments.

scholarly journals Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 158488-158501
Author(s):  
Qianjun Wang ◽  
Sujie Shao ◽  
Shaoyong Guo ◽  
Xuesong Qiu ◽  
Zhili Wang
Keyword(s):  
Internet Of Things ◽  
Task Allocation ◽  
Edge Computing ◽  
Allocation Mechanism

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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.

Accurate and Privacy-Preserving Task Allocation for Edge Computing Assisted Mobile Crowdsensing

2021 ◽  
pp. 1-14
Author(s):  
Zhihua Wang ◽  
Chaoqi Guo ◽  
Jiahao Liu ◽  
Jiamin Zhang ◽  
Yongjian Wang ◽  
...  
Keyword(s):  
Task Allocation ◽  
Privacy Preserving ◽  
Edge Computing ◽  
Mobile Crowdsensing
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