A Fuzzy Path Selection Power-Based for MANET

2009 ◽  
pp. 1283-1291 ◽  
Author(s):  
Ning Chen ◽  
Qi-min Zhang ◽  
Shang-zhu Jin
Keyword(s):  
Path Selection

Resource-Aware Path Selection in Heterogeneous Self-Organizing Wireless Networks

2010 ◽  
Vol E93-B (12) ◽  
pp. 3647-3650
Author(s):  
Bongjhin SHIN ◽  
Hoyoung CHOI ◽  
Daehyoung HONG
Keyword(s):  
Wireless Networks ◽  
Path Selection ◽  
Resource Aware ◽  
Self Organizing

scholarly journals Network Attack Path Selection and Evaluation Based on Q-Learning

2020 ◽  
Vol 11 (1) ◽  
pp. 285
Author(s):  
Runze Wu ◽  
Jinxin Gong ◽  
Weiyue Tong ◽  
Bing Fan
Keyword(s):  
Distribution System ◽  
Path Selection ◽  
Cyber Attacks ◽  
Power Distribution System ◽  
Q Learning ◽  
Attack Model ◽  
Cross Domain ◽  
Time Operation ◽  
Attack Path ◽  
The Impact

As the coupling relationship between information systems and physical power grids is getting closer, various types of cyber attacks have increased the operational risks of a power cyber-physical System (CPS). In order to effectively evaluate this risk, this paper proposed a method of cross-domain propagation analysis of a power CPS risk based on reinforcement learning. First, the Fuzzy Petri Net (FPN) was used to establish an attack model, and Q-Learning was improved through FPN. The attack gain was defined from the attacker’s point of view to obtain the best attack path. On this basis, a quantitative indicator of information-physical cross-domain spreading risk was put forward to analyze the impact of cyber attacks on the real-time operation of the power grid. Finally, the simulation based on Institute of Electrical and Electronics Engineers (IEEE) 14 power distribution system verifies the effectiveness of the proposed risk assessment method.

scholarly journals Optimization algorithms for resilient path selection in networks

2021 ◽  
Vol 128 ◽  
pp. 105191
Author(s):  
Marco Casazza ◽  
Alberto Ceselli
Keyword(s):  
Optimization Algorithms ◽  
Path Selection

Where To: Crowd-Aided Path Selection by Selective Bayesian Network

2021 ◽  
pp. 1-1
Author(s):  
Chen Zhang ◽  
Haodi Zhang ◽  
Weiteng Xie ◽  
Nan Liu ◽  
Kaishun Wu ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Path Selection

TripRes

2021 ◽  
Vol 17 (2) ◽  
pp. 1-21
Author(s):  
Xiaolong Xu ◽  
Zijie Fang ◽  
Lianyong Qi ◽  
Xuyun Zhang ◽  
Qiang He ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Discrete Distribution ◽  
Resource Provisioning ◽  
Path Selection ◽  
Urban Traffic ◽  
Traffic Flows ◽  
Multimedia Services ◽  
Traffic Flow Prediction ◽  
The City ◽  
Multimedia Surveillance

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

Sign in / Sign up

Export Citation Format

Share Document