scholarly journals Traditional PageRank Versus Network Capacity Bound

2019 ◽  
pp. 236-249
Author(s):  
Robert A. Kłopotek ◽  
Mieczysław A. Kłopotek
Keyword(s):  
Network Capacity ◽  
Capacity Bound

Network Capacity Bound for Personalized Bipartite PageRank

2015 ◽  
pp. 189-204 ◽  
Author(s):  
Mieczysław A. Kłopotek ◽  
Sławomir T. Wierzchoń ◽  
Robert A. Kłopotek ◽  
Elżbieta A. Kłopotek
Keyword(s):  
Network Capacity ◽  
Capacity Bound

scholarly journals Dynamic Plan Control: An Effective Tool to Manage Demand Considering Mobile Internet Network Congestion

2020 ◽  
Vol 11 (1) ◽  
pp. 91
Author(s):  
Xiaoyu Ma ◽  
Jihong Zhang ◽  
Yuan Cao ◽  
Zhou He ◽  
Jonas Nebel
Keyword(s):  
Programming Problem ◽  
Service Provider ◽  
Service Providers ◽  
Control Method ◽  
Network Capacity ◽  
Mobile Internet ◽  
Mixed Integer ◽  
Mobile Service ◽  
Data Traffic ◽  
Potential Customers

Rapidly increasing mobile data traffic have placed a significant burden on mobile Internet networks. Due to limited network capacity, a mobile network is congested when it handles too much data traffic simultaneously. In turn, some customers leave the network, which induces a revenue loss for the mobile service provider. To manage demand and maximize revenue, we propose a dynamic plan control method for the mobile service providers under connection-speed-restriction pricing. This method allows the mobile service provider to dynamically set the data plans’ availability for potential customers’ new subscriptions. With dynamic plan control, the service provider can adjust data network utilization and achieve high customer satisfaction and a low churn rate, which reflect high service supply chain performance. To find the optimal control policy, we transform the high-dimensional dynamic programming problem into an equivalent mixed integer linear programming problem. We find that dynamic plan control is an effective tool for managing demand and increasing revenue in the long term. Numerical evaluation with a large European mobile service provider further supports our conclusion. Furthermore, when network capacity or potential customers’ willingness to join the network changes, the dynamic plan control method generates robust revenue for the service provider.

Multi-Objective Fleet Assignment Problem Based on Self-Adaptive Genetic Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2895-2900 ◽  
Author(s):  
Xiao Yang ◽  
Bo Jiang
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Assignment Problem ◽  
Network Capacity ◽  
Total Carbon ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective ◽  
Route Network ◽  
Assignment Model ◽  
Key Factor

Since the beginning of the twenty-first century, energy conservation has become the theme of the development of the world. China government set the emissions-reduction targets in various industries on the 12th Five-Year Plan. And the airlines were committed to reduce their carbon emissions. From an operational perspective, the airline model assignment problem is a key factor of the total carbon emissions on the entire route network. But the traditional aircraft assignment models approach did not account for this purpose to reduce carbon emissions. By constructing the multi-objective optimization models consider carbon emissions assignment model using a genetic algorithm, numerical example shows that the model is able to meet all aspects demand which include meeting route network capacity demand, minimizing operating costs and reducing total aircraft fleet carbon emissions.

Distributed task allocation with critical tasks and limited capacity

Robotica ◽  
2021 ◽  
pp. 1-25
Author(s):  
An Zhang ◽  
Mi Yang ◽  
Bi Wenhao ◽  
Fei Gao
Keyword(s):  
Numerical Simulations ◽  
Unmanned Aerial Vehicle ◽  
Task Allocation ◽  
Network Capacity ◽  
Allocation Problem ◽  
Limited Capacity ◽  
Performance Impact ◽  
Task List ◽  
Aerial Vehicle ◽  
Traditional Performance

Abstract This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms.

Improving the Energy Efficiency by Cooperative Transmission in Multi-Hop Wireless Ad-Hoc Networks

2021 ◽  
Author(s):  
Salah Abdulhadi
Keyword(s):  
Ad Hoc Networks ◽  
Wireless Ad Hoc Networks ◽  
Ad Hoc ◽  
Multiple Antennas ◽  
Network Capacity ◽  
Cooperative Relaying ◽  
Cooperative Transmission ◽  
Total Power ◽  
Potential Candidate ◽  
Hoc Networks

Cooperative transmission has been recently proposed as a promising technique to combat multi-path fading and increased link reliability. It represents a potential candidate to exploit the benefits of using multiple antennas system without requiring to implement multiple antennas per terminal. There has been extensive research investigating physical layer issues of such systems; however, higher layer protocols that exploit cooperative links in ad hoc networks are still emerging in cooperative ad hoc networks, and it is important to effectively use cooperation without affecting the performance of the network. In this dissertation, we proposed a novel a characterization of the optimal multi-hop cooperative routing in ad hoc networks, and developed a metric for both evaluation. The key advantages of cooperative links are to minimize the number of hops while maintaining the QoS requirements and to minimize the end-to-end total power for a given rate. Also we showed that energy can be used more efficiently if we determine the joint optimal packet size and the optimal power allocation for both the source and the relay. For multi-flow scenario, we have proposed a clique-based inter-flow interference abstraction, and used the linear programming formulation to study the capacity gain of ad-hoc cooperative network. It is observed that the network capacity in multi-hop multi-flow settings is severely affected by interference between links and this effect increases when the cooperative relaying is imposed.

scholarly journals Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2548 ◽  
Author(s):  
Run Tian ◽  
Lin Ma ◽  
Zhe Wang ◽  
Xuezhi Tan
Keyword(s):  
Spectrum Sharing ◽  
Network Performance ◽  
Signal To Noise Ratio ◽  
Interference Management ◽  
Interference Alignment ◽  
Network Capacity ◽  
High Signal ◽  
Noise Power ◽  
Optimal Capacity ◽  
Iot Devices

This paper considers interference management and capacity improvement for Internet of Things (IoT) oriented two-tier networks by exploiting cognition between network tiers with interference alignment (IA). More specifically, we target our efforts on the next generation two-tier networks, where a tier of femtocell serving multiple IoT devices shares the licensed spectrum with a tier of pre-existing macrocell via a cognitive radio. Aiming to manage the cross-tier interference caused by cognitive spectrum sharing as well as ensure an optimal capacity of the femtocell, two novel self-organizing cognitive IA schemes are proposed. First, we propose an interference nulling based cognitive IA scheme. In such a scheme, both co-tier and cross-tier interferences are aligned into the orthogonal subspace at each IoT receiver, which means all the interference can be perfectly eliminated without causing any performance degradation on the macrocell. However, it is known that the interference nulling based IA algorithm achieves its optimum only in high signal to noise ratio (SNR) scenarios, where the noise power is negligible. Consequently, when the imposed interference-free constraint on the femtocell can be relaxed, we also present a partial cognitive IA scheme that further enhances the network performance under a low and intermediate SNR. Additionally, the feasibility conditions and capacity analyses of the proposed schemes are provided. Both theoretical and numerical results demonstrate that the proposed cognitive IA schemes outperform the traditional orthogonal precoding methods in terms of network capacity, while preserving for macrocell users the desired quality of service.

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