scholarly journals A solution for transparent mobility with route optimization in the IP multimedia subsystem

2013 ◽  
Vol 36 (17-18) ◽  
pp. 1726-1744 ◽  
Author(s):  
Ivan Vidal ◽  
Jaime Garcia-Reinoso ◽  
Ignacio Soto ◽  
Antonio de la Oliva
Keyword(s):  
Route Optimization ◽  
Ip Multimedia Subsystem ◽  
Transparent Mobility

Route Optimization of Construction Machine by Deep Reinforcement Learning

2019 ◽  
Vol 139 (4) ◽  
pp. 401-408
Author(s):  
Shunya Tanabe ◽  
Zeyuan Sun ◽  
Masayuki Nakatani ◽  
Yutaka Uchimura
Keyword(s):  
Reinforcement Learning ◽  
Route Optimization ◽  
Construction Machine

Using JAIN SLEE as an Interaction and Policy Manager for Enabler-based Services in Next Generation Networks

2010 ◽  
Vol 56 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Mosiuoa Tsietsi ◽  
Alfredo Terzoli ◽  
George Wells
Keyword(s):  
Internal Structure ◽  
Next Generation Networks ◽  
Ip Multimedia Subsystem ◽  
Next Generation ◽  
Provision Of Services ◽  
Service Capability

Using JAIN SLEE as an Interaction and Policy Manager for Enabler-based Services in Next Generation Networks The IP Multimedia Subsystem is a telecommunications framework with a standard architecture for the provision of services. While the services themselves have not been standardised, standards do exist for basic technologies that can be re-used and aggregated in order to construct more complex services. These elements are called service capabilities by the 3GPP and service enablers by the OMA, both of which are reputable standards bodies in this area. In order to provide re-usability, there is a need to manage access to the service capabilities. Also, in order to build complex services, there is a further need to be able to manage and coordinate the interactions that occur between service capabilities. The 3GPP and the OMA have separately defined network entities that are responsible for handling aspects of these requirements, and are known as a service capability interaction manager (SCIM) and a policy enforcer respectively. However, the internal structure of the SCIM and the policy enforcer have not been standardised by the relevant bodies. In addition, as the SCIM and the policy enforcer have been defined through complementary yet separate processes, there is an opportunity to unify efforts from both bodies. This paper builds on work and standards defined by the bodies, and proposes the design of an interaction manager with features borrowed from both the SCIM and the policy enforcer. To help validate the design, we have identified a platform known as JAIN SLEE which we believe conforms to the model proposed, and we discuss how JAIN SLEE can be used to implement our ideas.

Cross-provincial Cross-region Power Trading Optimization Modeling

2020 ◽  
Vol 13 (8) ◽  
pp. 1183-1189
Author(s):  
Jing-wen Chen ◽  
Yan Xiao ◽  
Hong-she Dang ◽  
Rong Zhang
Keyword(s):  
Network Flow ◽  
Optimal Allocation ◽  
Route Optimization ◽  
Regional Power ◽  
Power Resources ◽  
Gray Theory ◽  
Power Trading ◽  
Study Results ◽  
Multi Agent ◽  
Regional Economic Disparities

Background: China's power resources are unevenly distributed in geography, and the supply-demand imbalance becomes worse due to regional economic disparities. It is essential to optimize the allocation of power resources through cross-provincial and cross-regional power trading. Methods: This paper uses load forecasting, transaction subject data declaration, and route optimization models to achieve optimal allocation of electricity and power resources cross-provincial and cross-regional and maximize social benefits. Gray theory is used to predict the medium and longterm loads, while multi-agent technology is used to report the power trading price. Results: Cross-provincial and cross-regional power trading become a network flow problem, through which we can find the optimized complete trading paths. Conclusion: Numerical case study results has verified the efficiency of the proposed method in optimizing power allocation across provinces and regions.

Spatially Enabled Pipeline Route Optimization Model

2004 ◽  
Author(s):  
S. Raza Wasi ◽  
J. Darren Bender
Keyword(s):  
Decision Making ◽  
Optimization Problem ◽  
Environmental Design ◽  
Route Optimization ◽  
Design Criteria ◽  
Least Cost Path ◽  
Ahp Model ◽  
Spatial Decision Making ◽  
Pipeline Route ◽  
Analytical Tools

An interesting, potentially useful, and fully replicable application of a spatially enabled decision model is presented for pipeline route optimization. This paper models the pipeline route optimization problem as a function of engineering and environmental design criteria. The engineering requirements mostly deal with capital, operational and maintenance costs, whereas environmental considerations ensure preservation of nature, natural resources and social integration. Typically, pipelines are routed in straight lines, to the extent possible, to minimize the capital construction costs. In contrast, longer pipelines and relatively higher costs may occur when environmental and social considerations are part of the design criteria. Similarly, much longer pipelines are less attractive in terms of capital costs and the environmental hazard associated with longer construction area. The pipeline route optimization problem is potentially a complex decision that is most often undertaken in an unstructured, qualitative fashion based on human experience and judgement. However, quantitative methods such as spatial analytical techniques, particularly the least-cost path algorithms, have greatly facilitated automation of the pipeline routing process. In the past several interesting studies have been conducted using quantitative spatial analytical tools for finding the best pipeline route or using non-spatial decision making tools to evaluate several alternates derived through conventional route reconnaissance methods. Most of these studies (that the authors are familiar with) have concentrated on integrating multiple sources of spatial data and performing quantitative least-cost path analysis or have attempted to make use of non-spatial decision making tools to select the best route. In this paper, the authors present a new framework that incorporates quantitative spatial analytical tools with an Analytical Hierarchical Process (AHP) model to provide a loosely integrated but efficient spatial Decision Support System (DSS). Specifically, the goal is to introduce a fully replicable spatial DSS that processes both quantitative and qualitative information, balances between lowest-cost and lowest-impact routes. The model presented in this paper is implemented in a four step process: first, integration of multiple source data that provide basis for engineering and environmental design criteria; second, creation of several alternate routes; third, building a comprehensive decision matrix using spatial analysis techniques; and fourth, testing the alternative and opinions of the stakeholder groups on imperatives of AHP model to simplify the route optimization decision. The final output of the model is then used to carry out sensitivity analysis, quantify the risk, generate “several what and if scenarios” and test stability of the route optimization decision.

Sign in / Sign up

Export Citation Format

Share Document