基于模糊语言变量的动态目标无线网络选择技术(英文)

Even though various wireless Network Access Technologies(NATs)with different specifications and applications have been developed in the recent years,no single wireless technology alone can satisfy the anytime,anywhere,and any service wireless-access needs of mobile users.A real seamless wireless mobile environment is only realized by considering vertical and horizontal handoffs together.One of the major design issues in heterogeneous wireless networks is the support of Vertical Handoff(VHO).VHO occurs when a multi-interface enabled mobile terminal changes its Point of Attachment(PoA)from one type of wireless access technology to another,while maintaining an active session.In this paper we present a novel multi-criteria VHO algorithm,which chooses the target NAT based on several factors such as user preferences,system parameters,and traffic-types with varying Quality of Service(QoS)requirements.Two modules i.e.,VHO Necessity Estimation(VHONE)module and target NAT selection module,are designed.Both modules utilize several"weighted"users’and system’s parameters.To improve the robustness of the proposed algorithm,the weighting system is designed based on the concept of fuzzy linguistic variables.

Weather-based interruption prediction in the smart grid utilizing chronological data

This unique study will demonstrate a combined effect of weather parameters on the total number of power distribution interruptions in a region.Based on common weather conditions,a theoretical model can predict interruptions and risk assessment with immediate weather conditions.Using daily and hourly weather data,the created models will predict the number of daily or by-shift interruptions.The weather and environmental conditions to be addressed will include rain,wind,temperature,lightning density,humidity,barometric pressure,snow and ice.Models will be developed to allow broad applications.Statistical and deterministic simulations of the models using the data collected will be conducted by employing existing software,and the results will be used to refine the models.Models developed in this study will be used to predict power interruptions in areas that can be readily monitored,thus validating the models.The application has resulted in defining the predicted number of interruptions in a region with a specific confidence level.Reliability is major concern for every utility.Prediction and timely action to minimize the outage duration improves reliability.Use of this predictor model with existing smart grid self-healing technology is proposed.