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Summary: With the promotion and application of IPv6 in the world, there is a growing demand for IPv6 alias resolution. How to resolve IPv6 alias efficiently and accurately becomes an urgent problem to be solved. After analyzing the features of IPv6 addresses, this paper proposes a large‐scale adaptive IPv6 alias resolution method based on fingerprint information by combining Too‐Big Trick, UAv6, APD, and other alias resolution algorithms. It sends ICMPv6 probe packets to different types of IPv6 addresses to get the fingerprint information of target hosts. After filtering the classified addresses, our proposed alias resolution method is adaptively selected to resolve alias addresses. In the experiment, we use multi‐thread method to resolve the aliases of large‐scale IPv6 addresses, which greatly improves the efficiency of detection. Meanwhile, we use IPv6 datasets collected from the organizations RIPE and CAIDA. By comparing with Speedtrap and Too‐Big Trick, we confirm the accuracy of this method and obtain more alias pairs. [ABSTRACT FROM AUTHOR]

Summary: Shared transportation is a new way of traveling generated by the rapid development of the "Internet +" and the sharing economy. Due to the limited energy carried by shared e‐bikes, the energy problem becomes the primary obstacle restricting its development. At present, one way to solve the energy problem is to use mobile charging vehicles (MCVs). However, how to schedule MCVs to achieve fast response, reduce energy consumption, and improve charging efficiency is crucial. In this paper, we propose a Charging Scheduling scheme based on Dynamic Characteristic of shared e‐bike in Obstacle Space (CSDC‐OS). Firstly, we design two MCV departure mechanisms to solve the problems of low utilization of MCVs and long waiting time for shared e‐bikes. Then, we design the schedulability conditions based on the A‐star algorithm and the dynamic change of e‐bikes to ensure that MCVs can return to the service station in time. Finally, we propose a charging sequence algorithm based on linear skyline query and a selective insertion algorithm to improve the charging efficiency of MCVs. Simulation results show that the CSDC‐OS can improve the response time, mobile consumption, and charging efficiency by more than 8% on average. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we formulate and solve the selfish allocation problem by using game theory, which is different from the previously studied researches in three ways that make it more accurately reflective of real world peer-to-peer (P2P) allocation: (i) we treat the nodes as strategic agents and treat the replica allocation as a deliberate auction where node is incentivized to give his true quality of service for obtaining the replica; (ii) our mechanism computes node utility for all possible replica destination and payments for those destination nodes, and the best appropriate node can be selected as the final placement destination; and (iii) we show how to carry out our scheme with a distributed algorithm that is a straightforward extension to P2P allocation method and causes an overhead in convergence time. Our design and analysis of a strategy proof, feasible, Vickrey-Clarke-Groves-based auction scheme provides a new, promising direction in distributed algorithmic mechanism design, which has heretofore been focused mainly on P2P application. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Green wireless local area network (WLAN) is an emerging technology to achieve both the purposes of power conservation and high-speed accessing to the Internet because of the working on-demand strategy adoption and high density access points (APs) deployment. Although it is good news to data traffic service, Green WLAN brings severe challenges to the indoor localization service based on fingerprint algorithm. Redundant APs will greatly enlarge the radio map and introduce a much heavier computation burden to the terminal for localization in the online phase. In addition, APs in Green WLAN are powered on and off to make balances between data traffic service demand and energy saving goals so that the received signal strength (RSS) sampled online and recorded in the radio map offline are rarely matched in the same detected AP number, which leads to asymmetric matching problem occurring in the fingerprint algorithm. In this paper, we propose to make a nonlinear dimensionality reduction on the RSS by local discriminant embedding algorithm to realize both the computation burden decreasing and asymmetric matching problem resolving for the fingerprint algorithm in Green WLAN. The simulation results show that our proposed methods could effectively reduce the computation burden in the online phase and make the fingerprint algorithm operate more robustly when the RSS is reduced to the intrinsic dimensionality in Green WLAN. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]