无线自组网中基于移动预测与功率调整的适应性分簇算法

传送功率控制是无线自组网中资源管理和控制干扰的中心技术.传统上功率控制只是用作消除信道消隐负作用的一种手段,当前观点一般认为功率控制是一种可以为单个用户提供服务质量的灵活机制.提出一种分簇算法以达到减轻网络拓扑的动态变化的同时节省电能.此提案是基于全球定位系统的(GPS).根据移动主机的历史轨迹预测它下一个最可能处在的位置,预先调整传送功率.为了最大化网络的吞吐量,算法自适应性的控制每个簇在合适的大小.在GlomoSim模拟器上仿真了提出的算法.仿真结果表明,它是无线自组网中有效的拓扑管理机制,对由高速运动主机组成的网络特别有效.     

基于价格的拥塞控制模型扩展

基于价格的拥塞控制模型将网络拥塞控制问题抽象为聚合效用最大化的优化问题,已经成为拥塞控制机制设计和分析的一般框架。然而简化的线性模型与现实网络环境有一定差距,不能准确有效地指导新协议的设计与分析。因此,近年来研究人员正试图结合新的技术和应用,扩展完善基于价格的模型,建立更为准确有效的模型来反映现实的网络环境。为了系统地了解该领域研究工作的进展,本文首先简要回顾了基于价格的模型,分析其不足,介绍近年来在模型扩展方面的研究工作,最后指出几个有意义的研究方向。     

Equilibrium, Games, and Pricing in Transportation and Telecommunication Networks

Network equilibrium models that have traditionally been used for transportation planning have penetrated in recent years to other scientific fields. These models have recently been introduced in the telecommunication networks literature, as well as in the field of game theory. Researchers in the latter fields are not always aware of the very rich literature on equilibrium models outside of their application area. On the other hand, researchers that have used network equilibrium models in transportation may not be aware of new application areas of their tools. The aim of this paper is to present some central research issues and tools in network equilibria and pricing that could bring closer the three mentioned research communities.     

Optimization of Communication Network Fault Identification Based on NB-IoT

The Internet of Things (IoT) has developed an industrial and medical application in dramatic growth in recent years. Narrow Band-Internet of Things (NB-IoT) is a wireless communication and low power wide area technology based on new IoT devices and enables various services to grow. NB-IoT communication significantly increases the power consumption of user equipment, computer capacity, and spectrum performance. Network communication is complex, and the amount of information these networks have made it extremely difficult to manage the transmissions. Since the communication network failure is inevitable, rapid detection, identification, and recovery are the most reliable; it is essential to the high-speed operation. Narrow Band-IoT (NB-IoT) networking technology has proposed the idea of detecting network communication fault. The NB-IoT technique can help save a lot of energy by significantly reducing the number of retransmission required. The proposed technology proves that this research is a powerful proposition for achieving the ideal energy potential. Performance analysis and simulation results show that the detection mechanism can improve transmission efficiency and effectively reduce network communication failures power consumption.     

A Study on Development of a Hybrid Aerial/Terrestrial Robot System for Avoiding Ground Obstacles by Flight

To date, many studies related to robots have been performed around the world. Many of these studies have assumed operation at locations where entry is difficult, such as disaster sites, and have focused on various terrestrial robots, such as snake-like, humanoid, spider-type, and wheeled units. Another area of active research in recent years has been aerial robots with small helicopters for operation indoors and outdoors. However, less research has been performed on robots that operate both on the ground and in the air. Accordingly, in this paper, we propose a hybrid aerial/terrestrial robot system. The proposed robot system was developed by equipping a quadcopter with a mechanism for ground movement. It does not use power dedicated to ground movement, and instead uses the flight mechanism of the quadcopter to achieve ground movement as well. Furthermore, we addressed the issue of obstacle avoidance as part of studies on autonomous control. Thus, we found that autonomous control of ground movement and flight was possible for the hybrid aerial/terrestrial robot system, as was autonomous obstacle avoidance by flight when an obstacle appeared during ground movement.      

Bounded Model Checking with SNF,Alternating Automata,and Büchi Automata

Model checking of LTL formulæis traditionally carried out by a conversion to Büchi automata, and there is therefore a large body of research in this area including some recent studies on the use of alternating automata as an intermediate representation.Bounded model checking has until recently been apart from this, typically using a direct conversion from LTL to propositional logic. In this paper we give a new bounded model checking encoding using alternating automata and focus on the relationship between alternating automata and SNF. We also explore the differences in the way SNF, alternating, and Büchi automata are used from both a theoretical and an experimental perspective.     

无线传感器网络拓扑控制的理论探讨

拓扑控制是无线传感器网络中重要的节能技术,并且已经形成了功率控制和睡眠调度两个主流研究方向。针对当前研究工作中存在的问题,对拓扑控制进行了基础性的研究。全面地考虑了网络的通信能耗和空闲能耗,在理想情况下给出了以最小化能耗为目标的拓扑控制问题的一个明确定义;证明了这个问题是NP-难的,同时非形式化地讨论了更实际的拓扑控制问题的计算复杂性;并且在此基础上,进一步提出了关于如何设计能量高效的拓扑控制协议的3个必要性原则。希望本研究成果有助于探索更好的拓扑控制协议。     

Performance of IEEE 802.15.4 Clusters with Power Management and Key Exchange

The IEEE 802.15.4 specification is a recent low data rate wireless personal area network standard.While basic security services are provided for,there is a lack of more advanced techniques which are indispensable in modern personal area network applications.In addition,performance implications of those services are not known.In this paper,we describe a secure data exchange protocol based on the ZigBee specification and built on top of IEEE 802.15.4 link layer.This protocol includes a key exchange mechanism.We assume that all nodes are applying power management technique based on the constant event sensing reliability required by the coordinator.Power management generates random sleep times by every node which in average fairly distributes the sensing load among the nodes.Key exchange is initiated by a cluster coordinator after some given number of sensing packets have been received by the coordinator.We develop and integrate simulation model of the key exchange and power management technique into the cluster's reliable sensing function.We evaluate the impact of security function and its periodicity on cluster performance.     

Two-Player Game: Playing Casual Video Games with Outgroup Members Reduces Levels of Prejudice Toward That Outgroup

Video games have traditionally held a dubious reputation in the media and have been linked to many antisocial behaviors. A large amount of research has borne out some of these concerns, linking video games with addiction and particularly aggression. However, recent work in this area has begun to examine the positive aspects of video gaming. In this work, we examine how playing casual, low-involvement video games with an outgroup member may reduce prejudice. In Study 1, participants played cooperatively or competitively with a (trivial) outgroup member or alone. In Studies 2 and 3, a meaningful social identity was used: students’ university affiliation. Participants played either cooperatively with a rival university student against the computer, or alone. Analyses of all three studies showed that attitudes toward the outgroup were more positive after playing with an outgroup member compared with control conditions. How these findings may be applied to real world groups and extensions for future research are then discussed.     

Analysis of cancer data: a data mining approach

Abstract: Even though cancer research has traditionally been clinical and biological in nature, in recent years data driven analytic studies have become a common complement. In medical domains where data and analytics driven research is successfully applied, new and novel research directions are identified to further advance the clinical and biological studies. In this research, we used three popular data mining techniques (decision trees, artificial neural networks and support vector machines) along with the most commonly used statistical analysis technique logistic regression to develop prediction models for prostate cancer survivability. The data set contained around 120 000 records and 77 variables. A k-fold cross-validation methodology was used in model building, evaluation and comparison. The results showed that support vector machines are the most accurate predictor (with a test set accuracy of 92.85%) for this domain, followed by artificial neural networks and decision trees.