Hamming distance and hop count based classification for multicast network topology inference

© 2005 IEEE.Topology information of a multicast network benefits significantly to many applications such as resource management, loss and congestion recovery. In this paper we propose a new algorithm, namely binary hamming distance and hop count based classification algorithm (BHC), to infer multicast network topology from end-to-end measurements. The BHC algorithm identifies multicast network topology using hamming distance of the sequences on receipt/loss of probe packets maintained at each pair of nodes and incorporating the hop count available at each node. We analyze the inference accuracy of the algorithm and prove that the algorithm can obtain accurate inference at higher probability than previous algorithms for a finite number of probe packets. We implement the algorithm in a simulated network and validate the algorithm’s performance in accuracy and efficiency.Hui Tian, Hong She

Topology Performance Inference Algorithm Based on Network Tomography

传统的网络测量主要依赖各种协议的协作和中间节点的反馈信息来推断网络的内部性能参数或者拓扑结构，但是随着网络异构性和分布化的不断提高，网络性能的可知性变得越来越差。网络层析成像技术通过端到端的测量来推断网络的内部性能参数和拓扑结构，无需借助路由协议或者中间节点的协作，保证了用户的信息安全，同时减少了对网络流量的影响，目前已经成为国内外网络测量的研究热点之一。 本文主要针对基于层析技术的网络拓扑推断算法进行研究。在单源网络拓扑推断方面，着重分析了GLT（generallosstreeclassificationalgorithm）算法和BHC（binaryHammingdistanceandho...Traditional network measurement infer the internal network performance or logic topology mainly relies on the cooperation of various protocols and the feedback from internal nodes.Owing to the continuous improvement on network heterogeneity and distribution, the acquisition of network performance using traditional network measurement is becoming increasingly difficult. Network tomography uses end-...学位：工学硕士院系专业：信息科学与技术学院通信工程系_信号与信息处理学号：2332007115217

Topology Performance Inference Algorithm Based on Network Tomography

传统的网络测量主要依赖各种协议的协作和中间节点的反馈信息来推断网络的内部性能参数或者拓扑结构，但是随着网络异构性和分布化的不断提高，网络性能的可知性变得越来越差。网络层析成像技术通过端到端的测量来推断网络的内部性能参数和拓扑结构，无需借助路由协议或者中间节点的协作，保证了用户的信息安全，同时减少了对网络流量的影响，目前已经成为国内外网络测量的研究热点之一。 本文主要针对基于层析技术的网络拓扑推断算法进行研究。在单源网络拓扑推断方面，着重分析了GLT（generallosstreeclassificationalgorithm）算法和BHC（binaryHammingdistanceandho...Traditional network measurement infer the internal network performance or logic topology mainly relies on the cooperation of various protocols and the feedback from internal nodes.Owing to the continuous improvement on network heterogeneity and distribution, the acquisition of network performance using traditional network measurement is becoming increasingly difficult. Network tomography uses end-...学位：工学硕士院系专业：信息科学与技术学院通信工程系_信号与信息处理学号：2332007115217

Algorithm of Multicast Network Topology Inference Based on Packet Loss Rate

在分析现有的网络拓扑推断算法的基础上,提出一种改进的基于丢包率的多播网络拓扑推断算法。结合接收节点的层次信息、汉明距离及节点接收的探测包数量,能够同时推断网络拓扑结构和链路丢包率,并根据链路丢包率的估计值动态地调整拓扑推断的判决门限值,提高了推断的准确性。仿真实验证明,与现有算法相比,该算法具有更好的性能。By analyzing the existing network topology inference algorithms,this paper presents an improved multicast network topology inference algorithm based on packet loss rate.It combines the hop count of receivers,the Hamming distance and the number of received probes and can infer multicast network topology and loss performance simultaneously.It adapts dynamically the value of threshold according to the estimation of link packet loss rates,it improves the accuracy of the inference.Simulation results show that compared with the existing algorithms,this algorithm has better performance.福建省自然科学基金资助项目(2006J0044

Performance analysis of communication model on position based routing protocol: Review analysis

Research on the Vanet system has its own challenges and obstacles with the communication system between nodes being the main issue. Four categories in the Vanet system topology, namely position based routing protocols, broadcast based routing protocols, cluster based routing protocols and multicast/geocast routing protocols, have fundamental differences, especially in the concept of sending data and information between nodes. For this reason, in this study, the selection of standardization and integration of data delivery between nodes is of particular relevance. The ability to send data properly in busy and fast traffic conditions is another challenge. For this, there are many variables that must be considered to improve communication between nodes

Leveraging Resources on Anonymous Mobile Edge Nodes

Smart devices have become an essential component in the life of mankind. The quick rise of smartphones, IoTs, and wearable devices enabled applications that were not possible few years ago, e.g., health monitoring and online banking. Meanwhile, smart sensing laid the infrastructure for smart homes and smart cities. The intrusive nature of smart devices granted access to huge amounts of raw data. Researchers seized the moment with complex algorithms and data models to process the data over the cloud and extract as much information as possible. However, the pace and amount of data generation, in addition to, networking protocols transmitting data to cloud servers failed short in touching more than 20% of what was generated on the edge of the network. On the other hand, smart devices carry a large set of resources, e.g., CPU, memory, and camera, that sit idle most of the time. Studies showed that for plenty of the time resources are either idle, e.g., sleeping and eating, or underutilized, e.g. inertial sensors during phone calls. These findings articulate a problem in processing large data sets, while having idle resources in the close proximity. In this dissertation, we propose harvesting underutilized edge resources then use them in processing the huge data generated, and currently wasted, through applications running at the edge of the network. We propose flipping the concept of cloud computing, instead of sending massive amounts of data for processing over the cloud, we distribute lightweight applications to process data on users\u27 smart devices. We envision this approach to enhance the network\u27s bandwidth, grant access to larger datasets, provide low latency responses, and more importantly involve up-to-date user\u27s contextual information in processing. However, such benefits come with a set of challenges: How to locate suitable resources? How to match resources with data providers? How to inform resources what to do? and When? How to orchestrate applications\u27 execution on multiple devices? and How to communicate between devices on the edge? Communication between devices at the edge has different parameters in terms of device mobility, topology, and data rate. Standard protocols, e.g., Wi-Fi or Bluetooth, were not designed for edge computing, hence, does not offer a perfect match. Edge computing requires a lightweight protocol that provides quick device discovery, decent data rate, and multicasting to devices in the proximity. Bluetooth features wide acceptance within the IoT community, however, the low data rate and unicast communication limits its use on the edge. Despite being the most suitable communication protocol for edge computing and unlike other protocols, Bluetooth has a closed source code that blocks lower layer in front of all forms of research study, enhancement, and customization. Hence, we offer an open source version of Bluetooth and then customize it for edge computing applications. In this dissertation, we propose Leveraging Resources on Anonymous Mobile Edge Nodes (LAMEN), a three-tier framework where edge devices are clustered by proximities. On having an application to execute, LAMEN clusters discover and allocate resources, share application\u27s executable with resources, and estimate incentives for each participating resource. In a cluster, a single head node, i.e., mediator, is responsible for resource discovery and allocation. Mediators orchestrate cluster resources and present them as a virtually large homogeneous resource. For example, two devices each offering either a camera or a speaker are presented outside the cluster as a single device with both camera and speaker, this can be extended to any combination of resources. Then, mediator handles applications\u27 distribution within a cluster as needed. Also, we provide a communication protocol that is customizable to the edge environment and application\u27s need. Pushing lightweight applications that end devices can execute over their locally generated data have the following benefits: First, avoid sharing user data with cloud server, which is a privacy concern for many of them; Second, introduce mediators as a local cloud controller closer to the edge; Third, hide the user\u27s identity behind mediators; and Finally, enhance bandwidth utilization by keeping raw data at the edge and transmitting processed information. Our evaluation shows an optimized resource lookup and application assignment schemes. In addition to, scalability in handling networks with large number of devices. In order to overcome the communication challenges, we provide an open source communication protocol that we customize for edge computing applications, however, it can be used beyond the scope of LAMEN. Finally, we present three applications to show how LAMEN enables various application domains on the edge of the network. In summary, we propose a framework to orchestrate underutilized resources at the edge of the network towards processing data that are generated in their proximity. Using the approaches explained later in the dissertation, we show how LAMEN enhances the performance of applications and enables a new set of applications that were not feasible

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

Application of Machine Learning Techniques to Delay Tolerant Network Routing

This dissertation discusses several machine learning techniques to improve routing in delay tolerant networks (DTNs). These are networks in which there may be long one-way trip times, asymmetric links, high error rates, and deterministic as well as non-deterministic loss of contact between network nodes, such as interplanetary satellite networks, mobile ad hoc networks and wireless sensor networks. This work uses historical network statistics to train a multi-label classifier to predict reliable paths through the network. In addition, a clustering technique is used to predict future mobile node locations. Both of these techniques are used to reduce the consumption of resources such as network bandwidth, memory and data storage that is required by replication routing methods often used in opportunistic DTN environments. Thesis contributions include: an emulation tool chain developed to create a DTN test bed for machine learning, the network and software architecture for a machine learning based routing method, the development and implementation of classification and clustering techniques and performance evaluation in terms of machine learning and routing metrics