Shigang Chen - Böcker

Traffic Measurement on the Internet presents several novel online measurement methods that are compact and fast. Traffic measurement provides critical real-world data for service providers and network administrations to perform capacity planning, accounting and billing, anomaly detection, and service provision. Statistical methods play important roles in many measurement functions including: system designing, model building, formula deriving, and error analyzing. One of the greatest challenges in designing an online measurement function is to minimize the per-packet processing time in order to keep up with the line speed of the modern routers. This book also introduces a challenging problem – the measurement of per-flow information in high-speed networks, as well as, the solution. The last chapter discusses origin-destination flow measurement.

RFID (radio frequency identification) tags are becoming ubiquitously available in object tracking, access control, and toll payment. The current application model treats tags simply as ID carriers and deals with each tag individually for the purpose of identifying the object that the tag is attached to. The uniqueness of RFID as an Infrastructure is to change the traditional individual view to a collective view that treats universally-deployed tags as a new infrastructure, a new wireless platform on which novel applications can be developed. The book begins with an introduction to the problems of tag estimation and information collection from RFID systems, and explains the challenges. It discusses how to efficiently estimate the number of tags in a large RFID system, considering both energy cost and execution time. It then gives a detailed account on how to collect information from a sensor-augmented RFID network with new designs that significantly reduce execution time.

This book presents several compact and fast methods for online traffic measurement of big network data. It describes challenges of online traffic measurement, discusses the state of the field, and provides an overview of the potential solutions to major problems.The authors introduce the problem of per-flow size measurement for big network data and present a fast and scalable counter architecture, called Counter Tree, which leverages a two-dimensional counter sharing scheme to achieve far better memory efficiency and significantly extend estimation range. Unlike traditional approaches to cardinality estimation problems that allocate a separated data structure (called estimator) for each flow, this book takes a different design path by viewing all the flows together as a whole: each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. A framework of virtual estimators is designed to apply the idea of sharing to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work. To conclude, the authors discuss persistent spread estimation in high-speed networks. They offer a compact data structure called multi-virtual bitmap, which can estimate the cardinality of the intersection of an arbitrary number of sets. Using multi-virtual bitmaps, an implementation that can deliver high estimation accuracy under a very tight memory space is presented. The results of these experiments will surprise both professionals in the field and advanced-level students interested in the topic. By providing both an overview and the results of specific experiments, this book is useful for those new to online traffic measurement and experts on the topic.

This book introduces applications of RFID on the Internet of things, under the emerging technologies for tag search, anonymous RFID authentication, and identification of networked tags. A new technique called filtering vector (a compact data structure that encodes tag IDs) is proposed to enable tag filtration, meeting the stringent delay requirements for real-world applications. Based on filtering vectors, a novel iterative tag search protocol is designed, which progressively improves the accuracy of search result and reduces the time of each iteration by using the information learned from the previous iterations. Moreover, the protocol is extended to work under noisy channel.The authors also make a fundamental shift from the traditional design paradigm for anonymous RFID authentication by following an asymmetry design principle that pushes most complexity to the readers while leaving the tags as simple as possible. A novel technique is developed to dynamically generate random tokens on demand for authentication. The token-based authentication protocol only requires O(1) communication overhead and online computation overhead per authentication for both readers and tags.Finally, the authors investigate the problem of networked-tag identification. The traditional contention-based protocol design will incur too much energy overhead in multihop tag systems, and a reader-coordinated design that significantly serializes tag transmissions performs much better. In addition, a solution based on serial numbers is proposed to achieve load balancing, thereby reducing the worst-case energy cost among the tags.Designed for researchers and professionals, this SpringerBrief will interest individuals who work in efficiency, security, and privacy. Advanced-level students focused on network design will also benefit from the content.

This book presents several compact and fast methods for online traffic measurement of big network data. It describes challenges of online traffic measurement, discusses the state of the field, and provides an overview of the potential solutions to major problems.The authors introduce the problem of per-flow size measurement for big network data and present a fast and scalable counter architecture, called Counter Tree, which leverages a two-dimensional counter sharing scheme to achieve far better memory efficiency and significantly extend estimation range. Unlike traditional approaches to cardinality estimation problems that allocate a separated data structure (called estimator) for each flow, this book takes a different design path by viewing all the flows together as a whole: each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. A framework of virtual estimators is designed to apply the idea of sharing to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work. To conclude, the authors discuss persistent spread estimation in high-speed networks. They offer a compact data structure called multi-virtual bitmap, which can estimate the cardinality of the intersection of an arbitrary number of sets. Using multi-virtual bitmaps, an implementation that can deliver high estimation accuracy under a very tight memory space is presented. The results of these experiments will surprise both professionals in the field and advanced-level students interested in the topic. By providing both an overview and the results of specific experiments, this book is useful for those new to online traffic measurement and experts on the topic.