Future RFID technologies will go far beyond today's widely used passive tags. Battery-powered active tags are likely to gain more popularity due to their long operational ranges and richer on-tag resources. With integrated sensors, these tags can provide not only static identification numbers but also dynamic, real-time information such as sensor readings. This paper studies a general problem of how to design efficient polling protocols to collect such real-time information from a subset M of tags in a large RFID system. We show that the standard, straightforward polling design is not energy-efficient because each tag has to continuously monitor the wireless channel and receive O(|M|) tag IDs, which is energy-consuming. Existing work is able to cut the amount of data each tag has to receive by half through a coding design. In this paper, we propose a tag-ordering polling protocol (TOP) that can reduce per-tag energy consumption by more than an order of magnitude. We also reveal an energy-time tradeoff in the protocol design: per-tag energy consumption can be reduced to O(1) at the expense of longer execution time of the protocol. We then apply partitioned Bloom filters to enhance the performance of TOP, such that it can achieve much better energy efficiency without degradation in protocol execution time. Finally, we show how to configure the new protocols for time-constrained energy minimization.

Radio frequency identification (RFID) technologies are poised to revolutionize retail, warehouse, and supply chain management. One of their interesting applications is to automatically detect missing tags in a large storage space, which may have to be performed frequently to catch any missing event such as theft in time. Because RFID systems typically work under low-rate channels, past research has focused on reducing execution time of a detection protocol to prevent excessively long protocol execution from interfering normal inventory operations. However, when active tags are used for a large spatial coverage, energy efficiency becomes critical in prolonging the lifetime of these battery-powered tags. Furthermore, much of the existing literature assumes that the channel between a reader and tags is reliable, which is not always true in reality because of noise/interference in the environment. Given these concerns, this paper makes three contributions. First, we propose a novel protocol design that considers both energy efficiency and time efficiency. It achieves multifold reduction in both energy cost and execution time when compared to the best existing work. Second, we reveal a fundamental energy-time tradeoff in missing-tag detection, which can be flexibly controlled through a couple of system parameters in order to achieve desirable performance. Third, we extend our protocol design to consider channel error under two different models. We find that energy/time cost will be higher in unreliable channel conditions, but the energy-time tradeoff relation persists.

Bloom filters have been extensively applied in many network functions. Their performance is judged by three criteria: query overhead, space requirement, and false positive ratio. Due to wide applicability, any improvement to the performance of Bloom filters can potentially have a broad impact in many areas of networking research. In this paper, we study Bloom-1, a data structure that performs membership check in one memory access, which compares favorably with the k memory accesses of a standard Bloom filter. We also generalize Bloom-1 to Bloom-g and Bloom-Q, allowing performance tradeoff between membership query overhead and false positive ratio. We thoroughly examine the variants in this family of filters, and show that they can be configured to outperform the Bloom filters with a smaller number of memory accesses, a smaller or equal number of hash bits, and a smaller or comparable false positive ratio in practical scenarios. We also perform experiments based on a real traffic trace to support our filter design.

Compared to the classical barcode system, radio frequency identification (RFID) extends the operational distance from inches to a number of feet (passive RFID tags) or even hundreds of feet (active RFID tags). Their wireless transmission, processing, and storage capabilities enable them to support full automation of many inventory management functions in industry. This paper studies the practically important problem of monitoring a large set of active RFID tags and identifying the missing ones-the objects that the missing tags are associated with are likely to be missing as well. This monitoring function may need to be executed frequently and therefore should be made efficient in terms of execution time in order to avoid disruption of normal inventory operations. Based on probabilistic methods, we design a series of missing-tag identification protocols that employ novel techniques to reduce the execution time. Our best protocol reduces the time for detecting the missing tags by an order of magnitude when compared to existing protocols.

Spreader classification is an online traffic measurement function that has many important applications. In order to keep up with ever-higher line speed, the recent research trend is to implement such functions in fast but small on-die SRAM. However, the mismatch between the huge amount of Internet traffic to be monitored and limited on-die memory space presents a significant technical challenge. In this paper, we propose an Efficient Spreader Classification (ESC) scheme based on dynamic bit sharing, a compact information storage method. We design a maximum likelihood estimation method to extract per-source information from the compact storage and determine the heavy spreaders. Our new scheme ensures that false positive/negative ratios are bounded. Moreover, given an arbitrary set of bounds, we develop a systematic approach to determine the optimal system parameters that minimize the amount of memory needed to meet the bounds. Experiments based on a real Internet traffic trace demonstrate that the proposed spreader classification scheme reduces memory consumption by 3–20 times when compared to the best existing work. We also investigate a new multi-objective spreader classification problem and extend our classification scheme to solve it.

An origin-destination (OD) flow between two routers is the set of packets that pass both routers in a network. Measuring the sizes of OD flows is important to many network management applications such as capacity planning, traffic engineering, anomaly detection, and network reliability analysis. Measurement efficiency and accuracy are two main technical challenges. In terms of efficiency, we want to minimize per-packet processing overhead to accommodate future routers that have extremely high packet rates. In terms of accuracy, we want to generate precise measurement results with small bias and standard deviation. To meet these challenges, we design a new measurement method that employs a compact data structure for packet information storage and uses a novel statistical inference approach for OD-flow size estimation. We perform simulations to demonstrate the effectiveness of our method.

Radio Frequency IDentification (RFID) technology has been adopted in many applications, such as inventory control, object tracking, theft prevention, and supply chain management. Privacy-preserving authentication in RFID systems is a very important problem. Existing protocols employ tree structures to achieve fast authentication. We observe that these protocols require a tag to transmit a large amount of data in each authentication, which costs significant bandwidth and energy overhead. Current protocols also impose heavy computational demand on the RFID reader. To address these issues, we design two privacy-preserving protocols based on a new technique called cryptographical encoding, which significantly reduces both authentication data transmitted by each tag and computation overhead incurred at the reader. Our analysis shows that the new protocols are able to reduce authentication data by more than an order of magnitude and reduce computational demand by about an order of magnitude, when comparing with the best existing protocol.

RFID (radio frequency identification) technologies are poised to revolutionize retail, warehouse and supply chain management. One of their interesting applications is to automatically detect missing tags (and the associated objects) in a large storage space. In order to timely catch any missing event such as theft, the detection operation may have to be performed frequently. Because RFID systems typically work under low-rate channels, past research has focused on reducing execution time of a detection protocol, in order to prevent excessively-long protocol execution from interfering normal inventory operations. However, when active tags are used to provide a large spatial coverage, energy efficiency becomes critical in prolonging the lifetime of these battery-powered tags. Existing literature lacks thorough study on how to conserve energy in the process of missing-tag detection and how to jointly optimize energy efficiency and time efficiency. This paper makes two important contributions: First, we propose a novel protocol design that takes both energy efficiency and time efficiency into consideration. It achieves multi-fold reduction in both energy cost and execution time when comparing with the best existing work. In some cases, the reduction is more than an order of magnitude. Second, we reveal a fundamental energy-time tradeoff in missing-tag detection. Through our analytical framework, we are able to flexibly control the tradeoff through a couple of system parameters in order to achieve desirable performance.

Traffic measurement provides critical real-world data for service providers and network administrators to perform capacity planning, accounting and billing, anomaly detection, and service provision. One of the greatest challenges in designing an online measurement module is to minimize the per-packet processing time in order to keep up with the line speed of the modern routers. To meet this challenge, we should minimize the number of memory accesses per packet and implement the measurement module in the on-die SRAM. The small size of SRAM requires extremely compact data structures to be designed for storing per-flow information. The best existing work, called counter braids, requires more than 4 bits per flow and performs six or more memory accesses per packet. In this paper, we design a fast and compact measurement function that estimates the sizes of all flows. It achieves the optimal processing speed: two memory accesses per packet. In addition, it provides reasonable measurement accuracy in a tight space where the counter braids no longer work. Our design is based on a new data encoding/decoding scheme, called randomized counter sharing. This scheme allows us to mix per-flow information together in storage for compactness and, at the decoding time, separate the information of each flow through statistical removal of the error introduced during information mixing from other flows. The effectiveness of our online per-flow measurement approach is analyzed and confirmed through extensive experiments based on real network traffic traces. We also propose several methods to increase the estimation range of flow sizes.

Radio frequency identification (RFID) has been gaining popularity for inventory control, object tracking, and supply-chain management in warehouses, retail stores, hospitals, etc. Periodically and automatically estimating the number of RFID tags deployed in a large area has many important applications in inventory management and theft detection. Prior works focus on designing time-efficient algorithms that can estimate tens of thousands of tags in seconds. We observe that for an RFID reader to access tags in a large area, active tags are likely to be used due to their longer operational ranges. These tags are battery-powered and use their own energy for information transmission. However, recharging batteries for tens of thousands of tags is laborious. Hence, conserving energy for active tags becomes critical. Some prior works have studied how to reduce energy expenditure of an RFID reader when it reads tag IDs. We study how to reduce the amount of energy consumed by active tags during the process of estimating the number of tags in a system. We design two energy-efficient probabilistic estimation algorithms that iteratively refine a control parameter to optimize the information carried in transmissions from tags, such that both the number and the size of transmissions are reduced. These algorithms can also take time efficiency into consideration. By tuning a contention probability parameter$\omega$, the new algorithms can make tradeoff between energy cost and estimation time.

Traffic measurement provides critical real-world data for service providers and network administrators to perform capacity planning, accounting and billing, anomaly detection, and service provision. One of the greatest challenges in designing an online measurement module is to minimize the per-packet processing time in order to keep up with the line speed of the modern routers. To meet this challenge, we should minimize the number of memory accesses per packet and implement the measurement module in the on-die SRAM. The small size of SRAM requires extremely compact data structures to be designed for storing per-flow information. The best existing work, called counter braids, requires more than 4 bits per flow and performs 6 or more memory accesses per packet. In this paper, we design a fast and compact measurement function that estimates the sizes of all flows. It achieves the optimal processing speed: 2 memory accesses per packet. In addition, it provides reasonable measurement accuracy in a tight space where the counter braids no longer work. Our design is based on a new data encoding/decoding scheme, called randomized counter sharing. This scheme allows us to mix per-flow information together in storage for compactness and, at the decoding time, separate the information of each flow through statistical removal of the error introduced during information mixing from other flows. The effectiveness of our online per-flow measurement approach is analyzed and confirmed through extensive experiments based on real network traffic traces.

Scan detection is one of the most important functions in intrusion detection systems. In order to keep up with the ever-higher line speed, recent research trend is to implement scan detection in fast but small SRAM. This leads to a difficult technical challenge because the amount of traffic to be monitored is huge but the on-die memory space for performing such a monitoring task is very limited. We propose an efficient scan detection scheme based on dynamic bit sharing, which incorporates probabilistic sampling and bit sharing for compact information storage. We design a maximum likelihood estimation method to extract persource information from the shared bits in order to determine the scanners. Our new scheme ensures that the false positive/false negative ratios are bounded with high probability. Moreover, given an arbitrary set of bounds, we develop a systematic approach to determine the optimal system parameters that minimize the amount of memory needed to meet the bounds. Experiments based on a real Internet traffic trace demonstrate that the proposed scan detection scheme reduces memory consumption by three to twenty times when comparing with the best existing work.

RFID tags have many important applications in automated warehouse management. One example is to monitor a set of tags and detect whether some tags are missing - the objects to which the missing tags are attached are likely to be missing, too, due to theft or administrative error. Prior research on this problem has primarily focused on efficient protocols that reduce the execution time in order to avoid disruption of normal inventory operations. This paper makes several new advances. First, we observe that the existing protocol is far from being optimal in terms of execution time. We are able to cut the execution time to a fraction of what is currently needed. Second, we study the missing-tag detection problem from a new energy perspective, which is very important when battery-powered active tags are used. The new insight provides flexibility for the practitioners to meet their energy and time requirements.

The Bloom filters have been extensively applied in many network functions. Their performance is judged by three criteria: processing overhead, space overhead, and false positive ratio. Due to wide applicability, any improvement to the performance of Bloom filters can potentially have broad impact in many areas of networking research. In this paper, we propose Bloom-1, a new data structure that performs membership check in one memory access, which compares favorably with the k memory accesses of a classical Bloom filter. We also generalize Bloom-1 to Bloom-g, allowing performance tradeoff between membership query overhead and false positive ratio. We thoroughly examine the variants in this new family of filters, and show that they can be configured to outperform the Bloom filters with a smaller number of memory accesses, a smaller or equal number of hash bits, and a smaller and comparable false positive ratio in practical scenarios. We also perform experiments based on a real traffic trace to support our new filter design.

Future RFID technologies will go far beyond today's widely-used passive tags. Battery-powered active tags are likely to gain more popularity due to their long operational ranges and richer on-tag resources. With integrated sensors, these tags can provide not only static identification numbers but also dynamic, real-time information such as sensor readings. This paper studies a general problem of how to design efficient polling protocols to collect such real-time information from a subset M of tags in a large RFID system. We show that the standard, straightforward polling design is not energy-efficient because each tag has to continuously monitor the wireless channel and receive O(|M|) tag IDs, which is energy-consuming. Existing work is able to cut the amount of data each tag has to receive by half through a coding design. In this paper, we propose a tag-ordering polling protocol (TOP) that can reduce per-tag energy consumption by more than an order of magnitude. We also reveal an energy-time tradeoff in the protocol design: per-tag energy consumption can be reduced to O(1) at the expense of longer execution time of the protocol. Finally, we apply partitioned Bloom filters to enhance the performance of TOP, such that it can achieve much better energy efficiency without degradation in protocol execution time.

The spread of a source host is the number of distinct destinations that it has sent packets to during a measurement period. A spread estimator is a software/hardware module on a router that inspects the arrival packets and estimates the spread of each source. It has important applications in detecting port scans and distributed denial-of-service (DDoS) attacks, measuring the infection rate of a worm, assisting resource allocation in a server farm, determining popular Web contents for caching, to name a few. The main technical challenge is to fit a spread estimator in a fast but small memory (such as SRAM) in order to operate it at the line speed in a high-speed network. In this paper, we design a new spread estimator that delivers good performance in tight memory space where all existing estimators no longer work. The new estimator not only achieves space compactness, but operates more efficiently than the existing ones. Its accuracy and efficiency come from a new method for data storage, called virtual vectors, which allow us to measure and remove the errors in spread estimation. We also propose several ways to enhance the range of spread values that the estimator can measure. We perform extensive experiments on real Internet traces to verify the effectiveness of the new estimator .

RFID promises to revolutionize the inventory management in large warehouses, retail stores, hospitals, transportation systems, etc. Periodically reading the IDs of the tags is an important function to guard against administration error, vendor fraud and employee theft. Given the low-speed communication channel in which a RFID system operates, the reading throughput is one of the most important performance metrics. The current protocols have reached the physical throughput limit that can possibly be achieved based on their design methods. To break that limit, we have to apply fundamentally different approaches. This paper investigates how much throughput improvement the analog network coding can bring when it is integrated into the RFID protocols. The idea is to extract useful information from collision slots when multiple tags transmit their IDs simultaneously. Traditionally, those slots are discarded. With analog network coding, we show that a collision slot is almost as useful as a non-collision slot in which exactly one tag transmits. We propose the framed collision-aware tag identification protocol that optimally applies analog network coding to maximize the reading throughput, which is 51.1% ~ 70.6% higher than the best existing protocols.

RFID has been gaining popularity for inventory control, object tracking, and supply chain management in warehouses, retail stores, hospitals, etc. Periodically and automatically estimating the number of RFID tags deployed in a large area has many important applications in inventory management and theft detection. The prior work focuses on designing time-efficient algorithms that can estimate tens of thousands of tags in seconds. We observe that, for a RFID reader to access tags in a large area, active tags are likely to be used. These tags are battery-powered and use their own energy for information transmission. However, recharging batteries for tens of thousands of tags is laborious. Unlike the prior work, this paper studies the RFID estimation problem from the energy angle. Our goal is to reduce the amount of energy that is consumed by the tags during the estimation procedure. We design several energy-efficient probabilistic algorithms that iteratively refine a control parameter to optimize the information carried in the transmissions from the tags, such that both the number and the size of the transmissions are minimized.

Comparing with the classical barcode system, RFID extends the operational distance from inches to a number of feet (passive RFID tags) or even hundreds of feet (active RFID tags). Their wireless transmission, processing and storage capabilities enable them to support the full automation of many inventory management functions in the industry. This paper studies the practically important problem of monitoring a large set of RFID tags and identifying the missing ones - the objects that the missing tags are associated with are likely to be missing, too. This monitoring function may need to be executed frequently and therefore should be made efficient in terms of execution time, in order to avoid disruption of normal inventory operations. Based on probabilistic methods, we design a series of missing-tag identification protocols that employ novel techniques to reduce the execution time. Our best protocol reduces the time for detecting the missing tags by 88.9% or more, when comparing with existing protocols.

The spread of a source host is the number of distinct destinations that it has sent packets to during a measurement period. A spread estimator is a software/hardware module on a router that inspects the arrival packets and estimates the spread of each source. It has important applications in detecting port scans and DDoS attacks, measuring the infection rate of a worm, assisting resource allocation in a server farm, determining popular Web contents for caching, to name a few. The main technical challenge is to fit a spread estimator in a fast but small memory (such as SRAM) in order to operate it at the line speed in a high-speed network. In this paper, we design a new spread estimator that delivers good performance in tight memory space where all existing estimators no longer work. The new estimator not only achieves space compactness but operates more efficiently than the existing ones. Its accuracy and efficiency come from a new method for data storage, called virtual vectors, which allow us to measure and remove the errors in spread estimation. We perform experiments on real Internet traces to verify the effectiveness of the new estimator.