Determining an object’s spatial pose (including orientation and position) plays a fundamental role in a variety of applications, such as automatic assembly, indoor navigation, and robot driving. In this paper, we design a fine-grained pose sensing system called Tag-Compass that attaches a single tag to an object (whose size may be small) and identifies the tagged object’s pose by determining the spatial orientation and position of the tag. We exploit thepolarizationproperties of the RF waves used in the communications between an RFID reader and the tag on the object. Polarization mismatch between the tag and the reader’s antenna affects the received signal strength at the reader. From the measured signal strength values, we are able to deduce the tag’s pose through a series of transformations and deviation minimization. We propose a system design for Tag-Compass and implement a prototype. We evaluate the performance of Tag-Compass through extensive experiments using the prototype. The experimental results show that Tag-Compass provides accurate estimate of object orientation with a median error of just 2.5° when the tag’s position is known and a median error of 3.8° when the tag’s position is unknown. In the latter case, Tag-Compass will provide an estimate of tag position as a byproduct of orientation sensing, with an accuracy comparable to the state of the art. It is practically appealing to find both the orientation and the position of an object using a single method, instead of having to deploy two different methods.

Traditional radio frequency identification (RFID) technologies allow tags to communicate with a reader but not among themselves. By enabling peer-to-peer communications among nearby tags, the emerging networked tags make a fundamental enhancement to today's RFID systems. This new capability supports a series of system-level functions in previously infeasible scenarios where the readers cannot cover all tags due to cost or physical limitations. This paper makes the first attempt to design a new communication model that is specifically tailored to efficient implementation of system-level functions in networked tag systems, in terms of energy cost and execution time. Instead of exploiting complex mechanisms for collision detection and resolution, we propose a collision-resistant communication model (CCM) that embraces the collision in tag communications and utilizes it to merge the data from different sources in a benign way. Two fundamental applications: RFID estimation and missing-tag detection, are presented to illustrate how CCM assists efficient system-level operations in networked tag systems. Simulation results show that the system-level applications through CCM are able to reduce the energy cost and execution time by one order of magnitude, compared with the ID-collection based solution.

In RFID-enabled applications, when a tag is put into use and associated with a specific object, the category-related information (e.g., the brands of clothes) about this object might be preloaded into the tag’s memory for the purpose of live query. Since such information reflects category attributes, all tags in the same category carry identical category information. To collect this information, we do not need to repeatedly interrogate each tag; one tag’s response in a category is sufficient. In this paper, we investigate the problem of category information collection in a multi-category RFID system, which is referred to asinformation sampling. We propose two time-efficiency protocols. The first is a two-phase sampling protocol (TPS) that works in the case of knowing tag IDs. By quickly zooming into a category and isolating a tag from this category, TPS is able to sample a category with small overhead. The second protocol, called back-and-forth sampling protocol (BFS), relaxes a key assumption in TPS and performs the sampling task efficiently without knowing any tag IDs or category IDs. By carrying out a step-forward frame and using the step-backward scheme, BFS is able to interrogate only 1.45 tags (close to the lower bound of one tag) on average for each category. We theoretically analyze the protocol performance of TPS and BFS and discuss the optimal parameter settings that minimize the overall execution time. Extensive simulations show that both the protocols outperform the benchmark, greatly improving the sampling performance.

Radio-frequency identification (RFID) technology has been widely adopted in various industries and people's daily lives. This paper studies a fundamental function of spatial-temporal joint cardinality estimation in distributed RFID systems. It allows a user to make queries over multiple tag sets that are present at different locations and times in a distributed tagged system. It estimates the joint cardinalities of those tag sets with bounded error. This function has many potential applications for tracking product flows in large warehouses and distributed logistics networks. The prior art is either limited to jointly analyzing only two tag sets or is designed for a relative accuracy model, which may cause unbounded time cost. Addressing these limitations, we propose a novel design of the joint cardinality estimation function with two major components. The first component is to record snapshots of the tag sets in a system at different locations and periodically, in a time-efficient way. The second component is to develop accurate estimators that extract the joint cardinalities of chosen tag sets based on their snapshots, with a bounded error that can be set arbitrarily small. We formally analyze the bias and variance of the estimators, and we develop a method for setting their optimal system parameters. The simulation results show that, under predefined accuracy requirements, our new solution reduces time cost by multiple folds when compared with the existing work.

RFID technologies are making their way into numerous applications, including inventory management, supply chain, product tracking, transportation, logistics, etc. One important application is to automatically detect anomalies in RFID systems, such as missing tags, unknown tags, or cloned tags due to theft, management error, or targeted attacks. Existing solutions are all designed to detect a certain type of RFID anomalies, but lack a general functionality for detecting different types of anomalies. This paper attempts to propose a general framework for anomaly detection in RFID systems, thereby reducing the complexity for readers and tags to implement different anomaly-detection protocols. We introduce a new concept of differential Bloom filter (DBF), which turns physical-layer signal data into a segmented Bloom filter that encodes the IDs of abnormal tags. As a case study, we propose a protocol that builds DBF for identifying all missing tags in an efficient way. We implement a prototype for missing-tag identification using USRP and WISP tags to verify the effectiveness our protocol, and use large-scale simulations for performance evaluation. The results show that our solution can significantly improve time efficiency, when comparing with the best existing work.

Identifying an object's spatial direction (or orientation) plays a fundamental role in a variety of applications, such as automatic assembly, indoor navigation, and robot driving. In this paper, we design a fine-grained direction finding system called Tag-Compass that attaches a single tag to an object (whose size may be small) and identifies the tagged object's orientation by determining the spatial direction of the tag. We exploit the polarization properties of the RF waves used in the communications between an RFID reader and the tag on the object. Polarization mismatch between the tag and the reader's antenna affects the received signal strength at the reader. From the measured signal strength values, we are able to deduce the tag's direction through a series of transformations and deviation minimization. We propose a system design for Tag-Compass and implement a prototype. We evaluate the performance of TagCompass through extensive experiments using the prototype. The experimental results show that Tag-Compass provides accurate direction estimates with a median error of just 2.5° when the tag's position is known and a median error of 3.8° when the tag's position is unknown.

Per-flow traffic measurement, which is to count the number of packets for each active flow during a certain measurement period, has many applications in traffic engineering, classification of routing distribution or network usage pattern, service provision, anomaly detection, and network forensics. In order to keep up with the high throughput of modern routers or switches, the online module for per-flow traffic measurement should use high-bandwidth SRAM that allows fast memory accesses. Due to limited SRAM space, exact counting, which requires to keep a counter for each flow, does not scale to large networks consisting of numerous flows. Some recent work takes a different approach to estimate the flow sizes using counter architectures that can fit into tight SRAM. However, existing counter architectures have limitations, either still requiring considerable SRAM space or having a small estimation range. In this paper, we design a scalable counter architecture called Counter Tree, which leverages a 2-D counter sharing scheme to achieve far better memory efficiency and in the meantime extend estimation range significantly. Furthermore, we improve the performance of Counter Tree by adding a status bit to each counter. Extensive experiments with real network traces demonstrate that our counter architecture can produce accurate estimates for flows of all sizes under very tight memory space.

Radio-frequency identification (RFID) technologies are making their way into retail products, library books, debit cards, passports, driver licenses, car plates, medical devices, and so on. The widespread use of tags in traditional ways of deployment raises a privacy concern: they make their carriers trackable. To protect the privacy of the tag carriers, we need to invent new mechanisms that keep the usefulness of tags while doing so anonymously. Many tag applications, such as toll payment, require authentication. This paper studies the problem of anonymous authentication. Since low-cost tags have extremely limited hardware resource, we propose an asymmetric design principle that pushes most complexity to more powerful RFID readers. With this principle, we develop a lightweight technique that generates dynamic tokens for anonymous authentication. Instead of implementing complicated and hardware-intensive cryptographic hash functions, our authentication protocol only requires tags to perform several simple and hardware-efficient operations such as bitwise XOR, one-bit left circular shift, and bit flip. The theoretical analysis and randomness tests demonstrate that our protocol can ensure the privacy of the tags. Moreover, our protocol reduces the communication overhead and online computation overhead to O(1) per authentication for both tags and readers, which compares favorably with the prior art.

Traditional radio frequency identification (RFID) technologies allow tags to communicate with a reader but not among themselves. By enabling peer communications between nearby tags, the emerging networked tags represent a fundamental enhancement to today's RFID systems. They support applications in previously infeasible scenarios where the readers cannot cover all tags due to cost or physical limitations. This paper is the first study on identifying state-free networked tags, which is a basic, fundamental function for most tagged systems. To prolong the lifetime of networked tags and make identification protocols scalable to large systems, energy efficiency and time efficiency are most critical. Our investigation reveals that the traditional contention-based protocol design will incur too much energy overhead in multihop tag systems. Surprisingly, a reader-coordinated design that serializes tag transmissions performs much better. In addition, we show that load balancing is important in reducing the worst case energy cost to the tags, and we present a solution based on serial numbers. We also show that, by leveraging the request aggregation and transmission pipelining techniques, the time efficiency of serialized ID collection can be greatly improved.

Radio frequency identification (RFID) technology has been widely used in Applications, such as inventory control, object tracking, and supply chain management. In this domain, an important research problem is called RFID cardinality estimation, which focuses on estimating the number of tags in a certain area covered by one or multiple readers. This paper extends the research in both temporal and spatial dimensions to provide much richer information about the dynamics of distributed RFID systems. Specifically, we focus on estimating the cardinalities of the intersection/differences/union of two arbitrary tag sets (called joint properties for short) that exist in different spatial or temporal domains. With many practical applications, there is, however, little prior work on this problem. We will propose a joint RFID estimation protocol that supports adaptive snapshot construction. Given the snapshots of any two tag sets, although their lengths may be very different depending on the sizes of tag sets they encode, we design a way to combine their information and more importantly, derive closed-form formulas to use the combined information and estimate the joint properties of the two tag sets, with an accuracy that can be arbitrarily set. By formal analysis, we also determine the optimal system parameters that minimize the execution time of taking snapshots, under the constraints of a given accuracy requirement. We have performed extensive simulations, and the results show that our protocol can reduce the execution time by multiple folds, as compared with the best alternative approach in literature.

For many practical applications, it is a fundamental problem to estimate the flow cardinalities over big network data consisting of numerous flows (especially a large quantity of mouse flows mixed with a small number of elephant flows, whose cardinalities follow a power-law distribution). Traditionally the research on this problem focused on using a small amount of memory to estimate each flow's cardinality from a large range (up to ${10}^{{9}}$ ). However, although the memory needed for each individual flow has been greatly compressed, when there is an extremely large number of flows, the overall memory demand can still be very high, exceeding the availability under some important scenarios, such as implementing online measurement modules in network processors using only on-chip cache memory. In this paper, instead of allocating a separated data structure (called estimator) for each flow, we take a different 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. We discover that sharing at the multi-bit register level is superior than sharing at the bit level. We propose a unified framework of virtual estimators that allows us to apply the idea of sharing to an array of cardinality estimation solutions, e.g., HyperLogLog and PCSA, achieving far better memory efficiency than the best existing work. Our experiment shows that the new solution can work in a tight memory space of less than 1 bit per flow or even one tenth of a bit per flow - a quest that has never been realized before.

Per-flow traffic measurement is a fundamental problem in the era of big network data, providing critical information for many practical applications including capacity planning, traffic engineering, data accounting, resource management, and scan/intrusion detection in modern computer networks. It is challenging to design highly compact data structures for approximate per-flow measurements. In this paper, we show that a highly compact virtual counter architecture can achieve fast processing speed (slightly more than 1 memory access per packet) and provide accurate measurement results under tight memory allocation. Extensive experiments based on real network trace data demonstrate its superior performance over the best existing work.

Radio-frequency identification (RFID) technologies have been widely used in many applications, including inventory management, supply chain, product tracking, transportation, logistics, etc. Tag estimation, which is to estimate the cardinality of a single tag set, is an important research topic. This paper expands the estimation research as follows: It performs joint estimation between two tag sets (which exist at different locations or at the same location but different times). More importantly the estimation is fine-grained in an effort to accommodate common practical scenarios, where each tag set consists of tags belonging to different categories. For any two given tag sets, we want to know the detailed information about the joint property of each category, instead of just the aggregate information of the whole sets. Furthermore, due to the open nature of RFID communications, it is often desirable that tag estimation can be performed in an anonymous way without revealing the tags' ID information. To support these requirements, we develop a new technique called mask bitmap that can encode a tag set without requiring the tags to report their IDs or category IDs. Any two mask bitmaps of different tag sets can be combined to perform category-level joint estimation. Through formal analysis, we determine how to set system parameters to meet a given accuracy requirement that can be arbitrarily set. Extensive simulation results confirm that the proposed solution can yield accurate category-level estimates in an efficient way, and preserve tags' anonymity as well.

Traditional radio frequency identification (RFID) technologies allow tags to communicate with a reader but not among themselves. By enabling peer-to-peer communications among nearby tags, the emerging networked tags make a fundamental enhancement to today's RFID systems. This new capability supports a series of system-level functions in previously infeasible scenarios where the readers cannot cover all tags due to cost or physical limitations. This paper makes the first attempt to design a new communication model that is specifically tailored to efficient implementation of system-level functions in networked tag systems. Instead of exploiting complex mechanisms for collision detection and resolution, we propose a collision-resistent communication model (CCM) that embraces the collision in tag communications and utilizes it to merge the data from different sources in a benign way. Two fundamental applications: RFID estimation and missing-tag detection, are presented to illustrate how CCM assists efficient system-level operations in networked tag systems. Simulation results show that system-level applications through CCM outperform those through ID collection.

Radio-frequency identification (RFID) technologies have been widely used in many applications. Tag estimation, which is to estimate the cardinality of a single tag set, is an important research topic. This paper expands the estimation research as follows: It performs joint estimation between two tag sets, and more importantly the estimation is fine-grained in an effort to accommodate common practical scenarios, where each tag set consists of tags belonging to different categories. For any two given tag sets, we want to know the detailed information about the joint property of each category, instead of just the aggregate information of the whole sets. Furthermore, due to the open nature of RFID communications, it is often desirable that category-level joint estimation can be performed in an anonymous way without revealing the tags' IDs. To support these requirements, we develop a new technique called mask bitmap that can encode a tag set without requiring the tags to report their IDs or category IDs. Any two mask bitmaps that encode different tag sets can be combined to perform category-level joint estimation. Simulation results confirm that the proposed solution can yield accurate category-level estimates and preserve tags' anonymity.

Radio-frequency identification (RFID) technology has been widely adopted by real-world industries. This paper presents a new application for distributively deployed RFID systems, wherein a user chooses multiple tag sets at will from different spatial or temporal domains, and then connects them by set operators (union, intersection and relative complement) to form a set expression. The user is allowed to query for the cardinality of an arbitrary set expression, which is called the joint property of multiple sets. We focus on the problem of estimating the joint property with bounded error, which has many potential applications. One of them is to allow users to check the number of tags in an arbitrary tag flow passing through a distributed RFID system. For this problem, we propose a solution with a novel design that supports versatile snapshot construction: Given the snapshots of multiple tag sets, although their lengths may be very different, our formulas can estimate their joint properties, with an accuracy that can be arbitrarily set. For the proposed estimator, we formally analyze its bias and variance, and also the optimal settings of protocol parameters to minimize the time cost of taking a snapshot of a tag set. The simulation results show that, under predefined accuracy requirement, our solution can reduce time cost by multiple folds as compared with existing works named DiffEstm and CCF, which require all tag sets must be encoded into snapshots with an equal length.

Radio frequency identification (RFID) technology has many applications in inventory management, supply chain, product tracking, transportation, and logistics. One research issue of practical importance is to search for a particular group of tags in a large-scale RFID system. Time efficiency is a crucial factor that must be considered when designing a tag search protocol to ensure its execution will not interfere with other normal inventory operations. In this paper, we design a new technique called filtering vector, which can significantly reduce transmission overhead during search process, thereby shortening search time. Based on this technique, we propose an iterative tag search protocol. In each round, we filter out some tags and eventually terminate the search process when the search result meets the accuracy requirement. Furthermore, we extend our protocol to work under noisy channel. The simulation results demonstrate that our protocol performs much better than the best existing work.

The grouping problem in RFID systems is to efficiently group all tags according to a given partition such that tags in the same group will have the same group ID. Unlike previous research on unicast transmission from a reader to a tag, grouping provides a fundamental mechanism for efficient multicast transmissions and aggregate queries in large RFID-enabled applications. A message can be transmitted to a group of$m$tags simultaneously in multicast, which improves the efficiency by$m$times when comparing with unicast. This paper studies this practically important but not yet thoroughly investigated grouping problem in large RFID system. We start with a straightforward solution called the Enhanced Polling Grouping EPG protocol. We then propose a time-efficient Filter Grouping FIG protocol that uses Bloom filters to remove the costly ID transmissions. We point out the limitation of the Bloom-filter based solution due to its intrinsic false positive problem, which leads to our final ConCurrent Grouping CCG protocol. With a drastically different design, CCG is able to outperform FIG by exploiting collisions to inform multiple tags of their group ID simultaneously and by removing any wasteful slots in its frame-based execution. We further enhance CCG to make it perform better with very large groups. Simulation results demonstrate that our best protocol CCG can reduce the execution time by a factor of 11 when comparing with a baseline polling protocol.

Most existing research adopts a “flat” view of radio frequency identification (RFID) systems to perform various functions of collecting tag IDs, estimating the number of tags, detecting the missing tags, etc. However, in practice, tags are often attached to objects of different groups, which may represent different product types in a warehouse, different book categories in a library, etc. As we move from a flat view to an organized group view, there arise many interesting problems. One of them, called multigroup threshold-based classification, is the focus of this paper. It is to determine whether the number of objects in each group is above or below a prescribed threshold value. Solving this problem is important for inventory tracking applications. If the number of groups is very large, it will be inefficient to measure the groups one at a time. The best existing solution for multigroup threshold-based classification is based on generic group testing, whose design is however geared toward detecting a small number of populous groups. Its performance degrades quickly when the number of groups above the threshold becomes large. In this paper, we propose a new classification protocol based on tag sampling and logical bitmaps. It achieves high efficiency by measuring all groups in a mixed fashion. In the meantime, we show that the new method is able to perform threshold-based classification with an accuracy that can be preset to any desirable level, allowing tradeoff between time efficiency and accuracy.

Radio frequency identification (RFID) technologies are widely used in many applications. The widespread use of tags in traditional ways of deployment raises a privacy concern: They make their carriers track able. This paper studies the problem of anonymous authentication. Due to resource constraints of low-cost tags, we develop a new technique to generate dynamic tokens for anonymous authentication by following an asymmetric design principle that pushes most complexity to more powerful RFID readers. Instead of implementing complicated cryptographic hash functions, our authentication protocol only requires tags to perform several simple hardware-efficient operations such as bitwise XOR, one-bit left circular shift and bit flip. Moreover, our protocol reduces the communication overhead and online computation overhead to O(1) per authentication for both tags and readers, which compares favorably with the prior art.

Searching for widespread events in large networks is a fundamental function that underlies many important applications of distributed anomaly detection, traffic measurement, online data mining, etc. This function can be performed by a cooperative monitoring system consisting of a central coordinator and a number of monitors that are deployed at a set of vantage points. We formulate a network primitive function, called multi-monitor joint detection, which is to find the common events observed by all or a given subset of monitors during each measurement period. It is a challenging problem because large-scale cooperative monitoring can generate tremendous communication overhead. Therefore, it is critical to design a solution for multi-monitor joint detection which controls communication overhead to a low level. We thoroughly examine existing techniques that may be applied, and identify their performance limitations. We then propose two new techniques, called combinable filters and progressive filtering, which address the performance limitations from different angles. We formally prove the correctness of our new solutions based on a probabilistic joint detection model. Numerical evaluation shows that our best solution achieves an overhead reduction in the range of 63% to 91% over the Bloom filter solution under various simulation settings when the number of monitors is 10 or more.

Per-flow traffic measurement, which is to count the number of packets for each active flow during a certain measurement period, has many applications in usage accounting, traffic engineering, service provision and anomaly detection. In order to maintain the high throughput of routers or switchers, the per-flow traffic measurement module should use high-bandwidth SRAM that allows fast memory accesses. Due to the limited SRAM space, exact counting, which requires to keep a counter for each flow, does not scale to large networks consisting of numerous flows. Some recent work takes a different path to accurately estimate the flow sizes using counter architectures that can fit into tight SRAM. However, existing counter architectures have some limitations, either still requiring considerable SRAM space, or having a very small estimation range. In this paper, we design a scalable counter architecture Counter Tree which leverages a two-dimensional counter sharing scheme to achieve far better memory efficiency and significantly extend estimation range. The extensive experiments with real network trace demonstrate that our counter architecture can produce accurate estimates for flows of all sizes even under a very tight memory space, e.g., 2 bits per flow.

Radio frequency identification (RFID) technologies are making their way into retail products, library books, debit cards, passports, driver licenses, car plates, medical devices, etc. The widespread use of tags in traditional ways of deployment raises a privacy concern: They make their carriers trackable. To protect the privacy of the tag carriers, we need to invent new mechanisms that keep the usefulness of tags while doing so anonymously. Many tag applications such as toll payment require authentication. This paper studies the problem of anonymous authentication. Since low-cost tags have extremely limited hardware resource, we propose an asymmetric design principle that pushes most complexity to more powerful RFID readers. Thus, we develop a lightweight technique that generates dynamic tokens for anonymous authentication. Instead of implementing complicated and hardware-intensive cryptographic hash functions, our authentication protocol only requires tags to perform several simple and hardware-efficient operations such as bitwise XOR, one-bit left circular shift, and bit flip. The theoretic analysis and randomness tests demonstrate that our protocol can ensure the privacy of the tags. Moreover, our protocol reduces the communication overhead and online computation overhead to O(1) per authentication for both tags and readers, which compares favorably with the prior art.

Traditional radio frequency identification (RFID) technologies allow tags to communicate with a reader but not among themselves. By enabling peer communications between nearby tags, the emerging networked tags represent a fundamental enhancement to today's RFID systems. They support applications in previously infeasible scenarios where the readers cannot cover all tags due to cost or physical limitations. This paper is the first study on identifying state-free networked tags, which is a basic, fundamental function for most tagged systems. To prolong the lifetime of networked tags and make identification protocols scalable to large systems, energy efficiency and time efficiency are most critical. Our investigation reveals that the traditional contention-based protocol design will incur too much energy overhead in multihop tag systems. Surprisingly, a reader-coordinated design that significantly serializes tag transmissions performs much better. In addition, we show that load balancing is important in reducing the worst-case energy cost to the tags, and we present a solution based on serial numbers.

Radio-frequency identification (RFID) technology has been widely used in applications such as inventory control, object tracking, supply chain management. An important research is to estimate the number of tags in a certain area covered by readers. This paper extends the research in both temporal and spatial dimensions to provide much richer information for monitoring the dynamics of distributed RFID systems. More specifically, we are interested in estimating the joint properties of any two snapshots taken at arbitrary locations and arbitrary times in a system. With many practical applications, there is however little prior work on this problem. We propose a joint RFID estimation protocol based on a simple yet versatile snapshot construction. Given the snapshots of any two tag sets, although their sizes may be very different, we design a way to combine their information and more importantly derive formulas to extract the joint properties of the two tag sets from the combined information, with an accuracy that can be arbitrarily set. Through formal analysis, we determine the optimal system parameters that minimize the execution time of taking snapshots, under the constraints of a given accuracy requirement. Our simulation results show that the proposed protocol can reduce the execution time by multifold when comparing with the best alternative approach in the literature.

Cardinality estimation over big network data consisting of numerous flows is a fundamental problem with many practical applications. Traditionally the research on this problem focused on using a small amount of memory to estimate each flow's cardinality from a large range (up to $10^9$). However, although the memory needed for each flow has been greatly compressed, when there is an extremely large number of flows, the overall memory demand can still be very high, exceeding the availability under some important scenarios, such as implementing online measurement modules in network processors using only on-chip cache memory. In this paper, instead of allocating a separated data structure (called estimator) for each flow, we take a different 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. We discover that sharing at the register (multi-bit) level is superior than sharing at the bit level. We propose a framework of virtual estimators that allows us to apply the idea of sharing to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work. Our experiment shows that the new solution can work in a tight memory space of less than 1 bit per flow or even one tenth of a bit per flow --- a quest that has never been realized before.

The ubiquitous use of RFID tags raises concern about potential security risks in RFID systems. Because low-cost tags are extremely resource-constrained devices, common security mechanisms adopted in resource-rich equipment such as computers are no longer applicable to them. Hence, one challenging research topic is to design a lightweight cipher that is suitable for low-cost RFID tags. Traditional cryptography generally assumes that the two communicating parties are equipotent entities. In contrast, there is a large capability gap between readers and tags in RFID systems. We observe that the readers, which are much more powerful, should take more responsibility in RFID cryptographic protocols. In this paper, we make a radical shift from traditional cryptography, and design a novel cipher called Pandaka1, in which most workload is pushed to the readers. As a result, Pandaka is particularly hardware-efficient for tags. We perform extensive simulations to evaluate the effectiveness of Pandaka. In addition, we present security analysis of Pandaka facing different attacks.

Radio frequency identification (RFID) technology has many applications in inventory management, supply chain, product tracking, transportation and logistics. One research issue of practical importance is to search for a particular group of tags in a large-scale RFID system. Time efficiency is a core factor that must be taken into consideration when designing a tag search protocol to ensure scalability. In this paper, we design a new technique called filtering vector, which can significantly reduce transmission overhead during search process, thereby shortening search time. Based on this technique, we propose an iterative tag search protocol. In each round, we filter out some tags and eventually terminate the search process when the search result meets the accuracy requirement. The simulation results demonstrate that our protocol performs much better than the best existing ones.