Haoxiang Liu

Towards adaptive continuous scanning in large-scale RFID systems

Radio Frequency Identification (RFID) technology plays an important role in supply chain logistics and inventory control. In these applications, a series of scanning operations at different locations are often needed to cover the entire inventory (tags). In such continuous scanning scenario, adjacent scans inevitably read overlapping tags multiple times. Most existing methods suffer from low scanning efficiency when the overlap is small, since they do not distinguish the size of overlap which is an important factor of scanning performance. In this paper, we analytically unveil the fundamental relationship between the performance of continuous scanning and the size of overlap, deriving a critical threshold for the selection of scanning strategy. Further, we design an accurate estimator to approximate the overlap. Combining the estimate and a compact data structure, an adaptive scanning scheme is introduced to achieve low communication time. Through detailed analysis and extensive simulations, we demonstrate that the proposed scheme significantly outperforms previous approach in total scanning time.

Generic Composite Counting in RFID Systems

Counting the number of RFID tags is a fundamental issue and has a wide range of applications in RFID systems. Most existing protocols, however, only apply to the scenario where a single reader counts the number of tags covered by its radio, or at most the union of tags covered by multiple readers. They are unable to achieve more complex counting objectives, i.e., counting the number of tags in a composite set expression such as (S_1 big cup S_2) - (S_3 big cap S_4). This type of counting has realistic significance since it provides more diversity than existing counting scenario, and can be applied in various applications. In this paper, we formally introduce the RFID composite counting problem, which aims at counting the tags in arbitrary set expression. We obtain strong lower bounds on the communication cost of composite counting. We then propose a generic Composite Counting Framework (CCF) that provides estimates for any set expression with desired accuracy. The communication cost of CCF is proved to be within a small factor from the optimal. We build a prototype system for CCF using USRP software defined radio and Intel WISP computational tags. Also, extensive simulations are conducted to evaluate the performance of CCF. The experimental results show that CCF is generic, accurate and time-efficient.

Fast and scalable counterfeits estimation for large-scale RFID systems

Many algorithms have been introduced to deterministically authenticate Radio Frequency Identification (RFID) tags, while little work has been done to address scalability issue in batch authentications. Deterministic approaches verify tags one by one, and the communication overhead and time cost grow linearly with increasing size of tags. We design a fast and scalable counterfeits estimation scheme, INformative Counting (INC), which achieves sublinear authentication time and communication cost in batch verifications. The key novelty of INC builds on an FM-Sketch variant authentication synopsis that can capture key counting information using only sublinear space. With the help of this well-designed data structure, INC is able to provide authentication results with accurate estimates of the number of counterfeiting tags and genuine tags, while previous batch authentication methods merely provide 0/1 results indicating the existence of counterfeits. We conduct detailed theoretical analysis and extensive experiments to examine this design and the results show that INC significantly outperforms previous work in terms of effectiveness and efficiency.

End-to-End Delay Measurement in Wireless Sensor Networks without Synchronization

The deployment of large scale Wireless Sensor Networks generally needs network management and measurement solutions. End-to-end delay is one of the most important metrics in assessing the network performance. Many efforts have been devoted to measuring the end-to-end delay efficiently and precisely. Unfortunately, existing approaches often require sensor nodes to be tightly time synchronized which is costly in resource limited sensor network. We propose a novel scheme that can measure the end-to-end delay for each packet to the granularity of tens of microseconds without clock synchronization. Through extensive experiments on our testbed, we examine the effectiveness of our approach. The results show that our scheme achieves high performance with low overhead. We also present observations about the network states by tracking and analyzing the end-to-end delay data.

Exploiting channel diversity for rate adaptation in backscatter communication networks

Backscatter communication networks receive much attention recently due to the small size and low power of backscatter nodes. As backscatter communication is often influenced by the dynamic wireless channel quality, rate adaptation becomes necessary. Most existing approaches share a common drawback: they do not distinguish channel qualities from different nodes or sub-channels. Consequently, the transmission rate may be improperly selected, resulting in low network throughput. Through extensive experimental studies, we observe that channel diversity plays a significant role in rate selection. Therefore, there are opportunities of exploiting channel diversity for better rate adaptation, improving network throughput. In this paper, we propose a Channel-Aware Rate Adaptation framework (CARA) for backscatter communication networks. By employing a lightweight channel probing scheme, we are able to obtain fine-grained channel information that enables accurate channel estimation. We further design a novel channel selection algorithm, benefiting as many backscatter nodes as possible. On each selected channel, CARA chooses data rate with respect to the node that has the best channel condition. We implement CARA on commercial readers and the experiment results show that CARA achieves up to 4× goodput gain compared with state-of-the-art rate adaptation scheme.

Fast Composite Counting in RFID Systems

Counting the number of tags is a fundamental issue and has a wide range of applications in RFID systems. Most existing protocols, however, only apply to the scenario where a single reader counts the number of tags covered by its radio, or at most the union of tags covered by multiple readers. They are unable to achieve more complex counting objectives, i.e., counting the number of tags in a composite set expression such as (S1 ∪ S2) - (S3 ∩ S4). This type of counting has realistic significance as it provides more diversity than existing counting scenario, and can be applied in various applications. We formally introduce the RFID composite counting problem, which aims at counting the tags in an arbitrary set expression and obtain its strong lower bounds on the communication cost. We then propose a generic Composite Counting Framework (CCF) that provides estimates for any set expression with desired accuracy. The communication cost of CCF is proved to be within a small factor from the optimal. We build a prototype system for CCF using USRP software defined radio and Intel WISP computational tags. Also, extensive simulations are conducted to evaluate the performance of CCF. The experimental results show that CCF is generic, accurate and time-efficient.

Fast and Adaptive Continuous Scanning in Large-Scale RFID Systems

Radio Frequency Identification (RFID) technology plays an important role in supply chain logistics and inventory control. In these applications, a series of scanning operations at different locations are often needed to cover the entire inventory (tags). In such continuous scanning scenario, adjacent scans inevitably read overlapping tags multiple times. Most existing methods suffer from low scanning efficiency when the overlap is small, since they do not distinguish the size of overlap which is an important factor of scanning performance. In this paper, we analytically unveil the fundamental relationship between the performance of continuous scanning and the size of overlap, deriving a critical threshold for the selection of scanning strategy. Further, we design an accurate estimator to approximate the overlap. Combining the estimate and a compact data structure, an adaptive scanning scheme is introduced to achieve low communication time. Through detailed analysis and extensive simulations, we demonstrate that the proposed scheme significantly outperforms previous approach in total scanning time.

Learning Resource Management Specifications in Smartphones

Over the past few years we have observed a phenomenal growth of smartphones. Smartphones are equipped with various hardware and software resources such as Bluetooth, camera and gravity sensors. If these resources are not managed appropriately, it may cause severe problems such as battery drains and system crashes. However, the specifications of resource management are usually implicit. In this paper, we investigate the problem of mining resource management specifications from off-the-shelf apps. Our key insight is that if a set of operations to a resource are frequently performed in a specific order, it must contain the specifications of how to manage the resource. We design a tool named Automatic Resource Specification Miner (ARSM), to automatically extract resource management specifications in smartphones. In our experiments, ARSM can mine tens of rules from 100 top rated Android apps within six hours. Our work is orthogonal to existing studies on diagnosing smartphone apps. With the resource management specifications discovered, ARSM can help them pinpoint more bugs in apps.

Channel-Aware Rate Adaptation for Backscatter Networks

Backscatter communication networks receive much attention recently due to the small size and low power of backscatter nodes. As backscatter communication is often influenced by the dynamic wireless channel quality, rate adaptation becomes necessary. Most existing approaches share a common drawback: they fail to take both spatial and frequency diversity into consideration at the same time. Consequently, the transmission rate may be improperly selected, resulting in low network throughput. In this paper, we propose a channel-aware rate adaptation framework (CARA) for backscatter networks. CARA incorporates three essential modules, a lightweight channel probing scheme that differentiates collisions from packet losses, a burstiness-aware channel selection mechanism benefiting as many backscatter nodes as possible, a rate selection method choosing the optimal rate, and a mobility detection that discovers location changes. We implement CARA on commercial readers, and the experiment results show that CARA achieves up to

Wonder: Efficient Tag Identification for Large-Scale RFID Systems

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