Qingjun Xiao

Reliable Anchor-Based Sensor Localization in Irregular Areas

Localization is a fundamental problem in wireless sensor networks and its accuracy impacts the efficiency of location-aware protocols and applications, such as routing and storage. Most previous localization algorithms assume that sensors are distributed in regular areas without holes or obstacles, which often does not reflect real-world conditions, especially for outdoor deployment of wireless sensor networks. In this paper, we propose a novel scheme called reliable anchor-based localization (RAL), which can greatly reduce the localization error due to the irregular deployment areas. We first provide theoretical analysis of the minimum hop length for uniformly distributed networks and then show its close approximation to empirical results, which can assist in the construction of a reliable minimal hop-length table offline. Using this table, we are able to tell whether a path is severely detoured and compute a more accurate average hop length as the basis for distance estimation. At runtime, the RAL scheme 1) utilizes the reliable minimal hop length from the table as the threshold to differentiate between reliable anchors and unreliable ones, and 2) allows each sensor to determine its position utilizing only distance constraints obtained from reliable anchors. The simulation results show that RAL can effectively filter out unreliable anchors and therefore improve the localization accuracy.

Multihop Range-Free Localization in Anisotropic Wireless Sensor Networks: A Pattern-Driven Scheme

This paper focuses on multihop range-free localization in anisotropic wireless sensor networks. In anisotropic networks, geometric distance between a pair of sensor nodes is not always proportional to their hop count distance, which undermines the assumption of many existing range-free localization algorithms. To tolerate network anisotropy, we propose a pattern-driven localization scheme, which is inspired by the observation that in an anisotropic network the hop count field propagated from an anchor exhibits multiple patterns, under the interference of multiple anisotropic factors. Our localization scheme therefore for different patterns adopts different anchor-sensor distance estimation algorithms. The average anchor-sensor distance estimation accuracy of our scheme, as demonstrated by both theoretical analysis and extensive simulations, is improved to be better than 0.4r when the average sensor density is above eight, and the sensor localization accuracy thus is approximately better than 0.5r. This localization accuracy can satisfy the needs of many location-dependent protocols and applications, including geographical routing and tracking. Compared with previous localization algorithms that declares to tolerate network anisotropy, our localization scheme excels in 1) higher accuracy stemming from its ability to tolerate multiple anisotropic factors, including the existence of obstacles, sparse and nonuniform sensor distribution, irregular radio propagation pattern, and anisotropic terrain condition, 2) localization accuracy guaranteed by theoretical analysis, rather than merely by simulations, and 3) a distributed solution with less communication overhead and enhanced robustness to different network topologies.

Efficient Misplaced-Tag Pinpointing in Large RFID Systems

Radio-Frequency Identification (RFID) technology brings many innovative applications. Of great importance to RFID applications in production economics is misplaced-tag pinpointing (MTP), because misplacement errors fail optimal inventory placement and thus significantly decrease profit. The existing MTP solution [1], originally proposed from a data-processing perspective, collects and processes a large amount of data. It suffers from time inefficiency (and energy-inefficiency as well if active tags are in use). The problem of finding efficient solutions for the MTP problem from the communication protocol design perspective has never been investigated before. In this paper, we propose a series of protocols toward efficient MTP solutions in large RFID systems. The proposed protocols detect misplaced tags using reader positions instead of tag positions to guarantee the efficiency and scalability as system scale grows, because RFID readers are much fewer than tags. Considering applications that employ active tags, we further propose a solution requiring responses from only a subset of tags in favor of energy saving. We also design a distributed protocol that enables each reader to independently detect misplaced tags. We then investigate how to apply the proposed protocols in scenarios with tag mobility. To evaluate the proposed protocols, we analyze their optimal performances to demonstrate their efficiency potential and also conduct extensive simulation experiments. The results show that the proposed protocols can significantly increase the time efficiency and the energy efficiency by over 70 percent on average when compared with the best existing work.

Differential estimation in dynamic RFID systems

Efficient estimation of tag population in RFID systems has many important applications. In this paper, we present a new problem called differential cardinality estimation, which tracks the population changes in a dynamic RFID system where tags are frequently moved in and out. In particular, we want to provide quick estimation on (1) the number of new tags that are moved in and (2) the number of old tags that are moved out, between any two consecutive scans of the system. We show that the traditional cardinality estimators cannot be applied here, and the tag identification protocols are too expensive if the estimation needs to be performed frequently in order to support real-time monitoring. This paper presents the first efficient solution for the problem of differential cardinality estimation. The solution is based on a novel differential estimation framework, and is named zero differential estimator. We show that this estimator can be configured to meet any pre-set accuracy requirement, with a probabilistic error bound that can be made arbitrarily small.

Efficient pinpointing of misplaced tags in large RFID systems

The Radio-Frequency Identification (RFID) technology has stimulated many innovative applications. Misplaced-tag pinpointing (MTP) is important to RFID applications in production economics because optimal inventory placement can significantly increase profit. Previous research from the database perspective needs to process a large amount of data which is time-consuming to collect (and energy-consuming if active tags are used). How to efficiently address the MTP problem from the protocol design perspective however has not been investigated. In this paper, we propose a series of protocols toward efficient MTP solution in large RFID systems. The proposed protocols detect misplaced tags based on reader positions instead of tag positions to guarantee the efficiency and scalability as system scale grows, because the number of readers is much smaller than that of tags. Considering applications to employ more and more popular active tags, we further propose a solution requiring responses from only partial tags in favor of energy saving. We analyze the optimal performances of proposed protocols to demonstrate their efficiency potential and conduct extensive simulation experiments to evaluate their performance under various scenarios. The results show that the proposed protocols can significantly increase the time efficiency and the energy efficiency by over 70% on average when compared with the state of the art.

Pandaka: A lightweight cipher for RFID systems

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.

Privacy-Preserving Transportation Traffic Measurement in Intelligent Cyber-physical Road Systems

Traffic measurement is a critical function in transportation engineering. We consider privacy-preserving point-to-point traffic measurement in this paper. We measure the number of vehicles traveling from one geographical location to another by taking advantage of capabilities provided by the intelligent cyberphysical road systems (CPRSs) that enable automatic collection of traffic data. The challenge is to allow the collection of aggregate point-to-point data while preserving the privacy of individual vehicles. We propose a novel measurement scheme, which utilizes bit arrays to collect “masked” data and adopts maximum-likelihood estimation (MLE) to obtain the measurement result. Both mathematical proof and simulation demonstrate the practicality and scalability of our scheme.

Robust localization against outliers in wireless sensor networks

In wireless sensor networks, a critical system service is the localization service that determines the locations of geographically distributed sensor nodes. The raw data used by this service are the distance measurements between neighboring nodes and the position knowledge of anchor nodes. However, these raw data may contain outliers that strongly deviate from their true values, which include both the outlier distances and the outlier anchors. These outliers can severely degrade the accuracy of the localization service. Therefore, we need a robust localization algorithm that can reject these outliers. Previous studies in this field mainly focus on enhancing multilateration with outlier rejection ability, since multilateration is a primitive operation used by localization service. But patch merging, a powerful operation for increasing the percentage of localizable nodes in sparse networks, is almost neglected. We thus propose a robust patch merging operation that can reject outliers for both multilateration and patch merging. Based on this operation, we further propose a robust network localization algorithm called RobustLoc. This algorithm makes two major contributions. (1) RobustLoc can achieve a high percentage of localizable nodes in both dense and sparse networks. In contrast, previous methods based on robust multilateration almost always fail in sparse networks with average degrees between 5 and 7. Our experiments show that RobustLoc can localize about 90% of nodes in a sparse network with 5.5 degrees. (2) As far as we know, RobustLoc is the first to uncover the differences between outlier distances and outlier anchors. Our simulations show that RobustLoc can reject colluding outlier anchors reliably in both convex and concave networks.

Toward collinearity-aware and conflict-friendly localization for wireless sensor networks

Localization aims at determining node positions and is essential for many applications in wireless sensor networks (WSNs). Most existing localization protocols adopt graph rigidity theory as the theoretical basis. The rigidity theory assumes that every three nodes are noncollinear in a two-dimensional graph; this assumption, however, may not always hold in WSNs. A lack of node collinearity verification places a limitation on localization accuracy. Furthermore, existing localization protocols explore only distance constraints for localization, giving rise to another limitation on localization percentage. Against these limitations, this paper presents two approaches toward collinearity-aware and conflict-friendly rigidity-based localization for WSNs. The proposed approaches are expected to increase both localization accuracy and percentage of traditional rigidity-based localization protocols. First, to achieve collinearity-awareness, we investigate node collinearity and propose a detection method to mitigate localization errors induced by probably collinear nodes. Second, to achieve conflict-friendliness, besides distance constraints, we explore distance conflicts to eliminate position ambiguities. Distance conflicts relax the sufficient condition of 3-connectivity for localizability to 2-connectivity; this relaxation can significantly improve localization percentage. For example, trilateration using distance conflicts yields a higher efficacy in both convex and non-convex WSNs and requires only a 25% lower average connectivity degree to locate 95% of sensors. The proposed approaches can be conveniently incorporated into existing localization protocols with small overhead. We validate their effectiveness of enhancing localization accuracy and percentage through both real and simulation experiments.

Imaging Seismic Tomography in Sensor Network

Tomography imaging, applied to seismology, requires a new, decentralized approach if high resolution calculations are to be performed in a sensor network configuration. The real-time data retrieval from a network of large-amount wireless seismic nodes to a central server is virtually impossible due to the sheer data amount and resource limitations. In this paper, we present a distributed multi-resolution evolving tomography algorithm for processing data and inverting volcano tomography in the network, while avoiding costly data collections and centralized computations. The new algorithm distributes the computational burden to sensor nodes and performs real-time tomography inversion under the constraints of network resources. We implemented and evaluated the system design in the CORE emulator. The experiment results validate that our proposed algorithm not only balances the computation load, but also achieves low communication cost and high data loss-tolerance.