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With the growing use of RFID-based devices, there are increasing attentions on utilizing RFID technology for localization. In this paper, we consider RFID reader localization which locates an object by attaching it with an RFID reader that communicates with passive RFID tags deployed in the environment. One difficulty in RFID reader localization is that frequent RFID faults can affect localization accuracy. More specifically, in a complex localization environment, metal, water, obstacles, etc., causes some tags to fail to communicate with the reader, and consequently the localization result may deviate from the real location. For permanent faults, existing localization approaches can tolerate only faults that occur at individual tags, by utilizing the redundant information from their neighboring tags. However, these approaches cannot handle permanent faults that occur at a group of neighboring tags in a region, which is referred to as regional permanent fault. They will suffer from serious localization errors if such kind of faults occurs. Moreover, existing work lacks quality measurement of localization results, hence the user may be not aware how serious the localization errors can be. In this paper, we propose an effective fault-tolerant RFID reader localization approach that can handle regional permanent fault, and provide quality measurement of localization results. Our approach is applied to both 2D and 3D localization applications. We further study the network localization problem where some objects know their locations and the other objects determine their locations by measuring the distances to their neighbors. Using the localization results and especially the quality information obtained by our approach, we solve the network localization problem with improved localization accuracy. Evaluation results show that our approach outperforms existing approaches in localization accuracy and can provide additional useful quality information. [ABSTRACT FROM PUBLISHER]

Accurate localization in wireless sensor networks is the foundation for many applications, such as geographic routing and position-aware data processing. In this paper, we develop a new localization protocol based on approximate convex decomposition (ACDL), with reliance on network connectivity information only. ACDL can calculate the node virtual locations for a large-scale sensor network with a complex shape. We first examine one representative localization algorithm and study the influential factors on the localization accuracy, including the sharpness of the angle at the concave point and the depth of the concave valley. We show that after decomposition, the depth of the concave valley becomes irrelevant. We thus define the concavity according to the angle at a concave point, which reflects the localization error. We then propose ACDL protocol for network localization. It consists of four main steps. First, convex and concave nodes are recognized and network boundaries are segmented. As the sensor network is discrete, we show that it is acceptable to approximately identify the concave nodes to control the localization error. Second, an approximate convex decomposition is conducted. Our convex decomposition requires only local information and we show that it has low message overhead. Third, for each convex section of the network, an improved MDS algorithm is proposed to compute a relative location map. Fourth, a fast and low complexity merging algorithm is developed to construct the global location map. Besides, by slight modification on the third step, we propose a variant of ACDL, denoted by ACDL-Tri, which is fully distributed and scalable while the localization accuracy is still comparable. We finally show the efficiency of ACDL by extensive simulations. [ABSTRACT FROM AUTHOR]

Radio frequency (RF) based technologies play an important role in indoor localization, since Radio Signal Strength (RSS) can be easily measured by various wireless devices without additional cost. Among these, radio map based technologies (also referred as fingerprinting technologies) are attractive due to high accuracy and easy deployment. However, these technologies have not been extensively applied on real environment for two fatal limitations. First, it is hard to localize multiple objects. When the number of target objects is unknown, constructing a radio map of multiple objects is almost impossible. Second, environment changes will generate different multipath signals and severely disturb the RSS measurement, making laborious retraining inevitable. Motivated by these, in this paper, we propose a novel approach, called Line-of-sight radio map matching, which only reserves the LOS signal among nodes. It leverages frequency diversity to eliminate the multipath behavior, making RSS more reliable than before. We implement our system MODLoc based on TelosB sensor nodes and commercial 802.11 NICs with Channel State Information (CSI) as well. Through extensive experiments, it shows that the accuracy does not decrease when localizing multiple targets in a dynamic environment. Our work outperforms the traditional methods by about 60 percent. More importantly, no calibration is required in such environment. Furthermore, our approach presents attractive flexibility, making it more appropriate for general RF-based localization studies than just the radio map based localization. [ABSTRACT FROM AUTHOR]

Jammers can severely disrupt the communications in wireless networks, and jammers' position information allows the defender to actively eliminate the jamming attacks. Thus, in this paper, we aim to design a framework that can localize one or multiple jammers with a high accuracy. Most of existing jammer-localization schemes utilize indirect measurements (e.g., hearing ranges) affected by jamming attacks, which makes it difficult to localize jammers accurately. Instead, we exploit a direct measurement--the strength of jamming signals (JSS). Estimating JSS is challenging as jamming signals may be embedded in other signals. As such, we devise an estimation scheme based on ambient noise floor and validate it with real-world experiments. To further reduce estimation errors, we define an evaluation feedback metric to quantify the estimation errors and formulate jammer localization as a nonlinear optimization problem, whose global optimal solution is close to jammers' true positions. We explore several heuristic search algorithms for approaching the global optimal solution, and our simulation results show that our error-minimizing-based framework achieves better performance than the existing schemes. In addition, our error-minimizing framework can utilize indirect measurements to obtain a better location estimation compared with prior work. [ABSTRACT FROM AUTHOR]

The knowledge of sensors' locations is crucial information for many applications in Wireless Sensor Networks (WSNs). When sensor nodes are deployed in hostile environments, the localization schemes are vulnerable to various attacks, e.g., wormhole attack, pollution attack, range enlargement/reduction attack, and etc. Therefore, sensors' locations are not trustworthy and need to be verified before they can be used by location-based applications. Previous verification schemes either require group-based deployment knowledge of the sensor field, or depend on expensive or dedicated hardware, thus they cannot be used for low-cost sensor networks. In this paper, we propose a lightweight location verification system that performs both “on-spot” and “in-region” location verifications. The on-spot verification intends to verify whether the locations claimed by sensors are far from their true spots beyond a certain distance. We propose two algorithms that detect abnormal locations by exploring the inconsistencies between sensors' claimed locations and their neighborhood observations. The in-region verification verifies whether a sensor is inside an application-specific verification region. Compared to on-spot verification, the in-region verification is tolerable to large errors as long as the locations of sensors don't cause the application to malfunction. We study how to derive the verification region for different applications and design a probabilistic algorithm to compute in-region confidence for each sensor. Experiment results show that our on-spot and in-region algorithms can verify sensors' locations with high detection rate and low false positive rate. They are robust in the presence of malicious attacks that are launched during the verification process. Moreover, compared with previous verification schemes, our algorithms are effective and lightweight because they do not rely on the knowledge of deployment of sensors, and they don't require expensive or dedicated hardware, so our algorithms can be used in any low-cost sensor networks. [ABSTRACT FROM AUTHOR]

Transceiver-free object tracking is to trace a moving object that does not carry any communication device in an environment with some monitoring nodes predeployed. Among all the tracking technologies, RF-based technology is an emerging research field facing many challenges. Although we proposed the original idea, until now there is no method achieving scalability without sacrificing latency and accuracy. In this paper, we put forward a real-time tracking system RASS, which can achieve this goal and is promising in the applications like the safeguard system. Our basic idea is to divide the tracking field into different areas, with adjacent areas using different communication channels. So, the interference among different areas can be prevented. For each area, three communicating nodes are deployed on the ceiling as a regular triangle to monitor this area. In each triangle area, we use a Support Vector Regression (SVR) model to locate the object. This model simulates the relationship between the signal dynamics caused by the object and the object position. It not only considers the ideal case of signal dynamics caused by the object, but also utilizes their irregular information. As a result, it can reach the tracking accuracy to around 1 m by just using three nodes in a triangle area with 4 m in each side. The experiments show that the tracking latency of the proposed RASS system is bounded by only about 0.26 m. Our system scales well to a large deployment field without sacrificing the latency and accuracy. [ABSTRACT FROM AUTHOR]

Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. In this paper, we propose to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for 1) detecting spoofing attacks; 2) determining the number of attackers when multiple adversaries masquerading as the same node identity; and 3) localizing multiple adversaries. We propose to use the spatial correlation of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. We then formulate the problem of determining the number of attackers as a multiclass detection problem. Cluster-based mechanisms are developed to determine the number of attackers. When the training data are available, we explore using the Support Vector Machines (SVM) method to further improve the accuracy of determining the number of attackers. In addition, we developed an integrated detection and localization system that can localize the positions of multiple attackers. We evaluated our techniques through two testbeds using both an 802.11 (WiFi) network and an 802.15.4 (ZigBee) network in two real office buildings. Our experimental results show that our proposed methods can achieve over 90 percent Hit Rate and Precision when determining the number of attackers. Our localization results using a representative set of algorithms provide strong evidence of high accuracy of localizing multiple adversaries. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are typically deployed to monitor phenomena that vary over the spatial region the sensor network covers. The sensor readings may also be dual-used for additional purposes. In this paper, we propose to use the inherent spatial variability in physical phenomena, such as temperature or ambient acoustic energy, to support localization and position verification. We first present the problem of localization using general spatial information fields, and then, propose a theory for exploiting this spatial variability for localization. Our Spatial Correlation Weighting Mechanism (SCWM) uses spatial correlation across different phenomena to isolate an appropriate subset of environmental parameters for better location accuracy. We then develop an array of algorithms employing environmental parameters using a two-level approach: first, we develop the strategies on how the subset of parameters should be chosen, and second, we derive mapping functions for position estimation. Our algorithms support our theoretical model for performing localization utilizing environmental properties. Finally, we provide an experimental evaluation of our approach by using a collection of physical phenomena measured across 100 locations inside a building. Our results provide strong evidence of the viability of using general sensor readings for location-aware applications. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the need for secure and efficient localization for wireless sensor networks in adversarial settings. An attack-resistant and efficient localization scheme is developed, which extends the scheme proposed in [1]. The method offers strong defense against not only distance reduction attacks but also distance enlargement attacks. Furthermore, our method does not employ any device-dependent variables, hence yields more accurate localization. An attack-driven model is also specified using Petri net, It provides a formal method for the verification of our scheme when considering distance enlargement attacks. The state analysis shows that the potential insecure states are unreachable, implying that the model can offer strong defense against these attacks. To the best of our knowledge, it is the first time that the Petri net has been introduced to validate security scheme for wireless sensor networks in the literature. [ABSTRACT FROM AUTHOR]

Jamming attacks are especially harmful when ensuring the dependability of wireless communication. Finding the position of a jammer will enable the network to actively exploit a wide range of defense strategies. In this paper, we focus on developing mechanisms to localize a jammer by exploiting neighbor changes. We first conduct jamming effect analysis to examine how the communication range alters with the jammer's location and transmission power using free-space model. Then, we show that a node's affected communication range can be estimated purely by examining its neighbor changes caused by jamming attacks and thus, we can perform the jammer location estimation by solving a least-squares (LSQ) problem that exploits the changes of communication range. Compared with our previous iterative-search-based virtual force algorithm, our LSQ-based algorithm exhibits lower computational cost (i.e., one step instead of iterative searches) and higher localization accuracy. Furthermore, we analyze the localization challenges in real systems by building the log-normal shadowing model empirically and devising an adaptive LSQ-based algorithm to address those challenges. The extensive evaluation shows that the adaptive LSQ-based algorithm can effectively estimate the location of the jammer even in a highly complex propagation environment. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have been considered as a promising tool for many location-dependent applications. In such deployments, the requirement of low system cost prohibits many range-based methods for sensor node localization; on the other hand, range-free approaches depending only on radio connectivity may underutilize the proximity information embedded in neighborhood sensing. In response to these limitations, this paper introduces a proximity metric called RSD to capture the distance relationships among 1-hop neighboring nodes in a range-free manner. With little overhead, RSD can be conveniently applied as a transparent supporting layer for state-of-the-art connectivity-based localization solutions to achieve better accuracy. We implemented RSD with three well-known algorithms and evaluated using two outdoor test beds: an 850-foot-long linear network with 54 MICAz motes, and a regular 2D network covering an area of 10,000 square feet with 49 motes. Results show that our design helps eliminate estimation ambiguity with a subhop resolution, and reduces localization errors by as much as 35 percent. In addition, simulations confirm its effectiveness for large-scale networks and reveal an interesting feature of robustness under unevenly distributed radio path loss. [ABSTRACT FROM AUTHOR]

Localization is an enabling technique for many sensor network applications. Real-world deployments demonstrate that, in practice, a network is not always entirely localizable, leaving a certain number of theoretically nonlocalizable nodes. Previous studies mainly focus on how to tune network settings to make a network localizable. However, the existing methods are considered to be coarse-grained, since they equally deal with localizable and nonlocalizable nodes. Ignoring localizability induces unnecessary adjustments and accompanying costs. In this study, we propose a fine-grained approach, localizability-aided localization (LAL), which basically consists of three phases: node localizability testing, structure analysis, and network adjustment. LAL triggers a single round adjustment, after which some popular localization methods can be successfully carried out. Being aware of node localizability, all network adjustments made by LAL are purposefully selected. Experiment and simulation results show that LAL effectively guides the adjustment while makes it efficient in terms of the number of added edges and affected nodes. [ABSTRACT FROM AUTHOR]

Localization is of great importance in mobile and wireless network applications. Time Difference of Arrival (TDOA) is one of the widely used localization schemes, in which the target (source) emits a signal and a number of anchors (receivers) record the arriving time of the source signal. By calculating the time difference of different receivers, the location of the target is estimated. In such a scheme, receivers must be precisely time synchronized. But time synchronization adds computational cost, and brings errors which may lower localization accuracy. Previous studies have shown that existing time synchronization approaches using low-cost devices are insufficiently accurate, or even infeasible under high requirement for accuracy. In our scheme (called Whistle), several asynchronous receivers record a target signal and a successive signal that is generated artificially. By two-signal sensing and sample counting techniques, time synchronization requirement can be removed, while high time resolution can be achieved. This design fundamentally changes TDOA in the sense of releasing the synchronization requirement and avoiding many sources of errors caused by time synchronization. We implement Whistle on commercial off-the-shelf (COTS) cell phones with acoustic signal and perform simulations with UWB signal. Especially we use Whistle to localize nodes of large-scale wireless networks, and also achieve desirable results. The extensive real-world experiments and simulations show that Whistle can be widely used with good accuracy. [ABSTRACT FROM PUBLISHER]

It is often important to obtain the real-time location of a small-sized ground robotic vehicle when it performs autonomous tasks either indoors or outdoors. We propose and implement LOBOT, a low-cost, self-contained localization system for small-sized ground robotic vehicles. LOBOT provides accurate real-time, 3D positions in both indoor and outdoor environments. Unlike other localization schemes, LOBOT does not require external reference facilities, expensive hardware, careful tuning or strict calibration, and is capable of operating under various indoor and outdoor environments. LOBOT identifies the local relative movement through a set of integrated inexpensive sensors and well corrects the localization drift by infrequent GPS-augmentation. Our empirical experiments in various temporal and spatial scales show that LOBOT keeps the positioning error well under an accepted threshold. [ABSTRACT FROM PUBLISHER]

The proliferation of wireless and mobile devices has fostered the demand for context-aware applications, in which location is one of the most significant contexts. Multilateration, as a basic building block of localization, however, has not yet overcome the challenges of 1) poor ranging measurements; 2) dynamic and noisy environments; and 3) fluctuations in wireless communications. Hence, multilateration-based approaches often suffer from poor accuracy and can hardly be employed in practical applications. In this study, we propose Quality of Trilateration (QoT) that quantifies the geometric relationship of objects and ranging noises. Based on QoT, we design a confidence-based iterative localization scheme, in which nodes dynamically select trilaterations with the highest quality for location computation. To validate this design, a prototype network based on wireless sensor motes is deployed and the results show that QoT well represents trilateration accuracy, and the proposed scheme significantly improves localization accuracy. [ABSTRACT FROM AUTHOR]

Scalable energy-efficient training protocols are proposed for wireless networks consisting of sensors and a single actor, where the sensors are initially anonymous and unaware of their location. The protocols are based on an intuitive coordinate system imposed onto the deployment area, which partitions the sensors into clusters. The protocols are asynchronous, in the sense that the sensors wake up for the first time at random, then alternate between sleep and awake periods both of fixed length, and no explicit synchronization is performed between them and the actor. Theoretical properties are stated under which the training of all the sensors is possible. Moreover, both worst-case and average case analyses of the performance, as well as an experimental evaluation, are presented showing that the protocols are lightweight and flexible. [ABSTRACT FROM AUTHOR]