Fig 3 - uploaded by Jun Luo
Content may be subject to copyright.
Hyperbola intersection problem of two hyperbolae. (a) For 3 collinear beacon positions, the effective region is a lens belt centered around the line. (b) With 4 beacons at the vertices of a kite, the effective region is the kite.
Source publication
Location-awareness is crucial to many applications of sensor networks. Existing location surveying approaches either rely on an inflexible infrastructure or suffer from high computation and communication load. In this paper, we present Non-intEractive lOcation Surveying (NEOS) to address certain deficiencies in the existing approaches. The key cont...
Contexts in source publication
Context 1
... by the geome- tries of the beacon positions. The larger the GDOP, the more sensitive the estimation accuracy is to the input (i.e., MDToA estimations) errors. Intuitively, the GDOP is minimized if the two hyperbolae are orthogonal at the intersection points and it increases with a decreasing an- gle between the two tangents. For example, in Fig. 3 (a), the two right node locations have much larger GDOPs than the two on the left. We omit the detailed analysis, which can be done by investigating the CRLB. Usually, we would like to have a large effective region (the region where GDOP is minimized) with a small number of beacon positions. In this work, we propose to have the 6 beacon ...
Context 2
... much larger GDOPs than the two on the left. We omit the detailed analysis, which can be done by investigating the CRLB. Usually, we would like to have a large effective region (the region where GDOP is minimized) with a small number of beacon positions. In this work, we propose to have the 6 beacon positions at the vertices of a kite, as shown in Fig. 3 (b). By choosing a proper coordinate system, the 2 measurements r 21 and r 43 along with the coordinates of the 4 beacon positions define two hyperbola ...
Similar publications
Transactional memory (TM) is an emerging synchronization mechanism that aims to solve most of the difficulties inherent in lock-based approaches. TM implementations may either rely on special hardware (HTM) or employ a software-only (STM) technique. While STM can be implemented and evaluated in current machines, HTM requires hardware modification a...
Software Engineering courses are core elements of the Computer Science curricula. While the main aim of such courses is to give students practical industry-relevant software engineering knowledge, often such courses fall short of this important target due to lack of industrial experience and support infrastructure, degenerating the course into "one...
Multiplayer games are representative of a large class of distributed applications that suffer from redundant communication,
bottlenecks, single points of failure and poor reactivity to changing network conditions. Many of these problems can be alleviated
through simple network adaptations at the infrastructure level. In this paper, we describe a mo...
Abstract Workflow became a mainstream formalism for complex scientific problems’ representation and is applied to different domains. In this paper we propose and analyze the interactive workflow model as the base for urgent computing (UC) infrastructures. Majority of research works in the area of urgent computing is focused on the deadline-driven s...
Citations
... In the real localization technique depends on the TOA method, few transmission paths among BS and UTs might be an NLOS path because the transmission environment that denotes NLOS error might damage the estimating efficiency and increases the average locating errors [5]. For overcoming this limitation, in [6], few NLOS mitigation methods (like WLS technique and maximal probability estimation) have been presented for solving the problems of position evaluating in the NLOS platform, thus the NLOS BS could be detected initially, and Later the LOS BS is utilized for estimating the unknown position of the UTs. With the help of NLOS mitigation methods, [7] proposed an advanced Chan-Taylor 3D locating method i.e., depending upon the least square estimate technique that could evade the effect of NLOS for a certain range. ...
Indoor localization is one of the emergent technologies in location based services, which find useful for commercial as well as civilian industries. Global position systems are a familiar solution for outdoor localization systems. But the presence of complicated obstacles in buildings poses a major challenge in indoor localization. Though few indoor localization techniques based on ranging and fingerprint based techniques are devised, they are time consuming and laborious. Therefore, this paper devises an efficient deer hunting optimization algorithm with weighted least square estimation (DHOA-WLSE) technique for accurate 3D indoor node localization technique. The proposed DHOA-WLSE technique has the ability to accomplish minimal localization error with least localization time. In DHOA-WLSE technique, the DHOA is used to estimate the primary target location to eliminate the non-line of sight errors. Based on the primary locations attained, the WLSE technique is applied to determine the accurate target’s final location. In order to validate the 3D indoor localization performance of the DHOA-WLSE technique, an extensive simulation analysis is performed and the results are investigated in terms of different measures. The simulation outcomes demonstrated the superior performance of the DHOA-WLSE technique over the recent state of art techniques.
... Range-based methods normally require pairwise distance measurements among all or part of the devices (or among various locations of the same device). The distance measurements are normally obtained through ToF/ToA [18], [19], TDoA [20], RSSI (with a certain propagation model) [21], and dead reckoning [22]. Measuring distance through ToF/ToA/TDoA requires either non-RF signal sources [18], [20] (so that the time can last long enough to be measurable) or a sophisticated design for RF signal [19] (which would not be usable for outdoor localization any sooner). ...
... The distance measurements are normally obtained through ToF/ToA [18], [19], TDoA [20], RSSI (with a certain propagation model) [21], and dead reckoning [22]. Measuring distance through ToF/ToA/TDoA requires either non-RF signal sources [18], [20] (so that the time can last long enough to be measurable) or a sophisticated design for RF signal [19] (which would not be usable for outdoor localization any sooner). Dead reckoning is useful for assisting user tracking in smallscale indoor space [22] (otherwise the accumulated errors can render the results unusable), but locating a user in a metropolitan area cannot solely rely on dead reckoning. ...
The ever-expanding scale of WiFi deployments in metropolitan areas has made accurate GPS-free outdoor localization become possible by relying solely on the WiFi infrastructure. Nevertheless, neither academic researches nor existing industrial practices seem to provide a satisfactory solution or implementation. In this paper, we propose WOLoc (WiFi-only Outdoor Localization) as a learning-based outdoor localization solution using only WiFi hotspots labeled by crowdsensing. On one hand, we do not take these labels as fingerprints as it is almost impossible to extend indoor localization mechanisms by fingerprinting metropolitan areas. On the other hand, we avoid the over-simplified local synthesis methods (e.g., centroid) that significantly lose the information contained in the labels. Instead, WOLoc adopts a semi-supervised manifold learning approach that accommodates all the labeled and unlabeled data for a given area, and the output concerning the unlabeled part will become the estimated locations for both unknown users and unknown WiFi hotspots. Moreover, WOLoc applies text mining techniques to analyze the SSIDs of hotspots, so as to derive more accurate input to its manifold learning. We conduct extensive experiments in several outdoor areas, and the results have strongly indicated the efficacy of our solution in achieving a meter-level localization accuracy.
... In the actual positioning method based on the TOA technique, some of the propagation paths between the base stations (BS) and the user terminals (UTs) may be a non-line-of-sight (NLOS) path due to the propagation environments, which have been indicated that the NLOS error may degrade the estimation performance and linearly increase the average positioning errors [14]. In order to overcome this shortcoming, in [15,16], some NLOS mitigation techniques (such as the weighted least squares (WLS) method and maximum likelihood estimator) have been proposed to solve the problem of location estimation in the NLOS environment, so that the NLOS BS can be identified first, and then the LOS BS can be used to estimate the unknown location of the UTs. ...
... The measured distance range r i is mainly the sum of the actual distance range and the NLOS error value, that is, r i = r i + n i , and the measured variance σ σ, r i r i . The least squares (LS) criterion can be used to estimate the single UT position as follows [15]: ...
Time of arrival (TOA) measurement is a promising method for target positioning based on a set of nodes with known positions, with high accuracy and low computational complexity. However, most positioning methods based on TOA (such as least squares estimation, maximum-likelihood, and Chan, etc.) cannot provide desirable accuracy while maintaining high computational efficiency in the case of a non-line of sight (NLOS) path between base stations and user terminals. Therefore, in this paper, we proposed a creative 3-D positioning system based on particle swarm optimization (PSO) and an improved Chan algorithm to greatly improve the positioning accuracy while decreasing the computation time. In the system, PSO is used to estimate the initial location of the target, which can effectively eliminate the NLOS error. Based on the initial location, the improved Chan algorithm performs iterative computations quickly to obtain the final exact location of the target. In addition, the proposed methods will have computational benefits in dealing with the large-scale base station positioning problems while has highly positioning accuracy and lower computational complexity. The experimental results demonstrated that our algorithm has the best time efficiency and good practicability among stat-of-the-art algorithms.
... The estimated location is the intersection of the hyperbolic curves, and this kind of technique is also known as multilateration. Luo et al. [19] is an instance of TDoA. The authors utilized the time difference of arrival instead of the time of arrival as an distance indicator. ...
As mobile devices, such as smart phones, become more and more popular, localization based on mobile devices has gained much attention in both the academia and industry. Although global positioning system (GPS) has been widely deployed in many fields, it has several disadvantages such as consuming too much power and requiring the line of sight to satellites. Cellular network-based localization is much more robust. However, this type of localization scheme cannot achieve enough accuracy for some localization-based services. With the existence of densely deployed WiFi access points, WiFi-based localization seems very promising in terms of positioning precision and power consumption. However, different than GPS and cellular network based localization, most of the existing WiFi-based localization schemes are supposed to be used in indoor environments. Note that indoor WiFi-based localization schemes cannot be simply ported to urban outdoor environments due to several factors such as weaker signal and more serious interference. In this paper, we present a hybrid outdoor localization scheme utilizing crowdsourced WiFi signal data and built-in sensors in smart phones, which can achieve high-positioning accuracy and low power consumption. Our experimental results show that the proposed hybrid localization scheme outperforms the widely-adopted GPS-based method in terms of both positioning accuracy and power consumption. In addition, the idea of crowdsourcing fingerprints is effective.
... Range-based methods normally require pairwise distance measurements among all or part of the devices (or among various locations of the same device). The distance measurements are normally obtained through ToF/ToA [18], [19], TDoA [20], RSSI (with a certain propagation model) [21], and dead reckoning [22]. Measuring distance through ToF/ToA/TDoA requires either non-RF signal sources [18], [20] (so that the time can last long enough to be measurable) or a sophisticated design for RF signal [19] (which would not be usable for outdoor localization any sooner). ...
... The distance measurements are normally obtained through ToF/ToA [18], [19], TDoA [20], RSSI (with a certain propagation model) [21], and dead reckoning [22]. Measuring distance through ToF/ToA/TDoA requires either non-RF signal sources [18], [20] (so that the time can last long enough to be measurable) or a sophisticated design for RF signal [19] (which would not be usable for outdoor localization any sooner). Dead reckoning is useful for assisting user tracking in small scale indoor space [22] (otherwise the accumulated errors can render the results unusable), but locating a user in an metropolitan area cannot solely rely on dead reckoning. ...
Given the ever-expanding scale of WiFi deployments in metropolitan areas, we have reached the point where accurate GPS-free outdoor localization becomes possible by relying solely on the WiFi infrastructure. Nevertheless, the existing industrial practices do not seem to have the right implementation to achieve an adequate accuracy, while the academic researches that are mostly attracted by indoor localization have largely neglected this outdoor aspect. In this paper, we propose WOLoc (WiFi-only Outdoor Localization) as a solution that offers meter-level accuracy , by holistically treating the large number of WiFi hotspot labels gather by crowdsensing. On one hand, we do not take these labels as fingerprints as it is almost impossible to extend indoor localization mechanisms by fingerprinting metropolitan areas. On the other hand, we avoid the oversimplified local synthesis methods (e.g., centroid) that significantly lose the information contained in the labels. Instead, we accommodate all the labeled and unlabeled data for a given area using a semi-supervised manifold learning technique, and the output concerning the unlabeled part will become the estimated locations for both users and WiFi hotspots. We conduct extensive experiments with WOLoc in several outdoor areas, and the results have strongly indicated the efficacy of our solution.
... In such cases, barrier coverage depends on the spatial distribution of locations of the sensors. In some work, sensors are capable of knowing their location information by GPS or a certain localization algorithm [28,29]. The problem of barrier coverage was studied in Ref. [30] when nodes have location errors. ...
For various applications, sensors are deployed to monitor belt regions to guarantee that every movement crossing a barrier of sensors will be detected in real-time with high accuracy and minimize the need for human support. The barrier coverage problem is introduced to model these requirements, and has been examined thoroughly in the past decades. In this survey, we state the problem definitions and systematically consider sensing models, design issues and challenges in barrier coverage problem. We also review representative algorithms in this survey. Furthermore, we provide discussions on some extensions and variants of barrier coverage problems.
... Instead, AMIL offers a lightweight way to obtain the direction and distance of all the neighboring mobile devices. The idea of mobility-induced TDoA in AMIL is not totally new, it has been proposed for localization in wireless sensor networks in [3]. This paper, however, has demonstrated for the first time that mobile devices can adopt this technique for localization with a set of common hardware and pure software implementation. ...
Smartphone users are often grouped to exchange files or perform
collaborative tasks when meeting together. We argue that the location
information of group members is critical to many mobile applications.
Existing localization solutions mostly rely on anchor nodes or
infrastructure to perform ranging and positioning. These approaches are
inefficient for ad hoc scenario. In this paper, we propose AMIL, an
Acoustic Mobility-Induced TDoA (Time-Difference-of-Arrival)-based
Localization scheme for smartphones. In AMIL, a smartphone user can use
simple gestures (e.g., hold the phone and draw a triangle in the air) to
quickly obtain the relative coordinates of neighboring mobile devices. We
have implemented and evaluated AMIL on off-the-shelf smartphones. The field
tests have shown that our scheme can achieve less than three degree
orientation errors and can successfully build a simple map of 12 people in
an office room with average error of 50cm.
... We assume that measurement and channel noise are independent for each location claim and each of its associated timestamps. In accordance with [16], we summarize all sources of noise in a zero-mean Gaussian random variable ∼ N (0, σ 2 ). The variance σ 2 depends on the accuracy of the system components involved in the verification process. ...
... The idea is to use all received location claims to estimate the error. As shown in [16], jointly estimating clock drift and measurement error is not feasible since the Cramer-Rao lower bound of the estimation error is too large. Therefore, we perform our verification in two steps. ...
... Conversely, secure track verification as we propose in this work does not require time synchronization which dramatically reduces the costs of infrastructure. The authors of [16] have used mobility-differentiated ToA as a special form of TDoA for location surveying in sensor networks . However, the algorithms proposed in their work assume non-adversarial settings whereas our approach is designed to be secure against location spoofing attacks. ...
We propose a new approach for securely verifying sequences of location claims from mobile nodes. The key idea is to exploit the inherent mobility of the nodes in order to constrain the degree of freedom of an attacker when spoofing consecutive location updates along a claimed track. We show that in the absence of noise, our approach is able to securely verify any 2-D track with a minimum of three verifiers or any 3-D track with four verifiers. Our approach is lightweight in the sense that it considerably relaxes the system requirements compared to previous secure location verification schemes which are all agnostic to mobility. As opposed to previous schemes, our track verification solution is at the same time (i) passive, (ii) does not require any time synchronization among the verifiers, (iii) does not need to keep the location of the verifiers secret, (iv) nor does it require specialized hardware. This makes our solution particularly suitable for large-scale deployments. We have evaluated our solution in a realistic air traffic monitoring scenario using real-world data. Our results show that 25 position claims on a track are sufficient to detect spoofing attacks with a false positive rate of 1.4% and a false negative rate of 1.2%. For tracks with more than 40 claims, the false positive and false negative rates drop to zero.
... The basic principle in localization is to use a set of sensors with fixed and known positions to collect radio signals from a mobile device carried by the mobile user [12][13][14][15] . With the obtained data, they perform location estimation such as using triangulation. ...
In this paper, we propose a novel pervasive business model for sales promotion in retail chain stores utilizing WLAN localization and near field communication (NFC) technologies. The objectives of the model are to increase the customers’ flow of the stores and their incentives in purchasing. In the proposed model, the NFC technology is used as the first mean to motivate customers to come to the stores. Then, with the use of WLAN, the movements of the customers, who are carrying smartphones, within the stores are captured and maintained in the movement database. By interpreting the movements of customers as indicators of their interests to the displayed items, personalized promotion strategies can be formulated to increase their incentives for purchasing future items. Various issues in the application of the adopted localization scheme for locating customers in a store are discussed. To facilitate the item management and space utilization in displaying the items, we propose an enhanced R-tree for indexing the data items maintained in the movement database. Experimental results have demonstrated the effectiveness of the adopted localization scheme in supporting the proposed model.
... Though Time-of-Flight (ToF) can be a good indicator of distance, extremely accurate clock is needed to measure RF ToF [27], unless one replaces and complements RF with acoustic signal or ultrasound [12,10,7]. Time-of-Arrival (ToA) and Time Difference of Arrival (TDoA) can also be used for ranging, but earlier technology only allows them to be measured for acoustic signal or ultrasound [11]. In theory, TDoA can be translated to AoA through the induced phase difference (see Section 3.1), and a system that derives AoA from WiFi signal has been implemented only recently [25]. ...
Whereas a few physical layer techniques have been proposed to locate a signal source indoors, they all deem multipath a "curse" and hence take great efforts to cope with it. Consequently, each sensor only obtains the information about the direct path; this necessitates a networked sensing system (hence higher system complexity and deployment cost) with at least three sensors to actually locate a source.
In this paper, we deem multipath a "bless" and thus innovatively exploit the power of it. Essentially, with minor knowledge of the geometry of an indoor space, each signal path may potentially contribute a new piece of information to the location of its source. As a result, it is possible to locate the source with only one hand-held device. At the same time, the extra information provided by multipath can help to at least partially reconstruct the geometry of the indoor space, which enables a floor plan generation process missing in most of the indoor localization systems.
To demonstrate these ideas, we implement a USRP-based radio sensor prototype named iLocScan; it can simultaneously scan an indoor space (hence generate a plan for it) and position the signal source in it. Through iLocScan, we mainly aim to showcase the feasibility of harnessing multipath in assisting indoor localization, rather than to rival existing proposals in terms of localization accuracy. Nevertheless, our experiments show that iLocScan offers satisfactory results on both source localization and space scanning.
