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Special Issue "Scalable Localization in Wireless Sensor Networks"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 July 2016).

Special Issue Editors

Prof. Dr. Lyudmila Mihaylova
E-Mail Website
Guest Editor
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, S1 3JD, UK
Tel. +44-114-222-5675
Interests: signal processing; Bayesian methods; Monte Carlo methods; nonlinear estimation; target tracking; sensor data fusion; control; autonomous and complex systems
Prof. Dr. Byung-Gyu Kim
E-Mail Website
Co-Guest Editor
Department of IT Engineering, Sookmyung Women's University, Seoul, Korea
Tel. +82-10-5037-3452
Interests: visual sensor network; real-time object segmentation; deep learning for object detection; facial expression recognition
Special Issues and Collections in MDPI journals
Assist. Prof. Debi Prosad Dogra
E-Mail Website
Co-Guest Editor
School of Electrical Sciences IIT Bhubaneswar India 751013
Tel. +91-674-2306-308
Interests: visual surveillance; sensor guided health-care automation; physical rehabilitation support designing; sensor guided traffic monitoring

Special Issue Information

Dear Colleagues,

Wireless Sensor Networks (WSNs) are becoming ubiquitous and have many applications, such as surveillance, health care, and intelligent transportation systems, including vehicular networks. Finding the location of mobile nodes is an important task, which, however, faces a number of challenges. These are due to signal propagation effects (such as multipath, shadowing components, and correlations in the measurements).

This Special Issue calls for both theoretical and practical oriented works in different domains of localization for indoor and outdoor environments. Particularly, GSM-based localization of single and multiple nodes with applications to Intelligent Transportation systems are welcome. Scalable algorithms, able to deal with large scale networks and massive data, are one area that is central for this issue. Routing algorithms, suitable for large scale sensor networks and development of efficient and reliable protocols, are also welcome. Additionally, the smart vision-based multi-objects detection, localization mechanisms, inter-domain cooperation for tracking, and high-level interpretation scheme of context/situation information will be discussed for smart camera networks and personal sensor networks.

Original contributed papers and technical review papers are welcome for this Special Issue.

Dr. Lyudmila Mihaylova
Guest Editor


Prof. Dr. Byung-Gyu Kim
Dr. Debi Prosad Dogra
Co-Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Localization
  • Large scale sensor networks, massive data
  • Scalable networks and algorithms
  • Detection, situation awareness
  • Wireless Sensor Networks
  • Smart sensors – cameras, GSMs, etc
  • Secured connections in sensor networks

Published Papers (20 papers)

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Open AccessArticle
A Self-Adaptive Model-Based Wi-Fi Indoor Localization Method
Sensors 2016, 16(12), 2074; https://doi.org/10.3390/s16122074 - 06 Dec 2016
Cited by 8
Abstract
This paper presents a novel method for indoor localization, developed with the main aim of making it useful for real-world deployments. Many indoor localization methods exist, yet they have several disadvantages in real-world deployments—some are static, which is not suitable for long-term usage; [...] Read more.
This paper presents a novel method for indoor localization, developed with the main aim of making it useful for real-world deployments. Many indoor localization methods exist, yet they have several disadvantages in real-world deployments—some are static, which is not suitable for long-term usage; some require costly human recalibration procedures; and others require special hardware such as Wi-Fi anchors and transponders. Our method is self-calibrating and self-adaptive thus maintenance free and based on Wi-Fi only. We have employed two well-known propagation models—free space path loss and ITU models—which we have extended with additional parameters for better propagation simulation. Our self-calibrating procedure utilizes one propagation model to infer parameters of the space and the other to simulate the propagation of the signal without requiring any additional hardware beside Wi-Fi access points, which is suitable for real-world usage. Our method is also one of the few model-based Wi-Fi only self-adaptive approaches that do not require the mobile terminal to be in the access-point mode. The only input requirements of the method are Wi-Fi access point positions, and positions and properties of the walls. Our method has been evaluated in single- and multi-room environments, with measured mean error of 2–3 and 3–4 m, respectively, which is similar to existing methods. The evaluation has proven that usable localization accuracy can be achieved in real-world environments solely by the proposed Wi-Fi method that relies on simple hardware and software requirements. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments
Sensors 2016, 16(12), 2055; https://doi.org/10.3390/s16122055 - 02 Dec 2016
Cited by 6
Abstract
Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has [...] Read more.
Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
A Context-Recognition-Aided PDR Localization Method Based on the Hidden Markov Model
Sensors 2016, 16(12), 2030; https://doi.org/10.3390/s16122030 - 30 Nov 2016
Cited by 7
Abstract
Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones [...] Read more.
Indoor positioning has recently become an important field of interest because global navigation satellite systems (GNSS) are usually unavailable in indoor environments. Pedestrian dead reckoning (PDR) is a promising localization technique for indoor environments since it can be implemented on widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the accumulation of positioning errors, and other external calibration sources should be used. In this paper, a context-recognition-aided PDR localization model is proposed to calibrate PDR. The context is detected by employing particular human actions or characteristic objects and it is matched to the context pre-stored offline in the database to get the pedestrian’s location. The Hidden Markov Model (HMM) and Recursive Viterbi Algorithm are used to do the matching, which reduces the time complexity and saves the storage. In addition, the authors design the turn detection algorithm and take the context of corner as an example to illustrate and verify the proposed model. The experimental results show that the proposed localization method can fix the pedestrian’s starting point quickly and improves the positioning accuracy of PDR by 40.56% at most with perfect stability and robustness at the same time. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm
Sensors 2016, 16(11), 1823; https://doi.org/10.3390/s16111823 - 01 Nov 2016
Cited by 6
Abstract
An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage [...] Read more.
An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Achieving Passive Localization with Traffic Light Schedules in Urban Road Sensor Networks
Sensors 2016, 16(10), 1662; https://doi.org/10.3390/s16101662 - 10 Oct 2016
Cited by 3
Abstract
Localization is crucial for the monitoring applications of cities, such as road monitoring, environment surveillance, vehicle tracking, etc. In urban road sensor networks, sensors are often sparely deployed due to the hardware cost. Under this sparse deployment, sensors cannot communicate with each other [...] Read more.
Localization is crucial for the monitoring applications of cities, such as road monitoring, environment surveillance, vehicle tracking, etc. In urban road sensor networks, sensors are often sparely deployed due to the hardware cost. Under this sparse deployment, sensors cannot communicate with each other via ranging hardware or one-hop connectivity, rendering the existing localization solutions ineffective. To address this issue, this paper proposes a novel Traffic Lights Schedule-based localization algorithm (TLS), which is built on the fact that vehicles move through the intersection with a known traffic light schedule. We can first obtain the law by binary vehicle detection time stamps and describe the law as a matrix, called a detection matrix. At the same time, we can also use the known traffic light information to construct the matrices, which can be formed as a collection called a known matrix collection. The detection matrix is then matched in the known matrix collection for identifying where sensors are located on urban roads. We evaluate our algorithm by extensive simulation. The results show that the localization accuracy of intersection sensors can reach more than 90%. In addition, we compare it with a state-of-the-art algorithm and prove that it has a wider operational region. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
An Enhanced Lightweight Anonymous Authentication Scheme for a Scalable Localization Roaming Service in Wireless Sensor Networks
Sensors 2016, 16(10), 1653; https://doi.org/10.3390/s16101653 - 07 Oct 2016
Cited by 9
Abstract
More security concerns and complicated requirements arise in wireless sensor networks than in wired networks, due to the vulnerability caused by their openness. To address this vulnerability, anonymous authentication is an essential security mechanism for preserving privacy and providing security. Over recent years, [...] Read more.
More security concerns and complicated requirements arise in wireless sensor networks than in wired networks, due to the vulnerability caused by their openness. To address this vulnerability, anonymous authentication is an essential security mechanism for preserving privacy and providing security. Over recent years, various anonymous authentication schemes have been proposed. Most of them reveal both strengths and weaknesses in terms of security and efficiency. Recently, Farash et al. proposed a lightweight anonymous authentication scheme in ubiquitous networks, which remedies the security faults of previous schemes. However, their scheme still suffers from certain weaknesses. In this paper, we prove that Farash et al.’s scheme fails to provide anonymity, authentication, or password replacement. In addition, we propose an enhanced scheme that provides efficiency, as well as anonymity and security. Considering the limited capability of sensor nodes, we utilize only low-cost functions, such as one-way hash functions and bit-wise exclusive-OR operations. The security and lightness of the proposed scheme mean that it can be applied to roaming service in localized domains of wireless sensor networks, to provide anonymous authentication of sensor nodes. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Ambient Sound-Based Collaborative Localization of Indeterministic Devices
Sensors 2016, 16(9), 1478; https://doi.org/10.3390/s16091478 - 14 Sep 2016
Abstract
Localization is essential in wireless sensor networks. To our knowledge, no prior work has utilized low-cost devices for collaborative localization based on only ambient sound, without the support of local infrastructure. The reason may be the fact that most low-cost devices are indeterministic [...] Read more.
Localization is essential in wireless sensor networks. To our knowledge, no prior work has utilized low-cost devices for collaborative localization based on only ambient sound, without the support of local infrastructure. The reason may be the fact that most low-cost devices are indeterministic and suffer from uncertain input latencies. This uncertainty makes accurate localization challenging. Therefore, we present a collaborative localization algorithm (Cooperative Localization on Android with ambient Sound Sources (CLASS)) that simultaneously localizes the position of indeterministic devices and ambient sound sources without local infrastructure. The CLASS algorithm deals with the uncertainty by splitting the devices into subsets so that outliers can be removed from the time difference of arrival values and localization results. Since Android is indeterministic, we select Android devices to evaluate our approach. The algorithm is evaluated with an outdoor experiment and achieves a mean Root Mean Square Error (RMSE) of 2.18 m with a standard deviation of 0.22 m. Estimated directions towards the sound sources have a mean RMSE of 17.5 ° and a standard deviation of 2.3 °. These results show that it is feasible to simultaneously achieve a relative positioning of both devices and sound sources with sufficient accuracy, even when using non-deterministic devices and platforms, such as Android. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
Sensors 2016, 16(9), 1391; https://doi.org/10.3390/s16091391 - 30 Aug 2016
Cited by 3
Abstract
This paper presents cooperative routing scheme to improve data reliability. The proposed protocol achieves its objective, however, at the cost of surplus energy consumption. Thus sink mobility is introduced to minimize the energy consumption cost of nodes as it directly collects data from [...] Read more.
This paper presents cooperative routing scheme to improve data reliability. The proposed protocol achieves its objective, however, at the cost of surplus energy consumption. Thus sink mobility is introduced to minimize the energy consumption cost of nodes as it directly collects data from the network nodes at minimized communication distance. We also present delay and energy optimized versions of our proposed RE-AEDG to further enhance its performance. Simulation results prove the effectiveness of our proposed RE-AEDG in terms of the selected performance matrics. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Practical Performance Analysis for Multiple Information Fusion Based Scalable Localization System Using Wireless Sensor Networks
Sensors 2016, 16(9), 1346; https://doi.org/10.3390/s16091346 - 23 Aug 2016
Cited by 3
Abstract
In practical localization system design, researchers need to consider several aspects to make the positioning efficiently and effectively, e.g., the available auxiliary information, sensing devices, equipment deployment and the environment. Then, these practical concerns turn out to be the technical problems, e.g., the [...] Read more.
In practical localization system design, researchers need to consider several aspects to make the positioning efficiently and effectively, e.g., the available auxiliary information, sensing devices, equipment deployment and the environment. Then, these practical concerns turn out to be the technical problems, e.g., the sequential position state propagation, the target-anchor geometry effect, the Non-line-of-sight (NLOS) identification and the related prior information. It is necessary to construct an efficient framework that can exploit multiple available information and guide the system design. In this paper, we propose a scalable method to analyze system performance based on the Cramér–Rao lower bound (CRLB), which can fuse all of the information adaptively. Firstly, we use an abstract function to represent all of the wireless localization system model. Then, the unknown vector of the CRLB consists of two parts: the first part is the estimated vector, and the second part is the auxiliary vector, which helps improve the estimation accuracy. Accordingly, the Fisher information matrix is divided into two parts: the state matrix and the auxiliary matrix. Unlike the theoretical analysis, our CRLB can be a practical fundamental limit to denote the system that fuses multiple information in the complicated environment, e.g., recursive Bayesian estimation based on the hidden Markov model, the map matching method and the NLOS identification and mitigation methods. Thus, the theoretical results are approaching the real case more. In addition, our method is more adaptable than other CRLBs when considering more unknown important factors. We use the proposed method to analyze the wireless sensor network-based indoor localization system. The influence of the hybrid LOS/NLOS channels, the building layout information and the relative height differences between the target and anchors are analyzed. It is demonstrated that our method exploits all of the available information for the indoor localization systems and serves as an indicator for practical system evaluation. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications
Sensors 2016, 16(8), 1043; https://doi.org/10.3390/s16081043 - 06 Aug 2016
Cited by 16
Abstract
In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal [...] Read more.
In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Gaussian Process Regression Plus Method for Localization Reliability Improvement
Sensors 2016, 16(8), 1193; https://doi.org/10.3390/s16081193 - 29 Jul 2016
Cited by 7
Abstract
Location data are among the most widely used context data in context-aware and ubiquitous computing applications. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor positioning. The most useful method is focused on the [...] Read more.
Location data are among the most widely used context data in context-aware and ubiquitous computing applications. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor positioning. The most useful method is focused on the received signal strength and provides a set of signal transmission access points. However, compiling a manual measuring Received Signal Strength (RSS) fingerprint database involves high costs and thus is impractical in an online prediction environment. The system used in this study relied on the Gaussian process method, which is a nonparametric model that can be characterized completely by using the mean function and the covariance matrix. In addition, the Naive Bayes method was used to verify and simplify the computation of precise predictions. The authors conducted several experiments on simulated and real environments at Tianjin University. The experiments examined distinct data size, different kernels, and accuracy. The results showed that the proposed method not only can retain positioning accuracy but also can save computation time in location predictions. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
Sensors 2016, 16(6), 937; https://doi.org/10.3390/s16060937 - 22 Jun 2016
Cited by 2
Abstract
Target tracking is a critical wireless sensor application, which involves signal and information processing technologies. In conventional target position estimation methods, an estimate is usually demonstrated by an average target position. In contrast, this work proposes a distributed information compression method to describe [...] Read more.
Target tracking is a critical wireless sensor application, which involves signal and information processing technologies. In conventional target position estimation methods, an estimate is usually demonstrated by an average target position. In contrast, this work proposes a distributed information compression method to describe the measurement uncertainty of tracking problems in cluster-based wireless sensor networks. The leader-based information processing scheme is applied to perform target positioning and energy conservation. A two-level hierarchical network topology is adopted for energy-efficient target tracking with information compression. A Level 1 network architecture is a cluster-based network topology for managing network operations. A Level 2 network architecture is an event-based and leader-based topology, utilizing the concept of information compression to process the estimates of sensor nodes. The simulation results show that compared to conventional schemes, the proposed data processing scheme has a balanced system performance in terms of tracking accuracy, data size for transmission and energy consumption. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones
Sensors 2016, 16(6), 910; https://doi.org/10.3390/s16060910 - 18 Jun 2016
Cited by 25
Abstract
The position accuracy of Global Navigation Satellite System (GNSS) modules is one of the most significant factors in determining the feasibility of new location-based services for smartphones. Considering the structure of current smartphones, it is impossible to apply the ordinary range-domain Differential GNSS [...] Read more.
The position accuracy of Global Navigation Satellite System (GNSS) modules is one of the most significant factors in determining the feasibility of new location-based services for smartphones. Considering the structure of current smartphones, it is impossible to apply the ordinary range-domain Differential GNSS (DGNSS) method. Therefore, this paper describes and applies a DGNSS-correction projection method to a commercial smartphone. First, the local line-of-sight unit vector is calculated using the elevation and azimuth angle provided in the position-related output of Android’s LocationManager, and this is transformed to Earth-centered, Earth-fixed coordinates for use. To achieve position-domain correction for satellite systems other than GPS, such as GLONASS and BeiDou, the relevant line-of-sight unit vectors are used to construct an observation matrix suitable for multiple constellations. The results of static and dynamic tests show that the standalone GNSS accuracy is improved by about 30%–60%, thereby reducing the existing error of 3–4 m to just 1 m. The proposed algorithm enables the position error to be directly corrected via software, without the need to alter the hardware and infrastructure of the smartphone. This method of implementation and the subsequent improvement in performance are expected to be highly effective to portability and cost saving. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Probabilistic Assessment of High-Throughput Wireless Sensor Networks
Sensors 2016, 16(6), 792; https://doi.org/10.3390/s16060792 - 31 May 2016
Cited by 3
Abstract
Structural health monitoring (SHM) using wireless smart sensors (WSS) has the potential to provide rich information on the state of a structure. However, because of their distributed nature, maintaining highly robust and reliable networks can be challenging. Assessing WSS network communication quality before [...] Read more.
Structural health monitoring (SHM) using wireless smart sensors (WSS) has the potential to provide rich information on the state of a structure. However, because of their distributed nature, maintaining highly robust and reliable networks can be challenging. Assessing WSS network communication quality before and after finalizing a deployment is critical to achieve a successful WSS network for SHM purposes. Early studies on WSS network reliability mostly used temporal signal indicators, composed of a smaller number of packets, to assess the network reliability. However, because the WSS networks for SHM purpose often require high data throughput, i.e., a larger number of packets are delivered within the communication, such an approach is not sufficient. Instead, in this study, a model that can assess, probabilistically, the long-term performance of the network is proposed. The proposed model is based on readily-available measured data sets that represent communication quality during high-throughput data transfer. Then, an empirical limit-state function is determined, which is further used to estimate the probability of network communication failure. Monte Carlo simulation is adopted in this paper and applied to a small and a full-bridge wireless networks. By performing the proposed analysis in complex sensor networks, an optimized sensor topology can be achieved. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Blind RSSD-Based Indoor Localization with Confidence Calibration and Energy Control
Sensors 2016, 16(6), 788; https://doi.org/10.3390/s16060788 - 31 May 2016
Cited by 4
Abstract
Indoor localization based on wireless sensor networks (WSNs) is an important field of research with numerous applications, such as elderly care, miner security, and smart buildings. In this paper, we present a localization method based on the received signal strength difference (RSSD) to [...] Read more.
Indoor localization based on wireless sensor networks (WSNs) is an important field of research with numerous applications, such as elderly care, miner security, and smart buildings. In this paper, we present a localization method based on the received signal strength difference (RSSD) to determine a target on a map with unknown transmission information. To increase the accuracy of localization, we propose a confidence value for each anchor node to indicate its credibility for participating in the estimation. An automatic calibration device is designed to help acquire the values. The acceleration sensor and unscented Kalman filter (UKF) are also introduced to reduce the influence of measuring noise in the application. Energy control is another key point in WSN systems and may prolong the lifetime of the system. Thus, a quadtree structure is constructed to describe the region correlation between neighboring areas, and the unnecessary anchor nodes can be detected and set to sleep to save energy. The localization system is implemented on real-time Texas Instruments CC2430 and CC2431 embedded platforms, and the experimental results indicate that these mechanisms achieve a high accuracy and low energy cost. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
On the Choice of Access Point Selection Criterion and Other Position Estimation Characteristics for WLAN-Based Indoor Positioning
Sensors 2016, 16(5), 737; https://doi.org/10.3390/s16050737 - 20 May 2016
Cited by 12
Abstract
The positioning based on Wireless Local Area Networks (WLAN) is one of the most promising technologies for indoor location-based services, generally using the information carried by Received Signal Strengths (RSS). One challenge, however, is the huge amount of data in the radiomap database [...] Read more.
The positioning based on Wireless Local Area Networks (WLAN) is one of the most promising technologies for indoor location-based services, generally using the information carried by Received Signal Strengths (RSS). One challenge, however, is the huge amount of data in the radiomap database due to the enormous number of hearable Access Points (AP) that could make the positioning system very complex. This paper concentrates on WLAN-based indoor location by comparing fingerprinting, path loss and weighted centroid based positioning approaches in terms of complexity and performance and studying the effects of grid size and AP reduction with several choices for appropriate selection criterion. All results are based on real field measurements in three multi-floor buildings. We validate our earlier findings concerning several different AP selection criteria and conclude that the best results are obtained with a maximum RSS-based criterion, which also proved to be the most consistent among the different investigated approaches. We show that the weighted centroid based low-complexity method is very sensitive to AP reduction, while the path loss-based method is also very robust to high percentage removals. Indeed, for fingerprinting, 50% of the APs can be removed safely with a properly chosen removal criterion without increasing the positioning error much. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment
Sensors 2016, 16(5), 692; https://doi.org/10.3390/s16050692 - 13 May 2016
Cited by 15
Abstract
In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate [...] Read more.
In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS), is proposed, which enables vehicle state estimation (VSE) with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student’s t-distribution is adopted in order to compute the probability distribution function (PDF) related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF) is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessArticle
Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process
Sensors 2016, 16(3), 381; https://doi.org/10.3390/s16030381 - 16 Mar 2016
Cited by 9
Abstract
Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense [...] Read more.
Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense signal database is labor intensive, expensive, and even impossible in some areas. Researchers are continually searching for better algorithms to create and update dense databases more efficiently. In this paper, we propose a scalable indoor positioning algorithm that works both in surveyed and unsurveyed areas. We first propose Minimum Inverse Distance (MID) algorithm to build a virtual database with uniformly distributed virtual Reference Points (RP). The area covered by the virtual RPs can be larger than the surveyed area. A Local Gaussian Process (LGP) is then applied to estimate the virtual RPs’ RSSI values based on the crowdsourced training data. Finally, we improve the Bayesian algorithm to estimate the user’s location using the virtual database. All the parameters are optimized by simulations, and the new algorithm is tested on real-case scenarios. The results show that the new algorithm improves the accuracy by 25.5% in the surveyed area, with an average positioning error below 2.2 m for 80% of the cases. Moreover, the proposed algorithm can localize the users in the neighboring unsurveyed area. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Open AccessReview
Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances
Sensors 2016, 16(5), 707; https://doi.org/10.3390/s16050707 - 16 May 2016
Cited by 170
Abstract
In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging [...] Read more.
In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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Other

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Open AccessLetter
Localisation of Sensor Nodes with Hybrid Measurements in Wireless Sensor Networks
Sensors 2016, 16(7), 1143; https://doi.org/10.3390/s16071143 - 22 Jul 2016
Cited by 10
Abstract
Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of [...] Read more.
Localisation in wireless networks faces challenges such as high levels of signal attenuation and unknown path-loss exponents, especially in urban environments. In response to these challenges, this paper proposes solutions to localisation problems in noisy environments. A new observation model for localisation of static nodes is developed based on hybrid measurements, namely angle of arrival and received signal strength data. An approach for localisation of sensor nodes is proposed as a weighted linear least squares algorithm. The unknown path-loss exponent associated with the received signal strength is estimated jointly with the coordinates of the sensor nodes via the generalised pattern search method. The algorithm’s performance validation is conducted both theoretically and by simulation. A theoretical mean square error expression is derived, followed by the derivation of the linear Cramer-Rao bound which serves as a benchmark for the proposed location estimators. Accurate results are demonstrated with 25%–30% improvement in estimation accuracy with a weighted linear least squares algorithm as compared to linear least squares solution. Full article
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
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