InPhase: Phase-based Ranging and Localization

Accurate localization or tracking of wireless devices is a crucial requirement for many emerging location-aware systems. Fields of applications include search and rescue, medical care, intelligent transportation, locationbased billing, security, home automation, industrial monitoring and control, location-assisted gaming, and social networking. During the last few years, there have been intensive research activities in this area and various solutions have been investigated. The main trend now is toward the integration of heterogeneous technologies to ensure global coverage and high accuracy in all possible scenarios, leading to a seamless localization system available anywhere anytime. While satellite-based navigation is well consolidated for open sky scenarios, localization in harsh environments (e.g., indoor or in urban canyons) is still an open issue that requires complementary wireless networks. Cellular systems, local/personal area networks, ad hoc, and wireless-sensor networks can be configured to support localization functionality. Indoor environments, however, are particularly challenging because of severe multipath and non-line-of-sight (NLOS) propagation. In this context, advanced signal processing algorithms must be employed in order to guarantee positioning robustness, such as NLOS identification and mitigation, fusion of data from different sources, and Bayesian methods to enclose any a priori information (e.g., dynamic models for mobile positioning). An important area of research is cooperative localization, which is expected to significantly improve both accuracy and coverage by exploiting all the available measurements on a peer-to-peer basis; efficient protocols and procedures have to be designed to minimize communication overheads and energy consumption. Measurement campaigns are essential for calibrating signal models and testing localization algorithms. A valuable tool for benchmarking algorithms is also provided by fundamental performance bounds, which are being actively analyzed as guidelines for the design of efficient positioning systems. The objective of this special issue, which was promoted under the auspices of the EC Network of Excellence in Wireless Communications NEWCOM++ (in particular, the Work Package WPR.B on Localization and Positioning Techniques), was to gather recent advances in both signal processing and communications areas, for localization in mobile wireless and sensor networks. Articles were solicited on both experimental and theoretical aspects, including new positioning algorithms and methodologies, system design and configuration, performance analysis and measurement campaigns. We received a total of 56 manuscripts addressing the above issues and challenges, of which 16 were selected for publication. Selection of each article was the result of a careful assessment by at least two (mostly three) independent reviewers with expertise on localization and wireless networking. Articles went through a minimum of two to a maximum of four revision phases before acceptance. Accepted articles belong to four main research areas: integration of positioning and communication functionalities, robustness to NLOS errors, indoor positioning, and localization in wireless sensor networks (WSNs). The first group of articles deals with the interaction of positioning and communications at different layers of the protocol stack. Connectivity issues are studied in Gao et al., which considers the relation between distance and communication hops, accounting for the border effect and dependence problems, for a model that is more realistic than the traditional unit-disk graph model. A related problem is investigated in the study of Moragrega et al., which deals with location-aware cluster formation. The authors propose LACFA, a distributed network formation algorithm that significantly increases the probability of localization of sensors in a cluster-tree topology. On the physical layer, Schmeink et * Correspondence: nicoli@elet.polimi.it Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milan, Italy Full list of author information is available at the end of the article Nicoli et al. EURASIP Journal on Wireless Communications and Networking 2011, 2011:197 http://jwcn.eurasipjournals.com/content/2011/1/197

Localization in wireless sensor networks has become a significant research challenge, attracting many researchers in the past decade. This paper provides a review of basic techniques and the state-of-the-art approaches for wireless sensors localization. The challenges and future research opportunities are discussed in relation to the design of the collaborative workspaces based on cooperative wireless sensor networks.

We develop a novel approach to source localization in mobile wireless sensor networks. Standard approaches make explicit assumptions relating to the statistical characteristics of the physical process and propagation environments which result from distributional model assumptions in a likelihood-based inference method. In contrast, we adopt an approach known in statistics as a non-parametric modeling framework which allows one to relax the number of required statistical assumptions, specifically with regard to the distributional properties of the received signal and the physical process. This is achieved via a re-formulation of the problem as a flexible non-parametric regression model via the framework of Gaussian Processes. Coupling this modeling perspective with a Bayesian optimization mechanism, we frame the global optimization objective as a sequential decision problem. We then develop an efficient algorithm to sequentially select the optimal location at which the mobile sensor should obtain observations under communication and mobility constraints. Simulation results demonstrate the efficiency of the algorithm at achieving accurate localization in a wireless sensor network.

Localization is a process of finding the coordinates of the deployed sensor nodes in the sensing area. Localization in wireless sensor networks (WSNs) is important as it results in location-stamped communication. Applications of WSNs like forest fire detection, nuclear or biological attacks, locating survivors post-disasters, etc. require location-aware information for real time and accurate response. Generally, the sensor nodes are not equipped with global positioning system due to its inefficiency for indoor and underwater regions. Other drawbacks include cost and power consumption. Localization in 3D WSNs is a complex task. The complexity increases due to an additional dimension leading to larger neighboring nodes and increase in coverage area (i.e., area coverage of a sensor node changes from circular to spherical). A lot of approaches have been proposed to localize the sensor nodes in 2D and are now being seen in 3D aspect. Although good efforts have been made for research summarization and guidance in 2D localization, it lacked attention by the researchers in 3D scope. This paper provides a comprehensive survey of the algorithms designed for localizing the sensor nodes in terrestrial and underwater regions. These algorithms have been classified based on the nature of anchor nodes. The localization methods have been categorized as static anchor node-based or mobile anchor node-based and further range-free or range-based depending upon the number of anchor nodes availability and distance estimation technique, respectively. The limitations and challenges of the proposed approaches have also been discussed briefly. The paper also tabulates these methods on the basis of attributes like localization process, complexity, number of sensor nodes used, etc.

In this paper, we are concerned with the problem of nonlinear inequalities defined on a graph. The feasible solution set to this problem is often infinity and Laplacian eigenmap is used as heuristic information to gain better performance in the solution. A continuous-time projected neural network, and the corresponding discrete-time projected neural network are both given to tackle this problem iteratively. The convergence of the neural networks are proven in theory. The effectiveness of the proposed neural networks are tested and compared with others via its applications in the range-free localization of wireless sensor networks. Simulations demonstrate the effectiveness of the proposed methods.

Wireless sensor networks (WSNs) localization is a key technology nowadays. Applications and protocols that need localization are called location-aware. One might think of equipping each node with a global position system (GPS) in WSN. In contrast, WSN constraints on power consumption and cost make it infeasible. The anchor nodes are one of the optimistic methods to limit unknown nodes, The anchor nodesequipped with GPS units to broadcast their current locations and used to find the unknown nodes with localization. Moreover, WSN nodes are non-uniformly distributed within the field with vacancies in different uses and different projects. Finally, Euclidean distance calculation between WSNs is giving wrong results with poor accuracy of node localization. Different topologies are possible in WSN localization problems. Concave shape, O shape, and concave shapes are important topologies. The proposed algorithm gives good results for all the topologies. Our results show that the error in the horizontal plane is less than 0.25 m while in the Z-axis is less than 0.5 m.

Localization is an important aspect in the field of wireless sensor networks that has attracted significant research interest recently. The interest in wireless sensor network localization is expected to grow further with the advances in the wireless communication techniques and the sensing techniques, and the consequent proliferation of wireless sensor network applications. This chapter provides an overview of various aspects involved in the design and implementation of wireless sensor network localization systems. These can be broadly classified into three categories: the measurement techniques in sensor network localization, sensor network localization theory and algorithms, and experimental study and applications of sensor network localization techniques. This chapter also gives a brief introduction to the other chapters in the book with a focus on explaining how these chapters are related to each other and how topics covered in each chapter fit into the architecture of this book and the big picture of wireless sensor network localization.

In wireless sensor networks (WSNs), location information of sensor nodes (or simply sensors) plays a vital role in both of the management of WNSs and many other applications. Due to the constraints on costs and energy, only a small portion of nodes in a WSN is deployed as anchor nodes or simply anchors with their locations a priori known or determined through certain hardware (e.g. GPS) to localize normal sensor nodes. However, the placement of such anchors has significant influence on the localization performance of sensor nodes. This paper tackles the problem of optimal anchor placement for localization in large-scale WSNs. But, differently from existing studies assuming independent and identically distributed measurement noises, this paper takes into account more practical distance dependent measurement noises. Then, provided that sensors' locations satisfy a homogeneous Poisson Point process, a theoretical analysis based on the average Cramer-Rao Lower Bound (CRLB) proves that it is optimal to place anchors in a regular fashion. In particular, given that each sensor can measure distances to nearby 3 anchors, the optimal anchor placement pattern is the equilateral triangle pattern, which is consistent with the optimal node deployment for 3-coverage and 6-connectivity. This study not only provides the knowledge for guiding the deployment of large-scale WSNs in practice, but also paves the way for building the theory of sensor localization in WSNs.

Localisation in wireless sensor networks (WSNs) not only provides a node with its geographical location but also a basic requirement for other applications such as geographical routing. Although a rich literature is available for localisation in static WSN, not enough work is done for mobile WSNs, owing to the complexity because of node mobility. Most of the existing techniques for localisation in mobile WSNs use Monte Carlo localisation (MCL), which is not only time consuming but also memory intensive. They, consider either the unknown nodes or anchor nodes to be static. In this study, the authors propose a technique called dead reckoning localisation for mobile WSNs (DRLMSN). In the proposed technique all nodes (unknown nodes as well as anchor nodes) are mobile. Localisation in DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are localised for the first time using three anchor nodes. For their subsequent localisations, only two anchor nodes are used. The proposed technique estimates two possible locations of a node using Bézout's theorem. A dead reckoning approach is used to select one of the two estimated locations. The authors have evaluated DRLMSN through simulation using Castalia simulator, and is compared with a similar technique called received signal strength‐MCL proposed by Wang and Zhu (2008).

Ground Based Wireless Positioning provides an in-depth treatment of non-GPS based wireless positioning techniques, with a balance between theory and engineering practice. The book presents the architecture, design and testing of a variety of wireless positioning systems based on the time-of-arrival, signal strength, and angle-of-arrival measurements. These techniques are essential for developing accurate wireless positioning systems which can operate reliably in both indoor and outdoor environments where the Global Positioning System (GPS) proves to be inadequate. The book covers a wide range of issues including radio propagation, parameter identification, statistical signal processing, optimization, and localization in large and multi-hop networks. A comprehensive study on the state-of-the-art techniques and methodologies in wireless positioning and tracking is provided, including anchor-based and anchor-free localisation in wireless sensor networks (WSN). The authors address real world issues such as multipath, non-line-of-sight (NLOS) propagation, accuracy limitations and measurement errors. Presenting the latest advances in the field, Ground Based Wireless Positioning is one of the first books to cover non-GPS based technologies for wireless positioning. It serves as an indispensable reference for researchers and engineers specialising in the fields of localization and tracking, and wireless sensor networks. Provides a comprehensive treatment of methodologies and algorithms for positioning and tracking Includes practical issues and case studies in designing real wireless positioning systems Explains non-line-of-sight (NLOS) radio propagation and NLOS mitigation techniques Balances solid theory with engineering practice of non-GPS wireless systems

Analysis and evaluation of adaptive RSSI-based ranging in outdoor wireless sensor networks

In the current global economy, localization in WSNs becomes a hot topic and it is attracting many researchers towards itself. WSN plays an important role in tracking objects in indoor as well as outdoor environments. The sensor data is useless until the location of the reporting node is unknown. The main aim of localization in wireless sensor networks (WSNs) is to determine the coordinates of target nodes in the sensing field by applying different approaches. This can be done either by the global positioning system (GPS) or any other methods. Usually, in localization methods, anchor nodes (GPS-equipped) broadcast beacon signals in the sensing field to help target nodes to localize themselves. The terms localization accuracy, localized nodes and computing time are mainly responsible for the performance of WSNs. The transmission range and density of anchor nodes directly affect the localization error. In this paper, the author surveys the various localization techniques optimized by nature-inspired algorithms and compares the salp swarm optimization algorithm with some well-known existing algorithms.

Network-wide localization in wireless sensor networks leverages the location information of a small set of nodes, called anchors, for estimating the location coordinates of non-anchor nodes. Traditionally triangulation based methods are used in an iterative manner to gradually localize all non-anchor nodes in the network. The challenge in these methods is how to control the propagation of initially small localizing errors that later magnify during the localization of remaining nodes. Machine learning based localizing algorithms, on the other hand, do not work in an iterative manner. However, one of the crucial challenges in this approach is dealing with relatively smaller training data sets typically derived from anchor nodes and the number of such nodes is normally small in the network. In order to overcome that problem, this paper proposes to use additional sampling locations, called virtual nodes, in the deployment area for generating additional training data. No physical nodes are required to be placed at these sampling points. Rather a suitable automated tool can be used for generating additional training data. Specifically, a wireless sniffer device can be used for generating additional feature vectors by way of recording the Received Signal Strength Indicator (RSSI) values at every sampling location. Our findings from simulations on how the localizing accuracy improves by exploiting different trade-offs regarding the use of virtual nodes are reported here.

Mobile device localization in wireless sensor networks is a challenging task. It has already been addressed when the WiFi propagation maps of the access points are modeled deterministically or estimated using an offline human training calibration. However, these techniques do not take into account the environmental dynamics. In this paper, the maps are assumed to be made of an average indoor propagation model combined with a perturbation field which represents the influence of the environment. This perturbation field is embedded with a distribution describing the prior knowledge about the environmental influence. The device is localized with Sequential Monte Carlo methods and relies on the estimation of the propagation maps. This inference task is performed online, using the observations sequentially, with a new online Expectation Maximization based algorithm. The performance of the algorithm is illustrated with Monte Carlo experiments using both simulated data and a true data set.

Node localization in mobile wireless sensor networks is a challenging task because of the changing environments and outlying sensor readings. A robust cooperative localization algorithm based on particle filter is proposed in this paper to deal with this non-Gaussian and non-linear estimation problem. Trace prediction, genetic crossover operation and node cooperation methods are used to optimize the particle quality and weight, so as to improve the localization accuracy of the algorithm. Only the inexpensive binary sensors are needed to provide observation information which makes the algorithm suit for almost every type of wireless sensor networks. Simulation results demonstrate that the proposed algorithm outperforms other classical localization methods while with low anchor node density.