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Indoor localization is an important primitive that can enable many ubiquitous computing applications. This paper improves the scheme of landmark and inertial navigation through crowd sensing. The location of landmark can calibrate the position of users, and accurate localization can optimize the landmark's location in turn. In this work, we define landmarks as certain characteristic structures with iBeacons. To tackle the challenge of low efficiency of a landmark, we combine Bluetooth signals and sensor readings to reflect the distinctive signature of a landmark and then design a method for a reliable detection of a landmark. Moreover, we investigate the calibration bias of a landmark to ensure the calibration accuracy. For improving the accuracy of inertial navigation, we personalize the step length model and correct the user's heading with the aid of crowd sensing. We have built an indoor localization system integrating the above modules and an indoor floor map, which can be further improved with more users using our system. We demonstrate for the first time a meter-level indoor localization system that is self-improving, user adaptive, and easy to deploy. Extensive experiments on users with mobile devices, with over 37 subjects walking over an aggregate distance of over 30 km, were carried out. Evaluation results show that our system can achieve a mean accuracy of 2 m initially and 1 m with the calibration of landmarks in a 39 m × 21 m testing area. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Wireless localization systems based on determination of signal runtime, such as time of arrival and time difference of arrival, are of great importance for a variety of applications. In many cases, synchronization of the clocks of the agent nodes to those of the network nodes (anchors) has to be performed together with the localization. The current paper investigates the fundamental accuracy limits of such a joint localization/synchronization. In particular, we analyze the impact of allocating power to the different anchor nodes and optimize this power allocation to maximize accuracy. Optimal trade-offs between localization and synchronization can be drawn according to the presented frameworks. Simulation results confirm the importance of proper power allocation; known special cases (time difference of arrival localization, localization with already-synchronized clocks) are recovered from our general solution. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of localization in sensor networks where, initially, a certain number of sensors are aware of their positions (either by using GPS or by being hand-placed) and are referred to as anchors. Our goal is to localize all sensors with high accuracy, while using a limited number of anchors. Sensors can be equipped with different technologies for signal and angle measurements. These measures can be altered by some errors because of the network environment that induces position inaccuracies. In this paper, we propose a family (AT-Family) of three new distributed localization techniques in wireless sensor networks: free-measurement (AT-Free) where sensors have no capability of measure, signal-measurement (AT-Dist) where sensors can calculate distances, and angle-measurement (AT-Angle) where sensors can calculate angles. These methods determine the position of each sensor while indicating the accuracy of its position. They have two important properties: first, a sensor node can deduce if its estimated position is close to its real position and contribute to the positioning of others nodes; second, a sensor can eliminate wrong information received about its position. This last property allows to manage measure errors that are the main drawback of measure-based methods such as AT-Dist and AT-Angle techniques. By varying the density and the error rate, simulations show that the three proposed techniques achieve good performances in term of high accuracy of localized nodes and less energy consuming while assuming presence of measure errors and considering low number of anchors. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, a new model utilizing all the information derived from connectivity-based sensor network localization is introduced. The connectivity information between any pair of nodes is modeled as convex and non-convex constraints. The localization problem is solved by searching for a solution that would satisfy all the constraints established in the problem. A two-objective evolutionary algorithm called Pareto Archived Evolution Strategy (PAES) is used to solve the localization problem. The solution can reach the most suitable configuration of the unknown nodes because the information on both convex and non-convex constraints related to connectivity has been utilized. From simulation results, a relationship between the communication range and accuracy is obtained. Furthermore, a two-level range connectivity-based sensor network localization method is proposed to enrich the connectivity information. The two-level range/indication of connectivity between each pair of nodes would indicate three levels of connectivity: strong, weak, or nil. A comparison on accuracy between the one-level and two-level ranges of connectivity is carried out by simulation using six different topological networks all containing 100 nodes. Simulation results have shown that better solution can be obtained by using two-level range connectivity compared with the usual one-level range connectivity-based localization. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), many applications require sensor nodes to obtain their locations. Now, the main idea in most existing localization algorithms has been that a mobile anchor node (e.g., global positioning system-equipped nodes) broadcasts its coordinates to help other unknown nodes to localize themselves while moving according to a specified trajectory. This method not only reduces the cost of WSNs but also gets high localization accuracy. In this case, a basic problem is that the path planning of the mobile anchor node should move along the trajectory to minimize the localization error and to localize the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) in WSNs. LMAT algorithm uses a mobile anchor node to move according to trilateration trajectory in deployment area and broadcasts its current position periodically. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper addresses the localization problem in wireless sensor networks using signal strength. We use a kernel function to measure the similarities between sensor nodes. The kernel matrix can be naturally defined in terms of the signal strength matrix. We show that the relative locations of sensor nodes can be obtained by solving a dimension reduction problem. To capture the structure of the whole network, we use the kernel spectral regression (KSR) method to estimate the relative locations of the sensor nodes. Given sufficient anchor nodes, the relative locations can be aligned to global locations. The key benefits of adopting KSR are that it allows us to define a graph to optimally preserve the topological structure of the sensor network, and a kernel function can capture the nonlinear relationship in the signal space. Simulation results show that we can achieve small average location error with a small number of anchors. We also compare our method with several related methods, and the results show that KSR is more efficient than the others in our simulated sensor networks. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Deployments over large geographical areas in the Internet of Things (IoT) pose a major challenge for single-hop localization techniques, giving rise to applications of multi-hop localizations. And while many proposals have been made on implementations for multi-hop localization, a close understanding of its characteristics is yet to be established. Such an understanding is necessary, and is inevitable in extending the reliability of location based services in IoT. In this paper, we study the characteristics of multi-hop localization and propose a new solution to enhance the performance of multi-hop localization techniques. We first examine popular assumptions made in simulating multi-hop localization techniques, and offer rectifications facilitating more realistic simulation models. We identify the introduced errors to follow the Gaussian distribution, and the estimated distance follows the Rayleigh distribution. We next use our simulation model to characterize the effect of the number of hops on localization in both dense and sparse deployments. We find that, contrary to common belief, it is better to use long hops in sparse deployments, while short hops are better in dense deployments - despite the traffic overhead. Finally, we propose a new solution that decreases and manages the overhead generated during the localization process. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper introduces a new vehicle localization approach for global positioning system-challenged road segments (e.g., tunnels), which takes advantage of roadside units (RSUs) and in-vehicle inertial navigation system (INS). In the proposed approach, namely RSU/INS-aided localization system (RIALS), vehicles only need one RSU in their transmission range for an accurate positioning. The beacons received from the RSU along with the information provided by the INS system are used for establishing and maintaining particular locus circles. After linearization, the system of locus circle equations is solved using the linear least square estimation technique, and the estimated vehicle's position is obtained. In the presence of speed variations and existing ranging and INS estimation errors, the proposed RIALS adaptively sets the sufficient number of required locus circles, aiming at keeping the localization error below a given threshold. We study the effects of ranging and INS estimation errors on localization accuracy of RIALS from analytical and simulation perspectives. Results of extensive simulations show that the localization error is more sensitive to the ranging error than the INS error. Moreover, the network traffic overhead of the proposed method is considerably lower than other competitive localization approaches. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In large-scale wireless sensor networks, cost-effective and energy-efficient localization of sensor nodes is an important research topic. In spite of their coarse accuracy, range-free (connectivity-based) localization methods are considered as cost-effective alternatives to the range-based localization schemes with specialized hardware requirements. In this paper, we derive closed-form expressions for the average minimum transmit powers required for the localization of sensor nodes, under deterministic path loss, log-normal shadowing, and Rayleigh fading channel models. The impacts of propagation environment and spatial density of anchor nodes on the minimum transmit power for node localization are evaluated analytically as well as through simulations. Knowledge of the minimum transmit power requirements for localizability of a sensor node enables improving energy efficiency and prolonging lifetime of the network. We also propose a novel distance metric for range-free localization in large-scale sensor networks. The target and anchor nodes are assumed to be positioned according to two statistically independent two-dimensional homogeneous Poisson point processes. Analytical expression for the average distance from a target node to its kth nearest neighbor anchor node is derived and is used for estimating the target-to-anchor node distances for localization. The Cramér-Rao lower bound on the localization accuracy for the new distance estimator is derived. Simulation results show the accuracy of the proposed distance estimate compared with some existing ones for range-free localization. The results of our investigation are significant for low-cost, energy-efficient localization of wireless sensor nodes. [ABSTRACT FROM AUTHOR]

Node localization is essential to wireless sensor networks (WSN) and its applications. In this paper, we propose a particle swarm optimization (PSO) based localization algorithm (PLA) for WSNs with one or more mobile anchors. In PLA, each mobile anchor broadcasts beacons periodically, and sensor nodes locate themselves upon the receipt of multiple such messages. PLA does not require anchors to move along an optimized or a pre-determined path. This property makes it suitable for WSN applications in which data-collection and network management are undertaken by mobile data sinks with known locations. To the best of our knowledge, this is the first time that PSO is used in range-free localization in a WSN with mobile anchors. We further derive the upper bound on the localization error using Centroid method and PLA. Simulation results show that PLA can achieve high performance in various scenarios. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Using geographic routing, like GPSR, is efficient for ad hoc and wireless sensor networks, but it requires that nodes be aware of their physical positions. However, if there are holes in the network, routing across them using GPSR will lead to a lot of overloaded nodes on their boundaries. In this paper, we propose a distributed protocol, named hexagonal virtual coordinate (HVC), for constructing a virtual coordinate system. After the HVC is constructed, the nodes in the network will be aware of the relative coordinates among the landmarks through the HVC chart. Based on the HVC chart, a source node can find an auxiliary routing path (ARP) to indicate the direction of the journey from the source to the destination. Simulation results show that our protocol can support geographic routing efficiently, and the landmarks found by our protocol are uniformly located in the network even if some holes exist within it. In addition, our protocol is resilient to various network shapes and can find a load balancing routing path to the destination even if this path comes across holes in the network. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Target localization and tracking are two of the critical tasks of sensor networks in many applications. Conventional localization and tracking techniques developed for wireless systems that rely on direction-of-arrival (DOA) or time-of-arrival (TOA) information are not suitable for low-power sensors with limited computation and communication capabilities. In this paper, we propose a low-complexity and energy-efficient method for target localization and tracking in noisy binary sensor networks, where the sensors can only perform binary detection, and the physical links are characterized by additive white Gaussian noise (AWGN) channels. The proposed method is based on known spatial topology. An efficient wake-up strategy is used to activate a particular group of sensors for cooperative localization and tracking. We analyze the localization error probability and tracking miss probability in the presence of prediction errors. Simulation results validate the theoretic analysis and demonstrate the effectiveness of the proposed approach. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Localization is essential for wireless sensor networks (WSNs). It is to determine the positions of sensor nodes based on incomplete mutual distance measurements. In this paper, to measure the accuracy of localization algorithms, a ranging error model for time of arrival (TOA) estimation is given, and the Cramer—Rao Bound (CRB) for the model is derived. Then an algorithm is proposed to deal with the case where (1) ranging error accumulation exists, and (2) some anchor nodes broadcast inaccurate/wrong location information. Specifically, we first present a ranging error-tolerable topology reconstruction method without knowledge of anchor node locations. Then we propose a method to detect anchor nodes whose location information is inaccurate/wrong. Simulations demonstrate the effectiveness of our algorithm. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Securing sensor networks has received much attention in the last few years. In a typical field deployment, a sensor network self-organizes, closely interacts with its physical environment, and works unattended possibly in a hostile environment such as a battlefield. In such environments, sensor networks are subject to node capture as well as a myriad of other attacks. Constrained energy, memory, and computational capabilities of sensor nodes mandate a clever design of security solutions to minimize overhead while maintaining secure communications over the lifespan of the network. In this paper, we propose a Dynamic Combinatorial Key management scheme, DCK, to provide efficient, scalable, and survivable dynamic keying in a clustered sensor network with a large number of sensor nodes. DCK employs the Exclusion-Basis Systems (EBS) as the underlying framework for key management at both the cluster and the sensor node levels. DCK enhances network security by localizing cluster key management functions, thus, limiting the impact of sensor node capture to the attacked cluster. Within a cluster, DCK decouples key generation, assignment, and distribution, and distributes the key management tasks among cluster nodes. Simulation results show that DCK is efficient in terms of energy consumption and storage. Also, it significantly outperforms other dynamic keying schemes, in particular with regards to energy consumed in key refreshment and re-keying after node capture. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Receiver Strength Signal Indication based Wireless Sensor Networks offer a cheap solution for location-aware applications. For a final breakthrough these systems need fast deployment and easy auto-configuration. In this study, we use the real-life iMinds test bed to expand a two-dimensional localization algorithm to the pseudo third dimension with very low additional computational time. Our experiments show that this fast three-dimensional algorithm has no outliers and avoids manual calibration. Our algorithm has lower position errors than a maximum likelihood algorithm with a mean square error cost function. Furthermore, with non-parametric statistical tests, we show that our previously designed two-dimensional preprocessing performs equally well in pseudo-three dimensions: the preprocessing reduces the position error in a statistically significant way.Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Accuracy is still the greatest challenge in the wireless sensor network localization efforts. Several diverse factors can give rise to localization errors. Modeling such diverse influencing factors to deliver a single, reasonably simple and practical solution is a difficult task. In order to address the problem of location inaccuracy, we propose a comparatively simple and ingenious approach, which is the simultaneous perturbation stochastic approximation (SPSA) localization engine. SPSA bypasses tedious modeling of the influencing factors where some of them are yet to be explored and random in nature. SPSA-based localization estimates the non-anchor node locations through minimizing the summation of estimated errors of all neighbors. However, the downside of SPSA is that it incurs errors in some specific relative neighborhood configurations often referred to as flip ambiguity. So, we further propose a solution to the flip ambiguity problem by implementing a constrained optimization with a penalty function method on the identified flip nodes. Most importantly, error propagation of the iterative localization algorithm is managed by incorporating a neighbor confidence matrix. We name this modified SPSA engine as simultaneous perturbation stochastic approximation by neighbor confidence (SPSA-NC). Experimental results show that SPSA-NC offers significantly better localization accuracy than its state-of-the-art competitors, namely, simulated annealing and the ordinary SPSA. The SPSA-NC program is available for downloading at . Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

There is recently an increasing interest in applications based on localization of mobile objects in outdoor environment. Many existing localization solutions rely primarily on a hybrid wireless network/dead reckoning (DR) scheme, as the regular wireless network can hardly estimate a position with satisfactory accuracy in bad channel conditions. However, the DR can involve considerable hardware investments and operating costs, moreover, it suffers from serious error accumulations in motion measurements of the object. To remedy drawbacks of the hybrid scheme, we present a mobility enhanced localization (MEL), in which the mobile object serves as a pseudo beacon. The proposed scheme enables network localization in those areas where traditional wireless networks may not work, and thus, avoids large investments the special hardware cost and error accumulations of DR. We further propose a weighted preprocessing method for classical localization algorithms, since theoretical analysis shows that they fail to consider some boundary conditions. Extensive real world Global Positioning System (GPS) experimental results demonstrate the superiority of the proposed MEL scheme. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]