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Abstract In sensors localization, it is not economically argumentative to equip many static nodes with GPS. The most widespread solution is to utilize from mobile location-aware nodes called mobile anchors. A substantial amount of research to propose mobile anchor trajectory to improve localization accuracy, localization latency, network coverage and traversed path has been reported. However, none of the existing static mobile anchor path planning mechanisms has emphasized on increasing mobile anchor lifetime and reliability that is necessary due to limited energy. In this paper we propose a novel mobile anchor trajectory planning scheme called Optimal Priority based Trajectory with Energy Constraint (OPTEC) in order to address these issues. The proposed scheme utilizes the Mixed Integer Linear Programming optimization (MILP) approach for optimal route planning in the presence of location uncertainty for deployed sensors. Several important evaluation metrics including localization coverage, localization success, ineffective beacon points and energy-location uncertainty product are also defined for a comprehensive comparison of static mobile anchor trajectory plans. In this paper, static sensors utilize a range-free localization algorithm. Simulation results reveal that the proposed mobile anchor trajectory planning approach can surpass other existing trajectories in terms of localization error, mobile anchor energy consumption and even sensors lifetime. [ABSTRACT FROM AUTHOR]

This paper proposes a novel node localization method in sparse underwater wireless sensor networks (UWSNs) where the locations of only a small number of anchor nodes are available. The proposed method estimates the Euclidean distances from anchor nodes to multi-hop sensor nodes with the help of angle of arrival (AoA) measurements in the local coordinate systems of the routing nodes. A new distance estimation method is proposed for sensor nodes with greater-than-two-hops to an anchor node by accurately estimating the rotation matrix between the routing nodes. By forwarding distances hop-by-hop through the network, the sensor nodes are able to obtain distance estimates to more than four or five anchor nodes. Then the location of the sensor node is solved by a weighted Least Squares method. This paper develops a practical table of weights for different number of hops and AoA estimation errors. Simulation results show that the proposed localization method outperforms the existing multi-hop localization algorithms, such as DV-hop, DV-distance, Euclidean, and Cosine-law methods, in terms of distance estimation and location estimation, even if the AoA measurement error is large. Meanwhile, the proposed method maintains almost the same high localization coverage as the existing methods. [ABSTRACT FROM AUTHOR]

Node localization is one of the key technologies in wireless sensor networks, which provides technical support for many location-based applications. In the process of node localization, due to the limitation of cost and energy consumption of wireless sensor networks, mobile anchor nodes more suitable for localization as a way to reduce costs. However, to the best of our knowledge, there has been no comprehensive and in-depth study dedicated to the problem of assisted localization of mobile anchor nodes for multidimensional wireless sensor networks in the last decade, as well as there are no latest research reports reflecting the latest progress in this field. In this paper, we focus on a detailed study of the mobile anchor node assisted localization problem for multidimensional wireless sensor networks. Firstly, a model for mobile anchor node assisted localization is described. Then static path planning and dynamic path planning methods for mobile anchor nodes in 2D and 3D environments are introduced, respectively, and compare them in terms of localization environments, number of mobile anchor nodes, mobile model, area full coverage, energy consumption of anchor nodes, localization ratio, localization error and path length. Finally, problems of the existing methods in this field and the possible future research directions are discussed. Intended to provide reference for researchers in this field and enable readers to gain a deeper understanding of Multi-Dimensional Path Planning Method for mobile anchor node Localization in wireless sensor networks (MDPPML), as well as research trends and future research directions in this field. [ABSTRACT FROM AUTHOR]

Several applications have exploited wireless sensor networks (WSNs), and localization is a key WSN technology. The range-free localization technique is substantially less expensive than conventional range-based localization strategies since it does not require distance or angle measurements between the anchor and unknown nodes. The Distance Vector-Hop (DV-Hop) technique is a widespread localization solution for WSNs because of its straightforward theory and minimal cost. The coordinate estimating precision of the classic DV-Hop algorithm needs further improvement due to its large localization error. In this paper, a DV-Hop-based method utilizing modified optimum anchor node subset (MOANS DV-Hop) is proposed to improve the localization performance of the DV-Hop algorithm. A strategy for anchor node deployment is proposed. An objective function is formulated to minimize the error in estimating coordinates of unknown nodes. With the MOANS DV-Hop algorithm, each anchor node first uses other anchor nodes to locate itself, then uses the Scaled General Learning Equilibrium Optimizer (SGLEO) algorithm to create an optimal subset made up of anchor nodes other than itself. The anchor node then updates its average hop size using the anchor node subset and broadcasts both the updated hop size and the anchor node subset to the nearby unknown nodes. An unknown node then determines its location using the Equilibrium Optimizer (EO) algorithm, the anchor node subset, and the updated hop size received from the closest anchor node. Simulation findings show that MOANS DV-Hop algorithm has a greater level of localization accuracy in coordinate estimation than both the classical and other improved DV-Hop methods. [ABSTRACT FROM AUTHOR]

Localization is a fundamental issue for many applications in wireless sensor networks. Without the need of additional ranging devices, the range-free localization technology is a cost-effective solution for low-cost indoor and outdoor wireless sensor networks. Among range-free algorithms, DV-hop (Distance Vector-hop) has the advantage to localize the mobile nodes which has less than three neighbour anchors. Based on the original DV-hop algorithm, this paper presents two improved algorithms (Checkout DV-hop and Selective 3-Anchor DV-hop). Checkout DV-hop algorithm estimates the mobile node position by using the nearest anchor, while Selective 3-Anchor DV-hop algorithm chooses the best 3 anchors to improve localization accuracy. Then, in order to implement these DV-hop based algorithms in network scenarios, a novel DV-hop localization protocol is proposed. This new protocol is presented in detail in this paper, including the format of data payloads, the improved collision reduction method E-CSMA/CA, as well as parameters used in deciding the end of each DV-hop step. Finally, using our localization protocol, we investigate the performance of typical DV-hop based algorithms in terms of localization accuracy, mobility, synchronization and overhead. Simulation results prove that Selective 3-Anchor DV-hop algorithm offers the best performance compared to Checkout DV-hop and the original DV-hop algorithm. [ABSTRACT FROM AUTHOR]

Coverage preservation during a mission is a crucial issue for wireless sensor networks (WSNs). There are numerous methods to measure the coverage globally, such as circular, grid and non-circular models, but only a few algorithms can be used to measure the coverage locally, using a sensor. A local coverage measurement algorithm uses only the location of a sensor and its neighbors to calculate the coverage, either by having the location information or by determining this information. Absolute localization for location-based services determines sensor positions according to the global Cartesian coordinate system, usually with the help of a global positioning system (GPS) device. In some applications, such as local coverage measurement, the sectional position of neighbors related to a sensor is sufficient. For a GPS-free environment, this paper develops a local position estimation algorithm (LPEA), using triangulation rules, which contains estimation and estimation correction procedures. Simulation results demonstrated that the algorithm is superior to previous work on finding the exact location of neighbors. Moreover, the estimation error in finding the location of neighbors in a 100-m wide field is less than 5 m whenever a minimum of eight neighbors is available. Furthermore, measuring coverage using the results of this LPEA is nearly identical to results where the locations of all sensors are known. In this paper, the circular model, the Delaunay triangulation (DT) coverage measurement model, was used, where the circular model, the circular model with shadowing effect and the circular probabilistic model are measurable through a DT coverage measurement. Simulation and real device experiments indicated that the algorithm is significantly accurate in terms of the local coverage measurement and accelerates the operation in coverage measurement algorithms in wireless sensor networks. [ABSTRACT FROM AUTHOR]

Abstract: This paper revisits the problem of range measurement based localization of a signal source or a sensor. The major geometric difficulty of the problem comes from the non-convex structure of optimization tasks associated with range measurements, noting that the set of source locations corresponding to a certain distance measurement by a fixed point sensor is non-convex both in two and three dimensions. Differently from various recent approaches to this localization problem, all starting with a non-convex geometric minimization problem and attempting to devise methods to compensate the non-convexity effects, we suggest a geometric strategy to compose a convex minimization problem first, that is equivalent to the initial non-convex problem, at least in noise-free measurement cases. Once the convex equivalent problem is formed, a wide variety of convex minimization algorithms can be applied. The paper also suggests a gradient based localization algorithm utilizing the introduced convex cost function for localization. Furthermore, the effects of measurement noises are briefly discussed. The design, analysis, and discussions are supported by a set of numerical simulations. [Copyright &y& Elsevier]

Abstract: Wireless sensor networks (WSNs) are a target technology for oil and gas pipeline monitoring because they offer benefits of low cost, ease of deployment and ability to cater for data acquisition at great spatial and temporal scales. In order for WSN to achieve trademark performance in remote monitoring of pipelines, and surpass the performance of present-day traditional monitoring systems, certain design requirements must be met. In this paper, we identify vital design issues that must be considered to facilitate the employment of WSN for pipeline monitoring. We classify these design issues into five different categories namely; sensing modality, power efficiency, energy harvesting, network reliability and localization. In addition, we discuss the concept of cooperative communication for pipeline-monitoring sensor networks deployed in sub-sea environments. We also study the employment of sensor networks for monitoring underground pipelines. Our findings are based on extensive study of the recent literature and comprehensive survey of existing WSN technologies. The WSN design considerations presented in this paper are particularly prolific for pipeline monitoring scenarios, they can however be easily extended to other oil and gas infrastructures. For example; well-head and heat exchanger monitoring, oil platform process monitoring, monitoring of natural gas storage facilities and data collection on coastal infrastructures that could support oil and gas exploration. [Copyright &y& Elsevier]

Abstract Estimating inter-node distances based on received radio signal strength (RSSI) is the foundation of RSSI-based outdoor localization in wireless sensor networks (WSNs). However, the accuracy of RSSI-based ranging depends on environmental and weather conditions. Therefore, it is important that RSSI-based ranging adapts to prevailing conditions to improve its range and location accuracy. This paper analyzes and evaluates RSSI-based ranging and adaptive techniques in outdoor WSNs to improve the range quality. The findings highlight the effects of path loss exponent (PLE) estimation error and temperature change on RSSI-based ranging. Consequently, we analyze techniques for mitigating these detrimental effects and propose an adaptive RSSI-based ranging algorithm in order to improve the ranging quality in changing outdoor conditions. The algorithm comprises link RSSI estimation, temperature compensation, PLE estimation, and inter-node distance estimation. Furthermore, we evaluate the performance of the proposed algorithm and compare different WSN-specific PLE estimation techniques by employing real measurement data of 2.4 GHz IEEE 802.15.4-compliant WSN nodes. The results indicate that although ranging error can be mitigated using the proposed adaptive techniques, the accuracy when a single PLE estimate is used is, in general, limited due to high inter-link PLE variation. [ABSTRACT FROM AUTHOR]

Indoor localization systems allow for innovative Industry 4.0 applications such as tracking of assets, people, or robots. Due to its cm-level accuracy, the Ultra Wide Band (UWB) technology seems to be a perfect fit as an enabler for these advanced use cases. Most current UWB research papers and commercial offerings assume that battery-powered mobile tags communicate with non-energy constrained infrastructure devices. However, in many deployments it is not possible to offer the required power cabling at the correct locations to provide energy to all infrastructure nodes. To this end, this paper proposes a novel power-aware Medium Access Control (MAC) protocol that uses a probed, secondary wake-up radio to maximize the battery lifetime on the anchor nodes. In order to show that battery powered infrastructure nodes are feasible, our solution accurately analyzes the long-term energy consumption of the infrastructure devices. Depending on the update rate and using a high capacity battery (10.4 Ah), it is possible to achieve a battery lifetime between 1 to 16 years, which is comparatively up to four times better compared to the current state-of-the-art TDMA-based solutions. [ABSTRACT FROM AUTHOR]

High vehicular mobility, frequent path link disruption and disconnection as well as random distribution of vehicular nodes make the establishment of path link for data dissemination highly challenging and the existing ad hoc routing protocols ineffective. The presence of traffic lights in metropolitan environments has a significant impact on connectivity because changes to traffic signals can induce variations in the geographical distribution of nodes. The designing of connectivity-aware geocast routing protocol using multi-hop communicational pattern, which is sensitive to traffic signal and traffic noise is a significant and contemporary issue in urban Internet of Vehicles (IoV). In this context, this paper proposes a Traffic Sensitive Connectivity-Aware Geocast Routing (TS-CAGR) protocol to minimize the impact of a traffic signals on link connectivity among vehicles along a path. First, a system connectivity model focusing on segmentation of a road into platoon and cell formation due to traffic light is presented. Secondly, connectivity model is presented to compute and analysis the connectivity probability using both platoon and cell formation. Thirdly, the routing algorithm is proposed comprising of an adaptive path selection mechanism to find suitable path segments based on the On-the-Fly Segment Density (OFSD) approach. Next Hop Vehicle (NHV) selection using Angular Zone-based two-hop four-neighbor information (AZ-2-H-4-NI) approach, and Store-Carry-Retransmit (SCR) approach to cope with the frequent link disconnection problem. By combining both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication patterns, the total probability of connectivity in cluster and cell-based vehicular node configurations has been computed. The performance of TS-CAGR is evaluated in an urban vehicular environment by considering performance parameters: throughput, end-to-end delay, packet delivery ration and routing overhead. The comparative performance evaluation attests to the superiority of the TS-CAGR protocol over the existing protocols. [ABSTRACT FROM AUTHOR]

Localization is the method of estimating the location of a wireless node using measured inputs such as distances from nodes with known locations. When the measured distances from anchor nodes are used for localization, compromised nodes that are involved in the process can give false information that produces inaccurate location estimates. This paper proposes a Generalized Likelihood Ratio (GLRT) based approach to find the compromised nodes that deliberately give false information. After detecting such malicious nodes, the measurements given by them are eliminated from the localization computation to improve the location estimate. The proposed method works for Gaussian range measurement errors, which is considered more realistic, as compared to a method available in literature that works only for uniformly distributed range errors. Extensive simulations were carried out to assess the performance of the algorithm under various conditions. The proposed method was found to give better localization accuracy as compared to previous methods available in the literature which address the same problem. Simulations also showed that the algorithm performs well even when some of the assumptions used in the algorithm do not hold true. [ABSTRACT FROM AUTHOR]

With the large use of wireless sensor devices, the interest in positioning and tracking by means of wireless sensor networks is expected to grow further. Particularly, accurate localization of a moving target is a fundamental requirement in several Machine to Machine monitoring applications. Tracking using Received Signal Strength Indicator (RSSI) has been frequently adopted thanks to the availability and the low cost of this parameter. In this paper, we propose an innovative target tracking algorithm which combines learning regression tree approach and filtering methods using RSSI metric. Regression Tree algorithm is investigated in order to estimate the position using the RSSI. This method is combined to filtering approaches yielding to more refined results. The suggested approach is evaluated through simulations and experiments. We also compare our method to existing algorithms available in the literature. The numerical and experimental results show the relevance and the efficiency of our method. [ABSTRACT FROM AUTHOR]

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes' locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors. [ABSTRACT FROM AUTHOR]

The location information of mobile target can be mapped between physical space and signal space, which is one of the key technologies for real-time tracking and multi-machine cooperation. Benefiting from the distributed perception and ubiquitous communication capabilities, wireless sensor networks (WSNs) can be used for target real-time positioning indoor and outdoor. However, various sensor noises and environmental interferences bring uncertainty to target positioning, resulting in inconsistent positioning performance. Hence, this paper proposes a coarse to accurate noise-tolerant positioning evaluation for mobile target based on modified genetic algorithm. Considering the uncertainty of various measurements in WSNs, the preliminary positioning results are estimated by total least squares. Taking the preliminary results as the initial search values, the modified genetic algorithm is used to refine the target positioning accuracy with use of adaptively adjusted crossover probability and improved mutation operation. In addition, the theoretical accuracy model is derived by the Cramer–Rao Lower Bound (CRLB) as a benchmark. Comprehensive experiments are conducted through the simulation and testing platform. The experimental results of the proposed algorithm have a 0.25 m average error and 0. 1 5 2 error variance. The result trends of simulation are consistent with the result of experimental platform, which can validate the superior accuracy of the proposed algorithm compared with relevant positioning algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a data fusion technique aimed at achieving highly accurate localization in a wireless sensor network with low computational cost. This is accomplished by fusing multiple types of sensor measurement data including received signal strength and angle of arrival. The proposed method incorporates a powerful data fusion technique, one that has never before been used in low cost localization of a stationary node, known as Dempster-Shafer Evidence Theory. Many useful functions of this theory, including sampling, aggregation, and plausibility, are integrated into the localization method. From there, the algorithm determines whether a set of given measurements belong to a particular county. Motivated by the flexible nature of Dempster-Shafer Theory, a multitude of network setups and combinations of available measurement features are tested to verify the performance of the proposed method. Performance of the proposed approach is evaluated using numerical results obtained from extensive simulations. When compared with the results of existing approaches in similarly constructed scenarios, the proposed localization technique achieves up to 98% accuracy in less than a tenth of the run-time required under presently established algorithms. [ABSTRACT FROM AUTHOR]

Systems using Bluetooth Low Energy (BLE) communication are widely used in various industrial applications, which benefit from the efficient low-power operation and pervasive availability of BLE transceivers in a large number of devices and sensors. It is no different in Smart Factory and Smart Farming settings, where BLE systems are already used for asset monitoring, management, tracking, and localization. The existing BLE-based localization systems aim at high accuracy and precision using radio propagation models and multilateration, or radio fingerprinting. Both methods use the received signal strength indicator (RSSI) measurements and the dependency of RSSI on the distance between the transmitter and the receiver. Because RSSI measurements are highly inaccurate and susceptible to radio propagation phenomena, high localization accuracy is only possible if the anchor devices are densely deployed, and the propagation conditions are stable. Unfortunately, these requirements do not hold in industrial systems where radio propagation is complex, the number of anchors is limited, and propagation conditions change because the environment is dynamic. Therefore, localization methods for industrial applications have to balance system complexity (e.g., anchor density, energy cost) and localization accuracy so that practical benefits can be attained. This paper presents a set of localization algorithms that require limited infrastructure, have low complexity, and can provide valuable location information at low costs. The proposed algorithms were verified in a real-life Smart Farming application where BLE is used to monitor well-being of farm animals. The animal localization was integrated with the already existing system and proved to operate reliably despite system-level constraints and varying propagation conditions — interference, signal attenuation, and unavailability of raw radio signal measurements. The proposed approaches allow localizing animals in the cowshed of 1600 m 2 , using only 10 anchors with an average positioning error below 8 m. The algorithms are based on signal strength measurement, so they can be applied to other radio technologies. [ABSTRACT FROM AUTHOR]

In this paper, Gaussian process regression (GPR) for fingerprinting based localization is presented. In contrast to general regression techniques, the GPR not only infers the posterior received signal strength (RSS) mean but also the variance at each fingerprint location. The GPR does take into account the variance of input i.e., noisy RSS data. The hyper-parameters of GPR are estimated using trust-region-reflective algorithm. The Cramér-Rao bound is analysed to highlight the performance of the parameter estimator. The posterior mean and variance of RSS data is utilized in fingerprinting based localization. The principal component analysis is employed to choose the k strongest wi-fi access points (APs). The performance of the proposed algorithm is validated using using real field deployments. Accuracy improvements of 10% and 30% are observed in two sites compared to the Horus fingerprinting approach. [ABSTRACT FROM AUTHOR]

Abstract: Hop Count based distance estimation is an important element for localization of devices in mobile ad hoc networks. Deriving distance estimates from hop counts is prone to error, especially in networks with low density. This paper shows that mobility can affect the accuracy of hop count based distance estimation. Two types of error are defined to describe and analyze the source of underestimation and overestimation of distances in a mobile ad hoc network. Different movement patterns are examined to get an insight of their impact on the hop counts and the estimated distances accordingly. Our experiments and analysis indicate that mobility can have a positive effect on the accuracy of distance estimates which results from a combination of asynchronous computation of hop counts and mobility of the nodes. At the same time, this positive effect can turn into a negative one with increasing mobility. Therefore, we determine characteristics, such as direction, speed, and similarity in movements of neighbors which are responsible for the disparity in the influences of the investigated mobility patterns. A study of these properties is presented and their individual effect is explained in detail. The difference between mobility and density induced error is discussed and their individual adverse effect is weighted against each other. In addition, we introduce a modified algorithm to determine hop counts which is designed to mitigate the effect of mobility. Two indicators are presented to identify and characterize the mobility of devices in a decentralized way. [Copyright &y& Elsevier]

Abstract: Accurate location awareness is of paramount importance in most ubiquitous and pervasive computing applications. Numerous solutions for indoor localization based on IEEE802.11, bluetooth, ultrasonic and vision technologies have been proposed. This paper introduces a suite of novel indoor positioning techniques utilizing signal-strength (SS) fingerprints collected from access points (APs). Our first approach employs a statistical representation of the received SS measurements by means of a multivariate Gaussian model by considering a discretized grid-like form of the indoor environment and by computing probability distribution signatures at each cell of the grid. At run time, the system compares the signature at the unknown position with the signature of each cell by using the Kullback–Leibler Divergence (KLD) between their corresponding probability densities. Our second approach applies compressive sensing (CS) to perform sparsity-based accurate indoor localization, while reducing significantly the amount of information transmitted from a wireless device, possessing limited power, storage, and processing capabilities, to a central server. The performance evaluation which was conducted at the premises of a research laboratory and an aquarium under real-life conditions, reveals that the proposed statistical fingerprinting and CS-based localization techniques achieve a substantial localization accuracy. [Copyright &y& Elsevier]

Abstract: The simple and cheap passive RFID tag systems have recently been used for efficient localization of indoor mobile robots, where each RFID tag stores its own absolute position and the mobile robot carrying an RFID reader scans the RFID tags to localize itself. The available localization schemes using passive RFID systems do not consider the scanning delay which may cause location estimation error, especially when the robot moves at a high speed. In this paper, a new Non-Blocking scanning (reading) scheme is proposed to avoid collisions so that the scanning delay, and consequently the localization error, can be reduced. This scheme avoids collisions among tag replies by assigning tag IDs based on the FCA coloring scheme. Theoretical and simulation studies indicate that the new Non-Blocking scheme combined with the new tag arrangement pattern can achieve a good RFID localization performance in terms of both estimation error and scanning delay with reduction of tag/reader required complexities. [Copyright &y& Elsevier]

Abstract: An ad hoc network of small robots (sensor nodes) adjusting their positions to establish network connectivity would be able to provide a communication infrastructure in an urban battlefield environment. A sensor node would be capable of moving to a particular position to establish network connectivity, provided it knows its current position, positions of other sensor nodes and the radio propagation characteristics of the sensor area. In this paper, we present a pseudo formation control based trajectory algorithm to determine the optimal trajectory of a moving beacon used in localization of the sensor nodes in real-time. The trajectory and the frequency of transmission of the GPS based position information of the moving beacon influences the accuracy of localization and the power consumed by the beacon to localize. Localization accuracy and reduction in the number of position information messages can be achieved, in real-time, by determining the optimal position from where the beacon should transmit its next position information. This will decrease the time required to localize, and power consumed by the beacon in comparison to random or predetermined trajectories. We first show that optimal position determination is a pseudo formation control problem. Next, we show the pseudo formation control problem formulated as an unconstrained optimization problem under the free space propagation model. We further present the modeling of the beacon incorporating the trajectory algorithm based on the pseudo formation control in a discrete event simulator. Simulation results, comparing the performance of localization with pseudo formation control based trajectory against random waypoint and predetermined trajectories for the beacon are presented. The simulation results show that the localization accuracy is significantly improved along with reduction in the number of position information messages transmitted when the beacon traverses along the pseudo formation control based trajectory. [Copyright &y& Elsevier]

Abstract: Localization is a fundamental problem in wireless sensor networks. Current localization algorithms mainly focus on checking the localizability of a network and/or how to localize as many nodes as possible given a static set of anchor nodes and distance measurements. In this paper, we study a new optimization problem, minimum cost localization problem, which aims to localize all sensors in a network using the minimum number (or total cost) of anchor nodes given the distance measurements. We show this problem is very challenging and then present a set of greedy algorithms using both trilateration and local sweep operations to address the problem. Extensive simulations have been conducted and demonstrate the efficiency of our algorithms. [ABSTRACT FROM AUTHOR]

Abstract: For Wireless Sensor Networks (WSNs), although many localization techniques have been proposed, development of effective localization techniques for sensor nodes, which do not require any hardware or computational burdens on them, continues to be a challenging task. Motivated by this need, this paper proposes a new location discovery scheme in WSNs so called Sectoral Sweeper based Location Estimation (SSLE). Sectoral Sweeper (SS) relies on integration of smart antenna processing capability only at a central node. By expanding the SS scheme, the key idea in the SSLE is to perform two dimensional (azimuth and range) sweeping process over a given sensor field with beamforming capable central node followed by range estimation and direction estimation algorithms. It does not require any hardware or computational load at tiny sensor nodes. Performance evaluation of the SSLE is given in terms of localization error, response message statistics, and complexity analysis for grid and randomly deployed sensor node scenarios under various sweeping conditions. The SSLE performance is also compared with the Centroid method. The advantage of the SSLE is that it is applicable to environments with hard-to-access sensor deployment scenarios and that desired localization accuracy can be obtained by properly adjusting sweeping parameters. [Copyright &y& Elsevier]

Abstract: Wireless sensor networks (WSN) have attached a great attention in the last few years. WSN offer several advantages over the traditional sensor networks, such as elimination of costly wires, security and larger area coverage. In the last few years, there has been great interest commercial applications based on Ultra Wideband (UWB). It has potentially low complexity, low cost and an excellent time domain resolution which facilitates location and tracking applications. Therefore, UWB provide a good combination of high performance with low complexity for WSN applications. Localization has attracted considerable attention in many fields. Accurate estimation of an individual location can be a difficult task, producing ambiguous results particularly in harsh environment. In this paper, we focus on feasibility study (via simulation and measurements) of using UWB based-WNS as future solution for localization in underground mines. [Copyright &y& Elsevier]

Abstract: In an adversarial environment, various kinds of attacks become possible if malicious nodes could claim fake locations that are different from their physical locations. In this paper, we propose a secure localization mechanism that detects existence of these nodes, termed as phantom nodes, without relying on any trusted entities, an approach significantly different from the existing ones. The proposed mechanism enjoys a set of nice features. First, it does not have any central point of attack. All nodes play the role of verifier, by generating local map, i.e. a view constructed based on ranging information from its neighbors. Second, this distributed and localized construction results in strong robustness against adversaries: even when the number of phantom nodes is greater than that of honest nodes, we can filter out most of the phantom nodes. Our analytical results as well as simulations under realistic noisy settings demonstrate that the proposed mechanism is effective in the presence of a large number of phantom nodes. [Copyright &y& Elsevier]

Abstract: Many applications that use sensor data from a wireless sensor network (WSN) require corresponding node position information as well. Therefore, it is not surprising that a common figure of merit for localization algorithms is the accuracy of the position estimate produced. Similarly, the amount of communication required by a localization algorithm is often of paramount interest as well since it is common knowledge that communication expends the most energy in a WSN. However, localization algorithms seldom characterize their communication cost. Furthermore, when they do it is often merely qualitative and is typically described as “expensive”. For two types of range-aware, anchor-free localization algorithms we found the opposite to be true. Rather than being expensive, the communication costs were quite modest. So much so that we maintain range-aware, anchor-free localization algorithms should be chosen on the basis of the accuracy required by the intended application independent of the communication cost. In this paper, we examine the effect of node degree, node distribution, range error and network size on distance error and communication cost for both incremental and concurrent versions of range-aware, anchor-free algorithms. The concurrent algorithm is twice as accurate as the incremental, but less efficient. Furthermore, node degree influences the energy cost of the algorithms the most, but neither algorithm uses more than a surprisingly small 0.8% of a 560mAh battery. This result indicates less energy efficient localization algorithms can be tolerated, especially if they provide better accuracy. Furthermore, if energy does need to be conserved, there is not much savings available within the localization algorithm and savings must be found in other areas such as the MAC protocol or routing algorithm. [Copyright &y& Elsevier]

Abstract: It is often useful to know the positions of nodes in a network. However, in a large network it is impractical to build a single global map. In this paper, we present a new approach for distributed localization called Positioning using Local Maps (PLM). Given a path between a starting node and a remote node we wish to localize, the nodes along the path each compute a map of their local neighborhood. Adjacent nodes then align their maps, and the relative position of the remote node can then be determined in the coordinate system of the starting node. Nodes with known positions can easily be incorporated to determine absolute coordinates. We instantiate the PLM framework using the previously proposed MDS-MAP(P) algorithm to generate the local maps. Through simulation experiments, we compare the resulting algorithm, which we call MDS-MAP(D), with existing distributed methods and show improved performance on both uniform and irregular topologies. [Copyright &y& Elsevier]

The emergence of IoT technology makes the applications of IoT wireless sensor networks (WSNs) attract more attentions, and most of the applications are based on the sensor's location. This paper addresses two main challenges encountered in the localization issue in IoT WSNs, i.e., limited energy in battery-powered sensors, and non-line-of-sight (NLOS) induced errors in harsh environments. We propose a node selection algorithm and an NLOS mitigation algorithm to modify the conventional cooperative localization algorithm for an improved performance. In the node selection algorithm, a node selection criterion is designed to select the most informative reference nodes for each agent, avoiding unnecessary energy consumption. An N -probabilistic hard weight (N -PHW) strategy is developed in the NLOS mitigation algorithm, in which a link condition indicator is set to quantify the quality of each link and NLOS errors are penalized based on this indicator. Numerical results show that by virtue of the proposed node selection algorithm, the energy consumption of the network is significantly reduced. Furthermore, the localization accuracy is improved with the proposed NLOS mitigation algorithm, especially in more severe NLOS environments. [ABSTRACT FROM AUTHOR]

In a wireless sensor network, the localized sensors are essential for efficient management of the network. In recent years, we have seen the implementation of various localization schemes. Few schemes presume to use mobile beacon because it has many potentialities. Despite many potentialities, the mobile beacon-based localization schemes suffer from two significant problems. First, the shorter beaconing interval increases the communication overhead and degrades the network lifetime. Second, the radio propagation irregularity minimizes the uniform coverage. For a better network lifetime, we need to reduce the communication overhead. Similarly, consistent coverage is improved through better path planning and enhanced beaconing range. However, most of the proposed geometric schemes in literature fails to address both the problems with better localization accuracy. In this paper, we address the underlying impact of shorter beaconing interval and the radio propagation irregularity. The proposed scheme utilizes the geometric least square curve fitting method for localization (GLSCFL). The results show that the proposed method provides better localization accuracy than other geometric schemes. [ABSTRACT FROM AUTHOR]

The massive multiple-input-multiple-output (mMIMO) antenna systems are well known for their capability to achieve high spectral efficiency in wireless communication systems thanks to millimeter waves (mmWaves) which allow a large number of antennas to be deployed at the base stations (BSs). Aside from communication-based services, mMIMO BSs are presently being exploited for location estimation of user equipment due to their high angular resolution, low-cost implementation, and excellent performance in the indoor and clutter urban environments where line-of-sight may not be available. Although various mMIMO localization solutions have been proposed, there are still pressing issues yet to be resolved. To this end, this article first provides an overview of recent and relevant state-of-the-art survey papers on localization. Further to this, we provide various foundational background concepts based on the existing localization techniques applicable to mMIMO localization systems. Furthermore, we discuss various methods under each technique and we also identify some critical factors to be considered in a practical radio environment. Based on these techniques, we provide a comprehensive review of recent works on mMIMO localization. Finally, we suggest key research directions to be addressed in the future and also we discuss key enabling technologies that will enhance the performance of localization systems in 6G communication networks. [ABSTRACT FROM AUTHOR]

This paper proposes a RSSI quantization and genetic algorithm based localization method. To quantize RSSI measurements from sensor nodes, we first divide sensing disks of nodes into multiple-rings and adopt genetic algorithm based on elitist preservation strategy to determine rings width. Secondly, we calculate the overlapping of rings and construct the mapping between a binary code sequence and the overlapping area. Thirdly, a density-based clustering algorithm is presented to solve the ambiguity of the target appearing area. Based on which, a two-stage centroid localization algorithm is proposed, which is especially useful for the case of irregular appearing area of the target. Finally, we construct an experimental environment to validate the performance of our algorithm. [ABSTRACT FROM AUTHOR]

Currently, numerous proprietary communication technologies are used by emergency response personnel to ensure uninterrupted coordination and effective situational awareness, especially when the telecommunication infrastructure gets damaged or destroyed. Affected victims and bystanders, however, lack access to such equipment and are thus deprived of information and unable to reach for help. To ameliorate the situation, this work details a promising networking architecture that capitalizes on beaconing-based technology already available on existing smartphone devices to provide emergency communications. On top of the proposed architecture, intelligent algorithms are developed to support efficient and resilient message exchange for communication and localization among the affected victims; both of which are necessary for prompt and effective response. The paper first provides an analytical performance evaluation of the proposed approach. It then delves into the development and experimentation details that lead to the implementation of a practical application that can be installed and used in time of need. Real-world experiments demonstrate that the proposed algorithms can sustain alert messaging for extended periods of time. [ABSTRACT FROM AUTHOR]

The Magnetic Induction (MI) communication techniques can enable or enhance many wireless applications in the complex environments where line-of-sight (LOS) links do not exist. The critical position information of each wireless device can also be derived by the same MI systems without additional hardware or infrastructure. However, while MI signals can penetrate most of the transmission media without significant attenuation or phase shifting, the obstacles with high conductivity can still influence the signal propagation, which incurs additional positioning errors in the MI-based localization. To address such challenge, this paper develops an environment-aware MI-based localization technique for wireless sensor networks in complex environments with significant amount of high-conductive obstructions. First, the system architecture of the MI-based environment-aware localization and the MI channel is introduced. The environment-aware capability is realized by analyzing the unique MI response information gathered by each MI-based sensor node. Then, a joint device localization and environment sensing algorithm is developed to estimated the position of each device in the network as well as the distribution of the high-conductive objects. Finally, the performance of the proposed solution is validated through both computer simulations and real-world experiments. [ABSTRACT FROM AUTHOR]