Search

Currently, the issue of delivering wood raw materials from remote forest areas, where the largest volumes are located, is relevant. Often, this delivery is economically justified only when using a network of medium and small rivers with the use of environmentally friendly technologies. On such rivers, it is preferable to use mobile anchors, in particular shore fillable ones, for fixing timber-rafting objects. A description has been given of a new modification of such an anchor, which differs from the prototype by a container having a flexible shell. This allows to significantly reduce the material consumption for manufacturing the container, the dimensions of the anchor in the transport position, and to produce anchors of any realistic size. The choice of the most rational option for such a container implies the scientific justification of its parameters. The aim of the study is to develop a scientific basis for this justification. Analytical formulas have been theoretically obtained for determining the basic geometric characteristics of the cross-section of the container under consideration. These formulas are useful in scientific research, but their application is very problematic in practical engineering calculations. This is due to the dependence of the specified characteristics on elliptic integrals and parameters, which are very difficult to measure in engineering practice. Having performed calculations using these formulas for a single cross-sectional area, we have obtained its specific geometric characteristics for various shapes, i.e. for various width-to-height ratios of a container to be filled. The results of the calculations have been approximating dependencies linking the named characteristics with the specified ratio, which is called the shape factor. Multiplying the specific characteristics by the square root of the cross-sectional area gives the absolute values of the corresponding characteristics. As a result, we have obtained formulas convenient for practical use, allowing to determine the width and height of the container to be filled, the width of its base, the perimeter of the cross-section, the heights of the zero pressure line and the extreme lateral point of the cross-section above the base of the container. The accuracy of calculations based on these and analytical formulas is almost the same. A formula for determining the specific tension of a flexible container shell has been obtained. The nature and degree of influence of the shape factor and length of the container on its other geometric characteristics have been established. An algorithm for substantiating the key parameters of a container to be filled with a flexible shell has been developed using the described results.

Implementing the Artificial Intelligence concept in higher education can be utilized in the context of Human Resource (HR) talent management. The lecturer portfolio provided by the Integrated Resource Information System (SISTER DIKTI) is expected to give an overview of the profiles of all lecturers and map competencies based on groups of fields of knowledge. However, the map of scientific fields based on SISTER data currently available is still subjective. The data is in the form of a group of lecturers' chosen fields of science, independently selected by each lecturer to recognize their expertise. This study discusses the problem of processing unstructured SISTER data. It looks for mapping solutions and classification methods by developing a strategy for classifying groups of scientific fields from unstructured data input. It is necessary to identify the suitability of the chosen field of science compared to that developed through the tri-dharma through identification based on a mapping of the group of fields that can be extracted from the tridharma activity, in this case, research represented by scientific publications recorded on SISTER. Therefore, we need an appropriate model to measure similarity, which can then be classified based on abstract documents and scientific publication titles for the classification of scientific fields using NLP based on classification run on DGX A100. This study aims to develop a classification method from titles and abstracts. Scientific publications contained in SISTER are unstructured data, so a corpus is formed to identify the lecturer's field of science. The results show that the classification method developed in this study can measure the similarity of lecturer publications based on abstracts and titles through a vector formation process based on bidirectional encoders and also produces a deep learning model to classify 24 categories of fields of science with an accuracy of 95,0345 percent on training data and 92.876 percent on the test data. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) rely on mobile anchor nodes (MANs) for network connectivity, data aggregation, and location information. However, MANs’ mobility can disrupt energy consumption and network performance. Effective path improvisation algorithms are needed for MANs to optimize energy use, reduce data loss, and maintain network connectivity in dynamic WSN environments. To overcome these issues, Topological Information Embedded Convolutional Neural Network based Lotus Effect Optimization for Path Improvisation of the Mobile Anchors in Wireless Sensor Networks (TIECNN-PIMA-OAC-WSN) was proposed. The approach establishes a robust network setup and energy model, employing TIECNN for initial cluster formation and cluster head selection. The chosen cluster head, termed the Mobile Anchor, undergoes optimization using the Lotus effect optimization algorithm to determine the most efficient and shortest path. This work enhances both the topological information processing and energy efficiency of mobile anchor paths. The simulation outcomes prove the proposed technique attains 33.12%, 39.56%, and 42% higher network lifespan for sensor nodes density 40; 38.22%, 29.66%, and 41.33% higher network lifespan for sensor nodes density 60; 37.45%, 35.55%, and 43.67% higher network lifespan for sensor nodes density 80; 32.45%, 29.45%, and 46.66% higher network lifespan for sensor nodes density 100 analysed to the existing methods. [ABSTRACT FROM AUTHOR]

Early bird Localization is to provide all sensor nodes with their geographical positions. A mobile anchor-based localization in wireless sensor networks uses a mobile anchor equipped with GPS, which travels along a predetermined path. At each specified beacon point, it broadcasts its current known position to help other sensor nodes with unknown locations estimate their positions. This paper analyzes the determination of beacon points based on a square grid. We propose an improved path planning model named Union-curve. Our proposed model incorporates all beacon points of five previously developed paths, namely, SCAN, HILBERT, S-type, Z-curve, and Σ-Scan on the commonly used square grid decomposition of area. Unknown sensor nodes estimate their positions using two techniques, APT and WCWCL-RSSI. Simulation results show that the proposed model has higher accuracy, with a big difference in error rate compared to the other models. In addition, this model guarantees maximum coverage with less path resolution value. [ABSTRACT FROM AUTHOR]

Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47.

Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47. [ABSTRACT FROM AUTHOR]

Considering the existing mobile anchor-assisted localization methods generally adopt a traverse strategy for the localization region, leading to an unnecessary increase in path length. The long path will cause long localization time and high energy consumption of mobile anchor. To reduce the long path of mobile anchor, this paper proposes a novel mobile anchor-assisted localization method based on the density of nodes distribution called Division of Regions Nested Equilateral Triangles path planning (DR-NET). Firstly, DR-NET divides the localization region into several small regions according to the density of nodes distribution, thereby reducing the movement in the distribution region of sensorless nodes. Secondly, the shortest path algorithm and genetic algorithm are used to calculate the shortest path of the inter-region and intra-region. Finally, the mobile anchor moves according to the planned path and assists the unknown nodes to locate. Compared with the existing classical methods LMAT, MAALRH, H-Curves and M-Curves, a series of experimental results demonstrate that DR-NET can effectively reduce the path length and energy consumption of mobile anchor by 8.56–30.66%, 4.26–28.59%, respectively. To further verify the validity and adaptability of DR-NET, we conduct experiments on localization regions with different shapes, areas, deployment strategies of nodes and structure types of nodes. The results show that DR-NET still maintains high stability in path length and localization success ratio. [ABSTRACT FROM AUTHOR]

Event detection is usually the primary purpose of wireless sensor networks (WSNs). Therefore, it is crucial to determine where and when an event occurs in order to map the event to its spatio-temporal domain. In WSN localization, a few anchor nodes are those aware of their locations via the Global Positioning System (GPS), which is energy-consuming. Non-anchor nodes self-localize by gathering information from anchor nodes to estimate their positions using a localization technique. Traditional algorithms use at least three static anchors for the localization process. Recently, researchers opted to replace multiple static anchors by a single mobile anchor during the localization process. This paper proposes a Kalman filter based on bounding box localization algorithm (KF-BBLA) in WSNs with mobile anchor node. We present a new mobile anchor localization strategy to minimize energy, hardware costs, and computation complexity, while improving accuracy and cost-effectiveness. Network connectivity measurement and the bounding box localization method are used in order to identify the bounded possible localization zone. The Kalman filter is then used to minimize the uncertainty produced by the connectivity process. We aim also to minimize the localization inaccuracies generated by the bounding box algorithm. Simulation results show that our proposed approach significantly reduces the localization error compared to other localization algorithms chosen from the recent literature by up to 20 % . We use the cumulative distribution function (CDF) as an indicator to assess the accuracy of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Aspect extraction is the basis for aspect-based sentiment analysis, aiming to find out the target of opinions from reviews. The existing neural network for aspect extraction model based on sequence labeling has a poor effect on the extraction of long aspect terms. To solve this problem, we propose a new aspect extraction framework, which uses a three-layer convolutional neural network (CNN) to learn multi-layer semantic features from reviews, and then uses the non-local attention mechanism to obtain dependence feature between different words. Moreover, conditional random field (CRF) has been used to reduce the probability of label conversion errors. CNN filters are good at learning local features without considering long-distance dependencies, while the non-local attention mechanism can strengthen the long-distance dependencies of words and ensure the integrity of long aspect terms. The proposed model is tested on two datasets of SemEval and compared with some baseline models. The experimental results show that the performance of the model is superior. [ABSTRACT FROM AUTHOR]

In an underwater wireless sensor network (UWSN), the location of the sensor nodes plays a significant role in the localization process. The location information is obtained by using the known positions of anchor nodes. For underwater environments, instead of using various static anchor nodes, mobile anchor nodes are more efficient and cost-effective to cover the monitoring area. Nevertheless, the utilization of these mobile anchors requires adequate path planning strategy. Mzost of the path planning algorithms do not consider irregular deployment, caused by the effects of water currents. Consequently, this leads towards the inefficient energy consumption by mobile anchors due to unnecessary transmission of beacon messages at unnecessary areas. Therefore, an efficient dynamic mobile path planning (EDMPP) algorithm to tackle the irregular deployment and non-collinear virtual beacon point placement, targeting the underwater environment settings is presented in this paper. In addition, EDMPP controls the redundant beacon message deployment and overlapping, for beacon message distribution in mobile assistant localization. The simulation results show that the performance of the EDMPP is improved by increasing the localization accuracy and decreasing the energy consumption with optimum path length. [ABSTRACT FROM AUTHOR]

The main function of a sensor node is to collect data from its environment and forward it to base station. In the absence of further information concerning their locations, those data will be unnecessary. Hence, developing algorithms for localizing all nodes of wireless sensor network is extremely important. We present in this paper, a new approach to determine geographical coordinates of unknown nodes, by using mobile anchor. The mobile anchor adopts a spiral trajectory, and diffuses its position periodically during its travel. The proposed approach uses Received Signal Strength Indicator to estimate distance with all broadcast messages received from mobile anchor. To calculate position, our approach determines a cloud of points that surround the solution; these points are selected from the set of intersection points of all beacons received by unknown node, by considering some constraints. The estimated position of unknown node represents the geometric center of this cloud. The behavior of our algorithm was studied by varying some metrics; the average error was minimized compared to those proposed in literature. [ABSTRACT FROM AUTHOR]

In most of the WSN applications, thousands of sensors have to be located very accurately. Localization system is required to provide position information to the nodes. Traditional localization method involves utilizing global positioning system (GPS) in all the nodes. It gives high precision, but is suitable only for outdoor environment. The energy consumption is also high. Recently, localization of each node was done using range-based and range-free techniques. Use of movable beacons to assist the nodes of a WSN in estimating their positions, has been proposed. A beacon is a node that is aware of its position (with GPS) and it will move in and around the sensor field. This beacon can be a robot. This paper gives an overview of the different localization techniques in WSN and an obstacle avoidance path planning localization algorithms, based on V Curve. This algorithm avoids the use of GPS in all sensor nodes except for the mobile beacon. And also in real-time environments, if a beacon is moving, it will be highly influenced by the obstacles. To avoid this obstacle, back-tracking technique is used. [ABSTRACT FROM AUTHOR]

For a connected graph G and a subset S of its vertices, the Steiner tree problem consists of finding a minimum-size connected subgraph containing S. The Steiner distance of S is the size of a Steiner tree for S, and the Steiner k-diameter of G is the maximum value of the Steiner distance over all vertex subsets S of cardinality k. Calculation of Steiner trees and Steiner distance is known to be NP-hard in general, so applications may benefit from using graphs where the Steiner distance and structure of Steiner trees are known. In this paper, we investigate the Steiner distance and Steiner k-diameter of the join, corona, and cluster of connected graphs, as well as threshold graphs. [ABSTRACT FROM AUTHOR]

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]

Underwater acoustic sensor networks (UWASNs) can revolutionize the subsea domain by enabling low-cost monitoring of subsea assets and the marine environment. Accurate localization of the UWASNs is essential for these applications. In general, range-based localization techniques are preferred for their high accuracy in estimated locations. However, they can be severely affected by variable sound speed, multipath spreading, and other effects of the acoustic channel. In addition, an inefficient localization scheme can consume a significant amount of energy, reducing the effective life of the battery-powered sensor nodes. In this paper, we propose robust, efficient, and practically implementable localization schemes for static UWASNs. The proposed schemes are based on the Time-Difference-of-Arrival (TDoA) measurements and the nodes are localized passively, i.e., by just listening to beacon signals from multiple anchors, thus saving both the channel bandwidth and energy. The robustness in location estimates is achieved by considering an appropriate statistical noise model based on a plausible acoustic channel model and certain practical assumptions. To overcome the practical challenges of deploying and maintaining multiple permanent anchors for TDoA measurements, we propose practical schemes of using a single or multiple surface vehicles as virtual anchors. The robustness of localization is evaluated by simulations under realistic settings. By combining a mobile anchor(s) scheme with a robust estimator, this paper presents a complete package of efficient, robust, and practically usable localization schemes for low-cost UWASNs.

In Wireless Sensor Networks (WSN) the wireless nodes work untethered and have limited power resource. Wireless charging provides an alternative approach to recharge the batteries of the wireless nodes. Localization provides the position information. Previous works have considered either on recharging techniques or on the localization. This paper proposes a wireless recharging and also localization using a single mobile anchor. First the mobile anchor locates the static node later it receives the battery level. Finally if the battery of the static node is lesser than the threshold limit β, it recharges the static nodes wirelessly. Also the mobile anchor follows an energy efficient path V curve algorithm. The proposed method can handle the dynamic energy demand efficiently. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, node locations are required for many applications. Usually, anchors with known positions are employed for localization. Sensor positions can be estimated more efficiently by using mobile anchors (MAs). Finding the best MA trajectory is an important problem in this context. Various path planning algorithms are proposed to localize as many sensors as possible by following the shortest path with minimum number of anchors. In this paper, path planning algorithms for MA assisted localization are proposed for both static and dynamic schemes. These approaches use MAs by stopping at minimum number of nodes to cover the monitoring area with shortest path length. A novel node localization algorithm based on alternating minimization is proposed. The performances of the proposed path planning algorithms are compared with previous approaches through simulations. The results show that more sensors are localized with less anchors in a shorter path and time for both schemes. [ABSTRACT FROM AUTHOR]

In operating and managing wireless sensor networks (WSNs) and their applications, the high accuracy of localization and the low operating costs are considered the substantial and key issues. The literature is rich in algorithms for localized WSN devices in hostile and unreachable outdoor environment. Majority of the literature considered mobile anchor as one of the solutions in locating sensor nodes. In this situation, the critical issue is the trajectory planning. All algorithms supposed that the mobile anchor should travel following the shortest path to determine the positions of sensor nodes with minimum localization error. A localization algorithm, which is called efficient localization algorithm based path planning for mobile anchors (ELPMA), is proposed. ELPMA is based on a one-mobile anchor moving in adjustable circular trajectory to scan the target area. It considers that the received signal strength indicator is the ranging function to determine the distance between mobile anchor and sensor nodes. ELPMA supposed the mobile anchor starts the motion from the center of the target area. The travelling paths are planned in advance by ELPMA based on the distance measurements between the mobile anchor and the sensor nodes. Simulation results demonstrate that ELPMA has better performance compared to other algorithms based on static path. This performance was evident in the localization accuracy and trajectory planning. [ABSTRACT FROM AUTHOR]

Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today's communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given. [ABSTRACT FROM AUTHOR]

While localization is essential to many applications, there are quantities of wireless nodes whose accurate locations remain unknown or hard to tell. As a remedy, this paper is leveraging a GPS-capable Android handset that features portability and sufficient computing capability to collect data and display localization results thereof. Our objective is to locate the unknown nodes under irregular radio coverage. To this end, we first devise a range-free approach that computes the most likely circumcenter from a convex polygon representative of radio coverage. Preliminary simulations corroborate the effectiveness of our development. Then the first approach is simplified by allowing for received signal strengths with the notion of estimative regions, namely rectangular areas enclosing an unknown node, to refine the area of interest for localization. Free from involved computations, our treatment mitigates the errors of estimation resulting from different radio signal propagation scenarios, by taking an additional parameter for ameliorating localization accuracy. Field experiments were conducted to demonstrate performance results overlying Google Maps on the handset and through quantitative comparisons in different settings. Performance results show that our development can get fielded in practice. [ABSTRACT FROM AUTHOR]

The objective of path planning for a mobile anchor is to find the path of minimum length that the anchor traverses to localize all sensors. The challenge is to design a movement strategy which reduces path length while meeting the requirements of a good range-free localization technique. A novel deterministic movement strategy is proposed in this paper that reduces path length and uses an existing range-free localization scheme which yields good positional accuracy. The mobile anchor moves in a hexagonal pattern to localize all the sensors which form a connected network. We compare performance of our algorithm with an existing path planning algorithm in terms of both path length and localization accuracy. Simulation results show that even in presence of irregular radio propagation, our algorithm achieves full localization. We have proposed another movement strategy for a mobile anchor using same hexagonal pattern to localize all the sensors lying in a rectangular region. Improvement in path length is shown theoretically compared to existing path planning schemes. [ABSTRACT FROM AUTHOR]

Identifying locations of sensor nodes in wireless sensor networks (WSNs) is significant for both network operations and most application level tasks. Although, geographical positioning system (GPS) based localization schemes are used for determining node locations but the cost of GPS devices and non-availability of GPS signals in indoor environments prevent their use in large scale WSNs. A substantial amount of research work exist that intend at obtaining precise and relative spatial locations of sensor nodes without requiring large amount of specialized hardware. Mobile anchor assisted localization is one typical approach that significantly reduces the implementation cost by using limited number of mobile anchors. In this survey, we present key issues and inherent challenges faced by the mobile anchor assisted localization techniques in WSNs. We take a closer look at the algorithmic approaches of various important fine-grained mobile anchor assisted localization techniques applicable in WSNs. In addition, we highlight the error refinement mechanisms adopted by the state-of-the-art works associated with their approaches. Well known mobile anchor trajectories presented in existing works are also reviewed. Finally, open research issues are discussed for future research scope in this field. [ABSTRACT FROM AUTHOR]

Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today’s communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.

Copyright of SCIENCE CHINA Information Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Localization is a fundamental problem in wireless sensor networks. Many approaches, including range-based and range-free techniques, have been proposed to determine the optimal location for sensor nodes. This paper presents a simple range-free localization scheme that is based on received signal strength indicator (RSSI) geometric localization for wireless sensor networks. The node locations are computed by performing simple geometric calculations that rely on the specific approximate RSSI values with the position of a mobile beacon equipped with a global positioning system. The simulation results showed that the proposed scheme is efficient, and that the node positions can be determined accurately. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), node's locations play a critical role in many applications. Having a GPS receiver on every sensor node is costly. In this paper, we propose an adaptive mobile anchor localization algorithm based on ant colony optimization, firstly, some virtual anchor nodes are distributed in the area, second, ant colony was used which has the maximum of transition probabilities to obtain the optimal path, last, the centroid-weighted localization algorithm was proposed to locate the position of unknown nodes. Simulation results show that the localization accuracy of the proposed algorithm is better than the traditional centroid algorithm, the more number of anchor nodes, namely, the density is bigger, the position errors is smaller. Under the same anchor nodes, the more number of unknown nodes, the position errors is smaller and the overall trend is downward. [ABSTRACT FROM AUTHOR]

Localization is a key issue in wireless sensor networks. The geographical location of sensors is important information that is required in sensor network operations such as target detection, monitoring, and rescue. These methods are classified into two categories, namely range-based and range-free. Range-based localizations achieve high location accuracy by using specific hardware or using absolute received signal strength indicator (RSSI) values, whereas range-free approaches obtain location estimates with lower accuracy. Because of the hardware and energy constraints in sensor networks, RSSI offers a convenient method to find the position of sensor nodes. However, in the presence of channel noise, fading, and attenuation, it is not possible to estimate the actual location. In this paper, we propose an RSSI-based localization scheme that considers the trend of RSSI values obtained from beacons to estimate the position of sensor nodes. Through applying polynomial modeling on the relationship between received RSSI and distance, we are able to locate the maximum RSSI point on the anchor trajectory. Using two such trajectories, the sensor position can be determined by calculating the intersection point of perpendiculars passing through the maximum RSSI point on each trajectory. In addition, we devised schemes to improve the localization method to perform under a variety of cases such as single trajectory, unavailability of RSSI trends, and so. The advantage of our scheme is that it does not rely on absolute RSSI values and hence, can be applied in dynamic environments. In simulations, we demonstrate that the proposed localization scheme achieves higher location accuracy compared with existing localization approaches. [ABSTRACT FROM PUBLISHER]

Localization is one of the most important issues in wireless sensor networks (WSNs). In the most widely proposed range-free algorithms, nodes estimate location by employing the geometric constraints imposed by the location of the mobile anchor. However, none of them addresses how the mobile anchor moves to optimize the improvement of location inaccuracies and minimize the anchor's movement. This paper assumes that previous range-free algorithms have been executed for a period of time and the deployed sensors are of different location inaccuracies. According to the size of the estimative region of each static sensor, an anchor-guiding mechanism is proposed to determine the beacon locations and construct an efficient path for the mobile anchor. Experimental study reveals that the proposed anchor-guiding mechanism effectively guides the mobile anchor to move along an efficient path, thereby saving the time required for improving or balancing the location inaccuracies of all sensor nodes. [ABSTRACT FROM PUBLISHER]

Bounding-box mechanism is a well-known low-cost localization approach for wireless sensor networks. However, the bounding-box location information cannot distinguish the relative locations of neighboring sensors, hence leading to a poor performance for some applications such as location-aware routing. This paper proposes a Differentiating Relative Locations (DRL) mechanism which uses a mobile anchor to broadcast tones and beacons aiming at distinguishing the relative locations of any two neighboring nodes. With the order of entering and leaving tone transmission range, each sensor is able to identify its relative location relation with each of its neighbors. In addition, two path planning mechanisms are proposed to guarantee that all sensors can identify their relative locations while the energy consumption of the mobile anchor can be effective. Theoretical analysis is developed to estimate the impacts of duty time of tone signals in terms of energy conservation and accuracy of relative locations. Experimental study reveals that the proposed mechanism effectively distinguishes relative locations of any two neighboring nodes and hence significantly improves the performance of location-aware routing in wireless sensor networks (WSNs). [ABSTRACT FROM PUBLISHER]

Underwater acoustic sensor networks (UWASNs) can revolutionize the subsea domain by enabling low-cost monitoring of subsea assets and the marine environment. Accurate localization of the UWASNs is essential for these applications. In general, range-based localization techniques are preferred for their high accuracy in estimated locations. However, they can be severely affected by variable sound speed, multipath spreading, and other effects of the acoustic channel. In addition, an inefficient localization scheme can consume a significant amount of energy, reducing the effective life of the battery-powered sensor nodes. In this paper, we propose robust, efficient, and practically implementable localization schemes for static UWASNs. The proposed schemes are based on the Time-Difference-of-Arrival (TDoA) measurements and the nodes are localized passively, i.e., by just listening to beacon signals from multiple anchors, thus saving both the channel bandwidth and energy. The robustness in location estimates is achieved by considering an appropriate statistical noise model based on a plausible acoustic channel model and certain practical assumptions. To overcome the practical challenges of deploying and maintaining multiple permanent anchors for TDoA measurements, we propose practical schemes of using a single or multiple surface vehicles as virtual anchors. The robustness of localization is evaluated by simulations under realistic settings. By combining a mobile anchor(s) scheme with a robust estimator, this paper presents a complete package of efficient, robust, and practically usable localization schemes for low-cost UWASNs. [ABSTRACT FROM AUTHOR]

Localizing machine-type communication (MTC) devices or sensors is becoming important because of the increasing popularity of machine-to-machine (M2M) communication networks for location-based applications. These include such as health monitoring, rescue operations, vehicle tracking, and wildfire monitoring. Moreover, efficient localization approaches for sensor-based MTC devices reduce the localization error and energy consumption of MTC devices. Because sensors are used as an integral part of M2M communication networks and have achieved popularity in underwater applications, research is being conducted on sensor localization in both underwater and terrestrial M2M networks. Major challenges in designing underwater localization techniques are the lack of good radio signal propagation in underwater, sensor mobility management, and ensuring network coverage in 3D underwater M2M networks. Similarly, predicting the mobility pattern of MTC devices, trading-off energy consumption and location accuracy pose great design challenges for terrestrial localization techniques. This article presents a comprehensive survey on the current state-of-the-art research on both terrestrial and underwater localization approaches for sensor-based MTC devices. It also classifies localization approaches based on several factors, identifies their limitations with potential solutions, and compares them. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]