Range-free Localization Schemes Research Articles

A Voronoi Diagram-Based Grouping Test Localization Scheme in Wireless Sensor Networks

The wireless sensor network (WSN) provides us with a cost-effective way to remotely monitor a large number of objects, locations, and environmental parameters. Taking advantage of WSNs to applications can add new capabilities to existing products and bring out new services. We propose a novel range-free localization scheme, the Voronoi diagram-based grouping test localization (VTL) scheme, to estimate the location efficiently for WSNs. VTL divides the anchor nodes into multiple groups and uses the corresponding closest Voronoi cells to compute the estimated location. Apart from improving the accuracy of location estimation, it also largely simplifies the implementation. Simulation results show that the VTL scheme has better performance compared with other range-free localization schemes. When reaching a certain anchor node density, the VTL scheme will have higher localization accuracy and a larger percentage of localizable nodes. Hence, VTL is likely more appropriate for upcoming WSN scenarios with large ratios of anchor nodes being available.

A Novel Range-Free Localization Scheme Based on Anchor Pairs Condition Decision in Wireless Sensor Networks

It is essential to acquire the location of sensor nodes in the wireless sensor networks (WSNs), since the data collected with nodes would become meaningless without their location. In the existing studies, range-free localization schemes have been proved suitable to large-scaled WSNs due to the low cost in hardware implementation. However, the defect of those schemes is their poor accuracy in anisotropic networks with coverage holes. To tackle this problem, we propose a range-free localization scheme that combines the advantages of geometric constraint and hop progress-based methods. The geometric information provided by the combination of anchor pairs and unknown nodes is used to design the discrimination conditions, and each node divides the anchor pairs into one of three proposed categories. For different categories of anchor pairs, corresponding methods are proposed to estimate the distancse between sensor nodes. In this way, the trade-off between distance estimation accuracy and anchor utilization can be achieved. Simulation results indicate that the proposed scheme outperforms other schemes in terms of localization accuracy and proportion of outliers with acceptable computational and time complexity, where the localization accuracy and proportion of outliers in the proposed scheme are improved by up to 47% and 61% compared to DV-maxHop.

Range-Free Localization With a Mobile Beacon via Motion Compensation in Underwater Sensor Networks

Localization accuracy is crucial for achieving reliable location tagging for underwater missions. However, the accuracy of conventional range-free localization schemes with a mobile beacon is distorted by ship dynamics ( i.e. , pitch, roll, and heave) over oceanic conditions, and the schemes suffer from two uncertainties: beacon point and geometric shape uncertainties. To reduce the effects of these uncertainties, we herein propose LMB-MC, a novel range-free localization scheme. LMB-MC compensates beacon point errors and employs an accurate geometric model instead of an unconditional circle-shaped model. Furthermore, we adopted real-world motion measurements from an operating ship and used motion data to support diverse simulation settings ( i.e. , Douglas sea scales). Result of extensive simulation studies verified that LMB-MC provided improved accuracy and reliability compared to the existing solutions in the considered oceanic environments.

Connectivity Based DV-Hop Localization for Internet of Things

Due to the cost-effective advantage, range-free localization schemes are attractive for low-cost Internet of Things applications. As a distinguishing range-free scheme, DV-Hop localization can localize those unknown nodes which have less than 3 or even no neighbor anchors. However, the localization results by existing DV-Hop based algorithm are found to be inconsistent with the real connectivity between nodes. The inconsistency inspires us to propose two algorithms to improve localization accuracy. First a Centralized Connectivity-based DV-Hop (CCDV-Hop) algorithm is proposed to optimize the accuracy of DV-Hop localization. Establishing an optimization problem which takes the real connectivity between any two nodes as the constraints, the proposed algorithm can make the localization results conform to the real connectivity. Then an algorithm with lower complexity is proposed, namely Distributed Connectivity-based DV-Hop (DCDV-Hop) algorithm, which can obtain near-optimal localization performance in distributed networks. Without including the connectivity of all nodes, the constraints in the proposed DCDV-Hop algorithm only consider the real connectivity within two hops. Simulation results show that despite higher complexity, the proposed algorithms can achieve much better accuracy than other DV-Hop based methods.

Resolution Limit of Positioning Error for Range-Free Localization Schemes

The metric of resolution limit of localization error plays an important role in the range-free localization schemes design, which can manifest the best error-recognition ability that the range-free schemes could achieve. The existing derived resolution limits are found to be smaller than the real resolution limit. Therefore, this article aims to obtain the real resolution limit of range-free localization error. To achieve the goal, two algorithms are proposed in ideal channel and shadow-fading channel, respectively. Specifically, in ideal channel, maximum movable distance is defined and its important features are introduced, then the concept of critical point and its related proposition are given accordingly. Subsequently, an algorithm is proposed to derive the real resolution limit in ideal channel. For the case of shadow-fading channel, based on stochastic channel model, first, at each time instant, the random variables are turned into definite values, then, the instant resolution limit is computed, and finally, the expectation of resolution limit can be calculated. Simulation results demonstrate the correctness of our derived resolution limit. The results also show that the resolution limits or lower bounds derived by the existing methods are much smaller than the real resolution limit.

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Current and future wireless applications strongly rely on precise real-time localization. A number of applications, such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety, and military systems require reliable and accurate localization techniques. Generally, the most popular localization/positioning system is the global positioning system (GPS). GPS works well for outdoor environments but fails in indoor and harsh environments. Therefore, a number of other wireless local localization techniques are developed based on terrestrial wireless networks, wireless sensor networks (WSNs), and wireless local area networks (WLANs). Also, there exist localization techniques which fuse two or more technologies to find out the location of the user, also called signal of opportunity-based localization. Most of the localization techniques require ranging measurements, such as time of arrival (ToA), time difference of arrival (TDoA), direction of arrival (DoA), and received signal strength (RSS). There are also range-free localization techniques which consider the proximity information and do not require the actual ranging measurements. Dimensionality reduction techniques are famous among the range free localization schemes. Multidimensional scaling (MDS) is one of the dimensionality reduction technique which has been used extensively in the recent past for wireless networks localization. In this paper, a comprehensive survey is presented for MDS and MDS-based localization techniques in WSNs, IoT, cognitive radio networks, and 5G networks.

DV-Hop Localization With Protocol Sequence Based Access

Among typical range-free localization schemes for sensor networks, DV-Hop has its distinguishing advantage of the capability to localize unknown nodes, which have less than three or even no neighbor anchors. However, existing DV-Hop based localization methods have not considered the problem of packet collisions, which can frequently happen during the throughout-whole-network broadcast of numerous data packets at the first two steps of DV-Hop. In order to effectively improve the success rate of packet transmission between nodes, this paper proposes a medium access control (MAC) method based on Chinese Remainder Theorem (CRT) protocol sequence for DV-Hop localization. First, a broadcasting strategy is designed for the proposed MAC method. Then, dedicated procedures for both anchor nodes and unknown nodes are presented to implement the designed MAC method in DV-Hop localization. Through simulation and evaluation, the performance of DV-Hop localization with the proposed MAC method is thoroughly investigated. Simulation results show that when the number of protocol sequences is larger than a certain number, the proposed MAC method can provide DV-Hop with better localization accuracy than E-CSMA/CA, while keeping similar localization time.

A hybrid model using fuzzy logic and an extreme learning machine with vector particle swarm optimization for wireless sensor network localization

Localization is one of the challenges in wireless sensor networks, especially those without the aid of a global positioning system. Use of a dedicated positioning device incurs additional cost and reduces battery life; therefore, a range-free localization scheme is promising as a cost-effective approach. However, the main limitation of this approach is that the estimation precision can be affected by factors such as node density, sensing coverage, and topology diversity. Thus, this study investigates and proposes a method for improving a traditional range-free-based localization method (centroid) that uses soft computing approaches in a hybrid model. This model integrates a fuzzy logic system into centroid and uses an extreme learning machine (ELM) optimization technique to capitalize on the strengths of both approaches: the former is properly used with low node density and short coverage, while the latter is used for the opposite—to achieve a robust location estimation scheme. The ratios of known nodes within the sensing coverage range to the total known nodes and of the sensing coverage range to the maximum coverage range are used as adaptive weights for this hybrid model. To further improve the efficiency, especially in heterogeneous topologies, the concept of resultant force vectors is applied to this hybrid model over particle swarm optimization to mitigate the effects of irregular deployments. The performance of the proposed method is extensively evaluated via simulations that demonstrate its effectiveness compared to other state-of-the-art soft-computing-based range-free localization schemes (i.e., centroid, a fuzzy logic system, and a support vector machine with a traditional ELM).

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

Location information for events, assets, and individuals, mostly focusing on two dimensions so far, has triggered a multitude of applications across different verticals, such as consumer, networking, industrial, health care, public safety, and emergency response use cases. To fully exploit the potential of location awareness and enable new advanced location-based services, localization algorithms need to be combined with complementary technologies including accurate height estimation, i.e., three dimensional location, reliable user mobility classification, and efficient indoor mapping solutions. This survey provides a comprehensive review of such enabling technologies. In particular, we present cellular localization systems including recent results on 5G localization, and solutions based on wireless local area networks, highlighting those that are capable of computing 3D location in multi-floor indoor environments. We overview range-free localization schemes, which have been traditionally explored in wireless sensor networks and are nowadays gaining attention for several envisioned Internet of Things applications. We also present user mobility estimation techniques, particularly those applicable in cellular networks, that can improve localization and tracking accuracy. Regarding the mapping of physical space inside buildings for aiding tracking and navigation applications, we study recent advances and focus on smartphone-based indoor simultaneous localization and mapping approaches. The survey concludes with service availability and system scalability considerations, as well as security and privacy concerns in location architectures, discusses the technology roadmap, and identifies future research directions.

Geometric Constraint-Based Range-Free Localization Scheme for Wireless Sensor Networks

Localization is one of the most challenging issues in wireless sensor networks. It is broadly classified into range-based and range-free. The range-based schemes are generally expensive, and require additional range determining hardware. Besides, the range free schemes are simple, inexpensive, and energy efficient, where the localization is performed using proximity information. In this paper, we have proposed an analytical geometry-based range-free localization scheme, where a mobile beacon is used as a reference node. The proposed scheme uses three non-collinear beacon points to derive the analytical geometry of an arc. For localization, the arc parameters are used to generate the chords on the virtual circle of the sensor node. Later, the perpendicular bisector of the chord and approximated radius are used to localize the sensor node. The accuracy of localization depends on the size of the constraint area, which is used for the approximation of the arc parameters. To provide better localization accuracy, we have used an adaptive mechanism that defines the approximation accuracy of the arc parameters at different sizes of the constraint area. The simulation and experimental validation show that the proposed scheme provides better localization accuracy than the existing range free localization schemes.

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages and disadvantages. The similarities and differences of each scheme are investigated on the basis of significant parameters, namely, localization accuracy, computational cost, communication cost, and number of samples. We discuss the challenges and direction of the future research work for each parameter.

A Range Free Geometric Technique for Localization of Wireless Sensor Network (WSN) Based on Controlled Communication Range

The current restricted area based range free localization schemes use various geometric shapes such as triangle, rectangle, and circle to determine the residence area of the sensor node. Then this residence area is used to localize the sensor node. However, the existing work requires higher density of anchor deployment to gain the acceptable accuracy of position estimation. In this paper, we overcome the density dependent accuracy of localization by proposing a new localization scheme called geometric anchor beacon (GAB). The proposed scheme is based on analytical geometry of an arc. In the proposed scheme, the $$Cramers \ rule$$Cramersrule is used, where the intersecting point of two perpendicular bisectors of the chords is taken as the estimated position of the sensor node. Simulation results show that, GAB shows less estimation error than any other range-free localization schemes such as CAB, Centroid, Convex, and APIT.

Soft computing-based localizations in wireless sensor networks

Recent advances in wireless sensor networks have been applied in a variety of applications, including environment, healthcare, and military, based on their distinctive characteristics, such as being small in size and including self-organizing sensor nodes with multi-sensing capabilities and cost-effective monitoring, but there are still a number of constraints that require further exploration. Several of the limitations involve routing and reliability, and one of the key challenges is localization without the aid of global positioning services. On the one hand, GPS tends to be efficiently used only in open areas, and on the other hand, if integrated, the hardware overhead cost could be increased and there could be a smaller the battery lifetime. Thus, with the distance estimation derived from the received signal strength, a range-free localization scheme is promising as a cost-effective approach in which one of the optimization approaches is based on soft computing (which is typically used to address uncertainty and approximation) to achieve practicability and robustness. Thus, this research investigates probable soft computing techniques that are comparatively applied to localizations in a variety of components and subsequently proposes an alternative scheme that utilizes an extreme learning machine to increase the estimation accuracy. To enhance its precision further with a time complexity trade-off, a greedy-based approach that combines modified mass–spring optimizations is integrated and then evaluated by the simulation; these techniques demonstrate effectiveness compared to other state-of-the-art soft-computing-based range-free localization schemes.

Range Free Localization Techniques in Wireless Sensor Networks: A Review

Recent developments in micro electro mechanical systems (MEMS) technology and wireless communication have propelled the growing applications of wireless sensor networks (WSNs). Wireless sensor network is comprised of large number of small and cheap devices known as sensors. One of the important functions of sensor network is collection and forwarding of data. In most of the applications, it is of much interest to find out the location of the data. This type of information can be obtained by use of localization techniques. So node localization is very crucial to find out the position of node with the help of localization algorithms. Hence, node localization becomes one of the fundamental challenges in WSNs. We make the rigorous reviews on different schemes of localization in sensor networks. On the basis of range measurements, the localization schemes can be broadly classified in two categories such as: range based and range free schemes. The cost and hardware limitation on sensing node preclude the use of range based localization schemes. In most of the sensor network application coarse accuracy is sufficient so range free localization schemes are considered as a substitute to range based schemes. In this paper, the detailed study has been carried out to understand and select the best range free localization algorithm for WSNs. At the end some issues are discussed for future research in the area of localization techniques for WSNs.

Accident aware localization mechanism for wireless sensor networks

Accurate location information is important for event reporting, coverage estimation, and location-aware routing in a Wireless Sensor Network (WSN). Recently, a number of range-free localization schemes have been proposed to provide each static sensor with location information, which is represented by a rectangular region. However, most WSN applications are applied in outdoor environments where the sensors’ location regions could be incorrect due to sudden accidents. This paper proposes an Active Location Correction Protocol, called ALCP, for detecting and correcting the occurrence of location error based on the bounding box technology. Performance study reveals that applying the ALCP to improve the location accuracies can enhance the performance of the well-known GPSR routing in terms of routing length, sensing coverage, and packet arrival rate.

A Low-Cost Anchor Placement Strategy for Range-Free Localization Problems in Wireless Sensor Networks

The deployment of anchor nodes (or beacon nodes) is a key element in the design of range-free localization schemes for wireless sensor networks (WSNs). The majority of range-free localization schemes deploy numerous anchor nodes within the monitored region. However, in certain cases (e.g., hazardous areas), it may not be possible to manually install anchor nodes within the monitored region of a WSN. In this study, very few anchor nodes are installed at the boundary of a WSN monitored region to estimate the locations of unknown sensors. The locations of the unknown sensors are estimated based on the minimum hop counts to the anchor nodes. The average hop distance is estimated by applying parameters derived from prior experiments. The simulation results show that the range error of the proposed localization scheme can be as low as 0.15. The localization accuracy, computation cost, and communication cost of the proposed scheme are compared with those of the well-known DV-Hop method.

Range-Free Node Localization Schemes for Wireless Sensor Networks

Two range-free node localization schemes modified from the conventional DV-Hop scheme are presented in this work. Different node position derivation algorithms are used to enhance the localization accuracy of DV-Hop. The principle of the algorithms and the improvement approach are illustrated. Simulation shows that the modified schemes outperform the original scheme in terms of the localization accuracy as the network connection topology varies.

Range Free Localization Schemes for Wireless Sensor Networks

Range Free Localization Schemes for Wireless Sensor Networks

Localization Accuracy Improved Methods for Range-Free Localization Schemes in Wireless Sensor Network

Location information is the most important for wireless sensor network (WSN). Due to the limitation of energy, cost and influence of environment, range-free localization schemes are more suitable for WSN despite of their low localization accuracy. Two methods, which are improved grids SCAN and multiple locating and averaging scheme, are presented to improve localization accuracy of range-free localization schemes and performed through performance evaluation based on ROCRSSI (Ring Overlapping based on Comparison of Received Signal Strength Indicator). By simulation it is concluded that ROCRSSI applying both methods can get better localization performances.

Extended Monte Carlo localization algorithm for mobile sensor networks

A real-world localization system for wireless sensor networks that adapts for mobility and irregular radio propagation model is considered. The traditional range-based techniques and recent range-free localization schemes are not well competent for localization in mobile sensor networks, while the probabilistic approach of Bayesian filtering with particle-based density representations provides a comprehensive solution to such localization problem. Monte Carlo localization is a Bayesian filtering method that approximates the mobile node’s location by a set of weighted particles. In this paper, an enhanced Monte Carlo localization algorithm—Extended Monte Carlo Localization (Ext-MCL) is proposed, i.e., the traditional Monte Carlo localization algorithm is improved and extended to make it suitable for the practical wireless network environment where the radio propagation model is irregular. Simulation results show the proposal gets better localization accuracy and higher localizable node number than previously proposed Monte Carlo localization schemes not only for ideal radio model, but also for irregular one.