Distributed Localization Algorithm Research Articles

Distributed Relative Localization Algorithms for Multi-Robot Networks: A Survey

For a network of robots working in a specific environment, relative localization among robots is the basis for accomplishing various upper-level tasks. To avoid the latency and fragility of long-range or multi-hop communication, distributed relative localization algorithms, in which robots take local measurements and calculate localizations and poses relative to their neighbors distributively, are highly desired. Distributed relative localization has the advantages of a low communication burden and better system robustness but encounters challenges in the distributed algorithm design, communication protocol design, local network organization, etc. This paper presents a detailed survey of the key methodologies designed for distributed relative localization for robot networks. We classify the distributed localization algorithms regarding to the types of measurements, i.e., distance-based, bearing-based, and multiple-measurement-fusion-based. The detailed design methodologies, advantages, drawbacks, and application scenarios of different distributed localization algorithms are introduced and summarized. Then, the research works that support distributed localization, including local network organization, communication efficiency, and the robustness of distributed localization algorithms, are surveyed. Finally, popular simulation platforms are summarized and compared in order to facilitate future research and experiments on distributed relative localization algorithms.

Bearing-only distributed localization for multi-agent systems with complex coordinates

This paper addresses the bearing-only distributed localization, which is more accurate and more reliable than traditional localization technologies. A new bearing-only distributed localization framework is proposed to solve localization problems for both static sensor networks and mobile multi-agent systems (MASs). Based on the complex coordinate representation, a novel bearing-only distributed localization algorithm for sensor networks is proposed. The algorithm combines the orientation estimation and the position estimation to solve the orientation alignment problem, which makes the compasses no longer needed for the localized networks. The localization for the mobile MASs is also studied and the corresponding localization algorithm is designed, which is more general and more challenging. The key to obtaining positions of moving agents, is the velocity estimator in the proposed localization algorithm which makes the estimated positions and velocities converge to the true value simultaneously. A distinctive advantage of the localization framework proposed in this paper is that the bearing-only localization algorithms can be applied to more general systems, such as sensor networks and MASs. Simulations and experiment are presented to verify the proposed algorithms.

A Trust Assessment-Based Distributed Localization Algorithm for Sensor Networks Under Deception Attacks

Dear Editor, This letter is concerned with the distributed localization issue for wireless sensor networks subject to deception attacks. It is assumed that malicious nodes randomly launch attacks to tamper the ranging information between sensors. In order to accommodate the effects of deception attacks, a trust assessment-based distributed localization algorithm is proposed. Combined with data fusion of barycentric coordinates, our algorithm can achieve accurate localization. Finally, numerical simulations are given to validate the effectiveness of the proposed localization algorithm.

Distributed Localization of Multiagent Systems With Imperfect Channels Based on Iterative Learning

The real-time localization problem for a class of multiagent systems with imperfect channels under directed graphs is investigated in this article. A successive distributed localization estimation algorithm based on iterative learning is proposed to solve the localization problem of the agent with bounded communication delays. The barycentric coordinates are used to represent the relative position between agents. An estimator is proposed to inspect whether the received data packet is the latest through the iteration stamp on the packet. Meanwhile, an intermittent distributed localization algorithm is devised to handle with the random data loss of agents in real-time localization problem. The relationship between the convergence rate and the probability of the data loss is given in the sense of mathematical expectations. Both numerical simulation and Qbot-2e robot experiment are provided to verify the effectiveness and the feasibility of the aforementioned methods.

Distributed Localization for Dynamic Multiagent Systems With Randomly Varying Trajectory Lengths

This article considered the persistent real-time localization problem of dynamic multiagent systems with repetitive running characteristics under directed graph. The trajectory length of the agent is randomly varying caused by system constraints and external environment. A novel distributed iterative learning localization estimation method with full historical average data compensation is designed. The average value of all available historical data in the previous operations is used to compensate the incomplete trajectory. The designed diagnosis mechanism and search mechanism are used to determine whether the agent has stopped running and to screen out the available historical data. In order to reduce the computation burden, an improved distributed localization algorithm with receding horizon historical average data compensation is proposed. The asymptotic convergence of the estimation algorithms in the sense of mathematical expectation is derived through the rigorous analysis. Meanwhile, the influence of the incomplete repetitive trajectory of the agent on estimation error and convergence rate is also analyzed. The experimental result based on QBot-2e robot platform verifies the realizability of the proposed method.

Triangular angle rigidity for distributed localization in 2D

Recent advances in sensing technology have enabled sensor nodes to measure interior angles with respect to their neighboring nodes. However, it is unknown which combination of angle measurements is necessary to make a sensor network localizable, and it is also unidentified if there is a distributed localization algorithm whose required communication only consists of the sensor nodes’ measured angles and estimated positions. Motivated by these two challenging problems, this paper develops triangular angle rigidity for those networks consisting of a set of nodes and triangular angle constraints in 2D. First, we transfer the geometric constraint of each triangle into an angle-induced linear constraint. Based on the linear constraint, we show that different from angle rigidity, triangular angle rigidity implies global triangular angle rigidity. More importantly, inspired by Laman’s theorem, we propose a topological, necessary and sufficient condition to check generic triangular angle rigidity. Based on the results on triangular angle rigidity, both algebraic and topological localizability conditions are developed, which are necessary and sufficient when the number of anchor nodes in the network is two. Both continuous and discrete localization algorithms are proposed, in which only measured angles and estimated positions are communicated among the sensor nodes. Finally, a simulation example with 32 sensor nodes is used to validate the effectiveness of the proposed approaches.

An Exploratory Distributed Localization Algorithm Based on 3D Barycentric Coordinates

This paper studies the exploratory distributed localization of networked mobile agents using only distance measurements in a GPS-Denied 3D environment. To deal with this challenging problem, an analytic solution is firstly provided to calculate the 3D barycentric coordinates for agents outside the convex hull formed by anchors. Then, a distributed Jacobi Under-Relaxation Iteration (JURI) algorithm is developed to address the static 3D localization problem in a noisy environment. Next, the proposed algorithm is extended for the localization of mobile targets, especially when the mobile agents cannot get enough neighbors' support. Since the proposed algorithms do not suffer from the constraint of the convex hull (exploratory localization); as a result, the proposed method can be scaled to the localization of large-scale networked agents. Finally, several simulation examples are given to validate the effectiveness of the proposed algorithm in different scenarios.

UAV-Aided Relative Localization of Terminals

Accurate location information is essential for many emerging applications of the Internet of Things. Compared with absolute positions, relative positions of nodes are often more relevant in tasks such as formation control and autonomous driving. In this article, we propose a relative localization scheme for terminals aided by unmanned aerial vehicles. We first derive the performance limit of the relative position error as the constrained Cramér-Rao lower bound (CRLB), and proved that the equivalent Fisher information of a subnetwork retains all the information for its relative localization. We then develop a distributed localization algorithm based on local geometry transforming for the proposed scheme with low computation complexity. Moreover, an iterative descent algorithm is designed for joint power and spectrum allocation for relative localization. Numerical results show that the proposed localization algorithm significantly outperforms existing algorithms and the optimal allocation scheme can reduce the relative CRLB by about 50% compared with the uniform allocation scheme.

Distributed localization algorithm for wireless sensor networks using range lookup and subregion stitching

One of the ways in which localization algorithms in wireless sensor networks (WSNs) have been categorized is whether they are range-based or range-free. Range-based algorithms use expensive hardware to measure one or more physical quantities and, in turn, use them to localize nodes with greater precision. In contrast, range-free algorithms use coarse-grained quantities like connectivity to localize nodes with limited precision. A middle way between these two approaches can be called a partial range-based approach that can utilize the existing received signal strength indicator (RSSI) readings from sensor nodes to improve the already existing coarse-grained localization methods. Another important consideration in WSNs is that a distributed localization algorithm is more computationally feasible as compared to its centralized counterpart. Keeping these two considerations in mind, a distributed localization algorithm is proposed here which falls in the aforementioned partial range-based category. The proposed algorithm called RangeLookup-MDS first creates subregions using connectivity information only. This is followed by the collection of RSSI readings from individual sensor nodes that are used to perform range lookup for inter-node distance estimates in a lookup table. After that, relative localization in every subregion is performed using multidimensional scaling, and then the relative maps are stitched together to create a consistent (but relative) coordinate system. The algorithm also has the capability to compute absolute coordinates in two-dimensional if the stitching step is executed with at least three non-collinear anchor nodes with known locations. Simulation results on uniform as well as irregular networks of various sizes show that the proposed algorithm provides improved localization accuracy and reduces localization error up to 25% in comparison to a previous partial range-based localization algorithm.

Resilient Privacy-Preserving Distributed Localization Against Dishonest Nodes in Internet of Things

Existing distributed localization methods rarely consider the location privacy preservation problem, which however is nonnegligible. Regarding location privacy, typical solutions rely on a curious-but-honest model, requesting that all participants follow the rule. Different from the existing studies, both honest and dishonest models are considered in this article. We first propose a privacy-preserving distributed localization algorithm (PP-DILOC) by adopting a noise-adding mechanism under the curious-but-honest model. The performance of localization and privacy preservation of PP-DILOC are both theoretically analyzed. Then, in the presence of dishonest nodes, we propose a resilient PP-DILOC (RPP-DILOC), where a time-varying relax factor and an adversary detection procedure are added into PP-DILOC. Theoretical results provide sufficient conditions for the convergence of RPP-DILOC. The privacy levels and the localization performance in the absence/presence of dishonest nodes are evaluated through numerical and experimental results.

3-D Distributed Localization With Mixed Local Relative Measurements

This paper studies 3-D distributed network localization using mixed types of local relative measurements. Each node holds a local coordinate frame without a common orientation and can only measure one type of information (relative position, distance, relative bearing, angle, or ratio-of-distance measurements) about its neighboring nodes in its local coordinate frame. A novel rigidity-theory-based distributed localization is developed to overcome the challenge due to the absence of a global coordinate frame. The main idea is to construct displacement constraints for the positions of the nodes by using mixed local relative measurements. Then, a linear distributed localization algorithm is proposed for each free node to estimate its position by solving the displacement constraints. The algebraic condition and graph condition are obtained to guarantee the global convergence of the proposed distributed localization algorithm.

Distributed Localization Algorithm Using Hybrid Cuckoo Search with Hill Climbing (CS-HC) Algorithm for Internet of Things

Distributed Localization Algorithm Using Hybrid Cuckoo Search with Hill Climbing (CS-HC) Algorithm for Internet of Things

Collision avoidance cooperative attack with multiple pursuers based on bearing-only measurements

This paper provides a novel collisions avoidance cooperative attack method for multiple pursuers system based on bearing-only measurements. In the cooperative attack problem, the control algorithm must drive all of the attackers to converge to the desired position (target). However, the traditional consensus protocol based on the relative position information may cause the intervehicle collisions before the attackers can hit the target. To solve this issue, a distributed cooperative consensus control algorithm is proposed. Under this algorithm the potential collisions are proved to be avoided while the pursuers can achieve the final attack. Further more, considering the relative distance information is unavailable for some types of the pursuers to get, a control algorithm based on bearing-only measurements is proposed by referencing the distributed localization algorithm used in the sensors network. The advantages of this method are summed up as fully distributed, collisions avoidance, and bearing-only measurements. Finally, the effectiveness of the results is illustrated by numerical simulation.

RETRACTED ARTICLE: Research on distributed localization algorithm for wireless sensor network nodes in multimedia data transmission process

Aiming at the need of massive transmission bandwidth and efficient transmission efficiency in the transmission of multimedia data in wireless sensor networks, this paper proposes an improved BP neural network localization algorithm based on artificial bee colony algorithm to solve the problem of network node localization in the transmission of multimedia data in wireless sensor networks. The algorithm first completes the coarse location of the nodes by the three side distributed location algorithm through signal strength. Then, the BP neural network is optimized by artificial bee colony to locate accurately. The distance weight is determined based on the precise location and the errors generated in the process of ranging are corrected. Finally, a localization algorithm with high accuracy and robustness for wireless sensor network nodes is formed. The simulation results show that the proposed algorithm has a significant improvement in the performance and efficiency of sensor node positioning, and has a broad prospect in the transmission of multimedia data.

A Distributed Localization Algorithm for Wireless Sensor Networks Based on Robust Statistic

This research presents a distributed and low-complexity algorithm for range-based localization in wireless sensor networks (WSNs), which is an extension of the Distributed Spatially Constrained Local (DSCL) algorithm. The proposed method uses robust functions suitable to avoid outliers derived from corrupted estimated distances among sensor nodes, where the estimated distances are generated under two well known range-estimation techniques. The accuracy of the proposed algorithm was tested using a set of 10 distributed WSNs with noisy distance measurements between sensor nodes, deployed arbitrarily on a 2-D area of 100 m x 100 m. Regarding to the DSCL algorithm, the proposed approach DSCL/Smooth-l_1 reduces, in average, around 57% the error on position estimates under the RSSI model. On the other hand, the scheme DSCL/Cauchy-Lorentzian greatly outperforms the original one in the presence of outliers in distance estimates.

Localization Technique Considering Position Uncertainty of Reference Nodes in Wireless Sensor Networks

This paper proposes a distributed localization algorithm in wireless sensor networks. The algorithm considers the position uncertainty of reference nodes, which have been ignored in the past research, in which imperfect position information of reference nodes intensifies the error accumulation phenomenon. The algorithm calculates position uncertainty by the dilution of precision and presents a balance point between range errors and position errors by applying the modified spring mass method. We verify the feasibility of the proposed algorithm by changing multiple parameters under various circumstances. The result proves that the overall performance of localization is enhanced by considering the position uncertainty.

Rigid graph-based three-dimension localization algorithm for wireless sensor networks

This paper investigates the node localization problem for wireless sensor networks in three-dimension space. A distributed localization algorithm is presented based on the rigid graph. Before location, the communication radius is adaptively increasing to add the localizability. The localization process includes three steps: firstly, divide the whole globally rigid graph into several small rigid blocks; secondly, set up the local coordinate systems and transform them to global coordinate system; finally, use the quadrilateration iteration technology to locate the nodes in the wireless sensor network. This algorithm has the advantages of low energy consumption, low computational complexity as well as high expandability and high localizability. Moreover, it can achieve the unique and accurate localization. Finally, some simulations are provided to demonstrate the effectiveness of the proposed algorithm.

Efficient Distributed Localization Algorithm for Ownership Identification of Reindeer Calf Using Wireless Sensor Networks

This paper presents a distributed algorithm for identifying the ownership of new-born reindeer calves using wireless sensor networks (WSNs). Reindeer are semi wild animals giving birth while in the wild. Although reindeer cows usually carry identification tags or signs of their owners, it is difficult to identify the ownership of the calves within a mixed herd. Currently, identification is performed in the traditional way which is stressful on both animals and herders and quite costly and time consuming. In this work, a localization algorithm implemented on a WSN is considered to get rid of matching mother reindeer to their calves in a fully automatic way. In particular, the proposed distributed localization algorithm is based on two phases where sensors nodes are attached to the reindeer in a confined space with the objective to identify the animals keeping close together the most of the time, which are reckoned to be the mother reindeer and her calf. At first, the initial estimated nodes’ positions are obtained from an existing biased-maximum likelihood estimator (B-MLE) based on the log-normal shadowing model (LNSM). Secondly, each sensor node seeks to estimate its local map (i.e., its own position and those from its neighbors) by using an on-line distributed stochastic approximation algorithm (DSA) such the identification mother-calves can be done. We present numerical are issued to the standard ZigBee IEEE 802.15.4 operating at 2:4 GHz by means of radio devices WSN430 (equipped with CC2420 chips).

Locality Preserving Canonical Correlation Analysis Distributed Localization Algorithm for Wireless Sensor Networks

Locality Preserving Canonical Correlation Analysis Distributed Localization Algorithm for Wireless Sensor Networks

Modified Energy-Efficient Range-Free Localization Using Teaching–Learning-Based Optimization for Wireless Sensor Networks

ABSTRACTThis paper presents an energy-efficient Modified Distance Vector Hop algorithm using Teaching–Learning-Based Optimization, viz. MDV-TLBO, which is range-free, distributed localization algorithm for wireless sensor network (WSN). In the proposed algorithm, the hop size of anchor node is modified by adding correction factor. The concept of collinearity is introduced in this paper to reduce location errors caused by anchor nodes which are collinear. TLBO is used to enhance the localization accuracy, which is parameter-free, efficient optimization technique. Target nodes estimate their final coordinates after location upgradation procedure. In MDV-TLBO, anchor nodes communicate only one time with target nodes to broadcast their location. Hop size modification, optimal selection of anchor nodes, location optimization, and location upgradation are done at target node level, resulting considerable reduction in communication between nodes, due to which energy consumption of the nodes has been significantly reduced. To show the applicability of proposed algorithm in real scenarios, radio irregularity model is considered. Simulation results show that our proposed algorithm achieves better localization accuracy, high positioning coverage with less energy consumption in comparison of the existing improved DV-Hop algorithms.