Localization Algorithm For Networks Research Articles

Reliable and Cooperative Target Tracking Based on WSN and WiFi in Indoor Wireless Networks

Indoor localization and tracking have attracted growing attention because of its widely application for indoor location-based services (LBSs). However, for indoor localization, only with single positioning technology, the positioning accuracy, and localizability are difficult to satisfy the requirement due to the complex and crowed indoor environment. An indoor cooperative localization and tracking algorithm (CLTA) based on grid is developed to solve above problems. The CLTA is divided into offline phase and online phase. In the offline phase, a cooperative localization fingerprint database is established based on reliable nodes. In the online phase, a region overlapping mechanism is used to narrow location area in multi-network surroundings at first. Then, we use a prediction mechanism to predict the mobile target position in order to further narrow the location area. At last, a cross grid strategy is used to update the data in fingerprint database if possible, aiming at improving the accuracy of localization. Simulation and experiment results show that the proposed algorithm is better than single network localization algorithm on localization and tracking performance.

An RSSI-based Wireless Sensor Network Localization Algorithm with Error Checking and Correction

This paper studies the wireless sensor network localization algorithm based on the received signal strength indicator (RSSI) in detail. Considering the large errors in ranging and localization of nodes made by the algorithm, this paper corrects and compensates the errors of the algorithm to improve the coordinate accuracy of the node. The improved node localization algorithm performs error checking and correction on the anchor node and the node to be measured, respectively so as to make the received signal strength value of the node to be measured closer to the real value. It corrects the weighting factor by using the measured distance between communication nodes to make the coordinate of the node to be measured more accurate. Then, it calculates the mean deviation of localization based on the anchor node close to the node to be measured and compensates the coordinate error. Through the simulation experiment, it is found that the new localization algorithm with error checking and correction proposed in this paper improves the localization accuracy by 5%-6% compared with the weighted centroid algorithm based on RSSI.

Development of MDS-based localization algorithm in wireless sensors networks:A survey

Wireless sensor networks (WSN), as a new information acquisition and processing technology in target tracking, intrusion detection, positioning and other related fields, has broad application prospects. Multidimensional Scaling, which can be run in both range-based and range-free conditions, and pr oved to achieve high accuracy in range-based cases, is attracting a lot of researches. In this paper, we review the development of WSNs and the location based on MDS-MAP algorithm. MDS-MAP is an inherently centralized algorithm and is therefore of limited utility in many applications. We summarize the improvements that have been made over the past 20 years, and make a comparison of various methods.

Weighted localization in mobile wireless networks

Localization of wireless devices is a crucial requirement for many emerging applications such as environmental monitoring, intelligent transportation, home automation, health‐care monitoring, and social networking. In this letter, we propose AWL a new aggregate weighted localization algorithm for mobile wireless networks. The proposed algorithm is distributed and requires low computational and communication overheads enabling its use in resource‐limited devices.

Simulation of Wireless Sensor Networks Based on Directed Diffusion Algorithm

In order to reduce the energy consumption and data congestion caused by flooding at the initial stage of the network, the cluster head and the boundary node are used as the key nodes to reduce the data flow in the network, so that the network has better delay performance. Various clustering algorithms in wireless sensor networks and the improved method of clustering protocols are introduced. On the basis of directed diffusion algorithm, a directed diffusion protocol based on CONID clustering is studied. Through the OMNeT++ basic platform, the wireless sensor network simulation platform is built on the basis of the MiXiM module. The platform uses a hierarchical structure, and its network layer implements a directed diffusion protocol based on CONID clustering. The application layer implements the two step WLS localization algorithm. The MAC layer uses the FIexiTP protocol. The physical layer uses the simplest flat path fading model. Because the sensor has both perception and data transmission characteristics, the dual channel communication model is adopted. The platform is suitable for wireless sensor network detection, localization and tracking algorithm simulation. Through the hierarchical function of the platform, it can analyze the practicability and effectiveness of the algorithm in more detail, and has certain value. It can also be used in the simulation of cross layer protocols, and can combine several parameters to perform joint analysis of system performance. Simulation results show that the CONID based directional diffusion protocol has better delay performance than the conventional directed diffusion protocol. Finally, the performance of the two step WLS location protocol is analyzed from several perspectives, which provides an example for the platform.

DV-maxHop: A Fast and Accurate Range-Free Localization Algorithm for Anisotropic Wireless Networks

Localization awareness is a fundamental requirement in many Internet of Things (IoT) and other wireless sensor applications. The information transmitted by an individual entity or node is of limited use without the knowledge of its location. Research in this area is mostly geared towards multi-hop range-free localization algorithm as that only utilizes connectivity (neighbors) information. This work focuses on anchor-based, range-free localization algorithm, particularly in anisotropic networks. We observe that the pioneer Distance Vector Hop or DV-Hop algorithm, which provides accurate estimation in isotropic networks, can be enhanced to compute localization estimation for anisotropic networks with similar or comparable accuracy. The recently proposed algorithms for anisotropic networks are complex with communication and computational overheads. These algorithms may also be overkill for several location dependent protocols and applications. This paper proposes a scheme, called DV-maxHop, which reaches comparable accuracy quickly utilizing simpler, practical and proven variant of the DV-Hop algorithm. We evaluate the performance of our scheme using extensive simulation on several topologies under the effect of multiple anisotropic factors such as the existence of obstacles, sparse and non-uniform sensor distribution, and irregular radio propagation pattern. Even for isotropic networks, our scheme out-performed recent algorithms with lower computational overheads as well as reduced energy or communication cost due to its faster convergence. We also introduce the formulation and simulation of Multi-objective Optimization to obtain the optimal solution.

Dynamic Range Normal Bisector Localization Algorithm for Wireless Sensor Networks

Node localization in wireless sensor networks (WSNs) is one of the most critical issues, as many WSN applications depend on precise location of sensor nodes. A number of range-based and range-free localization algorithms have been proposed in last two decades. Range-based schemes attain higher localization accuracy at the cost of extra ranging hardware whereas; range-free schemes are cost effective but show poor localization accuracy. In order to improve the localization accuracy, we have proposed dynamic range normal bisector (DRNB) algorithm which is a distributed range-free localization approach. In DRNB, connectivity between nodes, dynamic ranges of anchor nodes, and principle of normal bisector are used to determine the location of nodes. Each anchor node transmits beacon packets at two different range levels in the network. Each normal node estimates its location by calculating the centre point of the overlapping region of the communication ranges of neighbouring anchors where it lies. Normal bisector technique is used to find the centre point of this overlapping region. Sometimes estimated location of a node may fall out of this overlapping region. To overcome this problem, correction factor has also been proposed. DRNB is an energy efficient algorithm, as it doesn’t require the exchange of information between neighbouring nodes. Analysis and simulation results show that DRNB performs better compared to other existing range-free schemes (e.g. centroid, restricted area based localization and mid-perpendicular algorithm).

A Novel Game based Secure Localization Algorithm for Wireless Sensor Networks

A Novel Game based Secure Localization Algorithm for Wireless Sensor Networks

A Collaborative and Predictive Localization Algorithm for Wireless Sensor Networks

Accurate locating for the mobile target remains a challenge in various applications of wireless sensor networks (WSNs). Unfortunately, most of the typical localization algorithms perform well only in the WSN with densely distributed sensor nodes. The non-localizable problem is prone to happening when a target moves into the WSN with sparsely distributed sensor nodes. To solve this problem, we propose a collaborative and predictive localization algorithm (CPLA). The Gaussian mixture model (GMM) is introduced to predict the posterior trajectory for a mobile target by training its prior trajectory. In addition, the collaborative and predictive schemes are designed to solve the non-localizable problems in the two-anchor nodes locating, one-anchor node locating and non-anchor node locating situations. Simulation results prove that the CPLA exhibits higher localization accuracy than other tested predictive localization algorithms either in the WSN with sparsely distributed sensor nodes or in the WSN with densely distributed sensor nodes.

Decentralized Consensus Based Target Localization in Wireless Sensor Networks

Target localization is an attractive subject for modern systems that utilize different types of distributed sensors for location based services such as navigation, public transport, retail services and so on. Target localization could be performed in both centralized and decentralized manner. Due to drawbacks of centralized systems such as security and reliability issues, decentralized systems are become more desirable. In this paper, we introduce a new decentralized and cooperative target localization algorithm for wireless sensor networks. In cooperative consensus based localization, each sensor knows its own location and estimates the targets position using the ranging techniques such as received signal strength. Then, all nodes cooperate with their neighbours and share their information to reach a consensus on targets location. In our proposed algorithm, we weight the received information of neighbour nodes according to their estimated distance toward the target node. Simulation results confirm that our proposed algorithm is faster, less sensitive to targets location and improves the localization accuracy by 85% in comparison with distributed Gauss–Newton algorithm.

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

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

RSS-Based Localization in WSNs Using Gaussian Mixture Model via Semidefinite Relaxation

Energy-based source localization methods are normally developed according to the channel path-loss models in which the noise is generally assumed to follow Gaussian distributions. In this letter, we represent the practical additive noise by the Gaussian mixture model, and develop a localization algorithm based on the received signal strength to achieve a maximum likelihood location estimator. By using Jensen’s inequality and semidefinite relaxation, the initially proposed nonlinear and nonconvex estimator is relaxed into a convex optimization problem, which can be efficiently solved to obtain the globally optimal solution. Besides, the corresponding Cramer–Rao lower bound is derived for performance comparison. Simulation and experimental results show a substantial performance gain achieved by our proposed localization algorithm in wireless sensor networks.

A simple efficient anchor-free node localization algorithm for wireless sensor networks

In wireless sensor networks, node localization is the first operation after deployment, and the efficiency of localization can directly influence the performance of the wireless sensor network, so designing an efficient localization algorithm has been an important focus in wireless sensor network research. Currently, anchor-free algorithms are the focus of research in node localization because they have the advantages of flexible structure, low cost, high stability and good extensibility. In this article, related research studies of unique localization-, angle- and distance-obtaining methods are introduced, and existing algorithms are analysed. Based on the analysis of existing research, an anchor-free localization algorithm named ladder diffusion node localization algorithm is proposed, in which sensor nodes are located by directional diffused activation and acknowledgement packets with fixed energy. In ladder diffusion node localization algorithm, the time cost of localization is greatly reduced, the communication tasks in localization are allocated in a more balanced manner, and node locations are obtained mainly by rigorous computation instead of measurement, which can reduce transmission measurement error. Finally, the performance of ladder diffusion node localization algorithm is proved both in theory and by the simulation results of experiments.

An Indoor Localization Algorithm Based on RBF Neural Network Optimized by the Improved PSO

Aimed at the problem of large localization error based on indoor received signal strength indication (RSSI), a RBF neural network (RBFNN) localization algorithm is proposed optimized by improved particle swarm optimization (PSO). Combined with resource allocation network (RAN), the number of nodes in hidden layer increase dynamically to determine the center of RBFNN, the number of nodes in hidden layer and spread constant. The inertia weight of PSO is improved to advance the global search ability of PSO and optimize the output weight of RBFNN. Finally, the optimized RBFNN is used for indoor RSSI positioning. Simulation and experimental results show that the improved localization algorithm has higher positioning accuracy.

A hybrid localization algorithm for multi-hop mobile underwater acoustic networks

Self-localization is an essential issue for mobile underwater acoustic networks. Recently most localization schemes focus on the static networks, while only a few methods are for MUANs which also have some drawbacks such as low accuracy, small scale, large communication overhead, etc. In this paper, we propose a hybrid localization algorithm which separates the network into some stages. Nodes in different stages will use different methods to localize themselves and both range-based and range-free methods are adopted during the procedure. The proposed algorithm can be used in a multi-hop network and can improve the localization accuracy as well as reducing the communication cost. Besides the advantages above, the algorithm does not need most of the prior knowledge about movement speed which can be more easier to use in underwater environment. Simulation results show that the proposed algorithm can balance the trade-off between the localization accuracy and the communication cost; meanwhile, the performance of the proposed algorithm is better than the previews and can perform well in the underwater environment.

Pulse-Based Distance Accumulation Localization Algorithm for Wireless Nanosensor Networks

Wireless nanosensor networks (WNSNs) consist of nano-sized communication devices, which are equipped with nano-transceivers, nano-antennas, and other functional modules. A nanosensor is an integrated device that ranges from 10 to $100~\mu m^{2}$ in size. Due to the limited communication capabilities of WNSNs, existing localization algorithms and protocols for wireless sensor networks (WSNs) are no longer applicable to WNSNs. This paper proposes a pulse-based distance accumulation (PBDA) localization algorithm for WNSNs that can be utilized to estimate the distance between nodes with known positions and nodes with unknown positions. The algorithm adopts femtosecond-long pulse for terahertz band communication based on ON–OFF keying (OOK) modulation. A clustering algorithm is first employed to reduce the energy consumption and time delay, then the nano-device analyzes the value of the received pulse based on the OOK modulation and estimates the distance between nodes. MATLAB simulations are implemented to verify the performance of PBDA by comparing it against the flooding-based hop-counting algorithm and cluster based hop-counting algorithm in terms of estimated distance accuracy, energy consumption, and time delay. The trilateral positioning method is also utilized to compare the localization error of PBDA with that of distance vector (DV)-hop. The results show that PBDA is able to support WNSNs with very high density in ranging and locating.

A Time Difference of Arrival-based Localization Algorithm for Wireless Sensor Networks

Aimed at solving the problem of local divergence and low data accuracy, this paper introduces a new Time Difference of Arrival(TDOA)-based localization algorithm (TBL) for the large-scale, high-density wireless sensor networks which are designed for real-time surveillance and unexpected incidents management. In particular, several means to improve the accuracy of distance measurement are investigated, and the TDOA method, based on the sound wave and electromagnetic wave to locate in the large-scale WSN, is discussed. Also, the well-designed circular location process has the advantage of better positioning accuracy and coverage percentage. Simulation results have confirmed the effectiveness of the formed TBL algorithm.

Localization algorithm for large-scale wireless sensor networks based on FCMTSR-support vector machine

Sensor node localization is one of research hotspots in the applications of wireless sensor network field. A localization algorithm is proposed in this article which is based on improved support vector machine for large-scale wireless sensor networks. For a large-scale wireless sensor network, localization algorithm based on support vector machine faces to the problem of the large-scale learning samples. The large-scale training samples will lead to high burden of the training calculation, over learning, and low classification accuracy. In order to solve these problems, this article proposed a novel scale of training sample reduction method (FCMTSR). FCMTSR takes the training sample as point set, get the potential support vectors, and remove the non-boundary outlier data immixed by analyzing relationships between points and set. To reduce the calculation load, fuzzy C-means clustering algorithm is applied in the FCMTSR. By the FCMTSR, the training time is reduced and the localization accuracy is improved. Through the simulations, the performance of localization based on FCMTSR-support vector machine is evaluated. The results prove that the localization precision is improved 2%, the training time is reduce 55% than existing localization algorithm based on support vector machine without FCMTSR. FCMTSR-support vector machine localization algorithm also addresses the border problem and coverage hole problem effectively. Finally, the limitation of the proposed localization algorithm is discussed and future work is present.

Robust ANNs-Based WSN Localization in the Presence of Anisotropic Signal Attenuation

We propose a novel range-free localization algorithm for wireless sensor networks that is robust against the anisotropic signal attenuation induced by fading, shadowing, interference, etc., present in any wireless channel, and hereby, develop a new distance estimation (DE) approach able to efficiently derive distances' estimates in closed form. Exploiting artificial neural networks, we also develop a power-efficient DE correction mechanism that properly accounts for anisotropic signal attenuation. Simulation results show that the proposed algorithm significantly outperforms most representative range-free localization algorithms, not only in accuracy but also in robustness against anisotropic attenuation.

A distributed adaptive local searching algorithm for wireless sensor network localization

The location of sensors represents a relevant attribute in wireless sensor networks. In this article, we propose a distributed range-based localization algorithm for wireless sensor network. The algorithm is mainly based on an adaptively local circular searching area where each unknown sensor must find inside a position that minimizes the error distance with neighboring sensors using a local function. The new position of a sensor is broadcast to neighboring sensors allowing collaboratively to other sensors re-estimate their new positions. The iterative process is repeated until a stopping criterion is reached. The performance of the proposed algorithm is evaluated and compared with other state-of-the-art algorithms, and results indicate that the approach overcomes the other methods in both accuracy and time of convergence.