Wireless Sensor Node Localization Research Articles

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

Relative Localization of Wireless Sensor Nodes by Using the RSSI and ToA based distance estimations

Wireless sensor network is a popular area for both academic research and commercial applications. A wireless sensor network is made up of several sensor nodes connected over various wireless infrastructures such as Wi-Fi, Bluetooth, ZigBee, or WiMAX. Determining the relative or absolute position of the sensor nodes is essential information for many applications. In this work, we present a novel method for estimating the position of sensor nodes using the Received Signal Strength Indicator and Time of Arrival metrics of the preferred wireless communication infrastructure. Localization results based on both of the metrics and comparison of them with respect to distance between the nodes are presented, and a novel combined method using both the RSSI and ToA based distance estimations is presented. The proposed method estimates the position of the WSN using both methods, but the result of a single method is preferred depending on the distance between the nodes since within the first 5 m. The RSSI based method is superior to ToA and for farther distances ToA outperforms RSSI. The measurement results show that the combined method reduces the estimation error and performs better than both methods it is based on.

Wireless sensor node localization algorithm combined with PSO-DFP

Abstract In wireless communication technology, wireless sensor networks usually need to collect and process information in very harsh environment. Therefore, accurate positioning of sensors becomes the key to wireless communication technology. In this study, Davidon–Fletcher–Powell (DFP) algorithm was combined with particle swarm optimization (PSO) to reduce the influence of distance estimation error on positioning accuracy by using the characteristics of PSO iterative optimization. From the experimental results, among the average precision (AP) values of DFP, PSO, and PSO-DFP algorithms, the AP value of PSO-DFP was 0.9972. In the analysis of node positioning error, the maximum node positioning error of PSO-DFP was only about 21 mm. The results showed that the PSO-DFP algorithm had better performance, and the average positioning error of the algorithm was inversely proportional to the proportion of anchor nodes, node communication radius, and node density. In conclusion, the wireless sensor node location algorithm combined with PSO-DFP has a better location effect and higher stability than the traditional location algorithm.

Accurate range-based distributed localization of wireless sensor nodes using grey wolf optimizer

Various ranging techniques are frequently employed in wireless sensor networks (WSNs) to determine the distance between a node and its neighboring anchor nodes. The distance measurement, as mentioned earlier is subsequently employed to estimate the location of the node whose location is unknown. The present paper presents an Accurate Localization Scheme that utilizes Grey Wolf Optimization (GWO) and is based on the Radio Signal Strength (RSS) ranging technique. The efficiency of our technique has been proved through extensive simulations, showing a consistent improvement in localization accuracy ratios and a decrease in location errors while maintaining cost-effectiveness.

Distributed wireless sensor node localization based on penguin search optimization

Wireless sensor networks (WSNs) have become popular for sensing areas-of-interest and performing assigned tasks based on information on the location of sensor devices. Localization in WSNs is aimed at designating distinct geographical information to the inordinate nodes within a search area. Biologically inspired algorithms are being applied extensively in WSN localization to determine inordinate nodes more precisely while consuming minimal computation time. An optimization algorithm belonging to the metaheuristic class and named penguin search optimization (PeSOA) is presented in this paper. It utilizes the hunting approaches in a collaborative manner to determine the inordinate nodes within an area of interest. Subsequently, the proposed algorithm is compared with four popular algorithms, namely particle swarm optimization (PSO), binary particle swarm optimization (BPSO), bat algorithm (BA), and cuckoo search algorithm (CS). The comparison is based on two performance metrics: localization accuracy and computation time to determine inordinate nodes. The results obtained from the simulation illustrate that PeSOA outperforms the other algorithms, achieving an accuracy higher than 30%. In terms of computation time to determine inordinate nodes, the proposed algorithm requires 28% less time (on average) than the other algorithms do.

Radial basis function-based node localization for unmanned aerial vehicle-assisted 5G wireless sensor networks

Localization or positioning of wireless sensor nodes is an essential task for a wide range of applications in wireless sensor networks-based fifth generation (5G) networks. Node localization using mobile aerial beacon nodes (MABNs) provides high localization accuracy and less deployment cost compared to the localization using fixed ground beacon nodes because MABN deployed in unmanned aerial vehicles sends signals to unknown nodes (UNs) through reliable air to ground (AG) channel link. The classical received signal strength indicator (RSSI)-based multilateration and optimization-based least square localization (OLSL) schemes result high localization error because of the nonlinear distortions induced in the wireless channel. So, the highly nonlinear artificial neural network (ANN) models such as multilayer perceptron (MLP) and radial basis function (RBF) are used effectively for nonlinear node localization problems. ANN-based localization techniques are also capable of localizing mobile UNs. Further, the RBF with Gaussian activation has a little edge over the MLP with sigmoid activation because the wireless channel usually modelled with a Gaussian random variable. The simulation results included in this article also justify the efficacy of the proposed RBF localization with performance gain of about (7–70) % over MLP, OLSL and RSSI localization techniques.

Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor Networks.

The Distance Vector-Hop (DV-Hop) algorithm is the most well-known range-free localization algorithm based on the distance vector routing protocol in wireless sensor networks; however, it is widely known that its localization accuracy is limited. In this paper, DEIDV-Hop is proposed, an enhanced wireless sensor node localization algorithm based on the differential evolution (DE) and improved DV-Hop algorithms, which improves the problem of potential error about average distance per hop. Introduced into the random individuals of mutation operation that increase the diversity of the population, random mutation is infused to enhance the search stagnation and premature convergence of the DE algorithm. On the basis of the generated individual, the social learning part of the Particle Swarm (PSO) algorithm is embedded into the crossover operation that accelerates the convergence speed as well as improves the optimization result of the algorithm. The improved DE algorithm is applied to obtain the global optimal solution corresponding to the estimated location of the unknown node. Among the four different network environments, the simulation results show that the proposed algorithm has smaller localization errors and more excellent stability than previous ones. Still, it is promising for application scenarios with higher localization accuracy and stability requirements.

A High-Efficient and Low-Cost Localization System for Wireless Sensor Networks

The problem of localization of wireless sensor nodes has long been regarded as very difficult to solve, when considering the realities of real world environments. In this paper, we formally describe, design, implement and evaluate a novel localization system, called Spotlight. Our system uses the spatio-temporal properties of well controlled events in the network (e.g., light), to obtain the locations of sensor nodes. We demonstrate that a high accuracy in localization can be achieved without the aid of expensive hardware on the sensor nodes, as required by other localization systems. We evaluate the performance of our system in deployments of Mica2 and XSM motes. Through performance evaluations of a real system deployed outdoors, we obtain a 20cm localization error. A sensor network, with any number of nodes, deployed in a 2500m2 area, can be localized in under 10 minutes, using a device that costs less than $1000. To the best of our knowledge, this is the first report of a sub-meter localization error, obtained in an outdoor environment, without equipping the wireless sensor nodes with specialized ranging hardware.

Wireless Sensor Node Localization Algorithm Based on Particle Swarm Optimization and Quantum Neural Network

Aiming at the problem of node localization in wireless sensor networks, a location algorithm for optimizing distance vector hopping (DV-hop) by constructing a quantum neural network model based on particle swarm optimization (PSO) is proposed. According to the average distance obtained by the traditional DV-HOP and the distance from the measured nodes, the quantum neural network model is constructed, and the average distance is trained by the particle swarm optimization algorithm which would shorten the training time of the traditional artificial neural network and accelerate the convergence speed. By using the proposed model, the optimal mean value is obtained, and the optimization of the DV-HOP algorithm is realized. The simulation results show that compared with the traditional DV-HOP algorithm, the proposed algorithm can reduce the positioning error by about 20%, and the positioning accuracy is significantly improved.

3-D localization of wireless sensor nodes using near-field magnetic-induction communications

This paper proposes two localization methods for wireless sensor nodes that utilize an arbitrarily oriented tri-directional coil as both magnetic induction (MI) transmitter and receiver for wireless communications. Taking advantage of magnetic field measurements of a tri-directional coil antenna in the near-field, the two localization algorithms use only two anchor nodes to locate a sensor node in the 3-D space. Assuming each anchor node transmits the communication signals by three coils sequentially, which are received by the three coils at a sensor node simultaneously, this paper derives closed-form formulas for estimating the transmission distance and the polar angles to yield eight possible location points based on the signals of each anchor node. Then a rotation matrix (RM)-based method derives the orientation rotation matrix between the transmitter and receiver to find two possible location vectors with opposite directions in each anchor node. Then, we use maximum likelihood to estimate the location with two anchor nodes assisted. Another method called the distance-based method, taking into account the locations of the two anchor nodes and the two sets of eight possible location estimates of the sensor node, estimates the location by minimizing the distance. The RM-based method can achieve high localization accuracy while the distance-based method has less computational complexity. However, the distance-based method may encounter location ambiguity when the orientations of the two anchor nodes are the same. Simulations were performed to compare these two algorithms and the existing localization algorithm in this scenario. The results show that the proposed two localization algorithms and the derived closed-form formula of distance achieve good accuracy under large measurement errors.

Comparative Analysis of AODV Base and RSSI Base Wireless Sensor Node Localization Techniques

Comparative Analysis of AODV Base and RSSI Base Wireless Sensor Node Localization Techniques

Relative Localization of Wireless Sensor Nodes by Using the RSSI Data

Wireless sensor network is an emerging research field and a crucial infrastructure for Internet of Things (IoT) applications. Sensor nodes of a wireless network may connect via different wireless technologies like Wi-Fi, Bluetooth, ZigBee or WiMAX. In many applications, like field monitoring or smart buildings, the location of the nodes with respect to a global or relative coordinate system is essential information. The main focus of this work is the localization of wireless sensor nodes, using the Received Signal Strength Indicator (RSSI) metric of the wireless telecommunication infrastructure. We present both simulation and measurement results of the proposed method and compare the results with similar work.

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location

Abstract The paper aims at localization of the anchor node (fixed node) by pursuit nodes (movable node) in outdoor location. Two methods are studied for node localization. The first method is based on LNSM (Log Normal Shadowing Model) technique to localize the anchor node and the second method is based on Hybrid TLBO (Teacher Learning Based Optimization Algorithm)- Unilateral technique. In the first approach the ZigBee protocol has been used to localize the node, which uses RSSI (Received Signal Strength Indicator) values in dBm. LNSM technique is implemented in the self-designed hardware node and localization is studied for Outdoor location. The statistical analysis using RMSE (root mean square error) for outdoor location is done and distance error found to be 35 mtrs. The same outdoor location has been used and statistical analysis is done for localization of nodes using Hybrid TLBO-Unilateral technique. The Hybrid- TLBO Unilateral technique significantly localizes anchor node with distance error of 0.7 mtrs. The RSSI values obtained are normally distributed and standard deviation in RSSI value is observed as 1.01 for outdoor location. The node becomes 100% discoverable after using hybrid TLBO- Unilateral technique.

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO.

Efficient Energy Utilization and Node Localization in Dynamic DV-Hop Algorithm for Wireless Sensor Networks

Objective: This paper deals with the proposed Dynamic DV-Hop technique for wireless sensor node localization is used for the computing node location with better accuracy and less localization error. Method: DV-Hop is simply an algorithm of the range-free localization which can help us getting estimation by using the hop-distance. Since there is very low location accuracy found in DV-Hop localization algorithm. By figure-out the DV-Hop problem so it is essential improving the DV-Hop algorithm, a newly and the communication ranges. This method requires no extra supplementary estimated model is propounded by considering the relations in between the node-hop distance hardware support. However, this method can be instrumented in a distributed manner. Findings: The simulation work show that the proposed scheme decreases the total communication cost than the normal DV-Hop algorithm so in the proposed Dynamic DV-Hop method it represents less localization errors which is established by the graphs. In the present study, proposed scheme provides efficient energy utilization and management during the Dynamic DV-Hop localization method. Application/Improvement: The research work concludes by stating that very low location accuracy found in DV-Hop algorithm but this issue can be improved by proposed Dynamic DV-Hop algorithm and efficient energy utilization and management is performed.

Optics-based wireless sensor node localization using MEMS CCR

In this paper, we analyze the phenomena and problems that can occur in the sensor node localization of optics-based sensor networks using MEMS (Micro Electro Mechanical Systems) CCR (Corner Cube Retro-reflector). CCR reflects incident rays back in the opposite direction to calculate the distance between the sensor node and the base station and measuring the difference in source and sink detection time. The sensor node position can be measured from the ToF (Time of Flight) and the AoA (Angle of Arrival). However, when the MEMS CCR is used, reflected ray’s power is not strong enough to be detected. In the optics-based sensor network, sensor nodes are distributed randomly. Some sensor nodes cannot be detected due to the rectilinear propagation characteristics of light. To overcome these problems, we analyze the reflected ray’s power and verify the results experimentally. Based on the analysis, we replaced the photo detector to increase the detectable range. We also showed that multiple base stations can solve the randomly distributed sensor node issue and improve sensor node localization.

A distributed localization in wireless sensor networks utilizing AOD estimation and synthetical uniform circular array

The localization of wireless sensor nodes is one of the key technologies in wireless sensor networks (WSN). Recently, a distributed localization method has been proposed based on a novel concept of angle of departure (AOD) estimation, which can be employed by e.g., the single antenna sensors to estimate the angle information and achieve localization. Based on the AOD estimation, in this paper, we introduce a synthetic uniform circular array (SUCA) synthesized by a rotary antenna on anchor to get the distributed localization of the sensors in multi-path environments. The rotated operation of antenna could create the SUCA with a larger aperture to deal with AOD of multipath components (MPCs) and relax the condition that array aperture should be greater than the number of MPCs. This new localization method only relies on radio transceivers without requiring extra measuring equipments, and the sensor nodes conduct localization in a completely distributed manner instead of centralized processing, which is suitable for large-scale WSN. Meanwhile, the influence of carrier frequency offset (CFO) between the transceivers is also considered and it is eliminated by utilizing the signal emitted from an auxiliary antenna.

Wireless Sensor Networks Target Localization Based on Least Square Method and DV-Hop Algorithm

Wireless sensor node localization is one of the most crucial techniques on wireless sensor network. For reducing localization cost and improving localization accuracy, we propose an improved wireless sensor network DV-Hop localization algorithm. The mobile beacon node is introduced into DV-Hop and allows a moving beacon node to move on the pre-arranged way, continuously broadcast its position information to form multiple virtual beacons. The simulation experiment result shows our method can reduce the localization cost and the complexity of network while improving the node localization accuracy and efficiency

A Localization Algorithm of Multi-Hop Three Dimensional AOA with Space-Based Angle Transmission and its Application

Wireless sensor node's localization is a funda-mental technology in Wireless Sensor Networks. There are only quite a few study on three-dimensional (3D) localization which is suffered in slow progress, actually, is one of the main difficulties in WSN localization. Based on the study of the existing two-dimensional positioning algorithm and the application of terrain modeling, localization algorithm for sensor nodes in (3D) condition has been focus on as well as the application of terrain model. Using the idea proposed by representative algorithm--APS multi-hop AOA (Angle of Arrival), this paper proposed a new algorithm named Multi-hop Three Dimensional AOA With Space-based Angle Trans-mission (MSAT3D AOA). Using this technology, target nodes can use information of anchor nodes which are more than one hop away form. This paper also combined MSAT3D AOA algorithm with Delaunay triangulation algorithm for terrain modeling.