Node Localization Scheme Research Articles

Enhanced Dwarf Mongoose Optimization Based Node Localization Scheme for Underwater Wireless Sensor Networks

Enhanced Dwarf Mongoose Optimization Based Node Localization Scheme for Underwater Wireless Sensor Networks

Hybridized Black Widow-Honey Badger Optimization: Swarm Intelligence Strategy for Node Localization Scheme in WSN

Hybridized Black Widow-Honey Badger Optimization: Swarm Intelligence Strategy for Node Localization Scheme in WSN

A Range-Based Node Localization Scheme for UWASNs Considering Noises and Aided with Neurodynamics Model

A Range-Based Node Localization Scheme for UWASNs Considering Noises and Aided with Neurodynamics Model

NLOS node localization for improving warning message distribution in VANETS

SummaryVehicular Ad‐hoc Network (VANET) is a vehicle‐to‐vehicle communication technology, which is highly promising due to its continuous demand, applicability, and suitability for public safety. The vehicular nodes establish communication with each other for exchanging traffic data and road maps, especially when warning messages need to be broadcasted on the network during emergency situations. Therefore, accurate vehicle information is necessary in such circumstances to improve the accuracy of Non‐Line‐of‐Sight (NLOS) node localization. In this paper, a Driving Training‐Based Optimization for NLOS Node Localization (DTO‐NL) Scheme is proposed for facilitating better warning message delivery during emergency situations in vehicular networks through a dynamic increase and decrease in the search based on the position of unknown NLOS nodes from the considered anchor nodes employed for localization. The performance of the suggested technique has been analyzed based on parameters like warning message delivery rate, latency, localization error, channel utilization rate, energy consumption, and packet delivery ratio. The proposed DTO‐NL scheme enhances the warning message delivery rate by 9.81%, 11.28%, 14.38%, and 17.64% compared to WIDVBA , ROA‐APS , ILL‐WOA‐NLOS , and WDHPBDS techniques .

Metaheuristic Optimization Based Node Localization and Multihop Routing Scheme with Mobile Sink for Wireless Sensor Networks

Metaheuristic Optimization Based Node Localization and Multihop Routing Scheme with Mobile Sink for Wireless Sensor Networks

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

Distributed Subspace Projection Graph Signal Estimation with Anomaly Interference

The estimation of graph signal is a vital problem in many distributed networks, such as vehicular networks, smart grids, unmanned aerial vehicles (UAVs), and the Internet of Things. In those networks, anomaly interference widely exists, such as network attack, noise, device fault, which will hazard the healthy of the entire system. In the paper, the estimation of graph signals with anomaly interference is investigated. We show that the graph signal estimation problem can be treated as a bandlimited subspace optimization problem, and propose a distributed subspace projection graph signal estimation algorithm based on the graph filter (DispGF), which can achieve better performance with less communication burden. In addition, a graph filter matrix that produces subspace projection is proposed to replace the nonsparse projection matrix, which guarantees distributed implementation and projection accuracy. Different from previous work, here, graph signal estimation is studied with no prior anomaly information. To this end, for FDI attack, random attack, noise interference, we propose anomaly detection and node localization scheme based on smoothness, that can achieve similar performance compared with the case of prior anomaly information known. Numerical experiments verify the effectiveness of the proposed DispGF algorithm. The convergence of the algorithm is theoretically analyzed.

Enhanced seagull optimization based node localization scheme for wireless sensor networks

Enhanced seagull optimization based node localization scheme for wireless sensor networks

Node localization and data aggregation scheme using cuckoo search and neural network

AbstractAmong multi‐hop technology, wireless sensor network (WSN) has been extensively investigated owing to its potential application in vivid fields. However, a key issue probing WSN is node location that is also the major area of interest in the present paper. The paper takes advantage of cuckoo search (CS) as the swarm intelligence technique used to address the issues of identification of malicious or unknown nodes within the network. The distance vector (DV)‐hop is used to determine the distance between the anchor sensor node and the unknown or the node with compromised nature. Then, artificial neural network architecture is used to distinguish the nodes based on the characteristics. This is followed by the evaluation of the proposed scheme to offer reliable data transmission using CS optimized data aggregation scheme. The simulation analysis over 1000 deployed nodes shows that CS significantly decreases the localization error to 0.494 and localization time to 0.058 s along with 15%–20% improvement in the throughput and packet delivery ratio. This shows that the proposed CS optimized architecture is successful in identifying the position of unknown nodes as well as compromised nodes that significantly improved the reliability of the data transmission.

A range based node localization scheme with hybrid optimization for underwater wireless sensor network

SummaryA great interest among researchers to establish Underwater Wireless Sensor Networks (UWSN) with mobile nodes have been created in recent past because of numerous potential applications in aquatic science. In comparison of radio communication, acoustic communication has lower bandwidth and larger propagation delay. This enacts additional restrictions on any localization algorithm. Existing range‐based approaches involve increased localization latency and error even with large deployment of anchor nodes. Some of the optimized localization techniques involve higher energy consumption and cost. In order to solve these issues, a hybrid optimized localization technique is proposed that can accurately localize mobile nodes in UWSN. In this algorithm, a fitness function is concluded based on the number of hops from the anchor node, Time of Arrival (ToA) distance estimation error and transmission delay. In comparison of existing localization techniques, proposed algorithm helps in reducing the energy consumption, localization error, localization delay and localization cost and experiment results proved the same.

Energy Efficient Localization Through Node Mobility and Propagation Delay Prediction in Underwater Wireless Sensor Network

In Underwater Wireless Sensor Network, for mobile target localization schemes, there exists long propagation delay of sensor nodes' messages. Further, the flexibility of node localization schemes to adapt in harsh underwater environment is the major issue. As propagation delay is one of the principal aspects that disturb the time harmonisation of underwater sensor nodes for communication. It must be adjusted to attain precise and accurate localization. In this article, we propose an energy-efficient localization scheme based on mobility and propagation delay prediction. Precise synchronization time is accomplished by forecasting the long propagation delay. Anchor node mobility prediction procedure is intended to analyse and record its speed at every localization period. Then, the propagation delay is compensated to attain rapid reporting of localization. The ordinary nodes make localization, utilizing the expected speed vectors obtained from the anchor nodes. The proposed scheme is tested in simulator NS-2.30 all-in-one with multiple runs and average of these runs is considered. The replicating outcomes exhibit, that the proposed method increases overall energy efficiency by 16%, localization cost by 26%, and delay by 28%, which are the major concern for localization scheme.

Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks

Nodes localization in a wireless sensor network (WSN) aims for calculating the coordinates of unknown nodes with the assist of known nodes. The performance of a WSN can be greatly affected by the localization accuracy. In this paper, a node localization scheme is proposed based on a recent bioinspired algorithm called Salp Swarm Algorithm (SSA). The proposed algorithm is compared to well-known optimization algorithms, namely, particle swarm optimization (PSO), Butterfly optimization algorithm (BOA), firefly algorithm (FA), and grey wolf optimizer (GWO) under different WSN deployments. The simulation results show that the proposed localization algorithm is better than the other algorithms in terms of mean localization error, computing time, and the number of localized nodes.

Node Localization in Wireless Sensor Networks Using Butterfly Optimization Algorithm

Accurate localization of sensor nodes has a strong influence on the performance of a wireless sensor network. In this paper, a node localization scheme using the application of nature-inspired metaheuristic algorithm, i.e., butterfly optimization algorithm, is proposed. In order to validate the proposed scheme, it is simulated on different sizes of sensor networks ranging from 25 to 150 nodes whose distance measurements are corrupted by gaussian noise. The performance of the proposed novel scheme is compared with performance of some well-known schemes such as particle swarm optimization (PSO) algorithm and firefly algorithm (FA). The simulation results indicate that the proposed scheme demonstrates more consistent and accurate location of nodes than the existing PSO- and FA-based node localization schemes.

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme.

Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE) in ELM is replaced by the half-quadratic optimization technique. Simulation results show that our proposed scheme outperforms other traditional localization schemes.

Range-free 3D node localization in anisotropic wireless sensor networks

In this paper, we propose two computationally efficient ‘range-free’ 3D node localization schemes using the application of hybrid-particle swarm optimization (HPSO) and biogeography based optimization (BBO). It is considered that nodes are deployed with constraints over three layer boundaries, in an anisotropic environment. The anchor nodes are randomly distributed over the top layer only and target nodes distributed over the middle and bottom layers. Radio irregularity factor, i.e., an anisotropic property of propagation media and heterogenous properties of the devices are considered. To overcome the non-linearity between received signal strength (RSS) and distance, edge weights between each target node and neighboring anchor nodes have been considered to compute the location of the target node. These edge weights are modeled using fuzzy logic system (FLS) to reduce the computational complexity. The edge weights are further optimized by HPSO and BBO separately to minimize the location error. Both the proposed applications of the two algorithms are compared with the earlier proposed range-free algorithms in literature, i.e., the simple centroid method and weighted centroid method. The results of our proposed applications of the two algorithms are better as compared to centroid and weighted centroid methods in terms of error and scalability.

Collaborative Localization and Location Verification in WSNs.

Localization is one of the most important technologies in wireless sensor networks. A lightweight distributed node localization scheme is proposed by considering the limited computational capacity of WSNs. The proposed scheme introduces the virtual force model to determine the location by incremental refinement. Aiming at solving the drifting problem and malicious anchor problem, a location verification algorithm based on the virtual force mode is presented. In addition, an anchor promotion algorithm using the localization reliability model is proposed to re-locate the drifted nodes. Extended simulation experiments indicate that the localization algorithm has relatively high precision and the location verification algorithm has relatively high accuracy. The communication overhead of these algorithms is relative low, and the whole set of reliable localization methods is practical as well as comprehensive.

An Interactive and Energy-efficient Node Localization Scheme for Wireless Sensor Networks

How to obtain accurate position of sensor node is still a challenging problem for wireless sensor networks (WSNs). This work concentrates on the problem of node localization with mobile anchor node, which is applied to broadcast beacon packets in the region of interest (ROI). Node localization scheme SAA-ERL (speed adaptive adjustment-energy ratio localization) for WSNs is realized in way of interactive communications. SAA scheme determines the suitable moving speed and direction for the mobile anchor node, while the ERL mechanism generates virtual hyper-spheres to help unknown nodes acquire their locations. Based on the quantitative analysis of the localizing performances, the guidelines for system parameters are obtained. Simulation results validate that when system parameters are jointly designed, SAA-ERL can be adapted to scenarios of diversified applications.

A Modified DV-Hop Scheme for 3D Node Localization in Wireless Sensor Networks

DV-Hop is one of the most widely-used node localization schemes for WSNs in 2D applications. Its performance in 3D node localization is investigated and a modified scheme is presented in this paper. Simulation is provided to test the feasibility and capacity of the original DV-Hop scheme and the modified scheme in 3D applications in terms of the localization accuracy as the network connection topology varies.

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.

A Modified DV-Hop Scheme for 3D Node Localization in Wireless Sensor Networks

DV-Hop is one of the most widely-used node localization schemes for WSNs in 2D applications. Its performance in 3D node localization is investigated and a modified scheme is presented in this paper. Simulation is provided to test the feasibility and capacity of the original DV-Hop scheme and the modified scheme in 3D applications in terms of the localization accuracy as the network connection topology varies.