Node Location Error Research Articles

Node location using cuckoo search algorithm with grouping and drift strategy for WSN

Wireless sensor networks (WSNs) have been used extensively in many fields due to their convenience in data collection, processing, and transmission, and the stability and reliability of information processing and transmission is dependent on the accuracy of the locations of sensor nodes in the WSNs. Therefore, in order to improve the accuracy of WSN node localization, an improved cuckoo search algorithm (ICS-GD) based on population grouping and drifting strategy is proposed in this manuscript for solving the WSN node localization error problem. The first step is to design a Mahalanobis distance-based method of expressing population diversity, and population clustering is also implemented on this basis in concert with individual evolution; an information interaction based drift strategy is then proposed to improve the search capability of the algorithm at the local development stage depending on the clustering; finally, the effectiveness of the ICS-GD algorithm is verified through benchmark test functions and WSN node localization error experiments and superiority of the ICS-GD algorithm through benchmark test functions and WSN node localization error experiments. Experimental results show that the ICS-GD algorithm has more clear advantages over state-of-the-art algorithms such as IAGA, BBPSO, and HGWO, and the algorithm obtains more satisfactory performance in all of the 26 benchmark test functions, and the localization error is as small as 22.21% in the solution of the WSN node localization problem.

A range‐free localization algorithm for IoT networks

Internet of things (IoT) is a ubiquitous network that helps the system to monitor and organize the world through processing, collecting, and analyzing the data produced by IoT objects. The accurate localization of IoT objects is indispensable for most IoT applications, especially healthcare monitoring. Utilizing GPS as the positioning system is not cost-efficient and does not apply to some environments (e.g., deep forests, oceans, inside the buildings, etc.). Hereupon, copious position estimation approaches are developed in the literature. Among range-free approaches, distance vector-Hop (DV-Hop) is the widely used algorithm due to its straightforward applicability and can estimate the position of unknown objects that are far-off the anchors. Due to its low accuracy, various techniques were proposed to increase the accuracy of basic DV-Hop. In the most recent approach, meta-heuristic algorithms were used, the results of which were promising. In the present paper, Tunicate Swarm Algorithm and Harris hawk optimization were initially hybridized. Afterthought, the resulting hybrid algorithm was enhanced by appending a new phase. Then, the proposed hybrid algorithm was intermingled with the DV-Hop algorithm. In the first set of experiments, the proposed hybrid algorithm was evaluated on 50 test functions using average, SD, box plot, and p-value criteria. In the second part, the proposed localization algorithm's efficiency was investigated in twenty-eight different manners using node localization error, average localization error, and localization error variance metrics. The effectiveness of the contributions was evident from the experimental results.

Time Synchronized Node Localization Using Optimal H-Node Allocation in a Small World WSN

Time synchronization and node localization over a wireless sensor network (WSN) is a vital problem in applications such as Internet of Things (IoT) and context-aware pervasive systems. Addressing this problem leads to an efficient data fusion, power scheduling, bandwidth utilization, geographic routing, time-based channel sharing, and clustering among sensor nodes. In a WSN, sensor nodes utilize multi-hop data transmission model towards the data transfer. Utilization of large number of hops for the data transfer leads to poor time synchronization resulting in erroneous node location estimates. In this letter, we make use of a recent development in social networks termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">small world characteristics</i> and propose a novel method of time synchronized node localization over a small world WSN (SW-WSN). The results are obtained via simulations over a medium scale WSN. The performance of the proposed method is evaluated by conducting detailed analysis of time synchronization and node localization errors over the SW-WSN. The results obtained illustrate that the proposed small world model yields improved localization results when compared to results obtained over a regular WSN.

Node localization method for massive sensor networks based on clustering particle swarm optimization in cloud computing environment

In order to reduce the positioning error of wireless sensor network nodes and deal with the positioning of a large number of sensor network nodes, a mass sensor node positioning method based on particle swarm optimization (SNPSO) is proposed. The node location error of node distance correction value is corrected by SNPSO algorithm, and the sensing data is encoded by the index of cloud computing resources. The dynamic target strategy (DTS) algorithm is used to solve the strict deadline constraints. The algorithm focuses on optimizing execution time to meet deadline constraints, and once feasible solutions are obtained, it focuses on optimizing execution costs within deadline constraints. The performance of the algorithm is simulated and analyzed on the platform of MATLAB 2016. Compared with the sensor positioning method, SNPSO improves the positioning accuracy of mass sensor nodes. The simulation results verify the validity of SNPSO. Compared with the improved quantum genetic algorithm (IQGA) under different scale data scheduling and different deadlines constraints, the proposed algorithm can improve the positioning accuracy of mass sensor nodes. The proposed algorithm can find the optimal solution of cloud computing resource scheduling with lower execution cost under strict deadline constraints, and can more easily meet the needs of massive sensor network node location data processing.

Improved DV-Hop Localization Algorithm Based on Dynamic Anchor Node Set for Wireless Sensor Networks

Node localization is a key issue in wireless sensor networks (WSN) area, and distance vector hop (DV-Hop) algorithm is widely adopted in WSN localization. Existing DV-Hop based algorithms employ all anchor nodes to localize the unknown node. However there is a large error of the estimated distance from the unknown node to some anchor nodes, which also results in the final unknown node localization error large. To improve the localization accuracy, we propose an improved DV-Hop algorithm based on dynamic anchor node set (DANS IDV-Hop). Differently to the existing DV-Hop based algorithms which apply total anchor nodes, DANS IDV-Hop utilizes part of anchor nodes to participate in localization. Firstly, the selection of anchor nodes is abstracted into a combinatorial optimization problem. For selecting appropriate anchor nodes, a novel binary particle coding scheme and fitness function are designed. Subsequently, the binary particle swarm optimization (BPSO) algorithm is applied to construct the dynamic anchor node set (DANS), and the localization is carried out on the DANS. Finally, the continuous particle swarm optimization (PSO) algorithm is utilized to further optimize the unknown node coordinates. Simulation results show that DANS IDV-Hop has excellent localization accuracy than that of the original DV-Hop and other DV-Hop based improved algorithms.

Research of Space Network Node Route Planning and Localization in HAPS Space

Compared with the traditional method of detection tracking, space sensor networks(WSN) for its good character of the traditional tracking methods inadequate. For the localization algorithm and route planning of space sensor network exists a big positioning error in high altitude platform station (HAPS) space communication. Space sensor network communication system is researched. According to the anchor nodes and unknown nodes, location algorithm and node route planning method of WSN are proposed in this paper. The performance of the node density and location error are simulated and analyzed in HAPS. The simulation results show that the performance of proposed location error is better than the traditional algorithms.

Mobile node localization method based on KF-LSSVR algorithm

As the mobile node localization algorithm in three-dimensional environment cannot meet the demand of actual application, a hybrid mobile node localization algorithm for a wireless sensor network (WSN) in three-dimensional environment is proposed in this paper, which is based on the least squares support vector regression (LSSVR) and Kalman filter (KF). The proposed algorithm firstly constructs the LSSVR localization model by sampling measurement area and training sample sets. Then, the KF model is used to iterate and correct the measured distance in order to obtain the distance between the unknown node and each anchor node. Finally, the LSSVR localization model is employed to obtain the estimated location of the unknown node. The experiments were conducted and the experimental results were analyzed according to ranging errors, anchor node density, communication radius, moving speed, and node localization errors. Simulation results show that the proposed algorithm using a joint KF and LSSVR algorithm is superior to the KF algorithm and the LSSVR algorithm, and it can reduce the localization errors and improve the localization accuracy.

Evaluation of a Compound Node Selection Function for a Greedy Routing in Urban Vehicular Ad Hoc Networks

The greedy routing that reduces the Euclidean distance to destination continuously to the possible maximum in every forwarding is suitable for vehicular ad hoc networks with high node mobility due to operation simplicity and low overhead. However, the selection of a next forwarding node based on the forward progress distance alone suffers from varying link conditions by high node mobility and communication obstructions. Thus, a compound node selection function is developed to include link quality factor. In this function, the accuracy of the forward progress distance largely depends on the node location error that comes from either GPS measurement error or the elapse of time after measurement. Location prediction is conducted by the Kalman filter and link quality is assessed using the successfully transmitted Hello messages. The compound function is evaluated against variations of fading and shadowing effects and the injected GPS error and is applied to a greedy routing protocol to prove its superiority.

An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm

The Approximate Point-In-Triangulation (APIT) localization algorithm is a widely used indoor positioning technology due to its simplicity and low power consumption. However, in practice, In-to-Out misjudgments exist regularly in APIT, and a considerable amount of nodes cannot be positioned due to the low node density. To tackle this issue, a Collaborative Coefficient-triangle APIT Localization (CCAL) algorithm is proposed. Firstly, an effective triangle criterion is put forward to reduce the probability of In-to-Out misjudgment and reduce the computational complexity. Then, a further Received Signal Strength Indicator (RSSI) location and weighted triangle coordinate calculation method is adopted to reduce the positioning error. Meanwhile, the idea of iterative collaborative positioning of the positioned unknown nodes is introduced to remarkably expand the localization coverage rate. Simulation results show that the proposed algorithm outperforms APIT, RSSI, and other improved algorithms in terms of both node location error and localization coverage rate.

Node localization algorithm of wireless sensor networks with mobile beacon node

When maximum movement distance of beacon node is limited, in order to improve the localization accuracy of sensor nodes, node localization algorithm of wireless sensor networks with mobile beacon node (NLA_MB) is proposed. NLA_MB algorithm divides the movement area of beacon node into some hexagonal grids, and establishes optimization model of node localization errors according to the movement path constraint, movement distance constraint and other constraints of beacon node. Beacon node uses heuristic method to solve the model approximately based on virtual force theory and location information of guide sensor nodes, and obtains a moving path which is suitable for the current node distribution. According to location information of beacon node or anchor sensor nodes, sensor nodes use maximum likelihood estimation algorithm to calculate their own location coordinates. Simulation results show that NLA_MB algorithm is suitable for the distribution scenarios of sensor nodes, such as full coverage distribution, L-type distribution, Tian-type distribution and U-type distribution. It can increase the number of sojourn locations of beacon node and average number of anchor points of sensor nodes, and reduce average localization error of sensor nodes. Under certain conditions, NLA_MB algorithm outperforms DOUBLE_SCAN, ZSCAN, MLE, RSCAN and DBMS algorithms.

On the Particle-assisted Stochastic Search Mechanism in Wireless Cooperative Localization

The wireless cooperative localization plays a key role in location-aware service. However, its objective function, e.g., the posteriori probability function, is commonly nonconvex due to nonlinear measurement function and/or non-Gaussian system disturbance. Moreover, due to the unavoidable reference node location error, the associated objective function is commonly intractable, which further complicates the cooperative localization. In this paper, a novel particle-assisted stochastic search (PASS) algorithm is proposed to realize the cooperative localization. Given a nonconvex objective function, the proposed PASS method can find out the global optimum in probability, assisted with its search particles, detection particles, and proposal particles. In addition, the PASS algorithm can harness the reference node location uncertainties in cooperative localization, by employing its proposal particles. The associated Cramer-Rao lower bound (CRLB), localization error propagation, computational complexity, and convergence properties are also presented to assess the proposed PASS-based cooperative localization. Finally, received signal strength-based localization is simulated to validate the effectiveness of the proposed PASS approach.

Improvement of DV-Hop localization based on shuffled frog leaping algorithm

In order to reduce the node localization error of DV-Hop algorithm in Wireless Sensor Network(WSN),a calculation method of average distance per hop was adjusted by using the shuffled frog leaping algorithm.The improved DV-Hop algorithm makes the average distance per hop closer to the actual value,thereby reducing the localization error.The simulation results indicate that the improved DV-Hop algorithm reduces localization error effectively and has good stability without additional devices;therefore,it is a practical localization solution for WSN.

An improved node localization algorithm based on DV-Hop for wireless sensor networks

Sensor node localization is the basis for the entire wireless sensor networks. Because of restricted energy of the sensor nodes, the location error, costs of communication and computation should be considered in localization algorithms. DV-Hop localization algorithm is a typical positioning algorithm that has nothing to do with distance. In the isotropic dense network, DV-Hop can achieve position more precisely, but in the random distribution network, the node location error is great. This paper summed up the main causes of error based on the analysis on the process of the DV-Hop algorithm, aimed at the impact to the location error which is brought by the anchor nodes of different position and different quantity, a novel localization algorithm called NDVHop_Bon (New DV-Hop based on optimal nodes) was put forward based on optimal nodes, and it was simulated on Matlab. The results show that the new proposed location algorithm has a higher accuracy on localization with a smaller communication radius in the circumstances, and it has a wider range of applications.

An Improved DV-Hop Localization Algorithm with Reduced Node Location Error for Wireless Sensor Networks

In this paper, we propose a new localization algorithm and improve the DV-Hop algorithm by using a differential error correction scheme that is designed to reduce the location error accumulated over multiple hops. This scheme needs no additional hardware support and can be implemented in a distributed way. The proposed method can improve location accuracy without increasing communication traffic and computing complexity. Simulation results show the performance of the proposed algorithm is superior to that of the DV-Hop algorithm.

Direct optimization of nodal hypersurfaces in approximate wave functions

The fixed-node variant of the diffusion quantum Monte Carlo method (FN-DMC) is capable of obtaining the exact eigenvalues (albeit numerically with statistical error) of a many-electron Hamilton operator, provided that the nodal hypersurface of the exact wave function is given. The use of nodes of a trial wave function leads to the node location error. The authors have developed local criteria to assess the accuracy of the nodes based on the distances of the nodal hypersurfaces of PsiT, TPsiT, and HPsiT which coincide for the exact wave function. These criteria are used to develop direct optimization methods for the nodal hypersurface. The optimization of the nodes is demonstrated for simple wave functions of the Be atom and the C2 molecule and verified with FN-DMC calculations.

On the systematic improvement of fixed-node diffusion quantum Monte Carlo energies using pair natural orbital CI guide functions

While the diffusion quantum Monte Carlo method (DQMC) is capable, in principle, of calculating exact ground state energies, in practice the fixed-node (FN) approximation leads to node location errors which make FN-DQMC energies upper bounds. It is shown that the node location error can be reduced systematically and without prohibitive increase of computer time requirements by using nodes derived from pair natural orbital CI wave functions (PNO-CI). The reduction is demonstrated for the N atom and the molecules N2 and H2O. With the DQMC/PNOCI method, we obtain a variational energy of −109.520(3) H for the N2 molecule and −76.429(1) H for the ground state of the water molecule which is only 22 and 9 mH above the estimated nonrelativistic ground state energy, respectively.