Coordinates Of Unknown Nodes Research Articles

Weighted Centroid Amorphous Algorithm for Position Error Minimization in WSN

ABSTRACTWireless Sensor Networks (WSNs) have numerous applications, one of which is localization. Localization is crucial for determining the position of unknown sensor nodes in the deployment environment. Localization techniques are broadly classified into two categories: range based and range‐free. Range‐free localization techniques are gaining popularity as they are easy to implement and do not require external hardware. In this proposed work, a hybrid localization algorithm named the Weighted Centroid Amorphous algorithm is proposed to reduce the position error in WSN. Instead of the conventional centroid algorithm, a weighted centroid algorithm is used. The weight in this work is considered as a function of the hop value and hop size estimated by the Amorphous algorithm. Ten nearest beacon nodes are considered to determine the coordinates of a single unknown node. The hop value and hop size of that unknown node are calculated from the 10 nearest beacon nodes, and by using the hop value and hop size, the weight is calculated. After the weight estimation, the weighted sum is calculated, and using the weighted sum, the coordinates of an unknown node are determined. Simulation results indicate that the proposed Weighted Centroid Amorphous method achieves superior accuracy, with rates of 90.41%, 89.57%, and 86.10% compared to the traditional Amorphous, improved Amorphous, and Ensemble approach, respectively.

Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

MOANS DV-Hop: An anchor node subset based localization algorithm for wireless sensor networks

Several applications have exploited wireless sensor networks (WSNs), and localization is a key WSN technology. The range-free localization technique is substantially less expensive than conventional range-based localization strategies since it does not require distance or angle measurements between the anchor and unknown nodes. The Distance Vector-Hop (DV-Hop) technique is a widespread localization solution for WSNs because of its straightforward theory and minimal cost. The coordinate estimating precision of the classic DV-Hop algorithm needs further improvement due to its large localization error. In this paper, a DV-Hop-based method utilizing modified optimum anchor node subset (MOANS DV-Hop) is proposed to improve the localization performance of the DV-Hop algorithm. A strategy for anchor node deployment is proposed. An objective function is formulated to minimize the error in estimating coordinates of unknown nodes. With the MOANS DV-Hop algorithm, each anchor node first uses other anchor nodes to locate itself, then uses the Scaled General Learning Equilibrium Optimizer (SGLEO) algorithm to create an optimal subset made up of anchor nodes other than itself. The anchor node then updates its average hop size using the anchor node subset and broadcasts both the updated hop size and the anchor node subset to the nearby unknown nodes. An unknown node then determines its location using the Equilibrium Optimizer (EO) algorithm, the anchor node subset, and the updated hop size received from the closest anchor node. Simulation findings show that MOANS DV-Hop algorithm has a greater level of localization accuracy in coordinate estimation than both the classical and other improved DV-Hop methods.

A DV-Hop optimization localization algorithm based on topological structure similarity in three-dimensional wireless sensor networks

Localization technology performs an essential part in wireless sensor networks (WSNs). The Distance vector hop (DV-Hop) localization algorithm has been frequently used in the WSNs with its simplicity, feasibility and minimal hardware requirements. However, the low localization accuracy of the DV-Hop algorithm has limited its further application in WSNs. To overcome the accuracy problem of the DV-Hop algorithm, we propose an improved DV-Hop algorithm based on topological structure similarity, called TDSDV-Hop. Different from the traditional DV-Hop algorithm, the topological structure similarity is applied to correct the hop count, reducing the inaccuracy caused by the minimum hop count. The hop counts will be converted from discrete to precise continuous values by the topological structure similarity. In this paper, firstly, the topological structure similarity is defined, followed by the proof of the theory that the distance between two nodes is inversely proportional to the topological structure similarity. Secondly, a hop-correction equation that can correct the hop count between neighbor nodes is set up. Then the TDSDV-Hop algorithm incorporates hop-correction formula and simulates the optimal hop-correction parameters in order to acquire more accurate values of the hop count. And the coordinates of unknown nodes are calculated by the least square method. Finally, the levy adaptive improved bird swarm algorithm (LSABSA) is utilized to optimize coordinates of unknown nodes by constructing a new objective function. The simulation results show that the localization performance of the TDSDV-Hop algorithm is better than that of DV-Hop, GWODV-Hop, CRWDV-Hop and CVLR algorithm in terms of localization accuracy, localization time and communication cost.

Weighted Centroid Localization Algorithm Based on MEA-BP Neural Network and DBSCAN Clustering

In order to overcome RSSI ranging error and improve the accuracy of positioning results, a weighted localization algorithm based on MEA-BP Neural Network and DBSCAN clustering is proposed in this paper. This algorithm uses MEA-BP Neural Network (MEA-BP NN) model to optimize ranging information firstly, then it uses trilateral measurement method to get multiple initial localization results about unknown node and form a set. After clustering the results by DBSCAN and eliminating noise points, the estimated coordinate of unknown node in each cluster is obtained by using the weighted centroid localization algorithm based on collinearity. Next the number of core points in each cluster is regarded as weight value, the weighted centroid localization algorithm is used again, thus the final coordinates of unknown node can be got. Simulation results show that the localization accuracy of wireless sensor network can be improved significantly by using this algorithm in two-dimensional scene.

An Anchor Node Selection Scheme for Improving RSS-Based Localization in Wireless Sensor Network

Accurate location information is essential for various emerging applications in wireless sensor network (WSN). In order to improve localization accuracy, it is of paramount importance to reduce the effects of noisy distance measurements. This paper proposes an anchor node selection scheme for Received Signal Strength- (RSS-) based localization in WSN. In the proposed approach, the nodes are sorted firstly to select anchor nodes reasonably, and to further reduce the influence of range error, the weight is assigned to each selected anchor node. Finally, an effective modified cuckoo search algorithm is used to compute the coordinates of unknown nodes. Extensive experiments are conducted to study the effects of anchor node ratio, ranging error factor and node density on the localization accuracy performance of the proposed method. The experimental results demonstrate that the proposed method performs better in improving localization accuracy compared with the localization technique without special anchor selection scheme which selects all anchors’ information received and the localization technique selecting nearest anchors.

An Enhanced DV-Hop Positioning Scheme Based on Spring Model and Reliable Beacon Node Set

How to get the node position, as a hotter topic in the field of wireless communication, is the primary problem to be solved in wireless sensor networks (WSNs). The DV-Hop positioning scheme is a kind of range-free positioning scheme, which has been favored by researchers in recent years. However, the original DV-Hop scheme faces the shortcoming of low positioning precision and stability. To ameliorate the positioning capability of the DV-Hop, an enhanced DV-Hop scheme based on the spring model and reliable beacon node set has been presented in this paper. Firstly, the relationship between nodes in the network is abstracted as a spring model, and the distance between unknown nodes and beacon nodes is corrected by comprehensively considering the influence of each beacon node from the physical phenomenon of force vector decomposition. After that, based on the random sample consensus (RANSAC) algorithm, the reliable beacon nodes with smaller distance deviation to the unknown node are picked out to compute the unknown node coordinates, rather than all beacon nodes participating in the calculation. Finally, extensive experiments have been conducted on distance accuracy and positioning performance. Simulation results demonstrate that our proposed positioning scheme outperforms better than original DV-Hop and other existing improved schemes under different scenarios.

Improvement of triangle centroid localization algorithm based on PIT criterion (ITCL-PIT) for WSNs

One of the most significant study directions is node positioning in wireless sensor networks (WSNs). Because the existing RSSI-based triangle centroid localization technique is susceptible to the surrounding environment, this paper proposes an improved triangle centroid localization algorithm based on point-in-triangulation (PIT) criterion (ITCL-PIT) in terms of positioning accuracy and response speed. When combined with the actual placement situation in conventional triangle centroid localization, the suggested algorithm considers the estimated coordinates of the junction points as extra beacon nodes. As a result of the new beacon nodes, the size of the triangle in the junction region is decreased. Then, using the PIT criteria, keep calculating until the predicted position of the node is outside the triangle. Finally, the unknown node's coordinates are determined using the centroid approach. Based on the guaranteed response time, when the communication distance is 15–30 m, the ITCL-PIT method may enhance localization accuracy by up to five times when compared to the standard triangle centroid localization approach. Furthermore, the proposed technique has higher localization accuracy and faster response speed than the centroid iterative estimation approach, and the response time of the ITCL-PIT algorithm is reduced by around 17%. In addition, the experimental platform is built to ensure that the proposed strategy effectively lowers positioning error.

Improved Chan Algorithm Based Optimum UWB Sensor Node Localization Using Hybrid Particle Swarm Optimization

The localization of Wireless sensor networks (WSNs) has been recognized as one of the most challenging problems to overcome. Thus, much work has been given to solving this difficult problem. In emergency services, navigational systems, civil/military surveillance etc., locating the signal source in a WSN is essential. A novel approach for sensor node localization using range-based localization methodology has been proposed to overcome this issue. The problem is expressed in the form of a maximum probability distribution function. The use of an RSSI-based Time Difference of Arrival (TDOA) measurement model, along with the Chan algorithm, is used to find the coordinates of unknown nodes has been proposed. With the help of ultra-wideband, this research aims to develop new and precise localization algorithms for wireless sensor networks (WSNs). This work offers localization using two-hybrid localization algorithms, i.e., ELPSO (Ensemble learning particle swarm optimization) and PSO- BPNN (Back-propagation neural network optimized by particle swarm optimization). Further, the error optimization accuracy has been compared between those algorithms using simulations. The proposed techniques consistently offer a better localization accuracy than the conventional algorithms available in the literature. The new localization methods with optimal techniques reduce the error value to a minimal distance. The distance value of localization error is nearly2.7cms compared to other designs from the literature. It is noted as significantly less.

Localization by Salp Swarm Optimization with Doppler Effect in Wireless Sensor Networks

Wireless sensor networks (WSNs) have lately been widely used due to its abundant practice in methods that have to be spread over a large range. In any wireless application, the position precision of node is an important core component. Node localization intends to calculate the geographical coordinates of unknown nodes by the assistance of known nodes. In a multidimensional space, node localization is well-thought-out as an optimization problem that can be solved by relying on any metaheuristic’s algorithms for optimal outputs. This paper presents a new localization model using Salp Swarm optimization Algorithm with Doppler Effect (LOSSADE) that exploit the strengths of both methods. The Doppler effect iteratively considers distance between the nodes to determine the position of the nodes. The location of the salp leader and the prey will get updated using the Doppler shift. The performance validation of the presented approach simulated by MATLAB in the network environment with random node deployment. A detailed experimental analysis takes place and the results are investigated under a varying number of anchor nodes, and transmission range in the given search area. The obtained simulation results are compared over the traditional algorithm along with other the state-of-the-art methods shows that the proposed LOSSADE model depicts better localization performance in terms of robustness, accuracy in locating target node position and computation time.

Research on Location Algorithm Based on Beacon Filtering Combining DV-Hop and Multidimensional Support Vector Regression.

The DV-Hop algorithm is widely used because of its simplicity and low cost, but it has the disadvantage of a large positioning error. In recent years, although some improvement measures have been proposed, such as hop correction, distance-weighted correction, and improved coordinate solution, there is room for improvement in location accuracy, and the accuracy is affected in anisotropic networks. A location algorithm based on beacon filtering combining DV-Hop and multidimensional support vector regression (MSVR) is proposed in this paper. In the process of estimating the coordinates of unknown nodes, received signal strength indication (RSSI), MSVR, and weighted least squares method are combined. In addition, the verification error of beacon nodes is proposed, which can select the beacon nodes with smaller errors to reduce the location error. Simulation results show that in different distributions, the location accuracy of the proposed algorithm is at least 34% higher than that of the classical DV-Hop algorithm and at least 28% higher than that of the localization based on multidimensional support vector regression (LMSVR) algorithm. The proposed algorithm has the potential of application in small-scale anisotropic networks.

Adaptive bluetooth indoor positioning system based on greedy strategy

In order to solve the problem that current bluetooth indoor positioning system has low positioning accuracy in dynamic environment and can’t unable to adapt to dealing with complex situation of anchor circle, this paper proposes an adaptive bluetooth indoor positioning system based on greedy strategy. The system uses the deployed bluetooth to intelligently sense the real-time environment and measure the ibeacon signal attenuation model to reduce the ranging error caused by dynamic change of environment. In addition, the three anchor circles of the weighted centroid positioning into three groups and two different anchor circles are one group, and greedy strategy is used for getting the better weighted objects when each group of circles are intersected, or separated, or contained. Finally, the sum of the reciprocal radius of each group of anchor circles is used as the weight to obtain the estimated coordinates of unknown nodes. To verify the effectiveness of the positioning system by case study, the results show that the average error value is 0.60m. Compared with the centroid localization algorithm and weighted centroid algorithm, the system has the advantages of intelligent environment perception, higher positioning accuracy, and adaptive algorithm for dealing with various situations of anchor circle.

Geometric and decentralized approach for localization in wireless sensor network

The main function of a sensor node is to collect data from its environment and forward it to base station. In the absence of further information concerning their locations, those data will be unnecessary. Hence, developing algorithms for localizing all nodes of wireless sensor network is extremely important. We present in this paper, a new approach to determine geographical coordinates of unknown nodes, by using mobile anchor. The mobile anchor adopts a spiral trajectory, and diffuses its position periodically during its travel. The proposed approach uses Received Signal Strength Indicator to estimate distance with all broadcast messages received from mobile anchor. To calculate position, our approach determines a cloud of points that surround the solution; these points are selected from the set of intersection points of all beacons received by unknown node, by considering some constraints. The estimated position of unknown node represents the geometric center of this cloud. The behavior of our algorithm was studied by varying some metrics; the average error was minimized compared to those proposed in literature.

Research on DV-Hop improved algorithm based on dual communication radius

In order to solve the problem that the traditional DV-Hop location algorithm has a large error in locating unknown nodes, this paper proposes an improved DV-Hop algorithm based on dual communication radius. By adding a communication radius, the algorithm updates the minimum hop number obtained by the unknown node closer to the beacon node to a smaller hop value and keeps the minimum hop number information of unknown nodes farther away. This method reflects the gap between the actual distances in terms of hop number, which solves the problem of the large difference between the actual distances of the same hops to a certain extent. Therefore, it is more advantageous to estimate the more accurate average jump distance and can calculate the more precise distance between them and obtain the more precise coordinates of unknown nodes. MATLAB simulation results show that the improved algorithm based on dual communication radius DV-Hop can effectively reduce the location error and improve the location accuracy and stability compared with the traditional DV-Hop location algorithm.

Multi-node topology location model of smart city based on Internet of Things

Smart City is a new model and new form of urban development based on the Internet of Things, the advanced information technology, intelligent technology and multi-network integration. In most cases, wireless sensor networks need to be monitored in complex environments or in areas that humans cannot reach. Most of the nodes will adopt aircraft throwing and other methods, resulting in uncontrollable node positions. Such research cannot be carried out without knowing the specific source of the information. Therefore, the problem of node location in wireless sensor networks has become one of the key issues that must be solved. In this paper, a wireless sensor network topology based on correlation neighbor graph is constructed. The DV-Hop (Distance Vector-Hop) localization algorithm with hop number correction and average hop weighting is proposed. The signal strength value received by the node is converted into the distance between the nodes by the shadowing model; and the ratio of the distance value to the communication radius between the nodes is used to correct the hop value. In order to further improve the positioning performance of the algorithm, the MDV-Hop (Modified Distance Vector-Hop) algorithm is proposed. The MDV-Hop algorithm mainly improves the error caused by the number of hops between anchor nodes, the average hop distance and the network topology in the DV-Hop algorithm. Aiming at the problems of the maximum likelihood coordinate calculation method, the localization problem of nodes is transformed into the minimum problem of solving nonlinear equations, and the improved Bat algorithm is used to replace the maximum likelihood algorithm to obtain the coordinates of unknown nodes. The simulation shows that the unknown node has achieved better positioning results in the IoT positioning model based on wireless sensor network topology and has higher positioning accuracy.

A Novel Centralized Range-Free Static Node Localization Algorithm with Memetic Algorithm and Lévy Flight.

Node localization, which is formulated as an unconstrained NP-hard optimization problem, is considered as one of the most significant issues of wireless sensor networks (WSNs). Recently, many swarm intelligent algorithms (SIAs) were applied to solve this problem. This study aimed to determine node location with high precision by SIA and presented a new localization algorithm named LMQPDV-hop. In LMQPDV-hop, an improved DV-Hop was employed as an underground mechanism to gather the estimation distance, in which the average hop distance was modified by a defined weight to reduce the distance errors among nodes. Furthermore, an efficient quantum-behaved particle swarm optimization algorithm (QPSO), named LMQPSO, was developed to find the best coordinates of unknown nodes. In LMQPSO, the memetic algorithm (MA) and Lévy flight were introduced into QPSO to enhance the global searching ability and a new fast local search rule was designed to speed up the convergence. Extensive simulations were conducted on different WSN deployment scenarios to evaluate the performance of the new algorithm and the results show that the new algorithm can effectively improve position precision.

Improvement of Localization Algorithm for Wireless Sensor Networks Based on DV-Hop

In order to solve the problem that the DV-Hop localization algorithm has large errors in the wireless sensor network environment, this paper uses the minimum mean square criterion to determine the average hop distance of anchor nodes, and then calculates the mean value of the original average hop distance, which ensures that the improved average hop distance is closer to the real average hop distance of the whole network. The estimated distances between nodes are calculated by using the correction value corresponding to the average jump distance of the anchor node; in the positioning stage, when the anchor node is small, the estimated coordinates of unknown nodes are obtained by the minimum-maximum method; when the number of anchor nodes is large, the coordinates of unknown nodes are calculated by the maximum likelihood estimation method; this not only reduces the amount of calculation, but also the accuracy is more stable. This step is not only suitable for DV-Hop algorithm, but also can be used to estimate the coordinates when the distance between the unknown node and the anchor node is known. However, this improved method is only applicable to the premise that the simulation area is not large, so this improvement has its scope of adaptation, according to the needs of choice. Finally, the unknown node coordinates are iteratively optimized by using the quasi Newton method. Simulation results show that the proposed positioning algorithm has higher accuracy and better stability.

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.

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.

Study of range free centroid based localization algorithm and its improvement using particle swarm optimization for wireless sensor networks under log normal shadowing

Localization deals with the determination of coordinates of unknown nodes for proper routing of data in wireless sensor networks. The centroid based localization algorithm (CLA) has been explored to a great extent till date. Its basic and improved form suffers from large localization error. In the present work the basic range free centroid based localization algorithm is studied under log normal shadowing and evaluated in terms of localization error. Further the basic CLA is improved by using the particle swarm optimization (PSO) under the same environment. The localization error for basic and PSO based CLA is calculated by varying the anchor ratio, communication range, number of unknown nodes, and network area. In every condition the PSO based CLA performs well over basic CLA in terms of reduction in localization error. The localization error in the proposed work is smallest as compared to other known centroid based localization algorithms.