Average Hop Size Research Articles

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.

Accurate Range Free Location based Partial Derivative and Stochastic Feedforward Neural Network Hyperbolic-based Agriculture Sensor Network Formation

Wireless sensor networks (WSN) authorize the control of different source environmental aspects and crop states as far as precision agriculture is concerned. Nevertheless, the complicated agricultural environment brings about the WSN topology to change often and hence link association likelihood is laborious to predict. Information pertaining to agriculture is an essential distress for localization-based service in the domain of WSNs. Smooth planning and control as the most typical range-free localization method localization performance is said to be good in even distributed networks. Nevertheless, it demonstrated extremely poor accuracy under that in an urgent issue that required to be addressed. In this work a novel topology construction method called, Partial Derivative Laurent Approximation and Stochastic Feedforward Hyperbolic (PDLA-SFH) based Agriculture Sensor Network Formation is proposed. The proposed method is split into three steps. They are agriculture sensor topology construction, average hop size distance validation and position estimation. First topology construction is performed by employing Game Theory Partial Derivative Regression Coefficient-based Topology model. Second, Laurent Approximation-based Hop Size Distance validation model is designed for optimal topology formation. Finally the Stochastic Feedforward Hyperbolic-based Position Estimation is modeled. The simulation results performed in NS3 showed that the proposed localization algorithms can attain better localization performance in terms of accuracy, time and error rate in comparison with other existing methods such as basic Digital Twins and Autonomous Groups Particles Swarm Optimization (AGPSO) under distinct arbitrary network topologies.

DV-Hop-based localization algorithm using optimum anchor nodes subsets for wireless sensor network

Node localization has become a crucial technology in wireless sensor network (WSN) research field. Although the widely used DV-Hop localization algorithm is simple and easy to implement, its positioning accuracy still needs to be further enhanced. To improve the DV-Hop algorithm accuracy, a DV-Hop-based scheme using optimum anchor nodes subsets (OANS DV-Hop) is put forward in this article. For OANS DV-Hop algorithm, first of all, each anchor node localizes itself by using other anchor nodes, and employs binary particle swarm optimization (BPSO) algorithm to generate an optimum subset composed of other anchor nodes except itself. Then the anchor node applies OANS to recalculate its average hop size and broadcasts the new hop size and OANS to the nearest unknown nodes. The unknown node localizes itself by using OANS instead of all anchor nodes. Finally, a fitness function is designed based on OANS, and the continuous particle swarm optimization (PSO) algorithm is applied to further increase the localization accuracy. Simulation results demonstrate that OANS DV-Hop algorithm owns higher localization accuracy compared with the primal DV-Hop and other improved DV-Hop algorithms in various network environments.

Improved Wireless Sensor Network Localization Algorithm Based on Selective Opposition Class Topper Optimization (SOCTO)

One of the most important challenges in wireless sensor networks is the issue of localization. Furthermore, it is critical to monitor and evaluate the data gathered. With the aid of a collection of constructed nodes known as beacons, the localization technique can measure node location. For a variety of factors, such as upkeep, lifespan, and breakdown, the fixed density of these beacons may be increased or decreased. Because of its robustness, flexibility, and economic viability, a well-known technique for locating wireless sensor network nodes is the distance vector-hop (DV-Hop) algorithm. As a result, researchers continue to look for ways to develop it. Based on a proposed selective opposition class topper optimization (SOCTO), an enhanced DV-Hop localization algorithm is developed. It also focuses on an optimised formulation to compute the average hop-size with weight of beacon nodes in order to reduce the localization error within the estimated distance between the beacon and the dumb node, due to improved localization accuracy. The range-free localization algorithm in uniform multi-hop wireless sensor networks is the subject of a recent study. The results show that our proposed method outperforms the DV-Hop technique and related techniques in terms of average localization error (\(0.05\%\)) versus random beacon node deployment, average localization error (\(0.04\%\)) versus circular beacon node deployment, and average localization error (\(0.08\%\)) versus spiral beacon node deployment.

Multiple objective optimization-based DV-Hop localization for spiral deployed wireless sensor networks using Non-inertial Opposition-based Class Topper Optimization (NOCTO)

The problem of localization is one of most important issues in wireless sensor networks. Furthermore, it is critical to monitor and evaluate the data gathered. For a variety of factors, such as upkeep, lifespan, and breakdown, the fixed density of these beacons may be increased or decreased. Because of its robustness, flexibility, and economic viability, a well-known technique for locating wireless sensor network nodes is the Distance Vector-Hop (DV-Hop) algorithm. As a result, researchers continue to look for ways to develop it. A new Non-inertial Opposition based Class Topper Optimization (NOCTO) based enhanced DV-Hop localization algorithm is proposed. It also focuses through an optimized formulation to compute the average hop-size with weight of beacon nodes in order to reduce the localization error with estimated distance between the beacon and the dumb node, due to improved localization accuracy. For spiral deployed 2D wireless sensor networks, this paper proposes a multi-objective NOCTO-based DV-Hop localization. The simulation results indicate that our suggested multi-objective function outperforms some existing techniques.

An Enhanced DV-Hop Localization Scheme Based on Weighted Iteration and Optimal Beacon Set

Node localization technology has become a research hotspot for wireless sensor networks (WSN) in recent years. The standard distance vector hop (DV-Hop) is a remarkable range-free positioning algorithm, but the low positioning accuracy limits its application in certain scenarios. To improve the positioning performance of the standard DV-Hop, an enhanced DV-Hop based on weighted iteration and optimal beacon set is presented in this paper. Firstly, different weights are assigned to beacons based on the per-hop error, and the weighted minimum mean square error (MMSE) is performed iteratively to find the optimal average hop size (AHS) of beacon nodes. After that, the approach of estimating the distance between unknown nodes and beacons is redefined. Finally, considering the influence of beacon nodes with different distances to the unknown node, the nearest beacon nodes are given priority to compute the node position. The optimal coordinates of the unknown nodes are determined by the best beacon set derived from a grouping strategy, rather than all beacons directly participating in localization. Simulation results demonstrate that the average localization error of our proposed DV-Hop reaches about 3.96 m, which is significantly lower than the 9.05 m, 7.25 m, and 5.62 m of the standard DV-Hop, PSO DV-Hop, and Selective 3-Anchor DV-Hop.

Improved DV Hop Localization Algorithm for Wireless Sensor Networks

Considering the emerging applications of Wireless Sensor Network, the localization of sensor nodes is very important in some applications. Hence in this paper, proposed an improved Distance-Vector hop technique to reduce the localization error generated in the classical method of range free DV-hop algorithm. The proposed algorithm targets to minimize the error introduced in the average hop-size value by the series of mathematical corrections like minimum mean squared error method, modifying the average hop-value value calculated between known sensor nodes with respect to error-term calculated by comparing the estimated distance with actual distance or Euclidean value of anchor nodes, by using dynamic coefficient of weight with respect to minimum number of hopes, calculating x and y co-ordinates of unknown by using 2-D-hyperbolic method. The final corrections on the x and y values are done by using some geometrical methods. Simulations and results are executed on three different evolution models of network by varying radius of communication range, anchor node percentage and number of sensor nodes.

A Singular Value Decomposition Based Upgraded DV-Hop Localization Algorithm for Wireless Sensor Networks

Finding the location of unknown nodes using a computationally inexpensive range-free localization method is one of the most popular research areas in wireless sensor networks (WSNs). DV-hop localization is one of the most popular and localization methods in WSNs. In this article, the authors propose an upgraded DV-Hop based localization method that provides better location accuracy along with minimal computational complexity. In the proposed method, the second step of DV-Hop algorithm is modified by improving the effective average hop size and the third step of DV-Hop is modified to enhance the localization accuracy by minimizing the ranging errors. The said minimization of ranging errors has been achieved by using singular value decomposition-based estimation method. The authors have compared the upgraded DV-Hop localization method with original DV-Hop localization method as well as with improved versions of DV-Hop localization method. The simulation results show that the proposed method has less computational complexity and high location accuracy than the compared methods.

Improvement of DV-Hop localization algorithm for randomly deployed wireless sensor networks

The Distance Vector-Hop (DV-Hop) algorithm is a well-known technique for wireless sensor networks node location due to its robustness, simplicity and cost effectiveness. Therefore scientists are still searching for its improvement. Aiming at optimizing the localization accuracy in traditional DV-Hop algorithm, we propose a novel improved recursive DV-Hop localization algorithm in this paper. Our research focuses on range-free localization algorithm in homogeneous multi-hop wireless sensor networks. The proposed algorithm is based on a recursive position calculation of unknown nodes. It also focuses on second and third step of DV-Hop algorithm. We aim to use an optimized formulation to calculate the average hop-size of anchor nodes aiming to minimize the localization error in the estimated distance between anchor and unknown node, thus achieving better localization accuracy. Simulation results prove the performance of our proposed algorithm comparing with DV-Hop algorithm and DV-Hop based algorithms in different deployment scenarios.

Improved range-free localization for three-dimensional wireless sensor networks using genetic algorithm

This paper presents a distributed range-free node localization algorithm for three-dimensional (3D) wireless sensor networks (WSNs), named as 3D-GAIDV Hop (3D genetic algorithm based Improved Distance Vector Hop). In the proposed algorithm, the average hop size of anchor nodes is modified by updating a correction factor and the modified hop size is further optimized by line search algorithm. The concept of coplanarity is introduced to reduce location errors caused by the anchor nodes which are coplanar. The localization accuracy is further improved by applying genetic algorithm (GA). To improve the positioning coverage of the network, those target nodes which have been localized successfully in the first round of the localization process, are upgraded to assistant anchor node. All the processes and calculations are carried out at target node level, which makes the proposed algorithm energy efficient. Simulation results show that our proposed algorithm outperforms the similar kind of existing algorithms.

Accurate Range-Free Localization in Multi-Hop Wireless Sensor Networks

To localize the wireless sensor networks nodes, only the hop-based information (i.e., hops' number, average hop size, and so on) has been, so far, exploited by range-free techniques, with poor-accuracy, however. In this paper, we show that localization accuracy may greatly benefit from joint exploitation, at no cost, of the information already provided by the forwarding nodes (i.e., relays) between each anchor (i.e., position aware) and sensor nodes pair. As such, we develop a novel range-free localization algorithm, derive its average location estimation error (LEE) in closed-form, and compare it in LEE performance with the best representative algorithms in the literature. We show that the proposed algorithm outperforms them in accuracy. In contrast to the latter, we further prove that it is able to achieve an LEE average and variance of about 0 when the number of sensors is large enough, thereby achieving an unprecedented accuracy performance among range-free techniques.

Two Novel DV-Hop Localization Algorithms for Randomly Deployed Wireless Sensor Networks

Aiming at solving the problem of low accuracy in traditional distance vector-hop (DV-hop) algorithm used for wireless sensor networks node location, two refined localization algorithms, that is, hyperbolic-DV-hop localization algorithm and improved weighted centroid DV-hop localization algorithm (IWC-DV-hop), are proposed in this paper. Instead of taking the average HopSize of BeaconNode nearest to the UnknownNode, hyperbolic-DV-hop algorithm chooses the average of average HopSizes of all BeaconNodes, as the average HopSize of the UnknownNode. The ML localization algorithm is also replaced by hyperbolic location algorithm. IWC-DV-hop algorithm improves the accuracy by selecting appropriate BeaconNodes and replacing ML localization with centroid localization. And a weighted scheme is used in IWC-DV-hop so that the influence of different anchors is taken into consideration. Simulations have been conducted to prove that the accuracy is improved by both of the two algorithms and IWC-DV-hop is the best choice.

Study of Improved DV-Hop Localization Algorithm in Wireless Sensor Network

An improved method is put forward in this paper to settle the larger location error of the classic DV-Hop algorithm. The average hop size is corrected using the hop count based on both the local network connectivity and the global average hop size information of the network. The invalid result is defined and corrected based on the random number in a specified area at the same time. The simulation results show that the improved algorithm gets better localization accuracy than the classic algorithm and other existing improved algorithms. Moreover, it is simple and needs small amount of calculation. So it is a practical algorithm for nodes localization in wireless sensor network.

Power efficient range-free localization algorithm for wireless sensor networks

Considering energy consumption, hardware requirements, and the need of high localization accuracy, we proposed a power efficient range-free localization algorithm for wireless sensor networks. In the proposed algorithm, anchor node communicates to unknown nodes only one time by which anchor nodes inform about their coordinates to unknown nodes. By calculating hop-size of anchor nodes at unknown nodes one complete communication between anchor node and unknown node is eliminated which drastically reduce the energy consumption of nodes. Further, unknown node refines estimated hop-size for better estimation of distance from the anchor nodes. Moreover, using average hop-size of anchor nodes, unknown node calculates distance from all anchor nodes. To reduce error propagation, involved in solving for location of unknown node, a new procedure is adopted. Further, unknown node upgrades its location by exploiting the obtained information in solving the system of equations. In mathematical analysis we prove that proposed algorithm has lesser propagation error than distance vector-hop (DV-Hop) and other considered improved DV-Hop algorithms. Simulation experiments show that our proposed algorithm has better localization performance, and is more computationally efficient than DV-Hop and other compared improved DV-Hop algorithms.

Enhanced DV-Hop Algorithm with Reduced Hop-Size Error in Ad Hoc Networks

DV-Hop algorithm produces errors in location estimations due to inaccurate hop size. We propose a novel localization scheme based on DV-Hop to improve positioning accuracy with least error hop sizes of anchors and average hop sizes of unknowns. The least error hop size of an anchor minimizes its location error, but it may be far small or large. To cope with this inconsistent hop size, each unknown node calculates its average hop size with hop sizes from anchors. Simulation results show that the proposed algorithm outperforms the DV-Hop algorithm in location estimations.