Distance Vector Hop Research Articles

A hybrid improved compressed particle swarm optimization WSN node location algorithm

The improvement of positioning accuracy in Wireless Sensor Networks (hereafter, WSN) is crucial to develop advanced Internet of Things (IOT, for short) applications. However, the conventional distance vector-hop (DV-Hop) localization algorithm has shortcomings such as low accuracy and weak stability. To overcome these shortcomings, this paper proposes a hybrid improved compressed particle swarm optimization algorithm (HICPSO), which consists of a scheme of linearly decreasing inertia weights, compressed velocity vectors, population Gaussian variants and optimal boundary selection. Then, HICPSO is integrated with DV-Hop to gradually reduce the distance error of least squares method (LSM) estimated with the efficient search advantage of HICPSO. Our simulation results show that the HICPSO algorithm possesses better computational accuracy and search performance on the 22 benchmark test functions compared with the algorithms such as the Improved Adaptive Genetic Algorithm (IAGA) and Adaptive Weighted Particle Swarm Optimizer (AWPSO). Meanwhile, compared with IAGA and AWPSO, the positioning accuracy of HICPSO-based positioning algorithm is improved by 4.28% and 4.76% respectively, and the stability is improved by one order of magnitude.

An improved DVHop localization algorithm using a novel angle based node reduction and optimization technique

Wireless sensor networks are becoming increasingly popular across a range of applications. One notable use is in seismic exploration and monitoring for oil and gas reservoirs. This application involves deploying numerous sensor nodes across outdoor fields to measure backscattered waves, which are then used to create an image of the subsurface. These sensor nodes remain active in the field for several days and must be accurately localized to ensure efficient reservoir detection. However, the Distance Vector-Hop (DVHop) algorithm, despite its simplicity, is not suitable for accurate node localization in exploration fields due to obstructions. In this paper, we propose a modified DVHop algorithm specifically designed for precise localization in such environments. Proposed algorithm uses angles between intermediate nodes to identify and circumvent nodes affected by obstructions. Distance estimation is performed using this reduced set of nodes. The estimated distances between these nodes are subsequently solved using Velocity Pausing Particle Swarm Optimization to determine the nodes’ locations. When evaluated in environments resembling exploration fields, our algorithm demonstrated an improvement of 25% to 63% in Average Localization Accuracy compared to other hop-based localization algorithms under similar conditions.

Enhancing Smart Irrigation Efficiency: A New WSN-Based Localization Method for Water Conservation

The shortage of water stands as a global challenge, prompting considerable focus on the management of water consumption and irrigation. The suggestion is to introduce a smart irrigation system based on wireless sensor networks (WSNs) aimed at minimizing water consumption while maintaining the quality of agricultural crops. In WSNs deployed in smart irrigation, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a new localization method for localizing unknown sensor nodes in WSN-based smart irrigation systems using estimated range measurements. The proposed method can accurately determine the positions of unknown nodes, even when they are located at a distance from anchors. It utilizes the Levenberg–Marquardt (LM) optimization algorithm to solve a nonlinear least-squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the proposed method, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes. Specifically, it achieves 19% and 58% improvement in estimation accuracy when compared to distance vector-hop (DV-Hop) and semidefinite relaxation-LM (SDR-LM) algorithms, respectively. Additionally, the method exhibits robustness against measurement noise and scalability for large-scale networks. Ultimately, integrating the proposed localization method into the smart irrigation system has the potential to achieve approximately 28% reduction in water consumption.

Optimizing Sensor Localization and Cluster Performance in Wireless Sensor Networks through Internet of Thing (IoT) and Boosted Weight Centroid Algorithm

Localization is an extremely important component of applications that make use of wireless sensor networks. It has a substantial impact on academics as well as real-time sensor deployment applications in the aim of lowering the amount of energy that is used while simultaneously locating unknown nodes. The process of obtaining the coordinates along an axis that represent the locations of the sensor nodes is referred to as localization. The accuracy of locating the positions of the nodes varies depending on the environmental conditions, the type of nodes, the type of application, and the type of localization methods used. A standard localization method known as distance vector hop (DV-hop) localization will be able to determine the positions of unknown nodes with typical accuracy with the assistance of beacon nodes based on Internet of things. The DV-hop and improved weighted centroid localization algorithms, in addition to the suggested boosted weight centroid-based localization approach, are both addressed in this article. The suggested boosted weight centroid localization technique is utilized to find nodes in the remote area of the WSN while conserving energy. This is accomplished with the assistance of measurements involving both the nodes and the centroid. The modified weight metric is utilized in the process of carrying out the task of localisation of an unknown node. The performance of BWCLA is evaluated based on a number of different metrics, including accuracy in localization, average localization error, total packets utilized, and energy usage.

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 Hybrid Localization Algorithm for an Adaptive Strategy-Based Distance Vector-Hop and Improved Sparrow Search for Wireless Sensor Networks.

Wireless sensor networks (WSNs) are applied in many fields, among which node localization is one of the most important parts. The Distance Vector-Hop (DV-Hop) algorithm is the most widely used range-free localization algorithm, but its localization accuracy is not high enough. In this paper, to solve this problem, a hybrid localization algorithm for an adaptive strategy-based distance vector-hop and improved sparrow search is proposed (HADSS). First, an adaptive hop count strategy is designed to refine the hop count between all sensor nodes, using a hop count correction factor for secondary correction. Compared with the simple method of using multiple communication radii, this mechanism can refine the hop counts between nodes and reduce the error, as well as the communication overhead. Second, the average hop distance of the anchor nodes is calculated using the mean square error criterion. Then, the average hop distance obtained from the unknown nodes is corrected according to a combination of the anchor node trust degree and the weighting method. Compared with the single weighting method, both the global information about the network and the local information about each anchor node are taken into account, which reduces the average hop distance errors. Simulation experiments are conducted to verify the localization performance of the proposed HADSS algorithm by considering the normalized localization error. The simulation results show that the accuracy of the proposed HADSS algorithm is much higher than that of five existing methods.

New Online DV-Hop Algorithm via Mobile Anchor for Wireless Sensor Network Localization

In many applications of Wireless Sensor Networks (WSNs), event detection is the main purpose of users. Moreover, determining where and when that event occurs is crucial; thus, the positions of nodes must be identified. Subsequently, in a range-free case, the Distance Vector-Hop (DV-Hop) heuristic is the commonly used localization algorithm because of its simplicity and low cost. The DV-Hop algorithm consists of a set of reference nodes, namely, anchors, to periodically broadcast their current positions and assist nearby unknown nodes during localization. Another potential solution includes the use of only one mobile anchor instead of these sets of anchors. This solution presents a new challenge in the localization of rang-free WSNs because of its favorable results and reduced cost. In this paper, we propose an analytical probabilistic model for multi-hop distance estimation between mobile anchor nodes and unknown nodes. We derive a non-linear analytic function that provides the relation between the hop counts and distance estimation. Moreover, based on the recursive least square algorithm, we present a new formulation of the original DV-Hop localization algorithm, namely, online DV-Hop localization, in WSNs. Finally, different scenarios of path planning and simulation results are conducted.

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.

Enhanced DV-Hop using shuffled shepherd algorithm for localizing sensor nodes in 3-D space

For many wireless sensor networks applications, a powerful localization algorithm is required to determine the exact positions of sensor nodes. In this paper, a new localization algorithm is presented which combines the distance vector hop (DV-Hop) algorithm with the shuffled shepherd optimization algorithm to determine the position for each unknown node within 3-D space. Studying the localization problem in 3-D space is more realistic, but more challenging due to the enlarged search space. Comparison of our proposed algorithm with its alternatives in literature shows that it offers improved accuracy and more stable performance with respect to changes in nodes density and communication range.

A novel distance vector hop localization method for wireless sensor networks

Abstract Wireless sensor networks (WSNs) require accurate localization of sensor nodes for various applications. In this article, we propose the distance vector hop localization method (DVHLM) to address the node dislocation issue in real-time networks. The proposed method combines trilateration and Particle Swarm Optimization techniques to estimate the location of unknown or dislocated nodes. Our methodology includes four steps: coordinate calculation, distance calculation, unknown node position estimation, and estimation correction. To evaluate the proposed method, we conducted simulation experiments and compared its performance with state-of-the-art methods in terms of localization accuracy with known nodes, dislocated nodes, and shadowing effects. Our results demonstrate that DVHLM outperforms the existing methods and achieves better localization accuracy with reduced error. This article provides a valuable contribution to the field of WSNs by proposing a new method with a detailed methodology and superior performance.

A Novel Localization Technology Based on DV-Hop for Future Internet of Things

In recent years, localization has become a hot issue in many applications of the Internet of Things (IoT). The distance vector-hop (DV-Hop) algorithm is accepted for many fields due to its uncomplicated, low-budget, and common hardware, but it has the disadvantage of low positioning accuracy. To solve this issue, an improved DV-Hop algorithm—TWGDV-Hop—is put forward in this article. Firstly, the position is broadcast by using three communication radii, the hop is subdivided, and a hop difference correction coefficient is introduced to correct hops between nodes to make them more accurate. Then, the strategy of the square error fitness function is spent in calculating the average distance per hop (ADPH), and the distance weighting factor is added to jointly modify ADPH to make them more accurate. Finally, a good point set and Levy flight strategy both are introduced into gray wolf algorithm (GWO) to enhance ergodic property and capacity for unfettering the local optimum of it. Then, the improved GWO is used to evolve the place of each node to be located, further improving the location accuracy of the node to be located. The results of simulation make known that the presented positioning algorithm has improved positioning accuracy by 51.5%, 40.35%, and 66.8% compared to original DV-Hop in square, X-shaped, and O-shaped random distribution environments, respectively, with time complexity somewhat increased.

An environment safety monitoring system for agricultural production based on artificial intelligence, cloud computing and big data networks

Monitoring the agricultural production environment is crucial for optimal crop growth and resource efficiency. Cloud Computing, Artificial Intelligence (AI), and Big Data have revolutionized traditional agriculture, promising improved output and product quality. The popularity of these technologies drives their application in safety monitoring. This system facilitates data collection and transmission among equipment, overcoming challenges of traditional systems like investment, costs, and maintenance. In this paper, cloud computing-based AI optimization technology and big data network were proposed to monitor the safety of the agricultural production environment, and the shortcomings of traditional distance vector hop (DV hop) positioning algorithms were analyzed in depth. RSSI (Received Signal Strength Indication) technology improved the traditional DV Hop location method. The paper analyses direct and indirect transmission for data transmission between WSN and cloud nodes and favors indirect transmission because it consumes less invalid energy. Finally, the article compares several evaluations of alternative algorithms for monitoring system performance, including data transmission reliability, data reception rate, and data delay. The experimental results in this paper showed that in the data reception rate test, the data reception rate of System 2 was 97% at the lowest and 99% at the highest, both exceeding 95%.

A Distance Vector Hop-Based Secure and Robust Localization Algorithm for Wireless Sensor Networks

Location information of sensor nodes in a wireless sensor network is important. The sensor nodes are usually required to ascertain their positions so that the data collected by these nodes can be labeled with this information. On the other hand, certain attacks on wireless sensor networks lead to the incorrect estimation of sensor node positions. In such situations, when the location information is not correct, the data may be labeled with wrong location information that may subvert the desired operation of the wireless sensor network. In this work, we formulate and propose a distance vector hop-based algorithm to provide secure and robust localization in the presence of malicious sensor nodes that result in incorrect position estimation and jeopardize the wireless sensor network operation. The algorithm uses cryptography to ensure secure and robust operation in the presence of adversaries in the sensor network. As a result of the countermeasures, the attacks are neutralized and the sensor nodes are able to estimate their positions as desired. Our secure localization algorithm provides a defense against various types of security attacks, such as selective forwarding, wormhole, Sybil, tampering, and traffic replay, compared with other algorithms which provide security against only one or two types. Simulation experiments are performed to evaluate the performance of the proposed method, and the results indicate that our secure localization algorithm achieves the design objectives successfully. Performance of the proposed method is also compared with the performance of basic distance vector hop algorithm and two secure algorithms based on distance vector hop localization. The results reveal that our proposed secure localization algorithm outperforms the compared algorithms in the presence of multiple attacks by malicious nodes.

An Improvement of DV-Hop Localization Algorithm Based on Cyclotomic Method in Wireless Sensor Networks

Location information is one of the crucial and essential elements for monitoring data in wireless sensor networks. The distance vector-hop (DV-Hop) localization algorithm is of practical importance in improving its localization performance. To achieve global optimization, a DV-Hop algorithm based on the cyclotomic method and weighted normalization, also known as CMWN-DV-Hop, is nominated in this paper. Therefore, the segmentation and weighting factors are introduced and normalized. The weighted recursive least-squares (WRLS) algorithm is chosen to compute the coordinates of the unknown nodes. The effects of various factors on this algorithm are tested, including the number of nodes, the anchor node ratio, and the communication radius. The simulation results show that the proposed algorithm has a super performance in reducing the localization error.

DV-Hop Location Algorithm Based on RSSI Correction

To increase the positioning accuracy of Distance Vector-Hop (DV-Hop) algorithm in non-uniform networks, an improved DV-Hop algorithm based on RSSI correction is proposed. The new algorithm first quantizes hops between two nodes by the ratio of the RSSI value between two nodes and the benchmark RSSI value, divides the hops continuously, calculates the average hop distance according to the Minimum Mean Square Error (MMSE) criterion of the best index based on the quantized hops, and then adds hop distance matching factor to the fitness function of each anchor node into the calculation of the hop distance fitness function to weight the fitness function. The change index value is introduced to obtain more accurate hop distance value, and then the estimation error of unknown node (UN) coordinate is modified by using the distance relationship between the UN and the nearest beacon node (BN), and the modified coordination position is further modified by using the triangle centroid to improve the accuracy of node positioning in the irregular network. The experimental results show that compared with the original DV-Hop, improved DV-Hop1, improved DV-Hop2 and improved DV-Hop3, the localization error of the improved algorithm in this paper is reduced by 58%, 45%, 34%, and 29%, respectively, on average, in the two network environments. Without increasing the hardware cost and energy consumption, the improved algorithm has excellent localization performance.

Optimized Range-Free Localization Scheme Using Autonomous Groups Particles Swarm Optimization for Anisotropic Wireless Sensor Networks

Location information is a required concern in localization-based service application in the field of wireless sensor networks (WSNs). Distance Vector-Hop (DV-Hop) algorithm as the most typical range-free localization scheme is much fitter for large-scaled WSNs. Its localization performance is good in even distributed networks. However, it demonstrated extremely poor accuracy under anisotropic networks. It is an urgent problem that need to be addressed. Accordingly, an optimized DV-Hop localization algorithm is put forward in this study with considering several anisotropic factors. Accumulated hop size error and collinearity are two main reason that led to low accuracy and poor stability. Hence, hop size error of anchors is reduced by introducing distance gap based on anchors. Besides, weighted least square method is adopted to replace the least square method to against anisotropic factors caused by irregular radio patterns. Moreover, an Autonomous Groups Particles Swarm Optimization (AGPSO) is employed to further optimize the obtained coordinate in the first round. It developed a novel method to determine localization coverage. The localization coverage is also added to be one evaluation metric in our study, which makes up for the lack of this evaluation indicator in most of the studies. Simulation results display good localization accuracy and strong stability under anisotropic networks. In addition, it also concluded that metaheuristic optimization algorithm and weighted least square method are more suitable to conquer anisotropic factor. It briefly points out a new direction for the future research work in the localization area under anisotropic networks.

A Spherical Band-based DV-Hop localization technique for three-dimensional wireless sensor network

Wireless Sensor Networks have been the focal point of research for many years due to their wide range of application areas. Such networks consist of resource-constrained sensor nodes that are generally not equipped with any positioning component due to cost issues. This requires the adoption of suitable methodologies to infer the location of the deployed sensor nodes. Location information of such sensor nodes can be obtained with the help of some location-aware nodes. Numerous localization algorithms exist in the literature. Amongst them, Distance Vector Hop (DV-Hop) is a computationally less expensive algorithm that uses hop count values between sensor nodes and anchor nodes for location estimation of the deployed sensor nodes. However, the traditional DV-Hop algorithm produces a larger positioning deviation for a higher hop count value. Several existing works attempt to address this issue by either modifying the hop size or optimizing the estimated position resulting in comparatively higher localization errors and computationally expensive. This paper aims to solve the issue by modifying the hop size by dividing it into equal-sized spherical bands (SB). Sensor nodes use this SB value for computing their distances from anchor nodes and non-coplanar anchor nodes for location estimation. The simulation results demonstrate that the mean localization error of the proposed approach has reduced approximately by 75%, 66%, and 47% in comparison to traditional DV-Hop, 3D PSODV-Hop, and 3D GAIDV-Hop respectively.

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.

Improved DV-Hop Algorithm Based on Swarm Intelligence for AI and IoT-Federated Applications in Industry 4.0

In the Internet of Things (IoT) ecosystem, localization is critical for tracking and monitoring targets via nodes. The distance vector-hop (DV-Hop) technique is a good choice for localizing neighborhood in IoT networks. The conventional DV-Hop algorithm is a distributed localization approach that does not consider the distribution of the nodes into full deliberation when calculating the hop count from the source to destined nodes. The transfer distance and node positions thus do not attain higher efficiency while ascertaining the distance between sources and destined nodes. The study aims to resolve the pitfalls in the traditional algorithm by making enhancements in controlling the original DV-Hop algorithm’s hop count and transfer distance method by utilizing the particle swarm to estimate the node positions. Error rate in the distance between beacon nodes and unseen nodes is effectively reduced with the proposed technique that calculate error factors with corrections in a reversed fashion to revise hop counts. An escape factor is introduced to take control of updating particles’ velocity in the system, and the inertia weight is defined by a piecewise function to enlarge search space. This mechanism increases the diversity of the particle populations and mitigates the tendency of estimations on node positions to be trapped into local optima under stationary state. Also, the improved DV-Hop algorithm described in the paper has a better convergence speed due to the presence of random inertia weight logarithmic method. Finally, the problem of premature convergence is also tackled as a variation factor is adopted in collaboration with a fitness function that affects the particles’ movement range and assists in global convergence. The overall performance of improved DV-Hop is evaluated by statistical metrics and also compared with the traditional DV-Hop algorithm under simulated environment with the data collected from real-world scenarios. Industry 4.0 is fully dependent upon IoT and the count of hops is very important for deciding the routing from the source to destination for speedy transmission of data. The improved DV-hop algorithm can achieve better results and has reduced error rate by more than 30%. The DV-Hop algorithm plays an important role in IoT-enabled environment especially in Industry 4.0.