Distance Vector Hop Localization Algorithm Research Articles

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.

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.

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.

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.

Node Localization Algorithm Based on Modified Archimedes Optimization Algorithm in Wireless Sensor Networks

Node localization information plays an important role in wireless sensor networks (WSNs). To solve the problem of low localization accuracy of distance vector hop (DV-Hop) localization algorithm in wireless sensor networks, an improved localization algorithm called MAOADV-Hop based on the modified Archimedes optimization algorithm (MAOA) and DV-Hop is proposed, which can achieve the balance between the localization speed and the localization precision. Firstly, tent chaotic mapping and particle swarm optimization (PSO) algorithm are introduced into Archimedes optimization algorithm to improve the initial population diversity and change the update rules of density and volume, which improve the global convergence ability and convergence speed of the algorithm. Secondly, the MAOA is used to replace the least square part of the DV-Hop localization algorithm to improve the localization accuracy of the algorithm. Finally, MAOADV-Hop is verified through four different network environments and compared with DE_DV-Hop, BOA_DV-Hop, and DV-Hop. The simulation results show that the localization speed of the proposed approach is faster than that of DE_DV-Hop and BOA_DV-Hop, and the localization error is less than that of DV-Hop, DE_DV-Hop, and BOA_DV-Hop.

Efficient RSU Selection Scheme for Fog-Based Vehicular Software-Defined Network

Efficient traffic control center (TCC) management requires roadside units (RSUs) to frequently update vehicles with traffic messages (TMs). RSUs are costly to road operators in terms of deployment and maintenance. Therefore, vehicular users expect the TCC to deliver high-quality and rapid responses to query requests on TMs through RSUs. This paper presents a novel RSU selection scheme based on a software-defined network (SDN) for traffic message transmission. In the proposed scheme, the RSU selection is determined by comparing the utility function calculated at the SDN controller. The SDN controller discovers the network connectivity topology through a cluster approach in which a cluster head (CH) is elected to report the network topology to the SDN controller. Furthermore, the SDN controller relies on the RSU selection manager to load existing RSU deployment techniques. In addition, a distance vector hop localization algorithm (DV-hop) is used to locate hidden RSUs to control signaling between the SDN controller and local controllers at RSUs. The proposed scheme achieves minimum latency and appropriate RSU management within a higher packet reception rate, which improves the selection of RSUs and traffic message delivery.

Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks

As localization represents the main backbone of several wireless sensor networks applications, several localization algorithms have been proposed in the literature. There is a growing interest in the multi-hop localization algorithms as they permit the localization of sensor nodes even if they are several hops away from anchor nodes. One of the most famous localization algorithms is the Distance Vector Hop (DV-Hop). Aiming to minimize the large localization error in the original DV-Hop algorithm, we propose an improved DV-Hop algorithm in this paper. The distance between unknown nodes and anchors is estimated using the received signal strength indication (RSSI) and the polynomial approximation. Moreover, the proposed algorithm uses a recursive computation of the localization process to improve the accuracy of position estimation. Experimental results show that the proposed localization technique minimizes the localization error and improves the localization accuracy.

Online Sequential DV-Hop Localization Algorithm for Wireless Sensor Networks

One of the main issues of wireless sensor networks is localization. Besides, it is important to track and analyze the sensed information. The technique of localization can calculate node position with the help of a set of designed nodes, denoted as anchors. The set density of these anchors may be incremented or decremented because of many reasons such as maintenance, lifetime, and breakdown. The well-known Distance Vector Hop (DV-Hop) algorithm is a suitable solution for localizing nodes having few neighbor anchors. However, existing DV-Hop-based localization methods have not considered the problem of anchor breakdown which may happen during the localization process. In order to avoid this issue, an Online Sequential DV-Hop algorithm is proposed in this paper to sequentially calculate positions of nodes and improve accuracy of node localization for multihop wireless sensor networks. The algorithm deals with the variation of the number of available anchors in the network. We note that DV-Hop algorithm is used in this article to process localization of nodes by a new optimized method for the estimation of the average distance of hops between nodes. Our proposed localization method is based on an online sequential computation. Compared with the original DV-Hop and other localization methods from the literature, simulation results prove that the proposed algorithm greatly minimizes the average of localization error of sensor nodes.

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.

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.

Hop-Count Quantization Ranging and Hybrid Cuckoo Search Optimized for DV-HOP in WSNs

Localization technology occupies a very important position in wireless sensor network (WSN). Distance vector-hop (DV-HOP) algorithm is range-free localization algorithm and has advantages of low overhead and can handle the case where a normal node has less than three neighbor anchors. However, considering the problem of high localization error of DV-HOP algorithm in WSN. An hybrid DV-HOP localization algorithm based on hop-count quantization anchor and modified cuckoo search (HMCS-D) is proposed. The algorithm using the correction factor to correct the number of hops that can reduce the error caused by the recording inaccurate minimum hops. The nodes of the hop neighbor nodes in the network are divided into three disjoint subsets and the distance between the nodes is estimated according geometric method. Then according to the weight of each anchor node to select average jump distance of unknown nodes. Finally, introduced hybrid cuckoo search which can dynamically adjust the search step size and using the search algorithm to calculate the node coordinates instead of the maximum likelihood estimation method. Simulation results show that compare with DV-HOP and cuckoo search DV-HOP algorithm, the average positioning error of HMCS-D algorithm decrease 39.7%, 10.6% respectively. Prove that the HMCS-D algorithm effectively improve the node localization accuracy, reduce the positioning error and without affecting the hardware cost.

On Improved DV‐Hop Localization Algorithm for Accurate Node Localization in Wireless Sensor Networks

Node Localization is a fundamental issue for many critical applications in Wireless sensor networks (WSNs). Traditional DV-Hop localization algorithm and corresponding improved ones still cannot provide su.cient localization accuracy in such WSNs. To ensure accurate localization, this paper proposes an improved DistancevectorHop (DV-Hop) localization algorithm. Under such an algorithm, we determine a corrected average hopdistance of beacon nodes by employing the di.erences between actual and estimated distance among beacon nodes in WSNs. We propose a probability information based selective strategy for the selection of beacon nodes. Based on these selected beacon nodes, we adopt a two dimensional hyperbolic function to predict the locations of unknown nodes. Simulation results are provided to illustrate the localization accuracy of our algorithm compared with traditional DV-Hop algorithm and its two improved algorithms in WSNs.

A New Distance Vector-Hop Localization Algorithm Based on Half-Measure Weighted Centroid

Considering the defects of the Distance Vector-Hop (DV-Hop) localization algorithm making errors and having error accumulation in wireless sensor network (WSN), we proposed a new DV-Hop localization algorithm based on half-measure weighted centroid. This algorithm followed the two-dimensional position distribution, designed the minimum communication radius, and formed a reasonable network connectivity firstly. Then, the algorithm corrected the distance between the beacon node and its neighbour node to form a more accurate jump distance so that the shortest path can be optimized. Finally, we theorized the proposed localization algorithm and verified it in simulation experiments, including same communication radius, different communication radii, and different node densities in same communication radius, and have compared the localization error and localization accuracy, respectively, between the proposed algorithm and the DV-Hop localization algorithm. The experiment’s result shows that the proposed localization algorithm have reduced the localization’s average error and improved the localization’s accuracy.

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.

A Hybrid DV-Hop Algorithm Using RSSI for Localization in Large-Scale Wireless Sensor Networks.

With the increasing realization of the Internet-of-Things (IoT) and rapid proliferation of wireless sensor networks (WSN), estimating the location of wireless sensor nodes is emerging as an important issue. Traditional ranging based localization algorithms use triangulation for estimating the physical location of only those wireless nodes that are within one-hop distance from the anchor nodes. Multi-hop localization algorithms, on the other hand, aim at localizing the wireless nodes that can physically be residing at multiple hops away from anchor nodes. These latter algorithms have attracted a growing interest from research community due to the smaller number of required anchor nodes. One such algorithm, known as DV-Hop (Distance Vector Hop), has gained popularity due to its simplicity and lower cost. However, DV-Hop suffers from reduced accuracy due to the fact that it exploits only the network topology (i.e., number of hops to anchors) rather than the distances between pairs of nodes. In this paper, we propose an enhanced DV-Hop localization algorithm that also uses the RSSI values associated with links between one-hop neighbors. Moreover, we exploit already localized nodes by promoting them to become additional anchor nodes. Our simulations have shown that the proposed algorithm significantly outperforms the original DV-Hop localization algorithm and two of its recently published variants, namely RSSI Auxiliary Ranging and the Selective 3-Anchor DV-hop algorithm. More precisely, in some scenarios, the proposed algorithm improves the localization accuracy by almost 95%, 90% and 70% as compared to the basic DV-Hop, Selective 3-Anchor, and RSSI DV-Hop algorithms, respectively.

An improved distance vector-Hop localization algorithm based on coordinate correction

In order to improve the localization accuracy of distance vector-Hop algorithm under the random topology network scenarios, a novel algorithm named coordinates correction-distance vector-Hop is pro...

Design and Simulation of a Meteorological Data Monitoring System Based on a Wireless Sensor

A system for observing meteorological data based on a wireless sensor network (WSN) is designed to fulfill the business requirements for meteorological data observation in unattended areas. The WSN is based on the ZigBee communication protocol, which can implement a system with low power consumption, self-organizing capability, and a large scope. The hardware design is based on the CC2530 chip, which is the first ZigBee system on chip produced by the TI company. The WSN designed by the improved distance vector-hop localization algorithm for a wireless sensor using simulation software demonstrates the effectiveness of the algorithm. The system ensures the accuracy and reliability of the meteorological observation data.

Improved DV-Hop Localization Algorithm Based on Bat Algorithm in Wireless Sensor Networks

Obtaining accurate location information is important in practical applications of wireless sensor networks (WSNs). The distance vector hop (DV-Hop) is a frequently-used range-free localization algorithm in WSNs, but it has low localization accuracy. Moreover, despite various improvements to DV-Hop-based localization algorithms, maintaining a balance between high localization accuracy and good stability and convergence is still a challenge. To overcome these shortcomings, we proposed an improved DV-Hop localization algorithm based on the bat algorithm (IBDV-Hop) for WSNs. The IBDV-Hop algorithm incorporates optimization methods that enhance the accuracy of the average hop distance and fitness function. We also introduce a nonlinear dynamic inertial weight strategy to extend the global search scope and increase the local search accuracy. Moreover, we develop an updated solutions strategy that avoids premature convergence by the IBDV-Hop algorithm. Both theoretical analysis and simulation results show that the IBDV-Hop algorithm achieves higher localization accuracy than the original DV-Hop algorithm and other improved algorithms. The IBDV-Hop algorithm also exhibits good stability, search capability and convergence, and it requires little additional time complexity and energy consumption.

An Advanced DV-hop Localization Algorithm in Wireless Sensor Network

Position information is a fundamental element in WSN. As one of the range-free localization algorithm, the DV-Hop (Distance Vector-Hop) is widely applied. The DV-Hop localization algorithm employs the physical distance between the anchor nodes to estimate the average hop distance, instead of the actual transmission distance. The distance gap between the two kinds of distance undoubtedly leads to the error of the average hop distance as well as relatively high localization error. Aiming at solving the problem, the improved algorithm is proposed. The reason of error between the physical distance and actual transmission distance is analyzed in the paper, and summarized as the asymmetry of distance resulted from the nodes random distribution, as well as the deviation degree between the actual transmission path and the physical distance，and the distance compensation model is built based on the asymmetry and deviation degree to make the physical distance closer to the transmission distance . In the improved algorithm increases no extra hardware and complexity. Compared to the original DV-Hop algorithm, the simulation results show that the improved algorithm proposed in the paper increases the positioning accuracy of the unknown nodes. Keyword: Wireless Sensor Network; Positioning Algorithm; nonuniformity; nonlinear degree; topological structure; DV-Hop