Journal articles: 'Node Localization'

1

Li, Xiao Qin, and Guang Rong Chen. "A Sensor Node Localization Algorithm Based on Fuzzy RSSI Distance." Applied Mechanics and Materials 543-547 (March 2014): 989–92. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.989.

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The node self-localization is the basis of target localization for wireless sensor network (WSN), the WSN nodes localization algorithms have two types based on distance and non distance. The node localization based on RSSI is simple and widely used in application. According to the traditional WSN nodes localization algorithm, the RSSI signal intensity changes greatly and with nonlinearity. And it is converted into distance feature with a large deviation, which leads to inaccurate positioning and localization. In order to solve this problem, a sensor node localization algorithm is proposed based on fuzzy RSSI distance. The nodes information is collected based on RSSI ranging method. And the location information is processed with fuzzy operation. The disturbance from the environmental factors for the positioning is solved. The accuracy of the node localization is improved. Simulation result shows that this algorithm can locate the sensor nodes accurately. The localization accuracy is high, and the performance of nodes localization is better than the traditional algorithm. It has good application value in the WSN nodes distribution and localization design.

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Khajuria, Vinayak, and Manjot Kaur. "Advancement in range based scheme for node localization of underwater acoustic networks." International Journal of Engineering & Technology 7, no. 2.27 (August 6, 2018): 122. http://dx.doi.org/10.14419/ijet.v7i2.27.12730.

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The underwater acoustic network is the type of network which is deployed under the deep sea or ocean to gather underwater information. The sensor node position estimation is a major issue of the underwater acoustic network. The process of estimating node position is called node localization. In the existing RSSI based approach for the node localization has a high delay which reduces its efficiency. The technique needs to be designed which localize a number of nodes in less amount of time. This research is based on the advancement of the range-based scheme for node localization. In the proposed scheme mobile beacons are responsible for the node localization. The beacon nodes send beacon message in the network and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node. The sensor nodes which are already localized will not respond back to the beacon messages which reduce delay in the network for node localization. The simulation of proposed modal is performed in MATLAB and it shows that proposed scheme performs well in terms of a number of nodes localized.

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An, Peng. "An Algorithm of Mobile Robot Node Location Based on Wireless Sensor Network." International Journal of Online Engineering (iJOE) 13, no. 05 (May 14, 2017): 4. http://dx.doi.org/10.3991/ijoe.v13i05.7044.

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In the wireless sensor network, there is a consistent one-to-one match between the information collected by the node and the location of the node. Therefore, it attempts to determine the location of unknown nodes for wireless sensor networks. At present, there are many kinds of node localization methods. Because of the distance error, hardware level, application environment and application costs and other factors, the positioning accuracy of various node positioning methods is not in complete accord. The objective function is established and algorithm simulation experiments are carried out to make a mobile ronot node localization. The experimnettal results showed that the proposed algorithm can achieve higher localization precision in fewer nodes. In addition, the localization algorithm was compared with the classical localization algorithm. In conclusion, it is verified that the localization algorithm proposed in this paper has higher localization accuracy than the traditional classical localization algorithm when the number of nodes is larger than a certain number

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Puneetpal Kaur, Mohit Marwaha, and Baljinder Singh. "Design and Implement Beacon based scheme for Node Localization in Underwater Acoustic Network." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 20 (June 5, 2020): 53–64. http://dx.doi.org/10.24297/ijct.v20i.8756.

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A network that can sense the surroundings and collected all the information from the sensor nodes and passed it to the base station is known as a wireless sensor network. The underwater acoustic networks are the type of network deployed under the oceans and passed information to the base station. Due to the dynamic nature of the network, nodes change their location at any time. To maximum aggregate information from the sensor nodes, to estimate exact node location is very important. The sensor node position estimation is a major issue of the underwater acoustic network. The process of estimating node position is called node localization. In the existing RSSI based approach for the node, localization has a high delay, which reduces its efficiency. The technique needs to be designed, which localizes more nodes in less amount of time. This research is based on the advancement of the range based scheme for node localization. In the proposed scheme, mobile beacons are responsible for node localization. The beacon nodes send beacon messages in the network, and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node, the sensor nodes which are already localized will not respond back to the beacon messages, which reduce delay in the network for node localization.

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Xia, Xin Jiang, Gang Hu, and Qin Wei Wei. "A Research on Circular Localization Algorithm of Wireless Sensor Network." Applied Mechanics and Materials 58-60 (June 2011): 1657–63. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1657.

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This paper based on several common wireless sensor node localization algorithms. According to the concentric localization algorithm principle, we proposed an annular localization algorithm and its improved algorithm .The algorithm uses the anchor node to do node ring through certain rules, narrows unknown nodes estimate area continually, and until finally gets the minimum area contains unknown nodes. Then taking the minimum area centroid position as unknown node’s estimate coordinates. Through the simulation of concentric localization algorithm and its improved algorithm, circular localization algorithm and its improved algorithm, can conclude that: When the proportion of anchor node increases from 5% to 10%, the positioning accuracy is obviously improved in the situation of low energy consumption.

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Mittal, Rachit, and Manik Lal Das. "Secure Node Localization in Mobile Sensor Networks." International Journal of Wireless Networks and Broadband Technologies 3, no. 1 (January 2014): 18–33. http://dx.doi.org/10.4018/ijwnbt.2014010102.

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Secure node localization in wireless sensor networks (WSN) has become an important research topic. Although, Global Positioning System (GPS) based node localization has got significant attention from researchers, GPS-free node localization trend is evolving in recent times. GPS-free node localization in mobile sensor networks can be constructed in two ways: Beacon based (BB) and Without Beacon based (WBB). The BB approach has been studied extensively under adversarial model and many algorithms based on BB approach have been proposed in literature in order to localize nodes in a secure manner. In contrast, WBB approach for node localization under adversarial model has not received substantial attention from researchers. In this paper, the authors discuss WBB approach for node localization under adversarial model. The authors discuss static and dynamic key settings for node localization using WBB approach. The authors present an improved protocol for node localization in mobile sensor networks, aiming at minimizing the impact of node capture threats. The authors consider the LEAP (Localized Encryption and Authentication Protocol) (Zhu, Setia, & Jajodia, 2003) and the LOCK (Localized Combinatorial Keying) (Eltoweissy, Moharrum, & Mukkamala, 2006) as the building blocks of their proposed scheme. The authors show that the improved protocol provides effective node localization in a secure manner with minimal node capture threats.

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Rashid, Mofeed Turky, Abdulmuttalib Rashid, and Ammar Aldair. "Multi-Node Localization and Identity Estimation Based Multi-Beacon Searching Algorithm." Information Technology And Control 49, no. 4 (December 19, 2020): 511–29. http://dx.doi.org/10.5755/j01.itc.49.4.24902.

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The accuracy of multi-nodes localization and identity estimation algorithms directly affected the performance of multi-agent systems like WSN, multi-robot, cellular phone and so on. In this paper, a novel algorithm is introduced in order to achieve high accuracy for multi-nodes localization and identity estimation, this algorithm is named multi-beacon searching algorithm. In this algorithm, the concept of the grid is employed for estimating the location and identity of nodes, in which the environment represented by a grid of reference beacons, for each beacon, light-emitting diode (LED) is used. Whereas, each node in the environment is equipped with four LDR sensors which are used to sense the lighting of LEDs according to a proposed searching algorithm. The localization process achieved based on three proposed algorithms: Firstly, a modified binary search algorithm is utilized to estimate the approximate location of the node by a group of neighbor LEDs. Secondly, the accurate localization algorithm is used to find the accurate location of each node by reducing the number of neighbor LEDs. Finaly, two algorithms are introduced to evaluate the location and identification of each node: the centroid algorithm and the minimum bounded circle algorithm. In the minimum bounded circle algorithm, a new faster algorithm called the maxima boundaries convex hull algorithm for polygon convex hull construction is introduced instead of the Chan's algorithm. Several simulation processes have been implemented for testing the proposed algorithms. The obtained results show that the proposed algorithms have very good performance in estimating the accurate localizations of the nodes.

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Santhosh, M., and P. Sudhakar. "Nelder Mead with Grasshopper Optimization Algorithm for Node Localization in Wireless Sensor Networks." Journal of Computational and Theoretical Nanoscience 17, no. 12 (December 1, 2020): 5409–21. http://dx.doi.org/10.1166/jctn.2020.9434.

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Node localization in wireless sensor network (WSN) becomes essential to calculate the coordinate points of the unknown nodes with the use of known or anchor nodes. The efficiency of the WSN has significant impact on localization accuracy. Node localization can be considered as an optimization problem and bioinspired algorithms finds useful to solve it. This paper introduces a novel Nelder Mead with Grasshopper Optimization Algorithm (NMGOA) for node localization in WSN. The Nelder-Mead simplex search method is employed to improve the effectiveness of GOA because of its capability of faster convergence. At the beginning, the nodes in WSN are arbitrarily placed in the target area and then nodes are initialized. Afterwards, the node executes the NMGOA technique for estimating the location of the unknown nodes and become localized nodes. In the subsequent round, the localized nodes will be included to the collection of anchor nodes to perform the localization process. The effectiveness of the NMGOA model is validated using a series of experiments and results indicated that the NMGOA model has achieved superior results over the compared methods.

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9

Zhang, Kai Sheng, Ya Ming Xu, Wu Yang, and Qian Zhou. "Improved Localization Algorithm Based on Proportion of Differential RSSI." Applied Mechanics and Materials 192 (July 2012): 401–5. http://dx.doi.org/10.4028/www.scientific.net/amm.192.401.

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How to enhance the accuracy of sensor node self-localization for limited energy resource networks is an important problem in the study of wireless sensor networks (WSNs). Concerning the advantages and disadvantages of some main algorithms for senor node self-localization, an easy and simple algorithm is proposed to locate the unknown node itself. The algorithm is to improve weight centroid localization (WCL), by the way of determining weight through using the proportion of differential received signal strength indicator (RSSI) that are derived from unknown node and criterion nodes. In contrast to WCL, the algorithm has the strengths of less computation and better determination of weight, and the determination of weight shows more distinguished distinction in the effect on the localization of unknown node, which is caused by various beacon nodes. Simulations demonstrate that the algorithm has a higher localization accuracy than WCL

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Song, Ling, Xiaoyu Jiang, Liying Wang, and Xiaochun Hu. "Monte Carlo Node Localization Based on Improved QUARTE Optimization." Journal of Sensors 2021 (April 5, 2021): 1–12. http://dx.doi.org/10.1155/2021/6670061.

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Wireless sensor network (WSN) is a research hot spot of scholars in recent years, in which node localization technology is one of the key technologies in the field of wireless sensor network. At present, there are more researches on static node localization, but relatively few on mobile node localization. The Monte Carlo mobile node localization algorithm utilizes the mobility of nodes to overcome the impact of node velocity on positioning accuracy. However, there are still several problems: first, the demand for anchor nodes is large, which makes the positioning cost too high; second, the sampling efficiency is low, and it is easy to fall into the infinite loop of sampling and filtering; and third, the positioning accuracy and positioning coverage are not high. In order to solve the above three problems, this paper proposes a Monte Carlo node location algorithm based on improved QUasi-Affine TRansformation Evolutionary (QUATRE) optimization. The algorithm firstly selects the high-quality common nodes in the range of one hop of unknown nodes as temporary anchor nodes, and takes the temporary anchor nodes and anchor nodes as the reference nodes for positioning, so as to construct a more accurate sampling area; then, the improved QUATRE optimization algorithm is used to obtain the estimated location of unknown nodes in the sampling area. Simulation experiments show that the Monte Carlo node positioning algorithm based on the improved QUATRE optimization has higher positioning accuracy and positioning coverage, especially when the number of anchor nodes is relatively small.

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Zhang, Lieping, Zhenyu Yang, Shenglan Zhang, and Huanhuan Yang. "Three-Dimensional Localization Algorithm of WSN Nodes Based on RSSI-TOA and Single Mobile Anchor Node." Journal of Electrical and Computer Engineering 2019 (July 28, 2019): 1–8. http://dx.doi.org/10.1155/2019/4043106.

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Aimed at the shortcomings of low localization accuracy of the fixed multianchor method, a three-dimensional localization algorithm for wireless sensor network nodes is proposed in this paper, which combines received signal strength indicator (RSSI) and time of arrival (TOA) ranging information and single mobile anchor node. A mobile anchor node was introduced in the proposed three-dimensional localization algorithm for wireless sensor networks firstly, and the mobile anchor node moves according to the Gauss–Markov three-dimensional mobility model. Then, based on the idea of using RSSI ranging in the near end and TOA ranging in the far end, a ranging method combining RSSI and TOA ranging information is proposed to obtain the precise distance between the anchor node and the unknown node. Finally, the maximum-likelihood estimation method is used to estimate the position of unknown nodes based on the obtained ranging values. The MATLAB simulation results show that the proposed algorithm had a higher localization accuracy and lower localization energy consumption compared with the traditional RSSI localization method or TOA localization method.

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12

Guan, Bo, and Xin Li. "An RSSI-based Wireless Sensor Network Localization Algorithm with Error Checking and Correction." International Journal of Online Engineering (iJOE) 13, no. 12 (December 11, 2017): 52. http://dx.doi.org/10.3991/ijoe.v13i12.7892.

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This paper studies the wireless sensor network localization algorithm based on the received signal strength indicator (RSSI) in detail. Considering the large errors in ranging and localization of nodes made by the algorithm, this paper corrects and compensates the errors of the algorithm to improve the coordinate accuracy of the node. The improved node localization algorithm performs error checking and correction on the anchor node and the node to be measured, respectively so as to make the received signal strength value of the node to be measured closer to the real value. It corrects the weighting factor by using the measured distance between communication nodes to make the coordinate of the node to be measured more accurate. Then, it calculates the mean deviation of localization based on the anchor node close to the node to be measured and compensates the coordinate error. Through the simulation experiment, it is found that the new localization algorithm with error checking and correction proposed in this paper improves the localization accuracy by 5%-6% compared with the weighted centroid algorithm based on RSSI.

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Jiang, Rui, Xin Wang, and Li Zhang. "Localization Algorithm Based on Iterative Centroid Estimation for Wireless Sensor Networks." Mathematical Problems in Engineering 2018 (October 8, 2018): 1–11. http://dx.doi.org/10.1155/2018/5456191.

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According to the application of range-free localization technology for wireless sensor networks (WSNs), an improved localization algorithm based on iterative centroid estimation is proposed in this paper. With this methodology, the centroid coordinate of the space enclosed by connected anchor nodes and the received signal strength indication (RSSI) between the unknown node and the centroid are calculated. Then, the centroid is used as a virtual anchor node. It is proven that there is at least one connected anchor node whose distance from the unknown node must be farther than the virtual anchor node. Hence, in order to reduce the space enclosed by connected anchor nodes and improve the location precision, the anchor node with the weakest RSSI is replaced by this virtual anchor node. By applying this procedure repeatedly, the localization algorithm can achieve a good accuracy. Observing from the simulation results, the proposed algorithm has strong robustness and can achieve an ideal performance of localization precision and coverage.

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Lédeczi, Ákos, and Miklós Maróti. "Wireless sensor node localization." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1958 (January 13, 2012): 85–99. http://dx.doi.org/10.1098/rsta.2011.0216.

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For most wireless sensor network (WSN) applications, the positions of the sensor nodes need to be known. Global positioning systems have not fitted into WSNs very well owing to their price, power consumption, accuracy and limitations in their operating environment. Hence, the last decade has brought about a large number of proposed methods for WSN node localization. They show tremendous variation in the physical phenomena they use, the signal properties they measure, the resources they consume, as well as in their accuracy, range, advantages and limitations. This paper provides a high-level, comprehensive overview of this very active research area.

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Yuan, Hong Li, Jian Yin Lu, and Xiao Ming Zhang. "Research on Mobile Anchor Node Localization Method Based on Hierarchical." Applied Mechanics and Materials 654 (October 2014): 362–65. http://dx.doi.org/10.4028/www.scientific.net/amm.654.362.

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Localization technology is enabling technology for wireless sensor applications. In order to improve the localization accuracy of wireless sensor network node, this paper proposes a mobile anchor node localization based on a layered approach. First area for wireless sensor networks slicing. Each layer consists of a number of equilateral triangles. Anchor nodes in each layer moves along the edge of the equilateral triangle to locate unknown node. After receiving the position information of the anchor node, unknown node to determine their own position by the triangular positioning.

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Wang, Sen, Yun Lin, Hongxu Tao, Pradip Kumar Sharma, and Jin Wang. "Underwater Acoustic Sensor Networks Node Localization Based on Compressive Sensing in Water Hydrology." Sensors 19, no. 20 (October 19, 2019): 4552. http://dx.doi.org/10.3390/s19204552.

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Groundwater is an important source of human activities, agriculture and industry. Underwater Acoustic Sensor Networks (UASNs) is one of the important technologies for marine environmental monitoring. Therefore, it is of great significance to study the node self- localization technology of underwater acoustic sensor network. This paper mainly studies the node localization algorithm based on range-free. In order to save cost and energy consumption, only a small number of sensing nodes in sensor networks usually know their own location. How to locate all nodes accurately through these few nodes is the focus of our research. In this paper, combined with the compressive sensing algorithm, a range-free node localization algorithm based on node hop information is proposed. Aiming at the problem that connection information collected by the algorithm is an integer, the hop is modified to further improve the localization performance. The simulation analysis shows that the improved algorithm is effective to improve the localization accuracy without additional cost and energy consumption compared with the traditional method.

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Wang, Lei, and Chen Cai. "New Method of WSN Node Localization Based on MSVD." Advanced Materials Research 562-564 (August 2012): 1819–22. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1819.

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Node localization technology is one of the important research areas in Wireless Sensor Networks (WSN) applications. The coordinates of the unknown nodes can be determined by the Least Square Estimate (LSE), which is commonly used in WSN node localization. But in the experiments and applications, it is discovered that different spatial positions sometimes lead to large errors. This phenomenon is studied and it is found that the WSN node localization is an ill-posed problem. It is proposed that WSN nodes localization should be diagnosed by the condition number. If the ill-posed problem degree is weak, the node can be localized by LSE method. But if the ill-posed degree is serious, the eigenvalue close to zero should be diagnosed and the node is localized by the method of Modified Singular Value Decomposition (MSVD) Regularization. Test results indicate that the proposed mothod of WSN localization based on MSVD weaken the influence of the ill-posed problems, reduces the location errors to around 3 meters, and improve the accuracy of localization.

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Zou, Dong Yao, He Lv, Dao Li Zheng, and Teng Fei Han. "An Improved Weighted Centroid Localization Algorithm in WSN." Applied Mechanics and Materials 513-517 (February 2014): 3496–99. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3496.

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This paper presents a weighted centroid localization algorithm which based on the nearest beacon node. It utilizes Beacon nodes From the unknown node nearest to correct ranging error caused by RSSI distance, Increased unknown node capability which adapt neighboring nodes distributed environment. Improved the positioning accuracy,. Simulation results indicate that the Positioning accuracy of Improved Algorithm which is Proposed in this article has been significantly improved, the highest Enhanced can up to 30%.

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Kaundal, Vivek, Paawan Sharma, and Manish Prateek. "Wireless Sensor Node Localization based on LNSM and Hybrid TLBO- Unilateral technique for Outdoor Location." International Journal of Electronics and Telecommunications 63, no. 4 (November 27, 2017): 389–97. http://dx.doi.org/10.1515/eletel-2017-0054.

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Abstract The paper aims at localization of the anchor node (fixed node) by pursuit nodes (movable node) in outdoor location. Two methods are studied for node localization. The first method is based on LNSM (Log Normal Shadowing Model) technique to localize the anchor node and the second method is based on Hybrid TLBO (Teacher Learning Based Optimization Algorithm)- Unilateral technique. In the first approach the ZigBee protocol has been used to localize the node, which uses RSSI (Received Signal Strength Indicator) values in dBm. LNSM technique is implemented in the self-designed hardware node and localization is studied for Outdoor location. The statistical analysis using RMSE (root mean square error) for outdoor location is done and distance error found to be 35 mtrs. The same outdoor location has been used and statistical analysis is done for localization of nodes using Hybrid TLBO-Unilateral technique. The Hybrid- TLBO Unilateral technique significantly localizes anchor node with distance error of 0.7 mtrs. The RSSI values obtained are normally distributed and standard deviation in RSSI value is observed as 1.01 for outdoor location. The node becomes 100% discoverable after using hybrid TLBO- Unilateral technique.

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Sun, Chun Jie, and Zhi Hui Ye. "An Anchor Filtering Localization Algorithm to Improve the Localization Accuracy of DV-Hop." Advanced Materials Research 716 (July 2013): 548–53. http://dx.doi.org/10.4028/www.scientific.net/amr.716.548.

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By calculating the average distances between each anchor and the unknown nodes, the anchor filtering localization algorithm filtered the anchors with smaller average distances, and chose qualified anchors to improve localization precision, especially in the non-uniform sensor networks. Simulation results showed that the proposed AFDV-Hop algorithm achieved higher positioning accuracy either in uniform or non-uniform sensor networks. The improvement of positioning accuracy became more obvious as the non-uniformity of the nodes increased. The node positioning errors were reduced 20%~30% and 40%~50% by AFDV-Hop algorithm when the non-uniformities of the node distributions were 10%~20% and 30%~40% respectively.

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Hasan, Ola, Ramzy Ali, and Abdulmuttalib Rashid. "Centralized approach for multi-node localization and identification." Iraqi Journal for Electrical and Electronic Engineering 12, no. 2 (December 1, 2016): 178–87. http://dx.doi.org/10.37917/ijeee.12.2.8.

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A new algorithm for the localization and identification of multi-node systems has been introduced in this paper; this algorithm is based on the idea of using a beacon provided with a distance sensor and IR sensor to calculate the location and to know the identity of each visible node during scanning. Furthermore, the beacon is fixed at middle of the frame bottom edge for a better vision of nodes. Any detected node will start to communicate with the neighboring nodes by using the IR sensors distributed on its perimeter; that information will be used later for the localization of invisible nodes. The performance of this algorithm is shown by the implementation of several simulations.

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Yang, Xi, and Jun Liu. "Sequence Localization Algorithm Based on 3D Voronoi Diagram in Wireless Sensor Network." Applied Mechanics and Materials 644-650 (September 2014): 4422–26. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.4422.

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For nodes’ self-localization in wireless sensor networks (WSN), a new localization algorithm called Sequence Localization algorithm based on 3D Voronoi diagram (SL3V) is proposed, which uses 3D Voronoi diagram to divide the localization space.It uses the polyhedron vertices as the virtual beacon nodes and constructs the rank sequence table of virtual beacon nodes. Then it computes Kendall coefficients of the ranks in the optimal rank sequence table and that of the unknown node. Finally, it realizes the weighted estimate of the unknown node by normalization processing Kendall coefficients. Simulation experiments prove that itcan obviously improve the localization accuracy compared with the traditional 2D sequence-based localization and can satisfy the need of localization for 3D space.

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Miloud, Mihoubi, Rahmoun Abdellatif, and Pascal Lorenz. "Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network." International Journal of Distributed Systems and Technologies 10, no. 1 (January 2019): 82–109. http://dx.doi.org/10.4018/ijdst.2019010106.

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Recently developments in wireless sensor networks (WSNs) have raised numerous challenges, node localization is one of these issues. The main goal in of node localization is to find accurate position of sensors with low cost. Moreover, very few works in the literature addressed this issue. Recent approaches for localization issues rely on swarm intelligence techniques for optimization in a multi-dimensional space. In this article, we propose an algorithm for node localization, namely Moth Flame Optimization Algorithm (MFOA). Nodes are located using Euclidean distance, thus set as a fitness function in the optimization algorithm. Deploying this algorithm on a large WSN with hundreds of sensors shows pretty good performance in terms of node localization. Computer simulations show that MFOA converge rapidly to an optimal node position. Moreover, compared to other swarm intelligence techniques such as Bat algorithm (BAT), particle swarm optimization (PSO), Differential Evolution (DE) and Flower Pollination Algorithm (FPA), MFOA is shown to perform much better in node localization task.

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Jian Yin, Lu. "A New Distance Vector-Hop Localization Algorithm Based on Half-Measure Weighted Centroid." Mobile Information Systems 2019 (January 3, 2019): 1–9. http://dx.doi.org/10.1155/2019/9892512.

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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.

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Liu, Xiaoyang, and Chao Liu. "Wireless Sensor Network Dynamic Mathematics Modeling and Node Localization." Wireless Communications and Mobile Computing 2018 (May 31, 2018): 1–8. http://dx.doi.org/10.1155/2018/1082398.

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With the rapid development of wireless sensor network (WSN) technology and its localization method, localization is one of the basic services for data collection in WSN. The localization accuracy often depends on the accuracy of distance estimation. Because of the constraint in size, power, and cost of sensor nodes, the investigation of efficient location algorithms which satisfy the basic accuracy requirement for WSN meets new challenges. This paper proposes a novel intelligent node localization algorithm in WSN based on beacon nodes to improve the precision in location estimation. Firstly, system model of WSN node localization is constructed according to the WSN environment. Then traditional WSN node localization methods such as DV-HOP, GA, and PSO are studied. Localization algorithm of WSN is proposed by using dynamic mathematics modeling. And the result of simulation, which is compared to the traditional algorithm, indicated that this algorithm is better to improve the accuracy and coverage of WSN. The simulation results show that the performance of the proposed WSN location algorithm is better than the traditional localization algorithms.

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Gao, Jun Xiang, and Yan Tian. "An Accurate Localization Method for Video Sensor Network Nodes." Advanced Materials Research 225-226 (April 2011): 531–35. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.531.

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Localization of the sensor nodes is a major obstacle for practical applications of video sensor networks. This paper present a novel localization technique based on vision in wireless sensor networks. On the assumption that sensor nodes can be recognized in an image, a sensor node firstly direct its Field-of-View (FoV) to an anchor or localized node, then we can get the orientation of anchor node relate to the sensor node to be localized. If two or more anchors can be found in sensing area, a series of equations will be obtained. They can be solved using minimum mean square error rule, and the solution of the overdetermined equations mentioned above is the estimation of the node position. The experiments indicate that a promising performance can be achieved in determining the exact node location using a small number of anchors.

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Shin, Soo Young, and Ifa Fatihah Mohamed Zain. "Binary Particle Swarm Optimization (BPSO) Algorithm for Distributed Node Localization." Applied Mechanics and Materials 556-562 (May 2014): 3666–69. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3666.

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In this paper, we propose a binary particle swarm optimization (BPSO) algorithm for distributed node localization in wireless sensor networks (WSNs). Each unknown node performs localization under the distance measurement from three or more neighboring anchors. The node that gets localized will be used as a reference for remaining nodes. A comparison of the performances of PSO and BPSO in terms of localization error and computation time is presented using simulations in Matlab.

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Fan, Shi Ping, Yong Jiang Wen, and Lin Zhou. "An Enhanced Monte Carlo Localization Algorithm for Mobile Node in Wireless Sensor Networks." Applied Mechanics and Materials 401-403 (September 2013): 1800–1804. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1800.

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There are some common problems, such as low sampling efficiency and large amount of calculation, in mobile localization algorithm based on Monte Carlo localization (MCL) in wireless sensor networks. To improve these issues, an enhanced MCL algorithm is proposed. The algorithm uses the continuity of the nodes movement to predict the area where the unknown node may reach, constructs high posteriori density distribution area, adds the corresponding weights to the sample points which fall in different areas, and filters the sample points again by using the position relations between the unknown node and its one-hop neighbors which include anchor nodes and ordinary nodes. Simulation results show that the localization accuracy of the algorithm is superior to the traditional localization algorithm. Especially when the anchor node density is lower or the unknown nodes speed is higher, the algorithm has higher location accuracy.

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Xue, Yuan, Wei Su, Dong Yang, Hongchao Wang, and Weiting Zhang. "RMLNet—A Reliable Wireless Network for a Multiarea TDOA-Based Localization System." Sensors 19, no. 20 (October 10, 2019): 4374. http://dx.doi.org/10.3390/s19204374.

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Ultrawideband (UWB) wireless communication is a promising spread-spectrum technology for accurate localization among devices characterized by a low transmission power, a high rate and immunity to multipath propagation. The accurately of the clock synchronization algorithm and the time-difference-of-arrival (TDOA) localization algorithm provide precise position information of mobile nodes with centimeter-level accuracy for the UWB localization system. However, the reliability of target node localization for multi-area localization remains a subject of research. Especially for dynamic and harsh indoor environments, an effective scheme among competing target nodes for localization due to the scarcity of radio resources remains a challenge. In this paper, we present RMLNet, an approach focus on the medium access control (MAC) layer, which guarantees general localization application reliability on multi-area localization. Specifically, the design requires specific and optimized solutions for managing and coordinating multiple anchor nodes. In addition, an approach for target area determination is proposed, which can approximately determine the region of the target node by the received signal strength indication (RSSI), to support RMLNet. Furthermore, we implement the system to estimate the localization of the target node and evaluate its performance in practice. Experiments and simulations show that RMLNet can achieve localization application reliability multi-area localization with a better localization performance of competing target nodes.

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30

Carroll, Patrick, Shengli Zhou, Hao Zhou, Xiaoka Xu, Jun-Hong Cui, and Peter Willett. "Underwater Localization and Tracking of Physical Systems." Journal of Electrical and Computer Engineering 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/683919.

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We investigate the problem of localizing an underwater sensor node based on message broadcasting from multiple surface nodes. With the time-of-arrival measurements from a DSP-based multicarrier modem, each sensor node localizes itself based on the travel time differences among multiple senders to the receiver. Using one-way message passing, such a solution can scale to accommodate a large number of nodes in a network. We consider the issue from not only the physical layer, but also at the node processing layer by incorporating a tracking solution. We present simulation results, testing results in a swimming pool featuring both stationary and moving receivers, and results from a lake test with a mobile receiver.

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Madagouda, Basavaraj K., Varsha M. Patil, and Pradnya Godse. "Localization of Sensor Nodes using Flooding in Wireless Sensor Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, no. 3 (July 15, 2013): 1153–61. http://dx.doi.org/10.24297/ijct.v9i3.3341.

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The accuracy of localization is a significant criterion to evaluate the practical utility of localization algorithm in wireless sensor networks (WSN). In mostly localization algorithms, one of the main methods to improve localization accuracy is to increase the number of anchor nodes. But the number of anchor nodes is always limited because of the hardware restrict, such as cost, energy consumption and so on. In this paper, we propose a novel which uses forwarding a query message in flooding technique for localization using anchor nodes and once a node localized it acts as virtual anchor node and it helps to localize remaining sensor nodes. It is scheme to increase and upgrade the virtual anchor nodes, while the real number of physical anchors is the same as before.

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Cui, Huanqing, Yongquan Liang, Chuanai Zhou, and Ning Cao. "Localization of Large-Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection." Wireless Communications and Mobile Computing 2018 (December 2, 2018): 1–18. http://dx.doi.org/10.1155/2018/2473875.

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Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.

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33

Liu, Yu, Xiao Yi, and You He. "A Novel Centroid Localization for Wireless Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 1 (January 1, 2012): 829253. http://dx.doi.org/10.1155/2012/829253.

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Self-localization of sensor nodes is one of the key issues in wireless sensor networks. Based on the analysis of traditional range-free algorithms such as centroid and APIT (approximate perfect point in triangulation test) schemes, the effect of random deployment of all nodes on node localization is researched. And then, an improved centroid localization algorithm (ICLA) based on APIT and the quality of perpendicular bisector is proposed. In ICLA, nodes are categorized into several kinds and localized, respectively. Extensive simulation results indicate that ICLA obtains a better localization result in random topology networks without any additional hardware. Therefore, ICLA can be an alternate solution for the node self-localization problem in large-scale wireless sensor networks.

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Li, Dina. "Research on Localization of Unknown Nodes in Wireless Sensor Network based on Centroid Iteration." International Journal of Online Engineering (iJOE) 13, no. 03 (March 28, 2017): 149. http://dx.doi.org/10.3991/ijoe.v13i03.6867.

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<span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">This paper proposes the localization of unknown nodes based on the centroid iteration algorithm. It uses the range-free localization method and proposes the iteration stopping criterion for centroid iteration algorithm and node localization flow chart according to the location of the unknown nodes based on the plane enclosed by N beacon nodes and those where the received signals of the unknown node are gradually weakened, and finally achieves the accurate localization of unknown communication nodes. Simulation results show that with the proportion of beacon nodes increasing, the relative error of calculation is gradually reduced; when the proportion of beacon nodes remains the same, with the radio range of the node increasing, the relative error of localization gradually decreases. When the radio range is small (R=15m, R=20m), with the proportion of beacon nodes increasing, the relative error of localization increases instead of decreasing. Compared with other three traditional centroid localization algorithms, the algorithm proposed in this paper can achieve the minimum relative localization error at different radio ranges. Under this algorithm, the localization is accurate and highly resistant to RSSI errors. Calculation results show that the optimal radio range R = 30m.

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Liu, Ying, Xu Zhang, Dan Liu, and Zhi Qiang Han. "Study of Location Algorithm for Wireless Sensor Networks Based on Newton Iteration." Advanced Materials Research 645 (January 2013): 285–89. http://dx.doi.org/10.4028/www.scientific.net/amr.645.285.

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The TDOA localization algorithm for wireless sensor networks can help us measure the time differences of an unknown node to more anchor nodes, which establish nonlinear equation of the differential distance between unknown node and anchor node. Newton iteration is the crucial method for nonlinear equation numerical solutions, so using the Newton iterative method can better achieve nonlinear optimization in TDOA location. The simulation results indicate that the algorithm can meet the requirement of localization and possesses the preferable localization precision.

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Zhang, Nan, Jian Hua Zhang, Jian Ying Chen, and Xiao Mei Qu. "An Immune-Based Node Localization Algorithm for WSN." Advanced Materials Research 490-495 (March 2012): 1207–11. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1207.

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Node localization technology is the premise and foundation of all applications in wireless sensor network. An improved DV-Hop algorithm was proposed aimed at the low-power requirement of wireless sensor networks. The distances between nodes and anchor nodes were used to calculate the node location in DV-Hop algorithm, and the immune algorithm was used to optimize the estimated location in the third stage of DV-Hop algorithm. The improved algorithm does not require additional hardware devices, and has smaller additional amount of communication and computation.

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Kapase, Niraj Bhupal, Santosh P. Salgar, Mahesh K. Patil, and Prashant P. Zirmite. "Anchor Movement Strategy for Conjecture Geometry Based Localization Scheme in Wireless Sensor Network." IAES International Journal of Robotics and Automation (IJRA) 5, no. 4 (December 1, 2016): 255. http://dx.doi.org/10.11591/ijra.v5i4.pp255-261.

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Abstract—Localization of sensor node with least error is one of the major concern in wireless sensor network as some of the application require sensor node to know their location with high degree of precision. For mobile anchor based localization many of the path planning schemes already developed which includes scan, double scan, Circles & S- Curves. These path planning schemes have some limitations like localization error, Number of sensor nodes covered in the network, Trajectory length of mobile anchor node. This paper represents anchor movement strategy which is based on Scan path, with modifications are made in such a way that it satisfies the requirements of localization scheme. This movement strategy ensures that trajectory of mobile anchor node will minimize localization error and also will cover majority of sensor node in the environment. The localization error yielded by Modified Scan algorithm is in the range of 0.2 to 0.4m which is quite lower than the other existing mentioned path planning strategies producing localization error in the range 0.6 to 1.8mKeywords—Localization; Mobile anchor node; Wireless sensor network; Modified Scan algorithm

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Gao, Baojian, Xiaoning Zhao, Jun Wang, and Xiaojiang Chen. "Decomposition Based Localization for Anisotropic Sensor Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/805061.

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Range-free localization algorithms have caused widespread attention due to their low cost and low power consumption. However, such schemes heavily depend on the assumption that the hop count distance between two nodes correlates well with their Euclidean distance, which will be satisfied only in isotropic networks. When the network is anisotropic, holes or obstacles will lead to the estimated distance between nodes deviating from their Euclidean distance, causing a serious decline in localization accuracy. This paper develops HCD-DV-Hop for node localization in anisotropic sensor networks. HCD-DV-Hop consists of two steps. Firstly, an anisotropic network is decomposed into several different isotropic subnetworks, by using the proposed Hop Count Based Decomposition (HCD) scheme. Secondly, DV-Hop algorithm is carried out in each subnetwork for node localization. HCD first uses concave/convex node recognition algorithm and cleansing criterion to obtain the optimal concave and convex nodes based on boundary recognition, followed by segmentation of the network’s boundary. Finally, the neighboring boundary nodes of the optimal concave nodes flood the network with decomposition messages; thus, an anisotropic network is decomposed. Extensive simulations demonstrated that, compared with range-free DV-Hop algorithm, HCD-DV-Hop can effectively reduce localization error in anisotropic networks without increasing the complexity of the algorithm.

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Zhang, Ting, Jingsha He, and Hong Yu. "Secure Localization in Wireless Sensor Networks with Mobile Beacons." International Journal of Distributed Sensor Networks 8, no. 10 (October 1, 2012): 732381. http://dx.doi.org/10.1155/2012/732381.

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We present a scheme, called SLMB, for secure sensor localization in WSNs in which we propose to use a mobile beacon node with the goal of reducing the overall energy consumption in sensor nodes during sensor localization. In the SLMB scheme, a mobile beacon node traverses through the network, collects information from unknown sensor nodes, figures out position relationship with these nodes, and sends the information to the base station where analysis and location calculation is carried out to relieve unknown sensor nodes from energy-consuming computation. The proposed SLMB scheme is also designed to resist wormhole attacks, and localization is developed based on a mathematical model to design a path for the mobile beacon node to traverse in order to cover the entire sensor network. To evaluate our scheme, we have performed simulations to demonstrate that the SLMB scheme can improve the success rate and the accuracy of sensor localization compared to other sensor localization schemes in hostile environments. Our simulation results also show that the SLMB scheme consumes much less energy than traditional distributed sensor localization schemes, which is an important metric in measuring the effectiveness and usefulness of any schemes targeted for applications in WSNs.

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Zhou, Chunyue, Hui Tian, and Baitong Zhong. "An improved MCB localization algorithm based on weighted RSSI and motion prediction." Computer Science and Information Systems 17, no. 3 (2020): 779–94. http://dx.doi.org/10.2298/csis200204020z.

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Aiming at the problem of low sampling efficiency and high demand for anchor node density of traditional Monte Carlo Localization Boxed algorithm, an improved algorithm based on historical anchor node information and the received signal strength indicator (RSSI) ranging weight is proposed which can effectively constrain sampling area of the node to be located. Moreover, the RSSI ranging of the surrounding anchors and the neighbor nodes is used to provide references for the position sampling weights of the nodes to be located, an improved motion model is proposed to further restrict the sampling area in direction. The simulation results show that the improved Monte Carlo Localization Boxed (IMCB) algorithm effectively improves the accuracy and efficiency of localization.

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41

Deng, Jian. "Study on Node Localization Algorithms in Wireless Sensor Network." Applied Mechanics and Materials 556-562 (May 2014): 3952–55. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3952.

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This paper introduces an improved localization algorithm that bases on distance and local coordinate system which is built up by beacon nodes. This algorithm evaluates the differences of several independent localization information to determine whether upgrade this node as beacon nodes according to the size of the difference whether beyond the prescribed or not. It can effectively prevent and reduce the accumulated and spread error in localization process by the audit of beacon nodes, and improve the positioning accuracy of nodes in the network.

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Fan, Yingsheng, Xiaogang Qi, Baoguo Yu, and Lifang Liu. "A Distributed Anchor Node Selection Algorithm Based on Error Analysis for Trilateration Localization." Mathematical Problems in Engineering 2018 (December 5, 2018): 1–12. http://dx.doi.org/10.1155/2018/7295702.

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This paper proposes a distributed anchor node selection algorithm based on error analysis for trilateration localization (EATL). The influence of distance measurement error on localization accuracy is discussed from two aspects: condition number of triangle formed by the three anchor nodes and the relative position between the unknown node and the three anchor nodes. Based on the error analysis, three principles for optimizing the selection of anchor nodes are given and then an algorithm for selecting anchor nodes on the ring is proposed.

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Kanoosh, Huthaifa M., Essam Halim Houssein, and Mazen M. Selim. "Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks." Journal of Computer Networks and Communications 2019 (February 19, 2019): 1–12. http://dx.doi.org/10.1155/2019/1028723.

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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.

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Wei, Ye Hua, and Wen Kang Wu. "A Node Localization Algorithm Based on Adaptive Inertia Weight Particle Swarm Optimization." Applied Mechanics and Materials 303-306 (February 2013): 302–6. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.302.

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Node localization is a key technology of wireless sensor network applications. Considering the resource constraints of senor nodes, node localization is transformed into an unconstrained optimization problem, then one distributed localization algorithm based on particle swarm with adaptive inertia weight is put forward. The unknown nodes construct particle region using the received information of their neighbor anchor nodes, which can reduce the search scope and save the computational cost of the algorithm. Mean deviation of the distances which are the particles to the global optima can be computed to characterize the distribution of particles, and is used to adaptively adjust the inertia weight to avoid falling into the local optima. Simulation shows that the proposed algorithm has a good localization performance.

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Qian, Kai Guo, Yu Jian Wang, Xiao Ming Li, and Zu Cheng Dai. "An Improved Bounding Box Localization Algorithm Based on Optimum Node Selection." Applied Mechanics and Materials 668-669 (October 2014): 1359–62. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1359.

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This paper mainly discusses the problem of localization algorithm for wireless sensor network. For improving the disadvantages of high positioning error and Low coverage, an improved algorithm is proposed based on the optimum node selection determined by the received RSSI strength and virtual anchor nodes. It uses anchor node to locate which selected within 0.5 communication scope of the node by RSSI signal strength. Nodes Completed positioning upgraded to virtual anchor nodes .Complete positioning upgraded to virtual anchor nodes to help neighbors to locate. It reduces the positioning error and improves the positioning accuracy. Simulation experiment shows that the positioning performance of improved algorithm is better than traditional Bounding Box algorithm.

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Meng, Yinghui, Qianying Zhi, Minghao Dong, and Weiwei Zhang. "A Node Localization Algorithm for Wireless Sensor Networks Based on Virtual Partition and Distance Correction." Information 12, no. 8 (August 16, 2021): 330. http://dx.doi.org/10.3390/info12080330.

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The coordinates of nodes are very important in the application of wireless sensor networks (WSN). The range-free localization algorithm is the best method to obtain the coordinates of sensor nodes at present. Range-free localization algorithm can be divided into two stages: distance estimation and coordinate calculation. For reduce the error in the distance estimation stage, a node localization algorithm for WSN based on virtual partition and distance correction (VP-DC) is proposed in this paper. In the distance estimation stage, firstly, the distance of each hop on the shortest communication path between the unknown node and the beacon node is calculated with the employment of virtual partition algorithm; then, the length of the shortest communication path is obtained by summing the distance of each hop; finally, the unknown distance between nodes is obtained according to the optimal path search algorithm and the distance correction formula. This paper innovative proposes the virtual partition algorithm and the optimal path search algorithm, which effectively avoids the distance estimation error caused by hop number and hop distance, and improves the localization accuracy of unknown nodes.

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Jiang, Wei Yong, Pin Wan, Yong Hua Wang, and Dong Liang. "PACMLA: A Localization Algorithm Based on Permutation and Combination Midnormal for Wireless Sensor Networks." Applied Mechanics and Materials 475-476 (December 2013): 564–68. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.564.

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Localization of sensors is one key technique in wireless sensor networks (WSN).Because the midnormal-based localization algorithm (MBLA) has shortcomings such as low accuracy, relatively large number of iterations, a localization algorithm based on permutation and combination midnormal (PACMLA) for WSN is proposed. Nodes are divided into anchor nodes and unknown nodes. In its own communication range, unknown node can communicate with anchor nodes. In PACMLA algorithm, the unknown node communicates with the anchor nodes in turn, and collects their coordinate information and RSSI value. Then by comparing the RSSI values received by unknown node, these RSSI values are formed an array in accordance with the order from small to large. Then starting from the first value of the RSSI array, each of these values and the value behind them will be combined into data sets. Finally, according to corresponding coordinate information of the RSSI value in the data sets, we will determine the position of the unknown node by Point In Which Side (PIWS) determination. In addition, our algorithm is a kind of Range-free algorithm, and it can cuts down the node energy cost. The experiment results illustrate that the PACMLA algorithm has lower error and higher accuracy.

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Safaie, Alireza, Reza Shahbazian, and Seyed Ali Ghorashi. "Cooperative Improved Target Localization in harsh Environments using Direction of Arrival." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 2 (August 1, 2016): 420. http://dx.doi.org/10.11591/ijeecs.v3.i2.pp420-427.

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Target localization is an important issue for many applications in wireless sensor networks. However, it is rather difficult to maintain the localization accuracy in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments as NLOS propagation leads to larger error than what LOS does. In this paper, we propose a new target localization method in mixed environments where NLOS is dominant and only one base node might be in LOS toward target. We use the cooperation between receiver nodes and the direction of arrival (DOA) of received signals to estimate the target’s location. The proposed cooperative target localization method tries to identify a base node that has LOS with respect to target node and use the LOS information for precise positioning of target node. We simulate the proposed method to analyze its performance. Simulation results confirm that our proposed method improves the localization accuracy on average by 20 percent in comparison with traditional cooperative methods.

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Mohd. Zaid Harith, Muhammad, Noorzaily Mohamed Noor, Mohd Idna Idris, and Emran Mohd. Tamil. "Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization." Sensors 18, no. 7 (July 19, 2018): 2344. http://dx.doi.org/10.3390/s18072344.

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The majority of the Wireless Sensor Network (WSN) localization methods utilize a large number of nodes to achieve high localization accuracy. However, there are many unnecessary data redundancies that contributes to high computation, communication, and energy cost between these nodes. Therefore, we propose the Intersection and Complement Set (IACS) method to reduce these redundant data by selecting the most significant neighbor nodes for the localization process. Through duplication cleaning and average filtering steps, the proposed IACS selects the normal nodes with unique intersection and complement sets in the first and second hop neighbors to localize the unknown node. If the intersection or complement sets of the normal nodes are duplicated, IACS only selects the node with the shortest distance to the blind node and nodes that have total elements larger than the average of the intersection or complement sets. The proposed IACS is tested in various simulation settings and compared with MSL* and LCC. The performance of all methods is investigated using the default settings and a different number of degree of irregularity, normal node density, maximum velocity of sensor node and number of samples. From the simulation, IACS successfully reduced 25% of computation cost, 25% of communication cost and 6% of energy consumption compared to MSL*, while 15% of computation cost, 13% of communication cost and 3% of energy consumption compared to LCC.

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Zhao, Ji, Yi Fu, and Han Bo Wang. "Localization Technology Based on Quantum-Behaved Particle Swarm Optimization Algorithm for Wireless Sensor Network." Applied Mechanics and Materials 220-223 (November 2012): 1852–56. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1852.

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This paper proposed a distributed iterative localization technology of wireless sensor networks (WSNs) to solve the problem of node localization. In this approch, once the nodes get localized, they act as references for the rest of nodes to localize. The ranging-based localization problem is formulated as a multidimensional optimization issue, and the quantum-behaved particle swarm optimization algorithm (QPSO) is used to exploit their quick convergence to quality solutions. Finally, the simulation results compared with the particle swarm optimization algorithm (PSO) algorithm show that QPSO outperforms the PSO and improve the node position accuracy, which prove the validity of the presented method.

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Journal articles on the topic 'Node Localization'

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