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Journal articles on the topic 'K-hop clustering'

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Published: 4 June 2021
Last updated: 28 January 2023

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1

Ahmed, Tanjil, Md Abdur, Ambreen Zaman, and Mahfida Amjad. "A Scalable K-hop Clustering Algorithm for Pseudolinear MANET." International Journal of Computer Applications 180, no. 35 (April 18, 2018): 62–68. http://dx.doi.org/10.5120/ijca2018916891.

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2

Batta, Mohamed Sofiane, Hakim Mabed, Zibouda Aliouat, and Saad Harous. "A Distributed Multi-Hop Intra-Clustering Approach Based on Neighbors Two-Hop Connectivity for IoT Networks." Sensors 21, no. 3 (January 28, 2021): 873. http://dx.doi.org/10.3390/s21030873.

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Under a dense and large IoT network, a star topology where each device is directly connected to the Internet gateway may cause serious waste of energy and congestion issues. Grouping network devices into clusters provides a suitable architecture to reduce the energy consumption and allows an effective management of communication channels. Although several clustering approaches were proposed in the literature, most of them use the single-hop intra-clustering model. In a large network, the number of clusters increases and the energy draining remains almost the same as in un-clustered architecture. To solve the problem, several approaches use the k-hop intra-clustering to generate a reduced number of large clusters. However, k-hop proposed schemes are, generally, centralized and only assume the node direct neighbors information which lack of robustness. In this regard, the present work proposes a distributed approach for the k-hop intra-clustering called Distributed Clustering based 2-Hop Connectivity (DC2HC). The algorithm uses the two-hop neighbors connectivity to elect the appropriate set of cluster heads and strengthen the clusters connectivity. The objective is to optimize the set of representative cluster heads to minimize the number of long range communication channels and expand the network lifetime. The paper provides the convergence proof of the proposed solution. Simulation results show that our proposed protocol outperforms similar approaches available in the literature by reducing the number of generated cluster heads and achieving longer network lifetime.
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3

Janakiraman, T. N., and J. Janet Lourds Rani. "Efficient clustering for mobile ad hoc networks using K-hop weighted paired domination." International Journal of Information and Communication Technology 2, no. 3 (2010): 228. http://dx.doi.org/10.1504/ijict.2010.032411.

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4

Leng, Supeng, Yan Zhang, Hsiao-Hwa Chen, Liren Zhang, and Ke Liu. "A novel k-hop Compound Metric Based Clustering scheme for ad hoc wireless networks." IEEE Transactions on Wireless Communications 8, no. 1 (January 2009): 367–75. http://dx.doi.org/10.1109/t-wc.2009.080186.

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5

Zhang, He Wei, Lei Sun, and Hong Zhang. "Research on Data Packets Clustering Algorithm in the Wireless Multiple Hop Network." Applied Mechanics and Materials 651-653 (September 2014): 1905–8. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1905.

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In view of the problems existing in the wireless multiple hop network such as consumption imbalance of node power, disunity of node transmission data efficiency, unfixed life within the scope of network, it has put forward the trade-off relationship between the wireless multiple hop network node energy consumption and multiple factors based on the k-means clustering method. The principle of steps and characteristics of the k-means clustering algorithm are first introduced; Then model the influence of the K-means polymerization on VoIP service quality, then use the k-means clustering method to make cluster analysis for network node data package, and mine the trade-off relationship between data transmission service quality and multiple hops node energy consumption; Finally carry on the simulation experiment to test the performance of this method. Simulation results show that the method not only improves the data transmission service quality of VoIP service, but also reduces the energy consumption of nodes and prolongs the life span of the wireless network.
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6

Leng, Supeng, Liren Zhang, Huirong Fu, and Jianjun Yang. "A Novel Location-Service Protocol Based on $k$-Hop Clustering for MobileAd HocNetworks." IEEE Transactions on Vehicular Technology 56, no. 2 (March 2007): 810–17. http://dx.doi.org/10.1109/tvt.2007.891425.

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7

Prajapat, Rajendra, Ram Narayan Yadav, and Rajiv Misra. "Energy-Efficient k-Hop Clustering in Cognitive Radio Sensor Network for Internet of Things." IEEE Internet of Things Journal 8, no. 17 (September 1, 2021): 13593–607. http://dx.doi.org/10.1109/jiot.2021.3065691.

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8

LIU, Min, Ji-hong HAN, and Ya-di WANG. "k-hop compound metric clustering algorithm based on trust in tactical Internet." Journal of Computer Applications 30, no. 2 (March 23, 2010): 521–24. http://dx.doi.org/10.3724/sp.j.1087.2010.00521.

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9

Misra, Rajiv, and Ram Narayan Yadav. "k-hop neighbour knowledge-based clustering in CRN under opportunistic channel access." International Journal of Communication Networks and Distributed Systems 19, no. 4 (2017): 369. http://dx.doi.org/10.1504/ijcnds.2017.087381.

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10

Misra, Rajiv, and Ram Narayan Yadav. "k-hop neighbour knowledge-based clustering in CRN under opportunistic channel access." International Journal of Communication Networks and Distributed Systems 19, no. 4 (2017): 369. http://dx.doi.org/10.1504/ijcnds.2017.10007987.

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11

Misbahuddin, Misbahuddin, Anak Agung Putri Ratna, and Riri Fitri Sari. "Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs." International Journal of Computers Communications & Control 13, no. 1 (February 12, 2018): 99. http://dx.doi.org/10.15837/ijccc.2018.1.3088.

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In multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. The cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. This paper proposes a dynamic multihop routing algorithm named Data Similarity Aware for Dynamic Multi-hop Routing Protocol (DSA-DMRP) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. The DSA-DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. In this mechanism, each node can recognize and note its similar neighbor nodes. Next, K-hop Clustering Algorithm (KHOPCA) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. The DSA-DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA and can satisfy the requirement of the dynamic multi-hop routing protocol.
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12

Gou, Pingzhang, Zhaoyang Yu, Xinyue Hu, and Kai Miao. "Three-Dimensional DV-Hop Localization Algorithm Based on Hop Size Correction and Improved Sparrow Search." Wireless Communications and Mobile Computing 2022 (May 11, 2022): 1–19. http://dx.doi.org/10.1155/2022/1540110.

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Acquiring precise localization information of sensor nodes is very important in wireless sensor networks. The 3DDV-hop localization algorithm suffers from large localization errors and high energy consumption. In order to improve positioning accuracy and reduce energy consumption, a 3DDV-hop node localization algorithm (3D-HCSSA) based on hop size correction and improved sparrow search optimization is proposed. The algorithm redefines the amendment factor and reduces the cumulative error caused by the hop counts in the traditional algorithm. A maximum distance similar link method based on a similar path search is proposed to find the most similar known node path pair from the target node to the noncoplanar known node link and correct the hop size between multihop counts. The sparrow search algorithm is improved by using the k -means clustering and sine cosine search strategy, which solves the problem that the traditional sparrow algorithm is easy to fall into the local optimum, accelerates the convergence speed, corrects the position deviation of the target node, and improves the positioning accuracy. Experiments demonstrate that the 3D-HCSSA algorithm can improve positioning accuracy and reduce energy consumption. Compared with the 3DDV-hop algorithm, 3D-GAIDV-hop algorithm, and HCLSO-3D algorithm, the 3D-HCSSA positioning accuracy is significantly improved.
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13

Pamungkas, Dimas W. L., and Radityo Anggoro. "An Improvement of Ant Colony Adhoc On-Demand Vector (ANT-AODV) with K-Means Clustering Nodes Method in Mobile Adhoc Network (MANET) Environment." Journal of Development Research 5, no. 1 (May 31, 2021): 1–6. http://dx.doi.org/10.28926/jdr.v5i1.133.

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MANET (Mobile Ad hoc Network) is a technology used for data communication on mobile media. MANET moves and speeds also randomly with different distances between nodes. In the AODV reactive protocol, the development of a routing protocol with an ant colony model can significantly improve performance. The measurement results will impact the decision on the development of the ant colony protocol implemented in AODV to obtain the best route for data transmission. Several studies have developed the K-Means clustering method to obtain the shortest and best route from the AODV protocol. It does not measure congestion, distance, energy, and signal strength of the wireless network. In this research, a combination of methods was carried out, by combining clustering and also ant colony method to get the best route while dealing with the problems that have been mentioned. Implementation is done by modifying the AODV protocol and adding the two methods. At the end of the research, metric measurements were carried out to determine the Packet Delivery Ratio, End to End Delay and also the Average Hop Count.
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14

Wu, Yi-Chao, and Chiu-Ching Tuan. "K-Hop Coverage and Connectivity Aware Clustering in Different Sensor Deployment Models for Wireless Sensor and Actuator Networks." Wireless Personal Communications 85, no. 4 (August 5, 2015): 2565–79. http://dx.doi.org/10.1007/s11277-015-2920-2.

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15

Jain, Aarti, and B. V. Ramana Reddy. "A Novel Method of Modeling Wireless Sensor Network Using Fuzzy Graph and Energy Efficient Fuzzy Based k-Hop Clustering Algorithm." Wireless Personal Communications 82, no. 1 (January 10, 2015): 157–81. http://dx.doi.org/10.1007/s11277-014-2201-5.

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16

Agbulu, G. Pius, G. Joselin Retna Kumar, and A. Vimala Juliet. "A lifetime-enhancing cooperative data gathering and relaying algorithm for cluster-based wireless sensor networks." International Journal of Distributed Sensor Networks 16, no. 2 (February 2020): 155014771990011. http://dx.doi.org/10.1177/1550147719900111.

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Despite unique energy-saving dispositions of cluster-based routing protocols, clustered wireless sensor networks with static sinks typically have problems of unbalanced energy consumptions, as the cluster head nodes around the sink are typically loaded with traffic from upper levels of clusters. This results in reduced lifetimes of the nodes and deterioration of other crucial performances. Meanwhile, it has been inferred from current literature that dedicated relay cooperation in cluster-based wireless sensor networks guarantees longer lifetime of the nodes and more improved performance. Therefore, to attain further enhanced performance among the current schemes, a lifetime-enhancing cooperative data gathering and relaying algorithm for cluster-based wireless sensor networks is proposed in this article. The proposed lifetime-enhancing cooperative data gathering and relaying algorithm shares the nodes into clusters using a hybrid K-means clustering algorithm that combines K-means clustering and Huffman coding algorithms. It makes full use of dedicated relay cooperative multi-hop communication with network coding mechanisms to achieve reduced data propagation cost from the various cluster sections to the central base station. The relay node selection is framed as a NP-hard problem, with regard to communication distances and residual energy metrics. Furthermore, to resolve the problem, a gradient descent algorithm is proposed. Simulation results endorse the proposed scheme to outperform related schemes in terms of latency, lifetime, and energy consumption and delivery rates.
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17

Fabrice Diédié, Gokou Hervé, Boko Aka, and Michel Babri. "Energy-Efficient Ant Colony-Based k-Hop Clustering and Transmission Range Assignment Protocol for Connectivity Construction in Dense Wireless Sensor Networks." Journal of Computer Science 14, no. 3 (March 1, 2018): 376–95. http://dx.doi.org/10.3844/jcssp.2018.376.395.

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18

Mukase, Sandrine, Kewen Xia, and Abubakar Umar. "Optimal Base Station Location for Network Lifetime Maximization in Wireless Sensor Network." Electronics 10, no. 22 (November 11, 2021): 2760. http://dx.doi.org/10.3390/electronics10222760.

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Wireless sensor networks have attracted worldwide attention in recent years. The failure of the nodes is caused by unequal energy dissipation. The reasons that cause unequal energy dissipation are, first and foremost, the distance between the nodes and the base station, and secondly, the distance between the nodes themselves. In wireless sensor networks, the location of the base station has a substantial impact on the network’s lifetime effectiveness. An improved genetic algorithm based on the crossover elitist conservation genetic algorithm (CECGA) is proposed to optimize the base station location, while for clustering, the K-medoids clustering (KMC) algorithm is used to determine optimal medoids among sensor nodes for choosing the appropriate cluster head. The idea is to decrease the communication distance between nodes and the cluster heads as well as the distance among nodes. For data routing, a multi-hop technique is used to transmit data from the nodes to the cluster head. Implementing an evolutionary algorithm for this optimization problem simplifies the problem with improved computational efficiency. The simulation results prove that the proposed algorithm performed better than compared algorithms by reducing the energy use of the network, which results in increasing the lifetime of the nodes, thereby improving the whole network.
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19

Yang, Bo, Xiangyu Bai, and Changxing Zhang. "Data Collection Method of Energy Adaptive Distributed Wireless Sensor Networks Based on UAV." Wireless Communications and Mobile Computing 2022 (June 28, 2022): 1–19. http://dx.doi.org/10.1155/2022/3469221.

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In sensor networks, UAVs are often introduced to assist data collection tasks. UAVs can operate as data ferry nodes, connecting distributed areas that are separated from each other. This paper proposes a data collection method for distributed wireless sensor networks based on UAV and introduces the idea of edge computing in it. In the single-hop transmission scenario, the K -means++ clustering method is used for sensor node clustering and cluster head election in the initial state. In the next rounds of data collection, UAV is used to assist in the election of new cluster heads and data collection tasks, taking into account the relative distance and the relative remaining energy relationship of the sensor nodes in their clusters. In addition, reasonable priorities are set for some nodes that have never been elected in the previous rounds and for the dead nodes. In the multihop transmission scenarios, for nodes that cannot deliver directly, the optimal relay node is selected for routing by comprehensively considering factors such as transmission angle, transmission distance, and remaining energy of the node in each cluster. The method proposed in this paper coordinates the overall energy consumption of sensor nodes in the environmental monitoring area, delays the death time of key sensor nodes, and extends the network lifetime. At the same time, an improved ACO is used to reasonably plan the data collection path of the UAV. Compared with the comparison scheme, the improved ACO can obtain a better shortest path length and has the fastest convergence speed when reaching the shortest path.
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20

Sekulić, Dimitrije, Branko Karadžić, Nevena Kuzmanović, Snežana Jarić, Miroslava Mitrović, and Pavle Pavlović. "Diversity of Ostrya carpinifolia Forests in Ravine Habitats of Serbia (S-E Europe)." Diversity 13, no. 2 (February 3, 2021): 59. http://dx.doi.org/10.3390/d13020059.

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We investigated vegetation in ravine habitats of Serbia, in order to classify hop hornbeam (Ostrya carpinifolia Scop.) forests in syntaxonomic terms, assess the effects of environmental factors on their floristic differentiation, and detect the biodiversity components of the analyzed communities. Both K-means clustering and Bayesian classification revealed five ecologically interpretable groups of forests that belong to the alliances Ostryo carpinifoliae-Fagion sylvaticae, Ostryo carpinifoliae-Tilion platyphylli, Fraxino orni-Ostryion carpinifoliae, Pseudofumario albae-Ostryion carpinifoliae, and Achilleo ageratifoliae-Ostryion carpinifoliae. Canonical correspondence analysis indicated that these alliances are clearly differentiated along a combined light–moisture gradient (from shade and mesic to sunny and xeric variants). The alpha diversity increases from xeric to mesic alliances. A lower alpha diversity in xeric forests may be explained by the stress conditions that prevent mesic species from colonizing the saxatile habitats. Extremely high—almost the greatest possible—values of both the species turnover and beta diversity were detected in all variants of the analyzed forests. Such high diversity may be the result of the strong environmental gradients in ravine habitats. The investigated forests represent an important pool of rare, paleo-endemic species that survived Quaternary glaciations in ravine refugia.
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21

Hu, Zhangxiang, Xiaodan Jiang, Xiajun Ding, Kai Fang, and Xiaolong Zhou. "A High-Performance Energy-Balanced Forwarding Strategy for Wireless Sensor Networks." Mobile Information Systems 2022 (May 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/3058499.

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The wireless sensor network (WSN) is composed of several sensor nodes organized by multi-hop self-organization, which is a typical network for the industrial internet in industrial application. However, the energy using and processing capacity of each node are greatly limited. Therefore, it is of great significance to study energy-saving and efficient communication protocols for WSN. To prolong the lifetime of WSN and improve network throughput, a high throughput routing protocol with balanced energy consumption is proposed. The designed protocol first employs the K-means clustering algorithm to cluster the nodes, then calculates the weights based on the residual energy of and distance between the nodes, and finally selects the best node as the cluster head. Moreover, the optimal size of the package is determined by the parameters of the wireless transceiver and the channel conditions. In the data transmission stage, the Dijkstra algorithm is used to calculate the multi-objective weight function as the link cost. Experimental results demonstrate the superior performance of the proposed protocol over the CERP and TEEN routing protocols in terms of energy saving of network nodes, so as to improve the throughput and survival time of the entire system.
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22

Khan, Asfandyar, Arif Iqbal Umar, Arslan Munir, Syed Hamad Shirazi, Muazzam A. Khan, and Muhammad Adnan. "A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids." Energies 14, no. 23 (December 6, 2021): 8171. http://dx.doi.org/10.3390/en14238171.

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The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.
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23

Liu, Fanpyn. "Majority Decision Aggregation with Binarized Data in Wireless Sensor Networks." Symmetry 13, no. 9 (September 10, 2021): 1671. http://dx.doi.org/10.3390/sym13091671.

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Wireless sensor networks (WSNs) are the cornerstone of the current Internet of Things era. They have limited resources and features, a smaller packet size than other types of networks, and dynamic multi-hop transmission. WSNs can monitor a particular area of interest and are used in many different applications. For example, during the COVID-19 pandemic, WSNs have been used to measure social distancing/contact tracing among people. However, the major challenge faced by WSN protocols is limited battery energy. Therefore, the whole WSN area is divided into odd clusters using k-means++ clustering to make a majority rule decision to reduce the amount of additional data sent to the base station (or sink) and achieve node energy-saving efficiency. This study proposes an energy-efficient binarized data aggregation (EEBDA) scheme, by which, through a threshold value, the collected sensing data are asserted with binary values. Subsequently, the corresponding cluster head (CH), according to the Hamming weight and the final majority decision, is calculated and sent to the base station (BS). The EEBDA is based on each cluster and divides the entire WSN area into four quadrants. All CHs construct a data-relay transmission link in the same quadrant; the binary value is transferred from the CHs to the sink. The EEBDA adopts a CH rotation scheme to aggregate the data based on the majority results in the cluster. The simulation results demonstrate that the EEBDA can reduce redundant data transmissions, average the energy consumption of nodes in the cluster, and obtain a better network lifetime when compared to the LEACH, LEACH-C, and DEEC algorithms.
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Ri, Yong Ae, Chol Ryong Kang, Kuk Hyon Kim, Yong Myong Choe, and Un Chol Han. "A New Method to Determine Cluster Number Without Clustering for Every K Based on Ratio of Variance to Range in K-Means." Mathematical Problems in Engineering 2022 (July 15, 2022): 1–14. http://dx.doi.org/10.1155/2022/6866747.

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In many clustering algorithms such as K-means and FCM, the cluster number K needs to be known beforehand. In this paper, we propose a new method to determine the cluster number without clustering for every K in K-means. We introduce a new statistics RVR (ratio of variance to range) and conduct Monte Carlo analysis of its characteristics. Based on the RVR, we propose an algorithm to determine the cluster number K and perform clustering utilizing it. We evaluate its effectiveness by performing a simulation test with different types of datasets; first, with real datasets, whose real number of clusters and components are known and second, with synthetic datasets. We observe a significant improvement in speed and quality of determining the cluster number and therefore clustering. Finally, we hope the proposed algorithm to be used efficiently and widely for clustering of multidimensional data.
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25

Li, Yiping, Xiangbing Zhou, Jiangang Gu, Ke Guo, and Wu Deng. "A Novel K-Means Clustering Method for Locating Urban Hotspots Based on Hybrid Heuristic Initialization." Applied Sciences 12, no. 16 (August 11, 2022): 8047. http://dx.doi.org/10.3390/app12168047.

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With rapid economic and demographic growth, traffic conditions in medium and large cities are becoming extremely congested. Numerous metropolitan management organizations hope to promote the coordination of traffic and urban development by formulating and improving traffic development strategies. The effectiveness of these solutions depends largely on an accurate assessment of the distribution of urban hotspots (centers of traffic activity). In recent years, many scholars have employed the K-Means clustering technique to identify urban hotspots, believing it to be efficient. K-means clustering is a sort of iterative clustering analysis. When the data dimensionality is large and the sample size is enormous, the K-Means clustering algorithm is sensitive to the initial clustering centers. To mitigate the problem, a hybrid heuristic "fuzzy system-particle swarm-genetic" algorithm, named FPSO-GAK, is employed to obtain better initial clustering centers for the K-Means clustering algorithm. The clustering results are evaluated and analyzed using three-cluster evaluation indexes (SC, SP and SSE) and two-cluster similarity indexes (CI and CSI). A taxi GPS dataset and a multi-source dataset were employed to test and validate the effectiveness of the proposed algorithm in comparison to the Random Swap clustering algorithm (RS), Genetic K-means algorithm (GAK), Particle Swarm Optimization (PSO) based K-Means, PSO based constraint K-Means, PSO based Weighted K-Means, PSO-GA based K-Means and K-Means++ algorithms. The comparison findings demonstrate that the proposed algorithm can achieve better clustering results, as well as successfully acquire urban hotspots.
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26

Ananda, Ridho, and Agi Prasetiadi. "Hierarchical and K-means Clustering in the Line Drawing Data Shape Using Procrustes Analysis." JOIV : International Journal on Informatics Visualization 5, no. 3 (September 23, 2021): 306. http://dx.doi.org/10.30630/joiv.5.3.532.

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One of the problems in the clustering process is that the objects under inquiry are multivariate measures containing geometrical information that requires shape clustering. Because Procrustes is a technique to obtaining the similarity measure of two shapes, it can become the solution. Therefore, this paper tried to use Procrustes as the main process in the clustering method. Several algorithms proposed for the shape clustering process using Procrustes were namely hierarchical the goodness-of-fit of Procrustes (HGoFP), k-means the goodness-of-fit of Procrustes (KMGoFP), hierarchical ordinary Procrustes analysis (HOPA), and k-means ordinary Procrustes analysis (KMOPA). Those algorithms were evaluated using Rand index, Jaccard index, F-measure, and Purity. Data used was the line drawing dataset that consisted of 180 drawings classified into six clusters. The results showed that the HGoFP, KMGoFP, HOPA and KMOPA algorithms were good enough in Rand index, F-measure, and Purity with 0.697 as a minimum value. Meanwhile, the good clustering results in the Jaccard index were only the HGoFP, KMGoFP, and HOPA algorithms with 0.561 as a minimum value. KMGoFP has the worst result in the Jaccard index that is about 0.300. In the time complexity, the fastest algorithm is the HGoFP algorithm; the time complexity is 4.733. Based on the results, the algorithms proposed in this paper particularly deserve to be proposed as new algorithms to cluster the objects in the line drawing dataset. Then, the HGoFP is suggested clustering the objects in the dataset used.
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27

Benmammar, Badr, Mohammed Housseyn Taleb, and Francine Krief. "Diffusing-CRN k-means: an improved k-means clustering algorithm applied in cognitive radio ad hoc networks." Wireless Networks 23, no. 6 (April 5, 2016): 1849–61. http://dx.doi.org/10.1007/s11276-016-1257-4.

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28

Abdulrazzak, Hazem Noori, Goh Chin Hock, Nurul Asyikin Mohamed Radzi, Nadia M. L. Tan, and Chiew Foong Kwong. "Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network." Mathematics 10, no. 24 (December 12, 2022): 4720. http://dx.doi.org/10.3390/math10244720.

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Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively.
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Muhariya, Ahmad, Bebas Widada, and Sri Siswanti. "Monitoring Program Keluarga Harapan Berbasis Mobile GIS Menggunakan K-Means Clustering." Techno.Com 20, no. 4 (November 22, 2021): 468–77. http://dx.doi.org/10.33633/tc.v20i4.4463.

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Poverty is a condition that is below the line of minimum requirement standard values, both for food and non-food. The Government of Indonesia has various programs to overcome poverty-based assistance social, including the family hope program. This family hope program is the provision of conditional cash assistance to very poor households in which there are pregnant women, toddlers, elementary, junior high, high school, elderly, and severe disabilities. The amount of assistance obtained based on the level of family poverty with poverty level parameters is seen from the many categories of very poor households concerned along with the obligation of participants to carry out important commitments in the field of Health and Education. The purpose of this research is the development of a mobile-based poor family monitoring application using the k-means clustering method. Validity test results using sample data 21, it can be concluded that the system can group poor families into 7 clusters with a thoroughness rate of 90.4%. Based on these results, K-Means Clustering can be said to have a high accuracy value for clustering poor families.
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Cai, Y., Li Ma, and Gang Liu. "A Night-time Anomaly Detection System of Hog Activities Based on Passive Infrared Detector." Applied Engineering in Agriculture 35, no. 4 (2019): 481–93. http://dx.doi.org/10.13031/aea.13007.

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Abstract. The amount of daily activity can be used as important data for the analysis and evaluation of the health, diseases, and environmental conditions of hog farms, which in turn can affect fertility rate and productivity. In this article, a monitoring system based on a passive infrared detector (PID) is proposed to analyze daily hog activity and abnormal behaviors. The hardware includes a high-accuracy acquisition system, which uses a 24-bit ADS1256 chip as its A/D conversion and signal input channel, and a PID, which ensures that the signal can be obtained uninterruptedly day and night. Based on the LabVIEW software platform, a real-time data acquisition, display, and storage system was programmed in which the activity curve can be displayed, and the system parameters can be modified if necessary. A simulation experiment was performed in a test laboratory (7 × 17 m) with a larger size than a typical hog room (7 × 15 m), and the appropriate orientation of the sensor, the installed position, and the lens were selected. Data for 90 days (day and night) were collected in a hog room to establish the model of daily activity. To find the abnormal behaviors during the night, an improved K-means clustering was constructed. The results indicated that the improved K-means clustering method performed satisfactorily in clustering and anomaly detection. The developed system for daily activities monitoring and night-time anomaly detection could be a potential technique to assist research in hog behavior detection and animal welfare improvement. Keywords: Animal activity, Hog, Motion sensor, PID, Signal processing.
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Sivagurunathan, S., V. Mohan, and P. Subathra. "Distributed Trust Based Authentication Scheme in a Clustered Environment Using Threshold Cryptography for Vehicular Ad Hoc Network." International Journal of Business Data Communications and Networking 6, no. 2 (April 2010): 1–18. http://dx.doi.org/10.4018/jbdcn.2010040101.

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A Vehicular Ad-Hoc Network, or VANET, is a form of Mobile Ad-Hoc Network to provide communications among nearby vehicles and between vehicles and nearby fixed equipments. Security has become a prime concern in providing communication between these vehicles. Unlike wired networks, the characteristics of Vehicular Ad Hoc Networks (VANETs) pose a number of non-trivial challenges to security design. In this paper, the authors present a threshold security mechanism with a mobility based Clustering for Open Inter Vehicle Communication Networks (COIN). Nodes that have a similar moving pattern are grouped into a cluster, and unlike other clustering algorithms, it takes the moving pattern of the vehicles into consideration with the driver’s intention. The stability of clusters is estimated based on relative mobility of cluster members. A threshold cryptographic scheme is employed on top of the clusters to protect routing information and data traffic. To ensure distributed trust in the clustered environment, the private key (k) is divided into n pieces in such a way that k is easily reconstructable from any p number of pieces.
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32

Kiley, Matthew R., and Md Shafaeat Hossain. "Who are My Family Members? A Solution Based on Image Processing and Machine Learning." International Journal of Image and Graphics 20, no. 04 (October 2020): 2050033. http://dx.doi.org/10.1142/s0219467820500333.

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Image creation and retention are growing at an exponential rate. Individuals produce more images today than ever in history and often these images contain family. In this paper, we develop a framework to detect or identify family in a face image dataset. The ability to identify family in a dataset of images could have a critical impact on finding lost and vulnerable children, identifying terror suspects, social media interactions, and other practical applications. We evaluated our framework by performing experiments on two facial image datasets, the Y-Face and KinFaceW, comprising 37 and 920 images, respectively. We tested two feature extraction techniques, namely PCA and HOG, and three machine learning algorithms, namely K-Means, agglomerative hierarchical clustering, and K nearest neighbors. We achieved promising results with a maximum detection rate of 94.59% using K-Means, 89.18% with agglomerative clustering, and 77.42% using K-nearest neighbors.
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Hajlaoui, Rejab, Eesa Alsolami, Tarek Moulahi, and Hervé Guyennet. "An adjusted K -medoids clustering algorithm for effective stability in vehicular ad hoc networks." International Journal of Communication Systems 32, no. 12 (June 14, 2019): e3995. http://dx.doi.org/10.1002/dac.3995.

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Chen, Joy Iong-Zong, and Hengjinda P. "Enhanced Dragonfly Algorithm based K-Medoid Clustering Model for VANET." Journal of ISMAC 3, no. 1 (April 7, 2021): 50–59. http://dx.doi.org/10.36548/jismac.2021.1.005.

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A VANET or vehicular Ad Hoc Network is known for its fast topology transition and node mobility, contributing to its attributes as an ad hoc network. The aspect of gathering the nodes, making this system extremely vigorous is known as clustering. However, in certain cases, it is not possible to keep track of the nodes which will results in network issues due to energy insufficiency during execution. Hence this will lead to primary energy management problems faced during the routing protocol which take into consideration the node lifetime. To address this discrepancy, we have proposed a novel optimization technique based on clustering. It has been observed that the proposed methodology will further improve the effectiveness of V2V communication. In this paper, clustering of the vehicle nodes is done using K-Medoid clustering model and are then used to improve energy efficiency. A metaheuristic algorithm is used to establish an energy efficient communication methodology. Based on the simulation analysis performed, it is seen that this methodology requires lesser execution time and improves the nodes’ energy efficiency.
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Suryadibrata, Alethea, and Julio Christian Young. "Visualisasi Algoritma sebagai Sarana Pembelajaran K-Means Clustering." Ultimatics : Jurnal Teknik Informatika 12, no. 1 (July 2, 2020): 25–29. http://dx.doi.org/10.31937/ti.v12i1.1523.

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Algorithm Visualization (AV) is often used in computer science to represents how an algorithm works. Educators believe that visualization can help students to learn difficult algorithms. In this paper, we put our interest in visualizing one of Machine Learning (ML) algorithms. ML algorithms are used in various fields. Some of the algorithms are used to classify, predict, or cluster data. Unfortunately, many students find that ML algorithms are hard to learn since some of these algorithms include complicated mathematical equations. We hope this research can help computer science students to understand K-Means Clustering in an easier way.
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Kumar, Bandani, Makam Subramanyam, and Kodati Prasad. "An Energy Efficient Clustering Using K-Means and AODV Routing Protocol in Ad-hoc Networks." International Journal of Intelligent Engineering and Systems 12, no. 2 (April 30, 2019): 125–34. http://dx.doi.org/10.22266/ijies2019.0430.13.

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37

Anitha, V. S., and M. P. Sebastian. "(k, r)-Dominating set-based, weighted and adaptive clustering algorithms for mobile ad hoc networks." IET Communications 5, no. 13 (September 5, 2011): 1836–53. http://dx.doi.org/10.1049/iet-com.2010.0370.

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38

Pandey, Ankur, Piyush Kumar Shukla, and Ratish Agrawal. "An adaptive Flying Ad-hoc Network (FANET) for disaster response operations to improve quality of service (QoS)." Modern Physics Letters B 34, no. 10 (March 27, 2020): 2050010. http://dx.doi.org/10.1142/s0217984920500104.

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Flying Ad-hoc Networks (FANETs) and Unmanned Aerial Vehicles (UAVs) are widely utilized in various rescues, disaster management and military operations nowadays. The limited battery power and high mobility of UAVs create problems like small flight duration and unproductive routing. In this paper, these problems will be reduced by using efficient hybrid K-Means-Fruit Fly Optimization Clustering Algorithm (KFFOCA). The performance and efficiency of K-Means clustering is improved by utilizing the Fruit Fly Optimization Algorithm (FFOA) and the results are analyzed against other optimization techniques like CLPSO, CACONET, GWOCNET and ECRNET on the basis of several performance parameters. The simulation results show that the KFFOCA has obtained better performance than CLPSO, CACONET, GWOCNET and ECRNET based on Packet Delivery Ratio (PDR), throughput, cluster building time, cluster head lifetime, number of clusters, end-to-end delay and consumed energy.
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Shi, Haobin, Renyu Zhang, Gang Sun, and Jialin Chen. "Clustering-based task coordination to search and rescue teamwork of multiple agents." International Journal of Advanced Robotic Systems 16, no. 2 (March 1, 2019): 172988141983115. http://dx.doi.org/10.1177/1729881419831154.

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It is important to have reasonable task coordination and path planning in rescue operations after a large-scale urban disaster. Whereas, there are many problems which can hamper rescue operations, such as communication obstacles, collapsed buildings, and secondary disaster. This article proposes a novel approach named ISODATA-K to achieve the task coordination and execution with heterogeneous ad hoc multi-agent. Inspired by the clustering analysis, ISODATA-K method, which does not require any input and threshold parameters, assigns the rescue agents to every area of the damaged city adaptively and efficiently. When the rescue agents get respective task, the path planning is done by A* algorithm which costs little time to find the relatively short route. The results of experiments demonstrate that the proposed method allows satisfactory rescue operations.
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Farahani, Gholamreza. "Black Hole Attack Detection Using K-Nearest Neighbor Algorithm and Reputation Calculation in Mobile Ad Hoc Networks." Security and Communication Networks 2021 (August 19, 2021): 1–15. http://dx.doi.org/10.1155/2021/8814141.

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The characteristics of the mobile ad hoc network (MANET), such as no need for infrastructure, high speed in setting up the network, and no need for centralized management, have led to the increased popularity and application of this network in various fields. Security is one of the essential aspects of MANETs. Intrusion detection systems (IDSs) are one of the solutions used to ensure security in this network. Clustering-based IDSs are very popular in this network due to their features, such as proper scalability. This paper proposes a new algorithm in MANETs to detect black hole attack using the K-nearest neighbor (KNN) algorithm for clustering and fuzzy inference for selecting the cluster head. With the use of beta distribution and Josang mental logic, the trust of each node will be calculated. According to the reputation and remaining energy, fuzzy inference will select the cluster head. Finally, the trust server checks the destination node. If allowed, it notifies the cluster head; otherwise, it detects the node as a malicious node in the black hole attack in each cluster. The simulation results show that the proposed method has improved the packet loss rate, throughput, packet delivery ratio, total network delay, and normalized routing load parameters compared with recent black hole detection methods.
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41

Bhowmick, Karan. "CLUSTERING ANALYSIS FOR RESIDENTIAL AREAS BASED ON NEIGHBORHOOD AMENITIES." International Journal of Advanced Research 9, no. 01 (January 31, 2021): 957–65. http://dx.doi.org/10.21474/ijar01/12376.

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The use of urban land in cities can be improved and the poor execution of Urban planning is related to the problem of housing. The problem of housing has become acute because of the tremendous increase of urban population and unplanned growth of the cities. Mumbai has a population of 20,411,000 thus it is the target of our analysis project. Affordable housing in Mumbai has become an unfathomable challenge, it one of the most complex probes in this city. About 42% of Mumbais housing comprises slums. With this in mind, our aim is to help the decision of buying houses, by recommending localities with basic amenities. We hope to make the process of scrutinizing residential buildings more streamlined. We also hope to underscore areas with housing potential in this study. We use K-Means Clustering to cluster the different neighborhoods of Mumbai, based on the availability of 31 amenities in the neighborhood. We have used Data from Wikipedia to get the list of neighborhoods in Mumbai, and we use Foursquare API to get a list of amenities in each area of the neighborhood. We then evaluate the model using silhouette score and plot a graph using folium to show the different clusters on the map of Mumbai.
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42

Mohamed Ali, Salam Saad, Ali Hakem Alsaeedi, Dhiah Al-Shammary, Hassan Hakem Alsaeedi, and Hadeel Wajeeh Abid. "Efficient intelligent system for diagnosis pneumonia (SARS-COVID19) in X-Ray images empowered with initial clustering." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (April 1, 2021): 241. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp241-251.

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<span>This paper proposes efficient models to help diagnose respiratory (SARS-COVID19) infections by developing new data descriptors for standard machine learning algorithms using X-Ray images. As COVID-19 is a significantly serious respiratory infection that might lead to losing life, artificial intelligence plays a main role through machine learning algorithms in developing new potential data classification. Data clustering by K-Means is applied in the proposed system advanced to the training process to cluster input records into two clusters with high harmony. Principle Component Analysis PCA, histogram of orientated gradients (HOG) and hybrid PCA and HOG are developed as potential data descriptors. The wrapper model is proposed for detecting the optimal features and applied on both clusters individually. This paper proposes new preprocessed X-Ray images for dataset featurization by PCA and HOG to effectively extract X-Ray image features. The proposed systems have potentially empowered machine learning algorithms to diagnose Pneumonia (SARS-COVID19) with accuracy up to %97.</span>
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43

Pulhani, Yashu, Ankur Singh Kang, and Vishal Sharma. "Performance analysis on self organization based clustering scheme for FANETs using K-means algorithm and firefly optimization." International Journal of Informatics and Communication Technology (IJ-ICT) 11, no. 2 (August 1, 2022): 148. http://dx.doi.org/10.11591/ijict.v11i2.pp148-159.

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<p><span>With the fast-increasing development of wireless communication networks, unmanned aerial vehicle (UAV) has emerged as a flying platform for wireless communication with efficient coverage, capacity, reliability, and its network is called flying ad-hoc network (FANET); which keeps changing its topology due to its dynamic nature, causing inefficient communication, and therefore needs cluster formation. In this paper, we proposed a cluster formation, selection of cluster head and its members, connectivity and transmission with the base station using the K-means algorithm, and choice of an optimized path for transmission using firefly optimization algorithm for efficient communication. Evaluation of performance with experimental results are obtained and compared using the K-means algorithm and firefly optimization algorithm in cluster building time, cluster lifetime, energy consumption, and probability of delivery success. On comparison of firefly optimization algorithm with firefly optimization algorithm, i.e., K-means algorithm results proved than without firefly optimization algorithm, better in terms of cluster building time, energy consumption, cluster lifetime, and also the probability of delivery success.</span></p>
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44

Yang, Xinwei, Tianqi Yu, Zhongyue Chen, Jianfeng Yang, Jianling Hu, and Yingrui Wu. "An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks." Sensors 22, no. 9 (April 22, 2022): 3236. http://dx.doi.org/10.3390/s22093236.

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Flying ad hoc networks (FANETs) have been gradually deployed in diverse application scenarios, ranging from civilian to military. However, the high-speed mobility of unmanned aerial vehicles (UAVs) and dynamically changing topology has led to critical challenges for the stability of communications in FANETs. To overcome the technical challenges, an Improved Weighted and Location-based Clustering (IWLC) scheme is proposed for FANET performance enhancement, under the constraints of network resources. Specifically, a location-based K-means++ clustering algorithm is first developed to set up the initial UAV clusters. Subsequently, a weighted summation-based cluster head selection algorithm is proposed. In the algorithm, the remaining energy ratio, adaptive node degree, relative mobility, and average distance are adopted as the selection criteria, considering the influence of different physical factors. Moreover, an efficient cluster maintenance algorithm is proposed to keep updating the UAV clusters. The simulation results indicate that the proposed IWLC scheme significantly enhances the performance of the packet delivery ratio, network lifetime, cluster head changing ratio, and energy consumption, compared to the benchmark clustering methods in the literature.
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45

Patra, Chiranjib, Samiran Chattopadhyay, Matangini Chattopadhyay, and Parama Bhaumik. "Analysing Topology Control Protocols in Wireless Sensor Network Using Network Evolution Model." International Journal of Distributed Sensor Networks 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/693602.

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In the study of wireless ad hoc and sensor networks, clustering is an important research problem as it aims at maximizing network lifetime and minimizing latency. A large number of algorithms have been devised to compute “good” clusters in a WSN but few papers have tried to characterize these algorithms in an analytical manner. In this paper, we use a local world model to understand and characterize the functioning of three tree based clustering algorithms. In particular, we have chosen simple tree, CDS Rule K, and A3 topology construction protocols. Using our theoretical framework based on a complex network model, we have also tried to quantify some of the observed features of these algorithms such as number of cluster heads and average degree of the resultant graph. The theoretically obtained measures have reasonably matched with measures obtained by simulation studies.
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46

Sindhwani, Manoj, Shippu Sachdeva, Akhil Gupta, Sudeep Tanwar, Fayez Alqahtani, Amr Tolba, and Maria Simona Raboaca. "A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks." Mathematics 11, no. 3 (January 18, 2023): 514. http://dx.doi.org/10.3390/math11030514.

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The Vehicular Ad-hoc Network (VANET) is an innovative technology that allows vehicles to connect with neighboring roadside structures to deliver intelligent transportation applications. To deliver safe communication among vehicles, a reliable routing approach is required. Due to the excessive mobility and frequent variation in network topology, establishing a reliable routing for VANETs takes a lot of work. In VANETs, transmission links are extremely susceptible to interruption; as a result, the routing efficiency of these constantly evolving networks requires special attention. To promote reliable routing in VANETs, we propose a novel context-aware reliable routing protocol that integrates k-means clustering and support vector machine (SVM) in this paper. The k-means clustering divides the routes into two clusters named GOOD and BAD. The cluster with high mean square error (MSE) is labelled as BAD, and the cluster with low MSE is labelled as GOOD. After training the routing data with SVM, the performance of each route from source to target is improved in terms of Packet Delivery Ratio (PDR), throughput, and End to End Delay (E2E). The proposed protocol will achieve improved routing efficiency with these changes.
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Maylawati, Dian Sa'adillah, Tedi Priatna, Hamdan Sugilar, and Muhammad Ali Ramdhani. "Data science for digital culture improvement in higher education using K-means clustering and text analytics." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 4569. http://dx.doi.org/10.11591/ijece.v10i5.pp4569-4580.

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This study aims to investigate the meaningful pattern that can be used to improve digital culture in higher education based on parameters of the technology acceptance model (TAM). The methodology used is the data mining technique with K-means algorithm and text analytics. The experiment using questionnaire data with 2887 respondents in Universitas Islam Negeri (UIN) Sunan Gunung Djati Bandung. The data analysis and clustering result show that the perceived usefulness and behavioral intention to use information systems are above the normal value, while the perceived ease of use and actual system use is quite low. Strengthened with text analytics, this research found that the EDA and K-means result in harmony with the hope or desire of academic society the information system implementation. This research also found how important the socialization and guidance of information systems, especially the new one information system, in order to improve digital culture in higher education.
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Mediayani, Melani, Yudi Wibisono, Lala Septem Riza, and Alejandro Rosales Pérez. "Determining Trending Topics in Twitter with a Data-Streaming Method in R." Indonesian Journal of Science and Technology 4, no. 1 (March 7, 2019): 148. http://dx.doi.org/10.17509/ijost.v4i1.15807.

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Trending topics in Twitter is a collection of certain topics that are widely discussed by users. This study aims to design a model and strategy for finding trending topics from data streams on Twitter. The research approach was carried out in four stages, namely twitter data collection, preprocessing data, data analysis with sequential K-Means clustering and information processing. Sequential K-Means is used because it can receive input data sequentially and the cluster center can be updated. Testing of the model is carried out in three scenarios where each scenario is distinguished between the amount of data, time and parameter values. After that, evaluation of the results of clustering will be done using the Dunn Index method. Trending topics twitter application were created using the R language and produce output in the form of histograms. There are five topics being the trending topics in New York before the new year. The topic of "Times" relates to the presence of a new year's celebration night concert in Times Square. The "Hours" topic deals with the calculation of time and seconds towards 2017. "Eve" and "Party" topics relate to celebrations and the topic "Resolution" relating to hope and change for New Yorkers in in 2017.
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Choukri, Ali, Younes Hamzaoui, Mohammed Amnai, and Youssef Fakhri. "Classification Algorithm Based on Nodes Similarity for MANETs." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 05 (March 14, 2019): 86. http://dx.doi.org/10.3991/ijoe.v15i05.9742.

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This article describes an algorithm of classification by similarity of nodes in a MANET (Clustering). To optimize a network performance without influencing others, we must act only on the cluster structure. Any additional calculation clutters more the system. To overcome this limitation, a strong classification method is needed. The purpose of classification algorithms is the search for an optimal partition. This optimum is obtained iteratively refining an initial pattern randomly selected by reallocating objects around mobile centers. In order to partition the nodes into clusters, we used this technique (iterative reallocation) from the well known k-means algorithm. The algorithm conception is based on the k-means method that we improved and adapted to make it suitable for mobile ad hoc networks. The algorithm is implemented on OLSR giving birth to a new routing protocol: OLSRKmeans.
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Dung, Tran Duc, Delowar Hossain, Shin-ichiro Kaneko, and Genci Capi. "Multifeature Image Indexing for Robot Localization in Textureless Environments." Robotics 8, no. 2 (May 3, 2019): 37. http://dx.doi.org/10.3390/robotics8020037.

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Robot localization is an important task for mobile robot navigation. There are many methods focused on this issue. Some methods are implemented in indoor and outdoor environments. However, robot localization in textureless environments is still a challenging task. This is because in these environments, the scene appears the same in almost every position. In this work, we propose a method that can localize robots in textureless environments. We use Histogram of Oriented Gradients (HOG) and Speeded Up Robust Feature (SURF) descriptors together with Depth information to form a Depth-HOG-SURF multifeature descriptor, which is later used for image matching. K-means clustering is applied to partition the whole feature into groups that are collectively called visual vocabulary. All the images in the database are encoded using the vocabulary. The experimental results show a good performance of the proposed method.
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