Gotowe bibliografie tematyczne / Node selection optimization

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Gotowa bibliografia na temat „Node selection optimization”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 6 lipca 2025

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Artykuły w czasopismach na temat "Node selection optimization"

Cao, Li, Yinggao Yue, and Yong Zhang. "A Data Collection Strategy for Heterogeneous Wireless Sensor Networks Based on Energy Efficiency and Collaborative Optimization." Computational Intelligence and Neuroscience 2021 (September 29, 2021): 1–13. http://dx.doi.org/10.1155/2021/9808449.

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In the clustering routing protocol, prolonging the lifetime of the sensor network depends to a large extent on the rationality of the cluster head node selection. The selection of cluster heads for heterogeneous wireless sensor networks (HWSNs) does not consider the remaining energy of the current nodes and the distribution of nodes, which leads to an imbalance of network energy consumption. A strategy for selecting cluster heads of HWSNs based on the improved sparrow search algorithm- (ISSA-) optimized self-organizing maps (SOM) is proposed. In the stage of cluster head selection, the proposed algorithm establishes a competitive neural network model at the base station and takes the nodes of the competing cluster heads as the input vector. Each input vector includes three elements: the remaining energy of the node, the distance from the node to the base station, and the number of neighbor nodes of the node. The best cluster head is selected through the adaptive learning of the improved competitive neural network. When selecting the cluster head node, comprehensively consider the remaining energy, the distance, and the number of times the node becomes a cluster head and optimize the cluster head node selection strategy to extend the network life cycle. Simulation experiments show that the new algorithm can reduce the energy consumption of the network more effectively than the basic competitive neural network and other algorithms, balance the energy consumption of the network, and further prolong the lifetime of the sensor network.

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Mrs. T. Nivetha, Dr. K. Prabhavathy. "Cluster Based Improved Particle Swarm Optimization for Optimum Cluster Head Election for Energy Efficient Routing in Wireless Sensor Networks." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 5223–37. http://dx.doi.org/10.52783/tjjpt.v44.i4.1877.

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The proposed methodology addresses critical challenges in Wireless Sensor Networks (WSN), focusing on optimizing cluster head and forwarding node selection. Leveraging an enhanced Particle Swarm Algorithm (PSO), the approach prioritizes residual energy and spatial balance in node selection. It efficiently assigns cluster head nodes to ordinary nodes and selects forwarding nodes within clusters. The algorithm incorporates proximity principles to ensure balanced positioning of nodes. Through iterative iterations, the method refines node selections, favoring candidates with higher residual energy and improved spatial distribution. This approach optimizes WSN performance, enhancing data transmission efficiency and network longevity by minimizing energy consumption. Moreover, it reduces communication overhead through piggybacking and ensures dynamic node adaptation for evolving network conditions.

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Zhang, Tao. "Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm." Journal of Electrical and Computer Engineering 2023 (January 13, 2023): 1–11. http://dx.doi.org/10.1155/2023/3748089.

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In recent years, the combination of cognitive radio and collaborative communication has been widely studied and applied because of its ability to increase user throughput and improve spectrum utilization in a flat-fading wireless channel environment. Such cognitive radio networks that use user collaboration to improve channel capacity and spectrum utilization are called collaborative cognitive radio networks. A Nash equilibrium game-based relay node selection algorithm is investigated, which aims to maximize the utility function of primary and cognitive users. Secondly, a Stackelberg game is introduced, which aims to select the better set of nodes to achieve spectrum sharing. Simulation results show that the algorithm proposed in the study maximizes the utility functions of both primary and cognitive users and enables the selection of a better set of nodes for spectrum sharing. Specifically, the Nash equilibrium game-based relay node selection algorithm at c = 0.3 ∗ 10−6 results in better utility values for both PU and CU, and the algorithm enables more CU to access the spectrum so that users can get longer access time. The relay node selection algorithm based on the Stackelberg game demonstrates high feasibility. Under the condition of parameter α = α ∗ , the algorithm can achieve high-quality cooperation, and CU in better positions can be used as relay cooperation nodes. The algorithm can improve the main user utility function by 20%–35%.

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Bharti, Rajendra Kumar, V. Bhoopathy, Parul Bhanarkar, et al. "Routing Path Selection and Data Transmission in Industry-Based Mobile Communications Using Optimization Technique." Wireless Communications and Mobile Computing 2022 (July 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/5431413.

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In a mobile network, nodes are share data packets; sometimes, that packets are totally flooding. The packet dropping node does not easily detect for routing time instance. The node trust level is minimum causing the packet loss; it affects the entire network performance, and it reduces throughput and increases communication overhead. Proposed exhaustive routing path allocation (ERP) technique is applied to select the legitimate node for broadcasting the data packets completely. The attacker nodes of that flooding packets are detected by using the legitimate detector which are present in network environment. The node credence level evaluation algorithm is planned to estimating each and every node authority range, whether the nodes have higher credence level basis efficient packet transmission in wireless nodes; otherwise, nodes have lesser credence level basis in effective packet broadcasting. These higher credence level nodes are assigned for communication process in movable network. It improves the throughput and minimizes the communication overhead. The performance metrics of the parameters are delay, communication overhead, throughput, network lifetime, energy consumption, and packet loss.

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Wang, Ruisong, Gongliang Liu, Wenjing Kang, Bo Li, Ruofei Ma, and Chunsheng Zhu. "Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks." Sensors 18, no. 8 (2018): 2568. http://dx.doi.org/10.3390/s18082568.

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Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total sensor nodes. The conventional compressed sensing scheme is to select some sensor nodes randomly. The network lifetime and the correlation of sensor nodes are not considered. Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance. In this paper, an optimized sensor node selection scheme is given based on Bayesian estimation theory. The advantage of Bayesian estimation is to give the closed-form expression of posterior density function and error covariance matrix. The proposed optimization problem first aims at minimizing the mean square error (MSE) of Bayesian estimation based on a given error covariance matrix. Then, the non-convex optimization problem is transformed as a convex semidefinite programming problem by relaxing the constraints. Finally, the residual energy of each sensor node is taken into account as a constraint in the optimization problem. Simulation results demonstrate that the proposed scheme has better performance than a conventional compressed sensing scheme.

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Parmanand, Parmanand, Sahdev Sahdev, and Anuradha Dwivedi. "Study the optimization of Dijkstra’s Algorithm." Journal of Ravishankar University (PART-B) 37, no. 2 (2024): 255–67. https://doi.org/10.52228/jrub.2024-37-2-18.

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This paper presents an optimized approach to the shortest path problem, a fundamental concern in graph theory, by improving node selection and data storage. The traditional Dijkstra's algorithm is enhanced by introducing a novel node selection strategy that prioritizes nodes with the most significant impact on the shortest path, minimizing redundant calculations and accelerating convergence. Additionally, a compact data storage structure is introduced, reducing memory requirements while maintaining accuracy. This optimized approach offers reduced storage needs, enhanced efficiency, and improved scalability, making it an ideal solution for real-world applications in network optimization, traffic routing, and logistics, enabling faster and more scalable solutions for large-scale graphs and complex networks.

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Kaur, Sandeep, Dr Rajeev Bedi, and Mohit Marwaha. "Optimization of Energy Efficient Advance Leach Protocol." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 5 (2021): 07–16. http://dx.doi.org/10.17762/ijritcc.v9i5.5472.

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In WSNs, the only source to save life for the node is the battery consumption. During communication with other area nodes or sensing activities consumes a lot of power energy in processing the data and transmitting the collected/selected data to the sink. In wireless sensor networks, energy conservation is directly to the network lifetime and energy plays an important role in the cluster head selection. A new threshold has been formulated for cluster head selection, which is based on remaining energy of the sensor node and the distance from the base station. Proposed approach selects the cluster head nearer to base station having maximum remaining energy than any other sensor node in multi-hop communication. The multi hop approach minimizing the inter cluster communication without effecting the data reliability.

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Niu, Haixu, Yonghai Li, Shuaixin Hou, et al. "Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT." Future Internet 17, no. 6 (2025): 253. https://doi.org/10.3390/fi17060253.

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Node localization is a critical challenge in Internet of Things (IoT) applications. The DV-Hop algorithm, which relies on hop counts for localization, assumes that network nodes are uniformly distributed. It estimates actual distances between nodes based on the number of hops. However, in practical IoT networks, node distribution is often non-uniform, leading to complex and irregular topologies that significantly reduce the localization accuracy of the original DV-Hop algorithm. To improve localization performance in non-uniform topologies, we propose an enhanced DV-Hop algorithm using Grey Wolf Optimization (GWO). First, the impact of non-uniform node distribution on hop count and average hop distance is analyzed. A binary Grey Wolf Optimization algorithm (BGWO) is then applied to develop an optimal anchor node selection strategy. This strategy eliminates anchor nodes with high estimation errors and selects a subset of high-quality anchors to improve the localization of unknown nodes. Second, in the multilateration stage, the traditional least square method is replaced by a continuous GWO algorithm to solve the distance equations with higher precision. Simulated experimental results show that the proposed GWO-enhanced DV-Hop algorithm significantly improves localization accuracy in non-uniform topologies.

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R., Saraswathi. "Forward Node Selection Using Particle Swarm Optimization (PSO) for Broadcasting in MANET." Journal of Advanced Research in Dynamical and Control Systems 12, no. 1 (2020): 287–94. http://dx.doi.org/10.5373/jardcs/v12i1/20201042.

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Rathore, Rajkumar Singh, Suman Sangwan, Sukriti Mazumdar, et al. "W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks." Sensors 20, no. 5 (2020): 1377. http://dx.doi.org/10.3390/s20051377.

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Underwater sensor networks (UWSNs) have witnessed significant R&D attention in both academia and industry due to their growing application domains, such as border security, freight via sea or river, natural petroleum production and the fishing industry. Considering the deep underwater-oriented access constraints, energy-centric communication for the lifetime maximization of tiny sensor nodes in UWSNs is one of the key research themes in this domain. Existing literature on green UWSNs are majorly adapted from the existing techniques in traditional wireless sensor network relying on geolocation and the quality of service-centric underwater relay node selection, without paying much attention to the dynamic underwater network environments. To this end, this paper presents an adapted whale and wolf optimization-based energy and delay-centric green underwater networking framework (W-GUN). It focuses on exploiting dynamic underwater network characteristics by effectively utilizing underwater whale-centric optimization in relay node selection. Firstly, an underwater relay node optimization model is mathematically derived, focusing on underwater whale dynamics for incorporating realistic underwater characteristics in networking. Secondly, the optimization model is used to develop an adapted whale and grey wolf optimization algorithm for selecting optimal and stable relay nodes for centric underwater communication paths. Thirdly, a complete workflow of the W-GUN framework is presented with an optimization flowchart. The comparative performance evaluation attests to the benefits of the proposed framework and is compared to state-of-the-art techniques considering various metrics related to underwater network environments.

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Rozprawy doktorskie na temat "Node selection optimization"

Baidas, Mohammed Wael. "Node Selection, Synchronization and Power Allocation in Cooperative Wireless Networks." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/26527.

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Recently, there has been an increasing demand for reliable, robust and high data rate communication systems that can counteract the limitations imposed by the scarcity of two fundamental resources for communications: bandwidth and power. In turn, cooperative communications has emerged as a new communication paradigm in which network nodes share their antennas and transmission resources for distributed data exchange and processing. Recent studies have shown that cooperative communications can achieve significant performance gains in terms of signal reliability, coverage area, and power savings when compared with conventional communication schemes. However, the merits of cooperative communications can only be exploited with efficient resource allocation in terms of bandwidth utilization and power control. Additionally, the limited network resources in wireless environments can lead rational network nodes to be selfish and aim at maximizing their own benefits. Therefore, assuming fully cooperative behaviors such as unconditionally sharing of one's resources to relay for other nodes is unjustified. On the other hand, a particular network node may try to utilize resources from other nodes and also share its own resources so as to improve its own performance, which in turn may prompt other nodes to behave similarly and thus promote cooperation. This dissertation aims to answer the following three questions: ``How can bandwidth-efficient multinode cooperative communications be achieved?'', ``How can optimal power allocation be achieved in a distributed fashion?'', and finally, ``How can network nodes dynamically interact with each other so as to promote cooperation?''. In turn, this dissertation focuses on three main problems of cooperation in ad-hoc wireless networks: (i) optimal node selection in network-coded cooperative communications, (ii) auction-based distributed power allocation in single- and multi-relay cooperative networks, and finally (iii) coalitional game-theoretic analysis and modeling of the dynamic interactions among the network nodes and their coalition formations. Bi-directional relay networks are first studied in a scenario where two source nodes are communicating with each other via a set of intermediate relay nodes. The symbol error rate performance and achievable cooperative diversity orders are studied. Additionally, the effect of timing synchronization errors on the symbol error rate performance is investigated. Moreover, a sum-of-rates maximizing optimal power allocation is proposed. Relay selection is also proposed to improve the total achievable rate and mitigate the effect of timing synchronization errors. Multinode cooperative communications are then studied through the novel concept of many-to-many space-time network coding. The symbol error rate performance under perfect and imperfect timing synchronization and channel state information is theoretically analyzed and the optimal power allocation that maximizes the total network rate is derived. Optimal node selection is also proposed to fully exploit cooperative diversity and mitigate timing offsets and channel estimation errors. Further, this dissertation investigates distributed power allocation for single-relay cooperative networks. The distributed power allocation algorithm is conceived as an ascending-clock auction where multiple source nodes submit their power demands based on an announced relay price and are efficiently allocated cooperative transmit power. It is analytically and numerically shown that the proposed ascending-clock auction-based distributed algorithm leads to efficient power allocation, enforces truth-telling, and maximizes the social welfare. A distributed ascending-clock auction-based power allocation algorithm is also proposed for multi-relay cooperative networks. The proposed algorithm is shown to converge to the unique Walrasian Equilibrium allocation which maximizes the social welfare when source nodes truthfully report their cooperative power demands. The proposed algorithm achieves the same performance as could be achieved by centralized control while eliminating the need for complete channel state information and signaling overheads. Finally, the last part of the dissertation studies altruistic coalition formation and stability in cooperative wireless networks. Specifically, the aim is to study the interaction between network nodes and design a distributed coalition formation algorithm so as to promote cooperation while accounting for cooperation costs. This involves an analysis of coalitions' merge-and-split processes as well as the impact of different cooperative power allocation criteria and mobility on coalition formation and stability. A comparison with centralized power allocation and coalition formation is also considered, where the proposed distributed algorithm is shown to provide reasonable tradeoff between network sum-rate and computational complexity. Ph. D.

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Oz, M. Tufan. "Optimization Of Selection Conditions And Agrobacterium Mediated Transformation Of Chickpea (cicer Arietinum L. Cv. Gokce)." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12605842/index.pdf.

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The objective of this study was to optimize an efficient selection system and Agrobacterium mediated transformation of chickpea (Cicer arietinum L.). Cotyledonary node explants of Turkish chickpea cultivar G&ouml k&ccedil e were used to determine the effects of selective agents, two antibiotics (Kanamycin, Hygromycin) and two herbicides (PPT, Glyphosate) as well as four antibiotics (Augmentin, Carbenicillin, Cefotaxime, Timentin) for eliminating Agrobacterium on multiple shoot and root induction. Selective agents and antibiotics were applied to explants at different concentrations for one month and numbers of regenerated shoots and roots were recorded. Kanamycin at 100 mg/L, Hygromycin at 20 mg/L, PPT at 3 mg/L and Glyphosate at 5 mg/L were found to be appropriate to select chickpea transformants. Lowest concentrations of all selective agents (50 mg/L Kanamycin, 10 mg/L Hygromycin, 3 mg/L PPT, 1 mg/L Glyphosate) totally inhibited rooting of the regenerated shoots. Among the Agrobacterium-eliminating antibiotics, Cefotaxime and Augmentin each up to 600 mg/L had no adverse effect on shoot induction, whereas Timentin (300 mg/L) significantly increased and Carbenicillin (300 mg/L) significantly decreased shoot induction after four weeks of culture. Augmentin was determined to have no effect on rooting capacities of chickpea shoots. However Cefotaxime at all concentrations significantly decreased root induction. On the other hand only high concentrations of Carbenicillin (300 mg/L) and Timentin (200 mg/L) significantly decreased rooting. Sulbactam in combination with Carbenicillin and Cefotaxime displayed effective inhibition of bacterial growth. Furthermore, Agrobacterium mediated transformation procedure for cotyledonary node explants of G&ouml k&ccedil e, was also optimized by monitoring transient uidA expression on 4th, 9th, and 16th days after transformation. Transformation procedure was improved via mechanical injury of axillary region of explants and application of vacuum infiltration at 200 mmHg for 40 minutes.

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Części książek na temat "Node selection optimization"

Li, Xiaohua(Edward). "Hop Optimization and Relay Node Selection in Multi-hop Wireless Ad-Hoc Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02080-3_17.

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Xu, Benlian, and Zhiquan Wang. "Bearings-Only Target Tracking Using Node Selection Based on an Accelerated Ant Colony Optimization." In Computational Intelligence and Security. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596981_129.

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Gao, Yuan, Hong Ao, Weigui Zhou, et al. "A Novel AI Based Optimization of Node Selection and Information Fusion in Cooperative Wireless Networks." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29513-4_2.

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Nithya, K. Jane, and K. Shyamala. "Mean Donkey and Smuggler Optimization (MDSO) Based Cluster Head Selection and Recurrent Neural Network Clone Node Detection (RNNCND) for Wireless Sensor Network (WSN)." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66410-6_12.

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Acevedo, Javier, Saturnino Maldonado, Sergio Lafuente, Hilario Gomez, and Pedro Gil. "Model Selection for Support Vector Machines Using Ant Colony Optimization in an Electronic Nose Application." In Ant Colony Optimization and Swarm Intelligence. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11839088_47.

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Saha, P., S. Ghorai, B. Tudu, R. Bandyopadhyay, and N. Bhattacharyya. "Optimization of Sensor Array in Electronic Nose by Combinational Feature Selection Method." In Sensing Technology: Current Status and Future Trends II. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02315-1_9.

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Khaddour, Lina A., and Siegfried K. Yeboah. "Multiple-Criteria Optimization of Residential Buildings Envelope Toward nZEBs: Simplified Approach for Damascus Post-war." In Springer Proceedings in Energy. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30960-1_21.

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AbstractSyria faces significant challenges in optimizing residential building energy consumption to subsequently reduce CO2 emissions due to its conventional construction methods and systems, exacerbated by the recent conflict. Post-war re-construction provides new opportunities for improvement in building standards through the 2009 BIC insulation code towards nearly Zero Energy Buildings (nZEBs). However, the decline in economy growth poses significant challenges. In this study, we formulate a simplified building envelope selection approach using multi-criterion optimization methodology based on simulated thermal loads using IESVE and cost-energy trade-off. IESVE was used to evaluate the thermal performances of five cases representing 5 different building envelope structures on existing buildings in Damascus, Syria. Four out of the five cases were BIC compliant, and their thermal performances and cost energy trade-offs were evaluated against that of a conventional building representing the construction-as-usual case. Payback on the investment in insulation improvement of the envelope structures were also calculated. The results overall shows that the envelope structures incorporating insulation layer reduced annual heating, cooling, and combined energy loads of those buildings. Comparatively, these improvements were slightly better under winter conditions than in summer. Based on payback period analysis, none of the improvements provided acceptable economical payback within five years, as energy consumption tariffs were extremely low and insulation material costs were extremely high. A Multi-Criteria Decision Making (MCDM) framework was developed and applied to the cases investigated. Based on the limitations of the BIC, no optimal solution was obtained. However, the framework provides a good basis for stakeholders to make sound decisions in transitioning buildings especially under post war context towards nZEBs.

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Lai Feng-Gang, Li Yu-Tai, and Shang Zhi-Jie. "Quantum Inspired Bee Colony Optimization Based Multiple Relay Selection Scheme." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-722-1-312.

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Multiple relay selection schemes for cooperative relaying considering maximizing the end-to-end signal to noise ratio (SNR) and power efficiency are researched in this paper. In cooperative multiple relay networks, the relay nodes which is selected are very important to the system performance. How to choose the best cooperative relay node number and which relay nodes are selected are an optimization problems. The exhaustive search scheme can solve the relay selection problem but the complexity will increase exponentially with the size of network, that is, the number of relays in the network. Two novel quantum bee colony optimization (QBCO) based relay selection schemes which optimize the SNR and power efficiency are proposed in this paper respectively. Simulation results show that the QBCO-based scheme has a much better performance compared with other schemes in literature.

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Chawra, Vrajesh Kumar, and Govind P. Gupta. "Salp." In Advances in Computer and Electrical Engineering. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1626-3.ch003.

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The formation of the unequal clusters of the sensor nodes is a burning research issue in wireless sensor networks (WSN). Energy-hole and non-uniform load assignment are two major issues in most of the existing node clustering schemes. This affects the network lifetime of WSN. Salp optimization-based algorithm is used to solve these problems. The proposed algorithm is used for cluster head selection. The performance of the proposed scheme is compared with the two-node clustering scheme in the term of residual energy, energy consumption, and network lifetime. The results show the proposed scheme outperforms the existing protocols in term of network lifetime under different network configurations.

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Parwekar, Pritee, and Sireesha Rodda. "Optimization of Clustering in Wireless Sensor Networks Using Genetic Algorithm." In Sensor Technology. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch039.

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The energy of a sensor node is a major factor for life of a network in wireless sensor network. The depletion of the sensor energy is dependent on the communication range from the sink. Clustering is mainly used to prolong the life of a network with energy consumption. This paper proposes optimization of clustering using genetic algorithm which will help to minimize the communication distance. The cluster overhead and the active and sleep mode of a sensor is also considered while calculating the fitness function to form the cluster. This approach helps to prolong the network life of sensor network. The proposed work is tested for different number of nodes and is helping to find the correct solution for the selection of cluster heads.

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Streszczenia konferencji na temat "Node selection optimization"

Wang, Xuan, Ruizhe Yang, Enchang Sun, Yihong Liu, Meng Li, and Tonghui Zhao. "Joint Optimization of Node Selection and Resource Allocation for Sharded Blockchain." In 2024 10th International Conference on Computer and Communications (ICCC). IEEE, 2024. https://doi.org/10.1109/iccc62609.2024.10942082.

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Ma, Sixing, Meng Li, Ruizhe Yang, Yang Sun, and Zhuwei Wang. "Intelligent Node Selection for Ad Hoc Networks-Assisted CBTC Systems Based on Proximal Policy Optimization." In 2024 10th International Conference on Computer and Communications (ICCC). IEEE, 2024. https://doi.org/10.1109/iccc62609.2024.10941915.

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Efendi, Yahya, Arinto Yudi Ponco Wardoyo, and Agus Naba. "Optimization of E-Nose Technology Using Learning Methods based on Feature Selection Algorithm." In 2024 IEEE International Conference on Smart Mechatronics (ICSMech). IEEE, 2024. https://doi.org/10.1109/icsmech62936.2024.10812332.

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Duewall, Clinton M., and Mahmoud M. El-Halwagi. "Constraint Formulations for Bayesian Optimization of Process Simulations: General Approach and Application to Post-Combustion Carbon Capture." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.170471.

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Some of the most highly trusted and ubiquitous process simulators have solution methods that are incompatible with algorithms designed for equation-oriented optimization. The natively unconstrained Efficient Global Optimization (EGO) algorithm approximates a black-box simulation with kriging surrogate models to convert the simulation results into a reduced-order model more suitable for optimization. This work evaluates several established constraint-handling approaches for EGO to compare their accuracy, computational efficiency, and reliability using an example simulation of an amine post-combustion carbon capture process. While each approach returned a feasible operating point in the number of iterations provided, none of them effectively converged to a solution, exploring the search space without effectively exploiting promising regions. Using the product of expected improvement and probability of feasibility as next point selection criteria resulted in the best solution value and reliability. Constraining probability of feasibility while solving for the next sample point was the least likely to solve, but the solutions found were most likely to be feasible operating points.

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Sun, Liqun, Yan Cheng, Shibo Wang, Shumin Sun, Xiaoqi Zhang, and Yunhai Lv. "Based on the Bi-level Optimization Framework for Site Selection and Capacity Determination of Renewable Energy Distribution Network Nodes." In 2025 8th International Conference on Energy, Electrical and Power Engineering (CEEPE). IEEE, 2025. https://doi.org/10.1109/ceepe64987.2025.11034061.

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Meng, Xiance, and Mangui Liang. "Node Selection-Based Anonymous Network Performance Optimization Method." In 2024 9th International Conference on Computer and Communication Systems (ICCCS). IEEE, 2024. http://dx.doi.org/10.1109/icccs61882.2024.10602902.

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Liu, Hongyu, Haoyang Liu, Yufei Kuang, Jie Wang, and Bin Li. "Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics." In GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion. ACM, 2024. http://dx.doi.org/10.1145/3638530.3664128.

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Wang Jia-ren, Dong En-qing, Qiao Fu-long, and Zou Zong-jun. "Wireless sensor networks node localization via Leader Intelligent Selection optimization algorithm." In 2013 19th Asia-Pacific Conference on Communications (APCC). IEEE, 2013. http://dx.doi.org/10.1109/apcc.2013.6766033.

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Dongare, Mahendra, Satish Jondhale, and Balasaheb Agarkar. "Energy-Efficient Approach for Node Selection in Routing Protocols of Wireless Sensor Networks." In 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT). IEEE, 2024. http://dx.doi.org/10.1109/icdcot61034.2024.10515331.

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Wang, Yu, Wenbin Zhou, and Fanfan Zhou. "Volunteer Sensor Networks Node Availability Prediction via Stronger Intelligent Selection Optimization Algorithm." In 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA). IEEE, 2014. http://dx.doi.org/10.1109/bwcca.2014.47.

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Raporty organizacyjne na temat "Node selection optimization"

Weller, Joel I., Ignacy Misztal, and Micha Ron. Optimization of methodology for genomic selection of moderate and large dairy cattle populations. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7594404.bard.

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The main objectives of this research was to detect the specific polymorphisms responsible for observed quantitative trait loci and develop optimal strategies for genomic evaluations and selection for moderate (Israel) and large (US) dairy cattle populations. A joint evaluation using all phenotypic, pedigree, and genomic data is the optimal strategy. The specific objectives were: 1) to apply strategies for determination of the causative polymorphisms based on the “a posteriori granddaughter design” (APGD), 2) to develop methods to derive unbiased estimates of gene effects derived from SNP chips analyses, 3) to derive optimal single-stage methods to estimate breeding values of animals based on marker, phenotypic and pedigree data, 4) to extend these methods to multi-trait genetic evaluations and 5) to evaluate the results of long-term genomic selection, as compared to traditional selection. Nearly all of these objectives were met. The major achievements were: The APGD and the modified granddaughter designs were applied to the US Holstein population, and regions harboring segregating quantitative trait loci (QTL) were identified for all economic traits of interest. The APGD was able to find segregating QTL for all the economic traits analyzed, and confidence intervals for QTL location ranged from ~5 to 35 million base pairs. Genomic estimated breeding values (GEBV) for milk production traits in the Israeli Holstein population were computed by the single-step method and compared to results for the two-step method. The single-step method was extended to derive GEBV for multi-parity evaluation. Long-term analysis of genomic selection demonstrated that inclusion of pedigree data from previous generations may result in less accurate GEBV. Major conclusions are: Predictions using single-step genomic best linear unbiased prediction (GBLUP) were the least biased, and that method appears to be the best tool for genomic evaluation of a small population, as it automatically accounts for parental index and allows for inclusion of female genomic information without additional steps. None of the methods applied to the Israeli Holstein population were able to derive GEBV for young bulls that were significantly better than parent averages. Thus we confirm previous studies that the main limiting factor for the accuracy of GEBV is the number of bulls with genotypes and progeny tests. Although 36 of the grandsires included in the APGD were genotyped for the BovineHDBeadChip, which includes 777,000 SNPs, we were not able to determine the causative polymorphism for any of the detected QTL. The number of valid unique markers on the BovineHDBeadChip is not sufficient for a reasonable probability to find the causative polymorphisms. Complete resequencing of the genome of approximately 50 bulls will be required, but this could not be accomplished within the framework of the current project due to funding constraints. Inclusion of pedigree data from older generations in the derivation of GEBV may result is less accurate evaluations.

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