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Journal articles on the topic 'Node selection optimization'
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1
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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S., J. Patil, Admuthe L.S., and R. Patil M. "CLBNSRM - CONFIDENCE LEVEL BASED UNBLEND NEIGHBOR SELECTION &BLEND NODE REPORT BASED OPTIMIZED ROUTE FORMATION IN MANET." International Journal of Computer Networks & Communications (IJCNC) 12, no. 2 (2020): 109–29. https://doi.org/10.5281/zenodo.3837058.
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A mobile Ad-hoc network (MANET) is an impulsive network that can be recognized with no predetermined infrastructure. To achieve safe path selection cryptographic key exchange was implemented mostly in turn of huge computational cost. Confidence based coordination in MANET focuses on routing challenges created by selfish nodes, as energy utilization & time factor are key issues in this aspect. The present protocol is focused on fuzzy optimization-based node confidence estimation and path selection with minimum energy utilization. The node with maximum confidence value will give high priority to include in the path for transmission. In the implemented protocol to build a novel confidence-based model multidimensional factors like confidence value, link cost, degree of node and node energy are included as decision-making factors. The proposed protocol CLBNSRM estimates confidence level in four steps to decide a trustworthiness of neighboring node. To estimate the efficiency of the present confidence model various protocols are compared by using attributes like the number of nodes, node speed, malicious node variation, etc. Moreover, different parameters like Packet delivery ratio, Throughput, Residual energy, and Packet dropped are considered with these attribute variations. Experimental results indicate that PDR and Throughput increase although in presence of malicious nodes, along with the utilization of minimal energy. Statistical analysis is carried out for mathematical modeling. This analysis shows that a linear model of an implemented protocol is better than compared protocol with all the aspects.
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Shaheen, Aaina, and Poonam Ghai. "An Energy Efficient Clustering Protocol Using CM-YSGA Optimization Approach in WSN." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 1705–12. http://dx.doi.org/10.22214/ijraset.2022.42635.
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Abstract: While dealing with the wireless sensor networks (WSNs), one of the greatest factors that must be taken into consideration is energy consumption of nodes. One of the most effective common way of preserving energy in sensor nodes is clustering technique in which CH selection is of great importance. In this manuscript, an improved energy efficient clustering protocol is proposed in which chaotic mapping algorithm is clubbed along with the advanced variant of Yellow Saddle Goatfish Algorithm (YSGA). The main objective of the proposed model is to reduce the energy consumption of nodes which ultimately prolongs the lifespan of wireless network. Initially, cluster are formed in the network by selecting the nodes randomly. After this, four important parameters which included, residual energy, distance to sink, average distance to CH neighbor node and delay are taken into consideration and on the basis of these factors’ fitness value is calculated by the chaotic-YSGA technique. The node whose fitness value came out to be highest of all other nodes, is selected as the CH in that particular cluster. In addition, the Huffman data compression technique is used in the proposed system to reduce energy consumption in nodes. Finally, the performance of the proposed Chaotic-YSGA model is analyzed and compared with conventional energy efficient models in MATLAB software in terms of alive node, dead nodes, throughput, residual energy, FND, HND and LND. Keywords: WSN, Energy Efficiency, CH selection, Optimization Algorithms.
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Kurnosov, M., and E. Tokmasheva. "Barrier Optimization on Asymmetrical NUMA Subsystems." Herald of the Siberian State University of Telecommunications and Informatics, no. 1 (March 18, 2021): 36–49. http://dx.doi.org/10.55648/1998-6920-2021-15-1-36-49.
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Algorithm MinNumaDist for barrier’s root selection is proposed. A root process allocates memory pages for shared counters and flags from its NUMA node. Total distance is minimized to all NUMA nodes (closeness centrality) by the algorithm. MinNumaDist reduces barrier’s time by 1035% for asymmetrical NUMA subsystems - for different number of processes on NUMA nodes or different number of NUMA nodes used from each socket.
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Ma, Chang Wei. "Coverage Optimization Mechanism Based on Weighted Genetic Algorithm and Constrained Genetic Algorithm." Applied Mechanics and Materials 416-417 (September 2013): 1574–79. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1574.
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As a basic problem of wireless sensor in network applications, coverage reflects the quality effect of monitoring and tracking of the network. In view of the high density of sensor nodes deployment, this article works on the node set selection problem, puts forward two kinds of coverage optimization mechanisms based on weighted genetic algorithm and constrained genetic algorithm according to the genetic algorithm operation of the fitness function generation, and calculates approximately optimal working nodes required in the sensor network fully covering area. The simulation results show that the algorithm can quickly converge to the optimal solution and complete the optimization of node set selection, thereby reducing network redundancy and prolonging the survival time of the network.
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K B, Manikandan. "SWARM OPTIMIZATION BASED IMPOSTER NODES AND RESOURCE LIMITATION AWARE NODE FAILURE DETECTION." ICTACT Journal on Communication Technology 11, no. 2 (2020): 2163–71. https://doi.org/10.21917/ijct.2020.0319.
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This research work studies about node failures, which can be prevented pretty good by providing the necessary resources rather than by establishing a route path again. This is done by clustering the mobile nodes in accordance with the on node significance level like it is done in the earlier work and the resources among the cluster members are shared with one another to guarantee that sufficient resources are made available. The cluster is established using the Fuzzy K-means clustering technique. The cluster head is accountable for selecting those clusters members, which can share their resources with one another whereas in this technical work, the cluster head selection is carried out with the help of Cuckoo Search based Hill climbing algorithm (CS-HC). Also, to prevent the wrong information on the node failure being spread by the imposter nodes acting as credible neighbour nodes, this work presents the Imposter Node Detection algorithm. This technique guarantees the optimum detection and suppression of node failures occurring in the mobile wireless networks by presenting effective methodologies. The overall process of realization of the proposed research approach is carried out in the NS2 simulation environment, which shows that the proposed technique ensures improved performance compared to the available research approaches.
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Rai, Ashok Kumar, Lalit Kumar Tyagi, Anoop Kumar, Swapnita Srivastava, and Naushen Fatima. "Enhancing Energy Efficiency in Cluster Based WSN using Grey Wolf Optimization." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 12, no. 1 (2023): e30632. http://dx.doi.org/10.14201/adcaij.30632.
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Wireless sensor networks (WSNs) are typically made up of small, low-power sensor nodes (SNs) equipped with capability for wireless communication, processing, and sensing. These nodes collaborate with each other to form a self-organizing network. They can collect data from their surrounding environment, such as temperature, humidity, light intensity, or motion, and transmit it to a central base station (BS) or gateway for additional processing and analysis. LEACH and TSEP are examples of cluster-based protocols developed for WSNs. These protocols require careful design and optimization of CH selection algorithms, considering factors such as energy consumption, network scalability, data aggregation, load balancing, fault tolerance, and adaptability to dynamic network conditions. Various research efforts have been made to develop efficient CH selection algorithms in WSNs, considering these challenges and trade-offs. In this paper, the Grey Wolf Optimization (GWO) algorithm is employed to address the problem of selecting CHs (CHs) in WSNs. The proposed approach takes into account two parameters: Residual Energy (RE) and the distance of node (DS)s from the BS. By visualizing and analyzing the GWO algorithm under variable parameters in WSNs, this research identifies the most appropriate node from all normal nodes for CH selection. The experimental results demonstrate that the proposed model, utilizing GWO, outperforms other approaches in terms of performance.
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Shashidhar, P. K., T. C. Thanuja, and Rajashekar Kunabeva. "Adaptive RPL Routing Optimization Model for Multimedia Data Transmission using IOT." Indian Journal Of Science And Technology 17, no. 5 (2024): 436–50. http://dx.doi.org/10.17485/ijst/v17i5.2627.
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Objectives: The main objectives of this research endeavor encompass the development of the Adaptive RPL Optimization (ARPLO) model to enhance data transmission efficiency within IoT networks. This includes constructing a grid-based network structure optimized for data transfer, selecting the most suitable nodes as grid head nodes to maximize network lifespan while minimizing energy consumption, implementing an innovative objective function-driven approach to optimize parent node selection, and integrating an Adaptive Deep Neural Network (ADNN) to accurately classify medical data. Methods: The research methodology entails several key steps. A grid-based network structure is established with IoT nodes and root nodes, where a DODAG process incorporating DIO messages is utilized for node ranking. To enhance energy efficiency, the Trickle algorithm is employed for control message optimization. Grid head nodes are chosen based on metrics such as root node fairness, residual energy, and load influence index. The novel Middle Order Optimal Routing (MOOR) objective function is utilized to optimize routing decisions. ADNN is implemented for precise medical data classification. The proposed model's performance is evaluated through simulation in a Python environment. Findings: The research findings demonstrate that the ARPLO model yields notable benefits compared to existing models. It achieves higher energy efficiency, improved throughput, enhanced packet delivery ratio (PDR), and an extended network lifespan. The Trickle algorithm contributes to efficient control message optimization. The MOOR-based routing approach improves multimedia medical data transfer. Moreover, the integration of ADNN enhances the accuracy of data classification, particularly in healthcare applications. The research outcomes align with the broader field's existing values and reports while offering novel insights that contribute to enhancing the existing knowledge base. ARPLO protocol performance reveals that there is increase of throughput of 31.2%, PDR by 7.12%, lifetime of 10.7 % with reduction of energy consumption by 12.72%, control overhead by 31.01% and end-to-end delay by 33.01%. Novelty: The novelty of this research lies in its comprehensive approach that integrates a grid-based network structure, MOOR-based optimization, and ADNN-based classification. The incorporation of the Trickle algorithm for energy-efficient communication is an innovative feature. The introduction of new metrics for grid head node selection, along with the application of the MOOR objective function for multimedia medical data routing, showcases the research's innovative contributions. Keywords: Internet of Things (IoT), RPL (Routing Protocol for LowPower and Lossy Networks), Optimization, Routing, Multimedia, Healthcare
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Hu, Aihua, Zhongliang Deng, Jianke Li, Yao Zhang, Yuhui Gao, and Di Zhao. "Optimization Selection Method of Post-Disaster Wireless Location Detection Equipment Based on Semi-Definite Programming." Electronics 11, no. 14 (2022): 2170. http://dx.doi.org/10.3390/electronics11142170.
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Signal propagation attenuation is greater in the post-disaster collapsed environment than that it is indoor or outdoor. The transmission environment is seriously affected by multi-path and non-line-of-sight transmission. When the signals penetrate the ruins and reach the receiver, their power may become very weak, which greatly affects the success rate of signal acquisition by the receiver. In the post-disaster environment, wireless signal propagation is severely blocked, which leads to serious signal attenuation and non-line-of-sight propagation, and signal acquisition distance and direction of detection equipment are limited. An optimization method of post-disaster wireless positioning detection equipment based on semi-deterministic programming was proposed, which allowed us to construct a location model of multiple detection equipment. The decision variable with variable nodes was generated. The reference node and penalty function were determined by fast rough positioning of the target. The node selection algorithm based on semi-definite programming was used to find the optimal node combination and complete the precise location. The performance of this algorithm was better than other known SDP optimization algorithms for reference nodes, and the correct selection accuracy of locating nodes was higher than 90%. Compared with the fixed main nodes, the positioning accuracy of the optimization algorithm was improved by 15.8%.
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Venkatasubramanian, S., A. Suhasini, and C. Vennila. "Cluster Head Selection and Optimal Multipath detection using Coral Reef Optimization in MANET Environment." International Journal of Computer Network and Information Security 14, no. 3 (2022): 88–99. http://dx.doi.org/10.5815/ijcnis.2022.03.07.
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Mobile Ad-hoc Network (MANET) data transfer between nodes in a multi-hop way offers a wide variety of applications. The dynamic feature of ad hoc network mobile nodes is primarily influenced by safety issues, which limit data forwarding rate in multipath routing. As a supplementary method to improve safe data delivery in a MANET, this paper propose and analyse the cluster head (CH) selection and optimum multipath scheme. The CHs are chosen based on the possibility values of each node in MANET, which are considered from the residual energy of each node. During the present phase, the total remaining node energy is used to calculate the mean energy of the entire network. The most likely nodes are picked as the CH, which gathers packets from the cluster members through multi-hop communication. The fundamental idea is to partition a top-secret communication into several shares and then forward the shares via numerous routes to the destination. The Coral Reef Optimization method is used in this work to perform optimum multipath routing. The thorough simulation findings validate the feasibility and efficacy of the suggested strategy in comparison to Butterfly optimization algorithm (BA), Whale Optimization algorithm (WOA) and BAT algorithm techniques.
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Yamada, Keigo, Yasuo Sasaki, Takayuki Nagata, Kumi Nakai, Daisuke Tsubakino, and Taku Nonomura. "Efficient Sensor Node Selection for Observability Gramian Optimization." Sensors 23, no. 13 (2023): 5961. http://dx.doi.org/10.3390/s23135961.
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Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-invariant, and discrete-time dynamical system are examined under the assumption of independent and identically distributed measurement noise. This study offers two novel selection algorithms, namely an approximate convex relaxation method with the Newton method and a gradient greedy method, and confirms the performance of the selection methods, including a convex relaxation method with semidefinite programming (SDP) and a pure greedy optimization method proposed in the previous studies. The matrix determinant of the observability Gramian was employed for the evaluations of the sensor subsets, while its gradient and Hessian were derived for the proposed methods. In the demonstration using numerical and real-world examples, the proposed approximate greedy method showed superiority in the run time when the sensor numbers were roughly the same as the dimensions of the latent system. The relaxation method with SDP is confirmed to be the most reasonable approach for a system with randomly generated matrices of higher dimensions. However, the degradation of the optimization results was also confirmed in the case of real-world datasets, while the pure greedy selection obtained the most stable optimization results.
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Liu, Chao, Qinghua Luo, Xiaozhen Yan, Yang Shao, Kexin Yang, and Chunyu Ju. "A distributed localization method for mobile nodes." IOP Conference Series: Materials Science and Engineering 1207, no. 1 (2021): 012001. http://dx.doi.org/10.1088/1757-899x/1207/1/012001.
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Abstract In Wireless Sensor Networks(WSNs), the location services are the basis of many application scenarios. However, for the range-based localization method, the localization accuracy and the system robustness of the distributed localization system are difficult to guarantee, due to the uncertainty of the distance estimation and position calculation are affected by the node state and communication uncertainty. In this paper, we propose the distributed localization method based on anchor node selection and Particle Filter optimization. In this method, we analyze the uncertainty of error propagation in the Least-squares method and find that there is a proportional relation between localization error and uncertainty propagation. According to this relationship, we propose the corresponding optimization criterion methods of anchor nodes. To optimize the initial localization results, we present the distributed localization method based on anchor node optimal selection and Particle Filter. Simulation results show that the methods we proposed could effectively improve the localization accuracy of the mobile nodes and the robustness of the system.
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Vijay Rathod, Et al. "A Network-Centred Optimization Technique for Operative Target Selection." Journal of Electrical Systems 19, no. 2 (2024): 87–96. http://dx.doi.org/10.52783/jes.694.
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The process of accomplishing strategic objectives by concentrating on effects as opposed to attrition-based destruction is known as effects-based operations, or EBO. Finding important nodes in an adversary network is a critical step in the EBO process for a successful implementation. In this paper, propose a network-based method to identify the most influential nodes by combining network centrality and optimization. To determine the node influence, the adversary's network structure is analyzed using degree and between centralities. Given the dynamic nature of the adversary network struct[1]ure and the centrality results, an optimization model that takes resource constraints into account chooses the key nodes. Our findings demonstrate that various network properties, such as between and degree centralities, influence the priorities of nodes as targets, and that using an optimization model yields better priorities with decreasing marginal properties. There is a discussion of the implications for theory and sensible decision-making.
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Wang, Shenglong, Jing Yang, Xiaoyu Ding, and Meng Zhao. "Detecting local communities in complex network via the optimization of interaction relationship between node and community." PeerJ Computer Science 9 (May 15, 2023): e1386. http://dx.doi.org/10.7717/peerj-cs.1386.
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The goal of local community detection algorithms is to explore the optimal community with a reference to a given node. Such algorithms typically include two primary processes: seed selection and community expansion. This study develops and tests a novel local community detection algorithm called OIRLCD that is based on the optimization of interaction relationships between nodes and the community. First, we introduce an improved seed selection method to solve the seed deviation problem. Second, this study uses a series of similarity indices to measure the interaction relationship between nodes and community. Third, this study uses a series of algorithms based on different similarity indices, and designs experiments to reveal the role of the similarity index in algorithms based on relationship optimization. The proposed algorithm was compared with five existing local community algorithms in both real-world networks and artificial networks. Experimental results show that the optimization of interaction relationship algorithms based on node similarity can detect communities accurately and efficiently. In addition, a good similarity index can highlight the advantages of the proposed algorithm based on interaction optimization.
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Du, Yong-wen, Zhang-min Wang, Gang Cai, and Jun-hui Gong. "Load-balanced routing algorithm based on cluster heads optimization for wireless sensor networks." MATEC Web of Conferences 232 (2018): 04050. http://dx.doi.org/10.1051/matecconf/201823204050.
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In order to solve the problem of unbalanced load consumption of nodes for wireless sensor networks (WSNs), this paper proposes a load-balanced routing algorithm based on cluster heads optimization for wireless sensor network. The proposed algorithm first applies first-order wireless transmission model to calculate the optimal number of clusters, then calculate nodes competitiveness rating by fuzzy algorithm considering the residual energy of node and distance from the node to base station, cluster head selection uses unequal clustering algorithm according to the competitiveness of nodes. By node competitiveness and energy management mechanism which cooperate with each other to select the best cluster heads. Use connected optimization between clusters to search multi-hop paths base station for reducing energy consumption of node, and consider transmission energy consumption, residual energy, transmission distance and other factors. The experimental results show that the proposed algorithm compared with LEACH and UCDP algorithm, can balance loading and effectively extend the life cycle of wireless sensor network.
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Jabeen, Tayyaba, Zain Ali, Wali Ullah Khan, et al. "Joint Power Allocation and Link Selection for Multi-Carrier Buffer Aided Relay Network." Electronics 8, no. 6 (2019): 686. http://dx.doi.org/10.3390/electronics8060686.
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In this paper, we present a joint power allocation and adaptive link selection protocol for an orthogonal frequency division multiplexing (OFDM)-based network consists of one source node i.e., base station (BS), one destination node i.e., (MU) and a buffer aided decode and forward (DF) relay node. Our objective is to maximize the average throughput of the system via power loading over different subcarriers at source and relay nodes. A separate power budget is assumed at each transmitting node to make the system more practical. In order to form our solution more tractable, a decomposition framework is implemented to solve the mixed integer optimization problem. Further, less complex suboptimal approaches have also been presented and simulation results are provided to endorse the efficiency of our designed algorithms.
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Wang, Kuiwu, Qin Zhang, Guimei Zheng, and Xiaolong Hu. "Multi-Target Tracking AA Fusion Method for Asynchronous Multi-Sensor Networks." Sensors 23, no. 21 (2023): 8751. http://dx.doi.org/10.3390/s23218751.
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Aiming at the problem of asynchronous multi-target tracking, this paper studies the AA fusion optimization problem of multi-sensor networks. Firstly, each sensor node runs a PHD filter, and the measurement information obtained from different sensor nodes in the fusion interval is flood communicated into composite measurement information. The Gaussian component representing the same target is associated with a subset by distance correlation. Then, the Bayesian Cramér–Rao Lower Bound of the asynchronous multi-target-tracking error, including radar node selection, is derived by combining the composite measurement information representing the same target. On this basis, a multi-sensor-network-optimization model for asynchronous multi-target tracking is established. That is, to minimize the asynchronous multi-target-tracking error as the optimization objective, the adaptive optimization design of the selection method of the sensor nodes in the sensor network is carried out, and the sequential quadratic programming (SQP) algorithm is used to select the most suitable sensor nodes for the AA fusion of the Gaussian components representing the same target. The simulation results show that compared with the existing algorithms, the proposed algorithm can effectively improve the asynchronous multi-target-tracking accuracy of multi-sensor networks.
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S, Silambarasan, and M. Savitha Devi. "ENHANCED LION SWARM OPTIMIZATION ALGORITHM WITH CENTRALIZED AUTHENTICATION APPROACH FOR SECURED DATA TRANSMISSION OVER WSN." ICTACT Journal on Communication Technology 12, no. 3 (2021): 2471–79. http://dx.doi.org/10.21917/ijct.2021.0365.
Full textAbstract:
Securing data accuracy in WSNs (Wireless Sensor Networks) is a major problem. Aggregation techniques for improving accuracy in data processing have been gaining attention of scholars, recently. Existing security systems using singular paths for transmission of data have delays in transmissions while being open to intrusions. Moreover, increased computational overheads and processing time increases the delay of data transmission in the given networks. To overcome these issues, this work proposes ELSOA-CA (Enhanced Lion Swarm Optimization Algorithm and Centralized Authentication) method. This method focuses on an optimal, faster and energy efficient data transmissions while ensuring accurate decisions on tomato crops. Multipath routing introduced in the proposed method ensures faster data transmissions by selecting optimal forwarder nodes which satisfy the constraints of delay and energy. Optimal forwarder node selection is done using ELOSA algorithm. Data transmissions are secured by centralized authentication involving third party nodes. Each and every node in a region gets registered with the authentication node where a node’s genuineness is checked the node is allowed into the forwarder node list, used for data routing. CA (Centralized Authentication) enhances the security level of data transmissions over WSN multi path routing. ELSOA-CA framework’s simulation results provide higher throughputs, reduced energy consumptions, improved network lifetimes PDRs (Packet Drop Ratios) and lesser delay times.
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Chung, Vincent, Hamzarul Alif Hamzah, Norah Tuah, Kit Guan Lim, Min Keng Tan, and Kenneth Tze Kin Teo. "CLUSTER HEAD SELECTION OPTIMIZATION IN WIRELESS SENSOR NETWORK VIA GENETIC-BASED EVOLUTIONARY ALGORITHM." ICTACT Journal on Communication Technology 11, no. 4 (2020): 2301–9. https://doi.org/10.21917/ijct.2020.0340.
Full textAbstract:
Wireless sensor network (WSN) is an embedded system comprises of spatially distributed sensor nodes where an energy-efficient mechanism is needed to prolong the network lifetime. Existing approaches for this optimization problem have several drawbacks, including non-adaptive network configuration that may cause premature death of sensor nodes. Genetic-based evolutionary algorithms such as Genetic Algorithm (GA) and Differential Evolution (DE) have been popularly used to optimize cluster head selection in WSN to improve energy efficiency for the extension of network lifetime. Therefore, the performances of GA and DE are evaluated through comparative analysis to determine their efficiency in cluster head selection optimization. Simulation results show that GA outperforms DE with higher round number for first node dies (FND) but lower round number for last node dies (LND) in terms of network lifetime. Besides, GA also leads to a network with lower number of transmission failures than DE. On the other hand, fitness convergence of GA is slower but it has higher fitness value of population.
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Yang, Cheng, Fei Zheng, Yujie Zou, et al. "MSGL+: Fast and Reliable Model Selection-Inspired Graph Metric Learning." Electronics 13, no. 1 (2023): 44. http://dx.doi.org/10.3390/electronics13010044.
Full textAbstract:
The problem of learning graph-based data structures from data has attracted considerable attention in the past decade. Different types of data can be used to infer the graph structure, such as graphical Lasso, which is learned from multiple graph signals or graph metric learning based on node features. However, most existing methods that use node features to learn the graph face difficulties when the label signals of the data are incomplete. In particular, the pair-wise distance metric learning problem becomes intractable as the dimensionality of the node features increases. To address this challenge, we propose a novel method called MSGL+. MSGL+ is inspired from model selection, leverages recent advancements in graph spectral signal processing (GSP), and offers several key innovations: (1) Polynomial Interpretation: We use a polynomial function of a certain order on the graph Laplacian to represent the inverse covariance matrix of the graph nodes to rigorously formulate an optimization problem. (2) Convex Formulation: We formulate a convex optimization objective with a cone constraint that optimizes the coefficients of the polynomial, which makes our approach efficient. (3) Linear Constraints: We convert the cone constraint of the objective to a set of linear ones to further ensure the efficiency of our method. (4) Optimization Objective: We explore the properties of these linear constraints within the optimization objective, avoiding sub-optimal results by the removal of the box constraints on the optimization variables, and successfully further reduce the number of variables compared to our preliminary work, MSGL. (5) Efficient Solution: We solve the objective using the efficient linear-program-based Frank–Wolfe algorithm. Application examples, including binary classification, multi-class classification, binary image denoising, and time-series analysis, demonstrate that MSGL+ achieves competitive accuracy performance with a significant speed advantage compared to existing graphical Lasso and feature-based graph learning methods.
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Nalinipriya, G., M. Geetha, D. Sudha, and T. Daniya. "Fuzzy Neighbors and Deep Learning-Assisted Spark Model for Imbalanced Classification of Big Data." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 31, no. 01 (2023): 141–62. http://dx.doi.org/10.1142/s0218488523500095.
Full textAbstract:
Big data is important in knowledge manipulation, assessment, and prediction. However, extracting and analyzing knowledge through big database are complex because of imbalance data distribution that leads to wrong decisions and biased classification outputs. Hence, an effective and optimal big data classification approach is designed using the proposed Bird Swarm Deer Hunting Optimization-Deep Belief Network (BSDHO-based DBN) algorithm based on spark architecture that follows the master and slave nodes. The proposed BSDHO is obtained by combining Deer Hunting Optimization algorithm and Bird Swarm Algorithm. The developed model poses two nodes, namely slave and master node. The training data is initially given to the master node in the spark architecture to perform transformation of data. Here, the transformation of data is done with an exponential log kernel, and then selection of feature is done with sequential forward selecting for choosing suitable features for enhanced processing. Consequently, oversampling process is performed with Fuzzy K-Nearest Neighbor (Fuzzy KNN) in the slave node using selected features to manage imbalance data. Then, in master node, classification is done with Deep belief Network, and trained using developed Bird swarm Deer Hunting Optimization (BSDHO) algorithm. On the other hand, the test data is taken as input, and is fed to the slave node to perform data transformation. Then, the transformed data is given to the master node for classification based on the proposed BSDHO. At last, the training data and testing data output produced the classified output. The proposed BSDHO-based DBN provided enhanced outcomes with highest specificity of 97.92%, accuracy of 96.92%, and sensitivity of 96.9%.
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Cui, Huanqing, Yongquan Liang, Chuanai Zhou, and Ning Cao. "Localization of Large-Scale Wireless Sensor Networks Using Niching Particle Swarm Optimization and Reliable Anchor Selection." Wireless Communications and Mobile Computing 2018 (December 2, 2018): 1–18. http://dx.doi.org/10.1155/2018/2473875.
Full textAbstract:
Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.
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Xiang Xu, Xiang Xu, 李儀 Xiang Xu, and Yi-Fan Wang Yi Li. "Particle Swarm Optimization with Long and Short Term Memory in Feature Selection." 電腦學刊 33, no. 5 (2022): 121–33. http://dx.doi.org/10.53106/199115992022103305011.
Full textAbstract:
<p>Taking each iteration of Particle swarm optimization (PSO) algorithm as a time node, the change of population in PSO algorithm can be regarded as a time series model. Particle population learns and evolves in multiple time nodes, which can be regarded as a dependent behavior on leader particles. In the traditional particle swarm optimization algorithm, this dependence behavior is independent of time, and its consideration standard is only the fitness value of particles. We deeply study the leadership mechanism of PSO algorithm in order to find a more robust leadership mechanism and improve the ability of PSO algorithm to explore the solution space, by extending the dependence behavior in the time dimension, we propose an improved PSO algorithm with long-term and short-term memory ability. In order to verify its performance, in the experimental part, we select 32 public data sets in UCI data to find the optimal feature subset. In a large number of feature selection experiments. The experimental results proofed that the performance of proposed algorithms is better than some state of the art algorithms. </p> <p>&nbsp;</p>
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Singh, Ravinder, and Rajdavinder Singh Boparai. "Dynamic Clustering and Cluster Head Selection for Energy Optimization under Wireless Sensor Network." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 1 (2018): 84. http://dx.doi.org/10.23956/ijarcsse.v8i1.529.
Full textAbstract:
Wireless sensor network is a field of networking that has been used for sensing information from environment. In WSN the sensor nodes are attached to a battery for sensing information. Each node utilizes three types of energy during its lifetime over the network. These energies are sensing energy, transmission or receiving energy and idle energy. During the sensing information the nodes consumes energy and transmission energy is used to transmit a data over a distance. Idle energy is that when node is not working but remains in on state. Due to deployment of WSN in unreachable area energy is main constraint for network to be cost effective.The major issue is network lifetime that must be increase so that network performs for long duration of time and provide cost effective for an n organization. To overcome this issue various methods had been proposed, chaining, pegasis, clustering and chain head selection are one of these methods.
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Maina Mwangi, Peter, John Gichuki Ndia, and Geoffrey Muchiri Muketha. "AN EXTENDED K-MEANS CLUSTER HEAD SELECTION ALGORITHM FOR EFFICIENT ENERGY CONSUMPTION IN WIRELESS SENSOR NETWORKS." International Journal of Network Security & Its Applications 15, no. 03 (2023): 65–83. http://dx.doi.org/10.5121/ijnsa.2023.15305.
Full textAbstract:
Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection optimization (PSO), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM) cluster head election algorithm have not fully reduced the issue of energy usage in WSN. The objective of this paper was to develop an extended K Mean Cluster Head selection(CHS) algorithm that uses remaining energy, distance between node and base station, distance between nodes and neighbour nodes, node density, node degree Maximum Cluster size, received signal strength indicator (RSSI) and Signal to Noise Ratio. The algorithm developed was used to enhance the lifespan of WSNs. The performance of the simulated variants of LEACH routing protocols is measured and evaluated using the quantitative research methodology. Utilizing residual node energy, packet delivery ratio, throughput, network longevity, average energy usage, and the number of live and dead node, the suggested approach is contrasted to previous approaches. From the study we observed that the proposed approach outperforms existing actual LEACH, Mod-LEACH and TSILEACH approaches.
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Peter, Maina Mwangi, Gichuki Ndia John, and Muchiri Muketha Geoffrey. "AN EXTENDED K-MEANS CLUSTER HEAD SELECTION ALGORITHM FOR EFFICIENT ENERGY CONSUMPTION IN WIRELESS SENSOR NETWORKS." International Journal of Network Security & Its Applications (IJNSA) 15, no. 3 (2023): 65–83. https://doi.org/10.5281/zenodo.8072861.
Full textAbstract:
Effective use of sensor nodes’ batteries in wireless sensor networks is critical since the batteries are difficult to recharge or replace. This is closely connected to the networks’ lifespan since once the battery is used up, the node is no longer useful. The entire network will not function if 60 to 80% of the nodes in it have completely depleted their energy. In order to minimize energy usage and sustain the network for a long time, many cluster head selection algorithms have been developed. However, the existing cluster head selection algorithms such as K-Means, particle swarm selection optimization (PSO), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM) cluster head election algorithm have not fully reduced the issue of energy usage in WSN. The objective of this paper was to develop an extended K Mean Cluster Head selection(CHS) algorithm that uses remaining energy, distance between node and base station, distance between nodes and neighbour nodes, node density, node degree Maximum Cluster size, received signal strength indicator (RSSI) and Signal to Noise Ratio. The algorithm developed was used to enhance the lifespan of WSNs. The performance of the simulated variants of LEACH routing protocols is measured and evaluated using the quantitative research methodology. Utilizing residual node energy, packet delivery ratio, throughput, network longevity, average energy usage, and the number of live and dead node, the suggested approach is contrasted to previous approaches. From the study we observed that the proposed approach outperforms existing actual LEACH, Mod-LEACH and TSILEACH approaches.
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Yu, Guoqing, Hongtao Ma, and Deden Witarsyah. "Optimal path selection algorithm for mobile beacons in sensor network under non-dense distribution." Open Physics 16, no. 1 (2018): 1066–75. http://dx.doi.org/10.1515/phys-2018-0127.
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Abstract When the traditional anchor aided location algorithm is used to select the mobile beacon path in the sensor network, there is no analysis of the energy imbalance of nodes in non-dense conditions, the optimal network node cannot be selected, and the selection error of the optimal path of the beacon is larger. A path selection algorithm for mobile beacons in a sensor network under non-dense distribution is proposed. Using the mobile beacon based wireless sensor network location algorithm, the weighted centroid algorithm and the extended Kalman filter (EKF) are used to obtain the accurate location results of the unknown nodes around the mobile beacon in the sensor network under non-dense distribution condition. The optimal node energy partition of the unknown node is obtained by the chaotic differential evolution method, and the optimal location of the optimal energy node in the wireless sensor network is calculated using the dynamic escape particle swarm optimization method, and the optimal beacon path is extracted. The experimental results show that the proposed algorithm can enhance the clustering performance of the optimal node in the wireless sensor network and has a better performance of dynamic node selection in wireless sensor network, and the convergence speed is faster and the operation time is shorter.
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Balaram, Allam, Rajendiran Babu, Miroslav Mahdal, et al. "Enhanced Dual-Selection Krill Herd Strategy for Optimizing Network Lifetime and Stability in Wireless Sensor Networks." Sensors 23, no. 17 (2023): 7485. http://dx.doi.org/10.3390/s23177485.
Full textAbstract:
Wireless sensor networks (WSNs) enable communication among sensor nodes and require efficient energy management for optimal operation under various conditions. Key challenges include maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer network lifetime contributes to improved data transfer durability, sensor conservation, and scalability. In this paper, an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource-efficient WSNs with minimal overhead is introduced. The proposed approach increases overall energy utilization and reduces inter-node communication, addressing energy conservation challenges in node deployment and clustering for WSNs as optimization problems. A dynamic layering mechanism is employed to prevent repetitive selection of the same cluster head nodes, ensuring effective dual selection. Our algorithm is designed to identify the optimal solution through enhanced exploitation and exploration processes, leveraging a modified krill-based clustering method. Comparative analysis with benchmark approaches demonstrates that the proposed model enhances network lifetime by 23.21%, increases stable energy by 19.84%, and reduces network latency by 22.88%, offering a more efficient and reliable solution for WSN energy management.
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Rani, Anju, and Amit Kumar Bindal. "Optimization of Energy Conservation in Wireless Sensor Networks." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2658–63. http://dx.doi.org/10.1166/jctn.2020.8962.
Full textAbstract:
Presently, Wireless Sensor Networks (WSNs) is quickest developing technology which broadly embracing for different application services including; climate observing, traffic expectation, reconnaissance, research and scholastic fields and so on. As the sensor nodes are haphazardly conveyed in remote condition, security measurements turns out to be most encouraging test where correspondence wirelesses systems confronting today. The Stable Election Protocol (SEP) is an enhanced algorithm of Adaptive Clustering Hierarchy (LEACH) with low energy in heterogeneous Wireless Sensor Network (WSN) for improving the life cycle. Be that as it may, the unequal energy circulation of cluster heads and nodes would diminish the lifetime. From one perspective, adding node vitality to cluster head selection to decrease the energy utilization of cluster heads; on the contrary, decline the energy utilization of nodes in cluster through not directly transmitted by interlude nodes. SEP, a protocol of heterogeneous-aware to drag out the time interim before the passing of the first node (we allude to as steady period), which is essential for some applications where the input from the sensor arrange must be solid. SEP depends on weighted election decision probabilities of every node to turn into cluster head as indicated by the rest of the energy in every node. The outcomes show that the E-SEP protocol functions admirably in adjusting the vitality utilization for improving the lifetime looking at LEACH and SEP protocol with enhanced SEP along with proposed E-SEP algorithm using MATLAB.
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Zheng, Xiandong, Wenlong Feng, Mengxing Huang, and Siling Feng. "Optimization of PBFT Algorithm Based on Improved C4.5." Mathematical Problems in Engineering 2021 (March 3, 2021): 1–7. http://dx.doi.org/10.1155/2021/5542078.
Full textAbstract:
Aiming at the problems of PBFT algorithm of consortium blockchain, such as high communication overhead, low consensus efficiency, and random selection of leader nodes, an optimized algorithm of PBFT is proposed. Firstly, the algorithm improves C4.5 and introduces weighted average information gain to overcome the mutual influence between conditional attributes and improve the classification accuracy. Then classify the nodes with improved C4.5, and select the ones with a high trust level to form the main consensus group. Finally, the integral voting mechanism is introduced to determine the leader node. Experimental results show that compared with traditional PBFT algorithm, the communication times of the improved PBFT algorithm are reduced greatly, which effectively alleviates the problem that the number of nodes in traditional PBFT algorithm increases and the traffic volume is too large, and significantly reduces the probability of the leader node doing evil and improves the consensus efficiency.
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Jiang, Dexia, and Leilei Li. "Node Selection Algorithm for Network Coding in the Mobile Wireless Network." Symmetry 13, no. 5 (2021): 842. http://dx.doi.org/10.3390/sym13050842.
Full textAbstract:
In the multicast network, network coding has proven to be an effective technique to approach maximum flow capacity. Although network coding has the advantage of improving performance, encoding nodes increases the cost and delay in wireless networks. Therefore, minimizing encoding nodes is of great significance to improve the actual network’s performance under a maximum multicast flow. This paper seeks to achieve partial improvements in the existing selection algorithm of encoding nodes in wireless networks. Firstly, the article gives the condition for an intermediate node to be an encoding node. Secondly, a maximum flow algorithm, which depends on the depth-first search method, is proposed to optimize the search time by selecting the larger augmentation flow in each step. Finally, we construct a random graph model to simulate the wireless network and the maximum multicast flow algorithm to analyze the statistical characteristics of encoding nodes. This paper aims at the optimization to find the minimal number of required coding nodes which means the minimum energy consumption. Meanwhile, the simulations indicate that the curve of coding nodes tends to be a geometric distribution, and that the curve of the maximum flow tends to be symmetric as the network scale and the node covering radius increase.
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Gao, He, Jun Li, Fu Qiang Zhou, Rong Zou, and Yi Cui. "Outage Probability Analysis and Adaptive Power Allocation for ISDF MIMO Cooperation Systems." Applied Mechanics and Materials 58-60 (June 2011): 2296–302. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2296.
Full textAbstract:
The outage probability performance is analyzed for the optimization of incremental selection decode-amplify-forward (ISDF) multi-node MIMO cooperative communication systems. Firstly, the system model for the proposed multi-node MIMO cooperative protocol. Then, the incremental selection decode-amplify-forward and optimal relay selection strategy (routing) based on the opportunistic relaying scheme is proposed. The mutual information and outage probability between source and destination nodes for the proposed scheme are formulated with the relays have the capability of maximum ratio combining (MRC) receiving and beam-forming transmitting capabilities. Finally, adaptive strategy is proposed for adaptive optimal power allocation (APA). Theoretical analysis and simulation results show that the proposed APA outperforms traditional equal power allocation (EPA) in outage performance.
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Saadi, Amnah A., and Osama A. Awad. "LIFETIME MAXIMIZATION OF A MOBILE WSN USING ZRP-FUZZY CLUSTERING PROTOCOL BASED ON ANT-LION OPTIMIZER." Iraqi Journal of Information and Communications Technology 1, no. 1 (2021): 70–82. http://dx.doi.org/10.31987/ijict.1.1.171.
Full textAbstract:
Wireless Sensor Networks require energy-efficient protocols for communication and data fusion to integrate data and extend the lifetime of the network. An efficient clustering algorithm for sensor nodes will optimize the energy efficiency of WSNs. However, the clustering process requires additional overhead, such as selection of cluster head, cluster creation, and deployment. This paper prepared a modified ZRP for mobile WSN clustering scheme and optimization using ant-lion optimization algorithm and so far named as mobility cluster head fuzzy logic based on the zone routing protocol (ZRP-FMC-ALO). Which proposed fuzzy logic approach based on three descriptors node for the selection of the CH nodes such as, residual energy, the concentration, and the centrality of the node and also exploited the concept of the mobility of the Base Station (BS) to prolong the life span of a WSN. The performance of the proposed protocol compared with the famous protocol such as LEACH. Using the MATLAB simulator and the result shows that it outperforms in terms of the WSN network lifetime, the average remaining-consuming energy, and the number of a live node.
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Jabbar, Sohail, Rabia Iram, Muhammad Imran, et al. "Energy Aware Simple Ant Routing Algorithm for Wireless Sensor Networks." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/194532.
Full textAbstract:
Network lifetime is one of the most prominent barriers in deploying wireless sensor networks for large-scale applications because these networks employ sensors with nonrenewable scarce energy resources. Sensor nodes dissipate most of their energy in complex routing mechanisms. To cope with limited energy problem, we present EASARA, an energy aware simple ant routing algorithm based on ant colony optimization. Unlike most algorithms, EASARA strives to avoid low energy routes and optimizes the routing process through selection of least hop count path with more energy. It consists of three phases, that is, route discovery, forwarding node, and route selection. We have improved the route discovery procedure and mainly concentrate on energy efficient forwarding node and route selection, so that the network lifetime can be prolonged. The four possible cases of forwarding node and route selection are presented. The performance of EASARA is validated through simulation. Simulation results demonstrate the performance supremacy of EASARA over contemporary scheme in terms of various metrics.
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Xing, Xiaoyou, Zhiwen Zhong, Xueting Li, and Yiyang Yue. "Node Selection and Path Optimization for Passive Target Localization via UAVs." Sensors 25, no. 3 (2025): 780. https://doi.org/10.3390/s25030780.
Full textAbstract:
The performance of passive target localization is affected by the positions of unmanned aerial vehicles (UAVs) at a large scale. In this paper, to improve resource utilization efficiency and localization accuracy, the node selection problem and the path optimization problem are jointly investigated. Firstly, the target passive localization model is established and the Chan-based time difference of arrival (TDOA) localization method is introduced. Then, the Cramer–Rao lower bound (CRLB) for Chan-TDOA localization is derived, and the problems of node selection and path optimization are formulated. Secondly, a CRLB-based node selection method is proposed to properly divide the UAVs into several groups, localizing different targets, and a CRLB-based path optimization method is proposed to search for the optimal UAV position configuration at each time step. The proposed path optimization method also effectively handles no-fly-zone (NFZ) constraints, ensuring operational safety while maintaining optimal target tracking performance. Also, to improve the efficiency of path optimization, particle swarm algorithm (PSO) is applied to accelerate the searching process. Finally, numerical simulations are performed to verify the validity and effectiveness of the proposed methods in this paper.
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Samuel, Rajula Angelin, and D. Shalini Punithavathani. "Designing a New Scalable Autoconfiguration Protocol with Optimal Header Selection for Large Scale MANETs." Journal of Circuits, Systems and Computers 29, no. 05 (2019): 2050068. http://dx.doi.org/10.1142/s0218126620500681.
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Autoconfiguration in mobile ad hoc network (MANET) is a challenging task to be accomplished in hostile environment. Moreover, a mobile node in MANET is usually configured with a unique IP address for providing better communication and to connect it with an IP network. Essentially, the nodes in wired networks are autoconfigured using a commonly known Dynamic Host Configuration Protocol (DHCP) server. However, MANET exhibits the intrinsic characteristics (i.e., distributed, dynamic and multi-hop) in nature; hence, it is hard to adopt DHCP server for autoconfiguration of nodes in MANET without applying significant modifications in auto-configuration scheme. This paper proposes an efficient IPV6 Duplicate address Elimination Autoconfiguration protocol for MANETs (IDEAM) which comprises the member and the cluster head (CH) nodes organized in a hierarchical fashion. Further, the proposed protocol considers the global connectivity exhibiting reduced communication overhead among the nodes. Initially, our proposed auto-configuration protocol encourages the Duplicate Address Detection (DAD) operation by selecting a controller node from the prefixed group members using a joining node in the network. In other words, the DAD operation is performed perfectly by a selected controller node on behalf of the new joining node. Thus, our proposed protocol becomes more effective and behaves better in the minimization of overhead by considerably eliminating the DAD messages broadcast in the network. Also, we imposed a new Flower pollination based gray wolf optimization (FPGWO) algorithm for selecting an optimal header among the group members by considering various node parameters (i.e., node location, resources and node density) to avoid unnecessary broadcasting of additional weight messages about each node in the network. The simulation results proved the efficiency of our proposed protocol in terms of scalability and in the minimization of overhead. Also, an effectual method provided by our proposed approach enhances the activity of marginal nodes over the group for healing the network that degrades its performance followed by the splitting and merging operation.
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Yang, Dingcheng, Chuanqi Zhu, Lin Xiao, Xiaomei Shen, and Tiankui Zhang. "An Energy-Efficient Scheme for Multirelay Cooperative Networks with Energy Harvesting." Mobile Information Systems 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5618935.
Full textAbstract:
This study investigates an energy-efficient scheme in multirelay cooperative networks with energy harvesting where multiple sessions need to communicate with each other via the relay node. A two-step optimal method is proposed which maximizes the system energy efficiency, while taking into account the receiver circuit energy consumption. Firstly, the optimal power allocation for relay nodes is determined to maximize the system throughput; this is based on directional water-filling algorithm. Secondly, using quantum particle swarm optimization (QPSO), a joint relay node selection and session grouping optimization is proposed. With this algorithm, sessions can be classified into multiple groups that are assisted by the specific relay node with the maximum energy efficiency. This approach leads to a better global optimization in searching ability and efficiency. Simulation results show that the proposed scheme can improve the energy efficiency effectively compared with direct transmission and opportunistic relay-selected cooperative transmission.
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Wang, Zhen, Jin Duan, Haobo Xu, Xue Song, and Yang Yang. "Enhanced Pelican Optimization Algorithm for Cluster Head Selection in Heterogeneous Wireless Sensor Networks." Sensors 23, no. 18 (2023): 7711. http://dx.doi.org/10.3390/s23187711.
Full textAbstract:
In the research of heterogeneous wireless sensor networks, clustering is one of the most commonly used energy-saving methods. However, existing clustering methods face challenges when applied to heterogeneous wireless sensor networks, such as energy balance, node heterogeneity, algorithm efficiency, and more. Among these challenges, a well-designed clustering approach can lead to extended node lifetimes. Efficient selection of cluster heads is crucial for achieving optimal clustering. In this paper, we propose an Enhanced Pelican Optimization Algorithm for Cluster Head Selection (EPOA-CHS) to address these issues and enhance cluster head selection for optimal clustering. This method combines the Levy flight process with the traditional POA algorithm, which not only improves the optimization level of the algorithm, but also ensures the selection of the optimal cluster head. The logistic-sine chaotic mapping method is used in the population initialization, and the appropriate cluster head is selected through the new fitness function. Finally, we utilized MATLAB to simulate 100 sensor nodes within a configured area of 100 × 100 m2. These nodes were categorized into four heterogeneous scenarios: m=0,α=0, m=0.1,α=2, m=0.2,α=3, and m=0.3,α=1.5. We conducted verification for four aspects: total residual energy, network survival time, number of surviving nodes, and network throughput, across all protocols. Extensive experimental research ultimately indicates that the EPOA-CHS method outperforms the SEP, DEEC, Z-SEP, and PSO-ECSM protocols in these aspects.
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Bhanu, Nageswaran Usha, Prathaban Banu Priya, Tiruveedhula Sajana, et al. "Dingo algorithm-based forwarder selection and huffman coding to improve authentication." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 1 (2023): 432. http://dx.doi.org/10.11591/ijeecs.v32.i1.pp432-440.
Full textAbstract:
<span>In wireless sensor network (WSN), the high volume of observe and transmitted data among sensor nodes make it requires to maintain the security. Even though numerous secure data transmission approaches designed over a network, an inadequate resource and the complex environment cause not able to used in WSNs. Moreover, secure data communication is a big challenging problem in WSNs especially for the military application. This paper proposes a dingo algorithm-based forwarder selection and huffman coding (DAHC) to improve authentication in internet of things (IoT) WSN. Initially, it detects the anomalous nodes by applying support vector machine (SVM) algorithm based on sensor node energy, node selfishness, and signal to noise ratio (SNR). Next, we using the dingo algorithm to select the forwarder node. This dingo algorithm computes the fitness function based on node degreee, node distance and node energy. Finally, the huffman coding to provide end to end authentication established on node energy from sender to receiver. During data transmission, the huffman coding to build the binary hop count value, it improves the authentication in the WSN. Performance results specify that this approach enhances the detection ratio and throughput.</span>
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Guo, Kai, and Yang Lv. "Optimizing Routing Path Selection Method Particle Swarm Optimization." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (2020): 2059042. http://dx.doi.org/10.1142/s0218001420590429.
Full textAbstract:
In view of the two shortcomings of the AODV routing protocol, they do not consider the bandwidth, delay and cost in the actual network, and the routing table has only one path from the basic node to the target node. This paper attempts to improve the AODV protocol by using particle swarm optimization. Through simulation experiments, this paper compares four improved particle swarm optimization algorithms, inertia weight, linear decline, shrinkage factor and chaos, and finds that ACPSO can find the optimal path faster and transmit data quickly. So, this paper uses chaotic particle swarm optimization (CACPSO) to improve AODV protocol. Finally, based on NS2 simulation platform, the improved AODV protocol is simulated and experimented. Different network environments are set up to test packet delivery rate, network delay and routing discovery frequency. The experimental results show that in the process of data transmission, the improved protocol has higher routing performance than AODV protocol, and can transmit data faster and more stably.
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Shashi, Raj K., and G. K. Siddesh. "MULTI-OBJECTIVE OPTIMIZATION ASSISTED NETWORK CONDITION AWARE QOS-ROUTING PROTOCOL FOR MANETS: MNCQM." International Journal of Computer Networks & Communications (IJCNC) 11, no. 4 (2019): 1–23. https://doi.org/10.5281/zenodo.3361215.
Full textAbstract:
The exponential rise in wireless communication systems and allied applications has revitalized academiaindustries to achieve more efficient data transmission system to meet Quality-of-Service (QoS) demands. Amongst major wireless communication techniques, Mobile Ad-hoc Network (MANET) is found potential to provide decentralized and infrastructure less communication among multiple distributed nodes across network region. However, dynamic network conditions such as changing topology, congestion, packet drop, intrusion possibilities etc often make MANET’s routing a tedious task. On the other hand, mobile network feature broadens the horizon for intruders to penetrate the network and causes performance degradation. Unlike classical MANET protocols where major efforts have been made on single network parameter based routing decision, this research paper proposes a novel Elitist Genetic Algorithm (EGA) Multi-Objective Optimization assisted Network Condition Aware QoS-Routing Protocol for Mobile Ad-hoc Networks (MNCQM). Our proposed MNCQM protocol exhibits two phase implementation where at first it performs node-profiling under dynamic network topology for which three factors; irregular MAC information exchange, queuing overflow and topological variations have been considered. Towards this objective node features like Packet Forwarding Probability (PFP) at the MAC layer, Success Probability of Data Transmission (SPDT) of a neighboring node, and Probability of Successful Data Delivery (PSDD) have been obtained to estimate Node-Trustworthiness Index (NTI), which is further used to eliminate untrustworthy nodes. In the second phase of implementation, a novel Evolutionary Computing assisted nondisjoint best forwarding path selection model is developed that exploits node’s and allied link’s connectivity and availability features to identify the quasi-sub-optimal forwarding paths. EGA algorithm intends to reduce hop-counts, connectivity-loss and node or link unavailability to estimate best forwarding node. One key feature of the proposed model is dual-supplementary forwarding path selection that enables alternate path formation in case of link outage and thus avoids any iterative network discovery phase.