Relevant bibliographies by topics / MCS selection algorithm

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  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'MCS selection algorithm'

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Published: 6 September 2023

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Journal articles on the topic "MCS selection algorithm"

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Li, Zhuo, Zecheng Li, and Wei Zhang. "Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing." Electronics 12, no. 4 (February 15, 2023): 960. http://dx.doi.org/10.3390/electronics12040960.

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In the field of mobile crowd sensing (MCS), the traditional client–cloud architecture faces increasing challenges in communication and computation overhead. To address these issues, this paper introduces edge computing into the MCS system and proposes a two-stage task allocation optimization method under the constraint of limited computing resources. The method utilizes deep reinforcement learning for the selection of optimal edge servers for task deployment, followed by a greedy self-adaptive stochastic algorithm for the recruitment of sensing participants. In simulations, the proposed method demonstrated a 20% improvement in spatial coverage compared with the existing RBR algorithm and outperformed the LCBPA, SMA, and MOTA algorithms in 41, 42, and 48 tasks, respectively. This research contributes to the optimization of task allocation in MCS and advances the integration of edge computing in MCS systems.
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Meng, Qing Min, Xiong Gu, Feng Tian, and Bao Yu Zheng. "k-NN Based MCS Selection in Distributed OFDM Wireless Networks." Advanced Materials Research 225-226 (April 2011): 974–77. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.974.

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Cognitive radio is seen as an intelligent wireless communication system that can learn and adapt the surrounding environment. Cognitive engine is the core component of implementation of cognitive radio. Information in knowledge base of cognitive engine can be obtained by using of machine learning. In this work, we consider wireless networks with clustered nodes and OFDM physical layer and present a combined sub-channel selection and modulation and coding rate selection based on k-Nearest Neighbor classification algorithm. Computer simulation results show that, in frequency selective fading channel, the scheme makes a new network node easy to choose appropriate modulation and coding rate.
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Cuevas, Erik, and Adolfo Reyna-Orta. "A Cuckoo Search Algorithm for Multimodal Optimization." Scientific World Journal 2014 (2014): 1–20. http://dx.doi.org/10.1155/2014/497514.

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Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration.
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Kalaiarasu, Dr M., and Dr J. Anitha. "Modified Cuckoo Search-Support Vector Machine (MCS-SVM) Gene Selection and Classification for Autism Spectrum Disorder (ASD) Gene Expression." NeuroQuantology 18, no. 11 (September 30, 2020): 01–13. http://dx.doi.org/10.14704/nq.2020.18.11.nq20228.

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Autism Spectrum Disorder (ASD) is a neuro developmental disorder characterized by weakened social skills, impaired verbal and non-verbal interaction, and repeated behavior. ASD has increased in the past few years and the root cause of the symptom cannot yet be determined. In ASD with gene expression is analyzed by classification methods. For the selection of genes in ASD, statistical philtres and a wrapper-based Geometric Binary Particle Swarm Optimization-Support Vector Machine (GBPSO-SVM) algorithm have recently been implemented. However GBPSO has provides lesser accuracy, if the dataset samples are large and it cannot directly apply to multiple output systems. To overcome this issue, Modified Cuckoo Search-Support Vector Machine (MCS-SVM) based wrapper feature selection algorithm is proposed which improves the accuracy of the classifier in ASD. This work consists of three major steps, (i) preprocessing, (ii) gene selection, and (iii) classification. Firstly, preprocessing is performed by mean or median ratios close to unity was removed from original gene dataset; based on this samples are reduced from 54,613 to 9454. Secondly, gene selection is performed by using statistical filters and wrapper algorithm. Statistical filters methods like Wilcox on Rank Sum test (WRS), Class Correlation (COR) function and Two-sample T-test (TT) were applied in parallel to a ten-fold cross validation range of the most discriminatory genes. In the wrapper algorithm, Modified Cuckoo Search (MCS) is also proposed to gene selection. This step decreases the number of genes of the dataset by removing genes. Finally, SVM classifier combined forms of gene subsets for grading. The autism microarray dataset used in the analysis was downloaded from the benchmark public repository Gene Expression Omnibus (GEO) (National Center for Biotechnology Information (NCBI)). The classification methods are measured in terms of the metrics like precision, recall, f-measure and accuracy. Proposed MCS-SVM classifier achieves highest accuracy when compared Linear Regression (LR), and GBPSO-SVM classifiers.
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Wang, Yanan, Guodong Sun, and Xingjian Ding. "Coverage-Balancing User Selection in Mobile Crowd Sensing with Budget Constraint." Sensors 19, no. 10 (May 23, 2019): 2371. http://dx.doi.org/10.3390/s19102371.

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Mobile crowd sensing (MCS) is a new computing paradigm for the internet of things, and it is widely accepted as a powerful means to achieve urban-scale sensing and data collection. In the MCS campaign, the smart mobilephone users can detect their surrounding environments with their on-phone sensors and return the sensing data to the MCS organizer. In this paper, we focus on the coverage-balancing user selection (CBUS) problem with a budget constraint. Solving the CBUS problem aims to select a proper subset of users such that their sensing coverage is as large and balancing as possible, yet without violating the budget specified by the MCS campaign. We first propose a novel coverage balance-based sensing utility model, which effectively captures the joint requirement of the MCS requester for coverage area and coverage balance. We then formally define the CBUS problem under the proposed sensing utility model. Because of the NP-hardness of the CBUS problem, we design a heuristic-based algorithm, called MIA, which tactfully employs the maximum independent set model to determine a preliminary subset of users from all the available users and then adjusts this user subset to improve the budget implementation. MIA also includes a fast approach to calculating the area of the union coverage with any complicated boundaries, which is also applicable to any MCS scenarios that are set up with the coverage area-based sensing utility. The extensive numeric experiments show the efficacy of our designs both in coverage balance and in the total coverage area.
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Pošík, Petr, Waltraud Huyer, and László Pál. "A Comparison of Global Search Algorithms for Continuous Black Box Optimization." Evolutionary Computation 20, no. 4 (December 2012): 509–41. http://dx.doi.org/10.1162/evco_a_00084.

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Four methods for global numerical black box optimization with origins in the mathematical programming community are described and experimentally compared with the state of the art evolutionary method, BIPOP-CMA-ES. The methods chosen for the comparison exhibit various features that are potentially interesting for the evolutionary computation community: systematic sampling of the search space (DIRECT, MCS) possibly combined with a local search method (MCS), or a multi-start approach (NEWUOA, GLOBAL) possibly equipped with a careful selection of points to run a local optimizer from (GLOBAL). The recently proposed “comparing continuous optimizers” (COCO) methodology was adopted as the basis for the comparison. Based on the results, we draw suggestions about which algorithm should be used depending on the available budget of function evaluations, and we propose several possibilities for hybridizing evolutionary algorithms (EAs) with features of the other compared algorithms.
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Gu, Zheng Gang, and Kun Hong Liu. "Microarray Data Classification Based on Evolutionary Multiple Classifier System." Applied Mechanics and Materials 130-134 (October 2011): 2077–80. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2077.

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Designing an evolutionary multiple classifier system (MCS) is a relatively new research area. In this paper, we propose a genetic algorithm (GA) based MCS for microarray data classification. We construct a feature poll with different feature selection methods first, and then a multi-objective GA is applied to implement ensemble feature selection process so as to generate a set of classifiers. When this GA stops, a set of base classifiers are generated. Here we use all the nondominated individuals in last generation to build an ensemble system and test the proposed ensemble method and the method that apply a classifier selection process to select proper classifiers from all the individuals in last generation. The experimental results show the proposed ensemble method is roubust and can lead to promising results.
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Alizadeh Moghaddam, S. H., M. Mokhtarzade, and S. A. Alizadeh Moghaddam. "A NEW MULTIPLE CLASSIFIER SYSTEM BASED ON A PSO ALGORITHM FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 71–75. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-71-2019.

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Abstract. Multiple classifier systems (MCSs) have shown great performance for the classification of hyperspectral images. The requirements for a successful MCS are 1) diversity between ensembles and 2) good classification accuracy of each ensemble. In this paper, we develop a new MCS method based on a particle swarm optimization (PSO) algorithm. Firstly, in each ensemble of the proposed method, called PSO-MCS, PSO identifies a subset of the spectral bands with a high J2 value, which is a measure of class-separability. Then, an SVM classifier is used to classify the input image, applying the selected features in each ensemble. Finally, the classification results of the entire ensembles are integrated using a majority voting strategy. Having the benefit of the PSO algorithm, PSO-MCS selects appropriate features. In addition, due to the fact that different features are selected in different runs of PSO, diversity between the ensembles is provided. Experimental results on an AVIRIS Indian Pine image show the superiority of the proposed method over its competitor, named random feature selection method.
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Abououf, Menatalla, Shakti Singh, Hadi Otrok, Rabeb Mizouni, and Ernesto Damiani. "Machine Learning in Mobile Crowd Sourcing: A Behavior-Based Recruitment Model." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–28. http://dx.doi.org/10.1145/3451163.

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With the advent of mobile crowd sourcing (MCS) systems and its applications, the selection of the right crowd is gaining utmost importance. The increasing variability in the context of MCS tasks makes the selection of not only the capable but also the willing workers crucial for a high task completion rate. Most of the existing MCS selection frameworks rely primarily on reputation-based feedback mechanisms to assess the level of commitment of potential workers. Such frameworks select workers having high reputation scores but without any contextual awareness of the workers, at the time of selection, or the task. This may lead to an unfair selection of workers who will not perform the task. Hence, reputation on its own only gives an approximation of workers’ behaviors since it assumes that workers always behave consistently regardless of the situational context. However, following the concept of cross-situational consistency, where people tend to show similar behavior in similar situations and behave differently in disparate ones, this work proposes a novel recruitment system in MCS based on behavioral profiling. The proposed approach uses machine learning to predict the probability of the workers performing a given task, based on their learned behavioral models. Subsequently, a group-based selection mechanism, based on the genetic algorithm, uses these behavioral models in complementation with a reputation-based model to recruit a group of workers that maximizes the quality of recruitment of the tasks. Simulations based on a real-life dataset show that considering human behavior in varying situations improves the quality of recruitment achieved by the tasks and their completion confidence when compared with a benchmark that relies solely on reputation.
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Krishnaveni P. and Balasundaram S. R. "Automatic Text Summarization by Providing Coverage, Non-Redundancy, and Novelty Using Sentence Graph." Journal of Information Technology Research 15, no. 1 (January 2022): 1–18. http://dx.doi.org/10.4018/jitr.2022010108.

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The day-to-day growth of online information necessitates intensive research in automatic text summarization (ATS). The ATS software produces summary text by extracting important information from the original text. With the help of summaries, users can easily read and understand the documents of interest. Most of the approaches for ATS used only local properties of text. Moreover, the numerous properties make the sentence selection difficult and complicated. So this article uses a graph based summarization to utilize structural and global properties of text. It introduces maximal clique based sentence selection (MCBSS) algorithm to select important and non-redundant sentences that cover all concepts of the input text for summary. The MCBSS algorithm finds novel information using maximal cliques (MCs). The experimental results of recall oriented understudy for gisting evaluation (ROUGE) on Timeline dataset show that the proposed work outperforms the existing graph algorithms Bushy Path (BP), Aggregate Similarity (AS), and TextRank (TR).
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Dissertations / Theses on the topic "MCS selection algorithm"

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Lee, Unghee. "A Proactive Routing Protocol for Multi-Channel Wireless Ad-hoc Networks." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/28127.

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Wireless mobile ad-hoc networks consist of a collection of peer mobile nodes that form a network and are capable of communicating with each other without help from stationary infrastructure such as access points. The availability of low-cost, com-modity network interface cards (NICs) has made the IEEE 802.11 medium access control (MAC) protocol the de facto MAC protocol for wireless mobile ad-hoc net-works, even though it is not optimal. The IEEE 802.11 MAC protocol is designed to have stations share a single channel in a given network. However, many of the IEEE 802.11 physical (PHY) layer specifications define multiple channels and allow the simultaneous, non-interfering use of some of these channels. Therefore, multiple communications can occur at the same time, offering the opportunity to increase the effective network capacity. We present an innovative routing protocol that utilizes multiple channels to im-prove the performance of wireless ad-hoc networks. The basic idea of the protocol is to use multiple channels so that multiple useful transmissions can occur simultane-ously, thus increasing network capacity. The proposed scheme requires minor changes to existing proactive ad-hoc routing protocols and no modifications to the current IEEE 802.11 MAC protocol. To reduce inefficiencies due to periodic updates in the proactive routing protocols, the proposed scheme divides the network layer into control and data planes. Nodes send routing updates using the control channel and user packets using the data channel. To demonstrate the multi-channel routing scheme, we extend the Destination-Sequenced Distance-Vector (DSDV), Open Shortest Path First-Minimal Connected Dominating Set (OSPF-MCDS), and Optimized Link State Routing (OLSR) protocol to multiple channel (MC) versions, denoted as DSDV-MC, OSPF-MCDS-MC, and OLSR-MC, respectively. Simulation results for DSDV-MC, OSPF-MCDS-MC, and OLSR-MC are presented and experimental results for OLSR-MC are presented. Simulation results indicate that DSDV-MC and OSPF-MCDS-MC effectively exploit multiple channels to improve network capacity. Goodput, the throughput consider-ing only useful error-free packets, increases with an increased number of available channels as the number of nodes and network load increase in both single-hop and multiple-hop networks. Experimental results with OLSR-MC also support that the proposed scheme increases network capacity without modification to the MAC proto-col in a real implementation. Although simulation and experimental results show that proposed scheme im-proves network capacity by exploiting multiple channels, problems exist with channel distribution. We introduce a new metric, the Channel Distribution Index (CDI) to in-vestigate these issues. The CDI indicates the fairness of the channel distribution. We identify the channel convergence problem, where a particular channel is over-utilized, and propose a channel reallocation scheme to mitigate the impact of the channel convergence problem using the CDI.
Ph. D.
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Wang, Ju-Chia, and 王汝嘉. "Joint Beam Training and MCS Mode Selection Algorithm under Multi-Path Fading Channel for WLANs." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/93043611866087509274.

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碩士
國立交通大學
資訊科學與工程研究所
105
As people’s need to internet needs increase, IEEE 802.11 is a standard for wireless local area network(WLAN) which had proposed an effective solution and is still continue developing. It uses 5GHz band that has higher bandwidth to replace the old 2.4GHz band, in order to provide a faster data transmit rate and a more stable signal. In 802.11ac standard, beamforming technology is included. The beamforming principle is to utilize multiple antennas in the space and to adjust the phase and amplitude for the sake of the signal can get the same phase, the same amplitude addition, and also the signal strength can become stronger in some space. However, on the contrary; some part of the space in the opposite phase, amplitude subtraction and the signal strength will become weak. Due to the directional of beamforming, we expect to rely on an effective algorithm to expect that the antenna will switch to the beam and use the beam to serve the user. And the signal strength can become maximum when users are under the particular beam. At the same time, by using the rate adaptation to maintain the best speed under the packet error rate tolerable. By watching the result of MATLAB simulation, we can verify the accuracy of the beam training algorithm and beam re-training algorithm. In addition, it also prove that our algorithm can effectively improve the overall performance.
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Book chapters on the topic "MCS selection algorithm"

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Wang, Hao, Andrew Chi Sing Leung, and John Sum. "MCP Based Noise Resistant Algorithm for Training RBF Networks and Selecting Centers." In Neural Information Processing, 668–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04179-3_59.

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Phinyomark, Angkoon, Franck Quaine, and Yann Laurillau. "The Relationship Between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface." In Computer Vision, 2234–68. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch098.

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Muscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition.
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Lucinska, Malgorzata, and Slawomir T. Wierzchon. "An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game." In Machine Learning, 1192–214. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch503.

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Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they will require the ability to cooperate, coordinate, and negotiate with each other. From a theoretical point of view such systems require a hybrid approach involving game theory, artificial intelligence, and distributed programming. On the other hand, biology offers a number of inspirations showing how these interactions are effectively realized in real world situations. Swarm organizations, like ant colonies or bird flocks, provide a spectrum of metaphors offering interesting models of collective problem solving. Immune system, involving complex relationships among antigens and antibodies, is another example of a multi-agent and swarm system. In this chapter an application of so-called clonal selection algorithm, inspired by the real mechanism of immune response, is proposed to solve the problem of learning strategies in the pursuit-evasion problem.
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Conference papers on the topic "MCS selection algorithm"

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Satapathy, Shaswat, Shivani Singh, and Debani Prasad Mishra. "MCS: A Distributed Multi-User Channel Selection Algorithm for Cognitive Radio Networks." In 2019 International Conference on Information Technology (ICIT). IEEE, 2019. http://dx.doi.org/10.1109/icit48102.2019.00015.

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Bojnordi, Ehsan, Seyed Jalaleddin Mousavirad, Gerald Schaefer, and Iakov Korovin. "MCS-HMS: A Multi-Cluster Selection Strategy for the Human Mental Search Algorithm." In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021. http://dx.doi.org/10.1109/ssci50451.2021.9660143.

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Xiaofei Zhao, Xinyu Gu, Xiang Zhang, Yi Gong, Lin Zhang, and Wenyu Li. "Investigation of different MCS selection algorithm to reduce the impact of CSI-RS on LTE legacy UEs." In TENCON 2015 - 2015 IEEE Region 10 Conference. IEEE, 2015. http://dx.doi.org/10.1109/tencon.2015.7373083.

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Junbo, Liu, Ding Shuiting, and Li Guo. "Influence of Random Variable Dimension on the Fast Numerical Integration Method of Aero Engine Rotor Disk Failure Risk Analysis." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23513.

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Abstract In the risk assessment of turbine rotor disks, the probability of failure of a certain disk type (after N flight cycles) is a vital criterion for estimating whether the disk is safe to use. Monte Carlo simulation (MCS) is often used to calculate the failure probability but is costly because it requires a large sample size. The numerical integration (NI) algorithm has been proven more efficient than MCS in conditions entailing three random variables. However, the previous studies on the NI method have not dealt with the influence of random variable dimension on calculation efficiency. Hence, this study aims to summarize the influence of variable dimensions on the time cost of a fastintegration algorithm. The time cost increases exponentially with the number of variables in the NI method. This conclusion provides a reference for the selection of probability algorithms involving multiple variables. The findings are expected to be of interest to the practice of efficient security design that considers multivariable conditions.
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Chien, Ying-Ren, Sheng-Teng Wu, and Hen-Wai Tsao. "Correntropy-based Data-Selective MCC Algorithm." In 2022 IEEE International Conference on Consumer Electronics - Taiwan. IEEE, 2022. http://dx.doi.org/10.1109/icce-taiwan55306.2022.9869130.

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Li, Bohan, Xindi Zhang, Shaowei Cai, Jinkun Lin, Yiyuan Wang, and Christian Blum. "NuCDS: An Efficient Local Search Algorithm for Minimum Connected Dominating Set." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/209.

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The minimum connected dominating set (MCDS) problem is an important extension of the minimum dominating set problem, with wide applications, especially in wireless networks. Despite its practical importance, there are few works on solving MCDS for massive graphs, mainly due to the complexity of maintaining connectivity. In this paper, we propose two novel ideas, and develop a new local search algorithm for MCDS called NuCDS. First, a hybrid dynamic connectivity maintenance method is designed to switch alternately between a novel fast connectivity maintenance method based on spanning tree and its previous counterpart. Second, we define a new vertex property called \emph{safety} to make the algorithm more considerate when selecting vertices. Experiments show that NuCDS significantly outperforms the state-of-the-art MCDS algorithms on both massive graphs and classic benchmarks.
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Mardini, Wail, Yaser Khamayseh, and Montaha Hani Khatatbeh. "Genetic algorithm for friendship selection in social IoT." In 2017 International Conference on Engineering & MIS (ICEMIS). IEEE, 2017. http://dx.doi.org/10.1109/icemis.2017.8273022.

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Abdullah, J., M. Y. Ismail, N. A. Cholan, and S. A. Hamzah. "GA-based QoS Route Selection Algorithm for Mobile Ad-Hoc Networks." In 2nd Malaysia Conferenced on Photonics (MCP). IEEE, 2008. http://dx.doi.org/10.1109/nctt.2008.4814299.

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Cho, Changgi, Hu Jin, Nah-Oak Song, and Dan Keun Sung. "MCS selection algorithms for a persistent allocation scheme to accommodate VoIP services in IEEE 802.16e OFDMA system." In 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2009). IEEE, 2009. http://dx.doi.org/10.1109/pimrc.2009.5449929.

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Ebian, Mohamed, Mohamed El-Sharkawy, and Salwa El-Ramly. "Adaptive error concealment algorithm for multiview coding based on lost MBs sizes and using dynamic selection of lower candidates MBs." In 2012 8th International Computer Engineering Conference (ICENCO). IEEE, 2012. http://dx.doi.org/10.1109/icenco.2012.6487085.

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