Academic literature on the topic 'Non-Centralized algorithms'
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Journal articles on the topic "Non-Centralized algorithms"
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Li, Xiang, and Yuxuan Ma. "Analysis of Multi-Robot Patrolling Algorithms." Journal of Physics: Conference Series 2419, no. 1 (January 1, 2023): 012100. http://dx.doi.org/10.1088/1742-6596/2419/1/012100.
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Abstract This article is dedicated to analyzing the common problems of robots when patrolling indoors and the corresponding solution algorithms. In the article, four problems of patrol robots (whether centralized or non-centralized) during operation are listed: the overload of data to be processed when the robotic sensors are transmitting the data, the repetitiveness and simultaneity of the robot work, the incorrect execution of the algorithm and how to deal with the inevitable unknown external factors. The simultaneity of the robots is to be eliminated because robots need to explore independently in the patrol task. Subsequently, four algorithms and functionalities are introduced (Monte Carlo Tree Planning Algorithm, Rapid Exploring Random Tree Planning, Mutual Exclusion Algorithm, and Kalman Filter Algorithm). The question of how the four algorithms address the corresponding factors will be illustrated by an elaboration of the process by which the algorithms solve the problem.
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GE, Quan-Bo, Wen-Bin LI, Ruo-Yu SUN, and Zi XU. "Centralized Fusion Algorithms Based on EKF for Multisensor Non-linear Systems." Acta Automatica Sinica 39, no. 6 (March 25, 2014): 816–25. http://dx.doi.org/10.3724/sp.j.1004.2013.00816.
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Dong, Da Wei, Xiao Guo Liu, and Tian Jing. "Channel Assignment Algorithm in Centralized WLAN." Applied Mechanics and Materials 721 (December 2014): 728–31. http://dx.doi.org/10.4028/www.scientific.net/amm.721.728.
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To reduce the number of inter-disturb access points and the interference among access points in same channel, with research on interference issus and channel assignment algorithms of wireless local area network, a scheme suitable for centralized wireless local area network was proposed aiming to minimize the total interference among access points, which comprehensively considerate the number of neighbor and the received power. And then the algorithm with cases was simulated and analyzed, the result of NS2 simulation indicated that the algorithm was simple, effective and feasible, which could realize dynamic adjustment to the wireless LAN RF channel and had a better load balance effect among non-overlapping channels.
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Xian, Wenhan, Feihu Huang, and Heng Huang. "Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10405–13. http://dx.doi.org/10.1609/aaai.v35i12.17246.
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Recently decentralized optimization attracts much attention in machine learning because it is more communication-efficient than the centralized fashion. Quantization is a promising method to reduce the communication cost via cutting down the budget of each single communication using the gradient compression. To further improve the communication efficiency, more recently, some quantized decentralized algorithms have been studied. However, the quantized decentralized algorithm for nonconvex constrained machine learning problems is still limited. Frank-Wolfe (a.k.a., conditional gradient or projection-free) method is very efficient to solve many constrained optimization tasks, such as low-rank or sparsity-constrained models training. In this paper, to fill the gap of decentralized quantized constrained optimization, we propose a novel communication-efficient Decentralized Quantized Stochastic Frank-Wolfe (DQSFW) algorithm for non-convex constrained learning models. We first design a new counterexample to show that the vanilla decentralized quantized stochastic Frank-Wolfe algorithm usually diverges. Thus, we propose DQSFW algorithm with the gradient tracking technique to guarantee the method will converge to the stationary point of non-convex optimization safely. In our theoretical analysis, we prove that to achieve the stationary point our DQSFW algorithm achieves the same gradient complexity as the standard stochastic Frank-Wolfe and centralized Frank-Wolfe algorithms, but has much less communication cost. Experiments on matrix completion and model compression applications demonstrate the efficiency of our new algorithm.
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Liu, Zonglin, and Olaf Stursberg. "Distributed control of networked systems with coupling constraints." at - Automatisierungstechnik 67, no. 12 (November 18, 2019): 1007–18. http://dx.doi.org/10.1515/auto-2019-0085.
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Abstract This paper proposes algorithms for the distributed solution of control problems for networked systems with coupling constraints. This type of problem is practically relevant, e. g., for subsystems which share common resources, or need to go through a bottleneck, while considering non-convex state constraints. Centralized solution schemes, which typically first cast the non-convexities into mixed-integer formulations that are then solved by mixed-integer programming, suffer from high computational complexity for larger numbers of subsystems. The distributed solution proposed in this paper decomposes the centralized problem into a set of small subproblems to be solved in parallel. By iterating over the subproblems and exchanging information either among all subsystems, or within subsets selected by a coordinator, locally optimal solutions of the global problem are determined. The paper shows for two instances of distributed algorithms that feasibility as well as continuous cost reduction over the iterations up to termination can be guaranteed, while the solutions times are considerably shorter than for the centralized problem. These properties are illustrated for a multi-vehicle motion problem.
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Capocasale, Vittorio. "Trapdoor proof of work." PeerJ Computer Science 10 (January 19, 2024): e1815. http://dx.doi.org/10.7717/peerj-cs.1815.
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Consensus algorithms play a crucial role in facilitating decision-making among a group of entities. In certain scenarios, some entities may attempt to hinder the consensus process, necessitating the use of Byzantine fault-tolerant consensus algorithms. Conversely, in scenarios where entities trust each other, more efficient crash fault-tolerant consensus algorithms can be employed. This study proposes an efficient consensus algorithm for an intermediate scenario that is both frequent and underexplored, involving a combination of non-trusting entities and a trusted entity. In particular, this study introduces a novel mining algorithm, based on chameleon hash functions, for the Nakamoto consensus. The resulting algorithm enables the trusted entity to generate tens of thousands blocks per second even on devices with low energy consumption, like personal laptops. This algorithm holds promise for use in centralized systems that require temporary decentralization, such as the creation of central bank digital currencies where service availability is of utmost importance.
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Huanca-Anquise, Candy A., Ana Lúcia Cetertich Bazzan, and Anderson R. Tavares. "Multi-Objective, Multi-Armed Bandits: Algorithms for Repeated Games and Application to Route Choice." Revista de Informática Teórica e Aplicada 30, no. 1 (January 30, 2023): 11–23. http://dx.doi.org/10.22456/2175-2745.122929.
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Multi-objective decision-making in multi-agent scenarios poses multiple challenges. Dealing with multiple objectives and non-stationarity caused by simultaneous learning are only two of them, which have been addressed separately. In this work, reinforcement learning algorithms that tackle both issues together are proposed and applied to a route choice problem, where drivers must select an action in a single-state formulation, while aiming to minimize both their travel time and toll. Hence, we deal with repeated games, now with a multi-objective approach. Advantages, limitations and differences of these algorithms are discussed. Our results show that the proposed algorithms for action selection using reinforcement learning deal with non-stationarity and multiple objectives, while providing alternative solutions to those of centralized methods.
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Zhao, Weiwei, Hairong Chu, Xikui Miao, Lihong Guo, Honghai Shen, Chenhao Zhu, Feng Zhang, and Dongxin Liang. "Research on the Multiagent Joint Proximal Policy Optimization Algorithm Controlling Cooperative Fixed-Wing UAV Obstacle Avoidance." Sensors 20, no. 16 (August 13, 2020): 4546. http://dx.doi.org/10.3390/s20164546.
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Multiple unmanned aerial vehicle (UAV) collaboration has great potential. To increase the intelligence and environmental adaptability of multi-UAV control, we study the application of deep reinforcement learning algorithms in the field of multi-UAV cooperative control. Aiming at the problem of a non-stationary environment caused by the change of learning agent strategy in reinforcement learning in a multi-agent environment, the paper presents an improved multiagent reinforcement learning algorithm—the multiagent joint proximal policy optimization (MAJPPO) algorithm with the centralized learning and decentralized execution. This algorithm uses the moving window averaging method to make each agent obtain a centralized state value function, so that the agents can achieve better collaboration. The improved algorithm enhances the collaboration and increases the sum of reward values obtained by the multiagent system. To evaluate the performance of the algorithm, we use the MAJPPO algorithm to complete the task of multi-UAV formation and the crossing of multiple-obstacle environments. To simplify the control complexity of the UAV, we use the six-degree of freedom and 12-state equations of the dynamics model of the UAV with an attitude control loop. The experimental results show that the MAJPPO algorithm has better performance and better environmental adaptability.
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Tan, Fuxiao. "The Algorithms of Distributed Learning and Distributed Estimation about Intelligent Wireless Sensor Network." Sensors 20, no. 5 (February 27, 2020): 1302. http://dx.doi.org/10.3390/s20051302.
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The intelligent wireless sensor network is a distributed network system with high “network awareness”. Each intelligent node (agent) is connected by the topology within the neighborhood which not only can perceive the surrounding environment, but can adjusts its own behavior according to its local perception information to constructs a distributed learning algorithms. Therefore, three basic intelligent network topologies of centralized, non-cooperative, and cooperative are intensively investigated in this paper. The main contributions of the paper include two aspects. First, based on algebraic graph, three basic theoretical frameworks for distributed learning and distributed parameter estimation of cooperative strategy are surveyed: increment strategy, consensus strategy, and diffusion strategy. Second, based on classical adaptive learning algorithm and online updating law, the implementation process of distributed estimation algorithm and the latest research progress of above three distributed strategies are investigated.
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Dr. C.V. Nageswara, Rao, and Putta Mr. Vihari. "RNGA based Centralized PI Controller for Multivariable Non Square Systems using Direct Synthesis Method." International Journal of Innovative Technology and Exploring Engineering 12, no. 6 (May 30, 2023): 1–10. http://dx.doi.org/10.35940/ijitee.f9518.0512623.
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Design of centralized PI controllers for multivariable non square systems is proposed in the present work. The centralized controller is designed based on the direct synthesis method. The method includes approximating the inverse of the process transfer matrix with the effective transfer function matrix. The effective transfer function for each element in the process transfer function matrix is derived by using the relative normalized gain array (RNGA), and relative average residence time array (RARTA) concepts proposed by Cai et al [1]. The transfer function models used in the present work include first order processes with time delay (FOPDT). Maclaurin series is applied to reduce the resulting controllers in to standard PI forms. The design method requires a single tuning parameter (filter time constant) to adjust the performance of the controller. Simulation study is carried out for various case studies and the results show the advantage of proposed method over the literature reported methods. The control algorithms are comparatively analyzed using standard robust stability measure. The designed controllers give a good performance with lesser interaction compared to the literature methods, Davison Method [2] and Tanttu and Lieslehto’s method [3].
Dissertations / Theses on the topic "Non-Centralized algorithms"
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Alvarez, Valera Hernan Humberto. "An energy saving perspective for distributed environments : Deployment, scheduling and simulation with multidimensional entities for Software and Hardware." Electronic Thesis or Diss., Pau, 2022. https://theses.hal.science/tel-04116013.
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De nos jours, la forte croissance économique et les conditions météorologiques extrêmes ont augmenté la demande mondiale d'électricité de plus de 6 % en 2021 après la pandémie de COVID. La reprise rapide de cette demande a rapidement augmenté la consommation d'électricité. Même si les sources renouvelables présentent une croissance significative, la production d'électricité à partir de sources de charbon et de gaz a atteint un niveau historique. D'autre part, la consommation d'énergie du secteur du numérique dépend de sa croissance et de son degré d'efficacité énergétique. À ce sujet, bien que les appareils à tous les niveaux de déploiement soient aujourd'hui économes en énergie, leur utilisation massive signifie que la consommation énergétique mondiale continue de croître.Toutes ces données montrent la nécessité d'utiliser l'énergie de ces appareils à bon escient. Pour cette raison, ce travail de thèse aborde le (re)déploiement dynamique de composants logiciels (conteneurs ou machines virtuelles) et de leurs données pour économiser de l'énergie. Dans cette mesure, nous avons conçu et développé des algorithmes intelligents d'ordonnancement distribué pour réduire la consommation électrique globale tout en préservant la qualité de service des applications.De tels algorithmes exécutent des procédures de migration et de duplication en tenant compte de la relation naturelle entre la charge/les fonctionnalités des composants matériels et la consommation d'énergie. Pour cela, ils mettent en place une nouvelle manière de négociations décentralisées basée sur un middleware distribué que nous avons créé (Kaligreen) et des structures de données multidimensionnelles.Pour exploiter et évaluer les algorithmes ci-dessus, des outils appropriés concernant les solutions matérielles et logicielles sont essentiels. Ici, notre choix a été de développer notre propreoutil de simulation appelé : PISCO.PISCO est un simulateur polyvalent et simple qui permet aux utilisateurs de se concentrer uniquement sur leurs stratégies de planification. Il permet d'abstraire les topologies de réseau sous forme de structures de données dont les éléments sont des dispositifs indexés par un ou plusieurs critères. De plus, il imite l'exécution de microservices en allouant des ressources selon diverses heuristiques de planification.Nous avons utilisé PISCO pour implémenter, exécuter et tester nos algorithmes de planificationNowadays, strong economic growth and extreme weather conditions increased global electricity demand by more than 6% in 2021 after the COVID pandemic. The fast recovery regarding this demand rapidly increased electricity consumption. Even though renewable sources present a significant growth, electricity production from both coal and gas sources has reached a historical level.On the other hand, the consumption of energy by the digital technology sector depends on its growth and its degree of energy efficiency. On this matter, although devices at all deployment levels are energy efficient today, their massive use means that global energy consumption continues to grow.All these data show the need to use the energy of these devices wisely. For that reason, this thesis work addresses the dynamic (re)deployment of software components (containers or virtual machines) and their data to save energy. To this extent, we designed and developed intelligent distributed scheduling algorithms to decrease global power consumption while preserving the applications' quality of service.Such algorithms execute migrations and duplications procedures considering the natural relation between hardware components' load/features and power consumption. For that, they implement a novel manner of decentralized negotiations based on a distributed middleware we created (Kaligreen) and multidimensional data structures.To operate and assess the algorithms above, appropriate tools regarding hardware and software solutions are essential. Here, our choice was to develop our ownsimulation tool called: PISCO.PISCO is a versatile and straightforward simulator that allows users to concentrate only on their scheduling strategies. It enables network topologies to be abstracted as data structures whose elements are devices indexed by one or more criteria. Additionally, it mimics the execution of microservices by allocating resources according to various scheduling heuristics.We have used PISCO to implement, run and test our scheduling algorithms
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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Östman, Alexander. "Non-centralized distributed algorithm to locate nearby servers based on player positions for a MMOG server cluster." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186714.
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In this thesis a non-centralized algorithm is proposed to locate nearby servers based on their players’ positions in a massive multiplayer online game server cluster. The purpose of this is to enable that players can visually see each other even though they are connected to different servers. By utilizing peer to peer connection between the servers the algorithm is tolerant against possible hardware failures. The algorithm simplifies the data sent over the network with a new concave polygon creation algorithm which works in linear execution time, enabling fast computations for real-time games. The algorithm works by finding colliding polygons from other servers and the closest polygons based on distance to find nearby servers which information should be shared with. Those two algorithms at this time work in quadratic execution time which is a point of improvement, which could require the concave polygon to be converted into one or several convex polygons. The algorithm is designed to give the user good access on the amount of network traffic sent over the cluster which gives better control and understanding on how much network traffic that will be sent in the cluster. It shows that the algorithm is dependent on how players in the world are distributed over the servers. By having players nearby each other on the same server improves the result of the algorithm. It is shown that compared to having a centralized server, the network traffic on every single node have reduced network traffic than compared to a centralized server.In den här uppsatsen presenteras en icke-centraliserad algoritm som hittar närliggande servrar baserat på deras spelares positioner i ett massivt multi-spelare online spel med flera servrar. Syftet är att möjliggöra att spelare från olika servrar kan se varandra visuellt även fast de är uppkopplade till olika servrar. Genom att använda sig av ”peer-to-peer” kommunikation i klustret blir algoritmen tolerant mot hårdvarufel. Algoritmen simplifierar data som skickas genom en ny typ av konkav polygon algoritm vilken fungerar i linjär exekveringstid, vilket möjliggör snabba beräkningar för realtidsspel. Algoritmen fungerar genom att hitta kolliderande polygoner från andra servrar och även de mest närliggande baserat på distans för att lokalisera närliggande servrar att dela information med. De här två algoritmerna arbetar i kvadratisk tid vilket skulle kunna förbättras. Detta kan kräva att konkava polygonen konverteras till en eller flera konvexa polygoner. Algoritmen är designad för att ge användaren bra tillgång till hur mycket nätverkstrafik som bör skickas inom klustret vilket ger en bättre kontroll och förståelse över hur mycket data som kommer att skickas totalt. Det visas att algoritmen är beroende av hur spelarna är distribuerade över servrarna. Genom att ha närliggande spelare i världen på samma server förbättras resultatet av algoritmen. Det visas även att jämfört med en centraliserad server så förbättras nätverkstrafiken på varje enskild nod jämfört med trafiken som mottogs av den centraliserade servern.
Book chapters on the topic "Non-Centralized algorithms"
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Albers, Susanne, and Alexander Eckl. "Explorable Uncertainty in Scheduling with Non-uniform Testing Times." In Approximation and Online Algorithms, 127–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80879-2_9.
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AbstractThe problem of scheduling with testing in the framework of explorable uncertainty models environments where some preliminary action can influence the duration of a task. In the model, each job has an unknown processing time that can be revealed by running a test. Alternatively, jobs may be run untested for the duration of a given upper limit. Recently, Dürr et al. [4] have studied the setting where all testing times are of unit size and have given lower and upper bounds for the objectives of minimizing the sum of completion times and the makespan on a single machine. In this paper, we extend the problem to non-uniform testing times and present the first competitive algorithms. The general setting is motivated for example by online user surveys for market prediction or querying centralized databases in distributed computing. Introducing general testing times gives the problem a new flavor and requires updated methods with new techniques in the analysis. We present constant competitive ratios for the objective of minimizing the sum of completion times in the deterministic case, both in the non-preemptive and preemptive setting. For the preemptive setting, we additionally give a first lower bound. We also present a randomized algorithm with improved competitive ratio. Furthermore, we give tight competitive ratios for the objective of minimizing the makespan, both in the deterministic and the randomized setting.
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Umar, Raza, and Wessam Mesbah. "Throughput-Efficient Spectrum Access in Cognitive Radio Networks." In Advances in Wireless Technologies and Telecommunication, 454–77. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6571-2.ch017.
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Cognitive radio based on dynamic spectrum access has emerged as a promising technology to meet the insatiable demand for radio spectrum by the emerging wireless applications. In this chapter, the authors address the problem of throughput-efficient spectrum access in Cognitive Radio Networks (CRNs) using Coalitional Game-theoretic framework. They model the problem of joint Coalition Formation (CF) and Bandwidth (BW) allocation as a CF game in partition form with non-transferable utility and present a variety of algorithms to dynamically share the available spectrum resources among competing Secondary Users (SUs). First, the authors present a centralized solution to reach a sum-rate maximizing Nash-stable network partition. Next, a distributed CF algorithm is developed through which SUs may join/leave a coalition based on their individual preferences. Performance analysis shows that the CF algorithms with optimal BW allocation provides a substantial gain in the network throughput over existing coalition formation techniques as well as the simple cases of singleton and grand coalition.
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Wang, Yunlong, Yang Cai, and Yuan Shen. "Cooperative Localization in Wireless Networks." In Advances in Wireless Technologies and Telecommunication, 232–73. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3528-7.ch006.
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This chapter describes cooperative localization in wireless networks, where mobile nodes with unknown positions jointly infer their positions through measuring and exchanging information with each other. The technique of cooperation localization, efficiently even in harsh propagation environment, enables amounts of location-based services that rely on high-accuracy position information of mobile nodes. After a brief introduction of cooperative localization, the Cramer-Rao lower bound is given as a standard metric for performance. Then the information in the temporal and spatial domain is illustrated with geometrical interpretations. Two classes of cooperative localization algorithms, namely, centralized and distributed algorithms, are presented to show the implementation of the cooperative localization in a wireless network. Then the performance of cooperative localization under non-line-of-sight condition is analyzed. Lastly, numerical results are given to illustrate the performance of cooperative localization algorithms.
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Prakash, Ved, Suman Pandey, and Deepti Singh. "A Perspective View of Bio-Inspire Approaches Employing in Wireless Sensor Networks." In IoT-enabled Sensor Networks: Architecture, Methodologies, Security, and Futuristic Applications, 18–31. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815049480124060004.
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In this chapter, we discuss a bio-inspired computational model that utilizes heuristic techniques. This model is robust and possesses optimization capabilities to address obscure and substantiated problems. Swarm intelligence is an integral part of this bio-inspired model, functioning within groups. The nature of these algorithms is non-centralized, drawing inspiration from self-management to solve real-life complex computational problems. Examples include the traveling salesman problem, the shortest path problem, optimal fitness functions, security systems, and the use of optimal computational resources in various areas. The deployment of a Wireless Sensor Network involves a group of sensor nodes, typically implemented at remote locations to observe environmental behaviors. However, these sensor nodes operate on batteries, making replacement or recharge nearly impossible once deployed. Energy is a crucial resource for wireless sensor networks to extend their lifetime. While numerous concepts have been proposed to improve the lifespan of wireless sensor networks, many issues in Wireless Sensor Networks (WSN) are designed as multi-dimensional optimization problems. The bio-inspired model offers a solution to overcome these challenges. Swarm Intelligence proves to be a simple, efficient, and effective computational methodology for addressing various issues in wireless sensor networks, including node localization, clustering, data aggregation, and deployment. The Swarm Intelligence methodology encompasses several algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Reactive Search Optimization (RSO), Fish Swarm Algorithm (FSA), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Differential Evolution (DE). This chapter introduces Swarm Intelligence-based optimization algorithms and explores the impact of PSO in wireless sensor networks.
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Anitha, V. S., and M. P. Sebastian. "Multi-Purpose DS-Based Cluster Formation and Management in Mobile Ad Hoc Networks." In Innovations in Mobile Multimedia Communications and Applications, 1–20. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-563-6.ch001.
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This chapter proposes a scenario-based and diameter-bounded algorithm for cluster formation and management in mobile ad hoc networks (MANETs). A (k, r) -Dominating Set is used for the selection of clusterheads and gateway nodes depending on the topology of the network. Here k is the minimum number of clusterheads per node in the network and r is the maximum number of hops between the node and the clusterhead. The non-clusterhead node selects the most qualified dominating node as its clusterhead from among the k dominating nodes. The quality of the clusterhead is a function of various metrics, which include connectivity, stability and residual battery power. The long-term service as a clusterhead depletes its energy, causing it to drop out of the network. Similarly, the clusterhead with relatively high mobility than its neighbors leads to frequent clusterhead election process. This perturbs the stability of the network and can adversely affect the network performance. Load balancing among the clusterheads and correct positioning of the clusterhead in a cluster are vital to increase the lifespan of a network. The proposed centralized algorithm periodically calculates the quality of all dominating nodes in the network and if it goes below the threshold level it resigns the job as the clusterhead and sends this message to all other members in the cluster. Since these nodes have k dominating nodes within the r -hop distance, it can choose the current best-qualified node as its clusterhead. Simulation experiments are conducted to evaluate the performance of the algorithm in terms of the number of elements in the (k, r)-DS, the load balancing factor, the number of re-affiliations per unit time and the number of dominating set updates per unit time. The results establish the potential of this algorithm for use in MANETs.
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Zardari, Munwar Ali, and Low Tang Jung. "Classification of File Data Based on Confidentiality in Cloud Computing Using K-NN Classifier." In Cloud Security, 678–97. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8176-5.ch034.
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Cloud computing is a new paradigm model that offers different services to its customers. The increasing number of users for cloud services i.e. software, platform or infrastructure is one of the major reasons for security threats for customers' data. Some major security issues are highlighted in data storage service in the literature. Data of thousands of users are stored on a single centralized place where the possibility of data threat is high. There are many techniques discussed in the literature to keep data secure in the cloud, such as data encryption, private cloud and multiple clouds concepts. Data encryption is used to encrypt the data or change the format of the data into the unreadable format that unauthorized users could not understand even if they succeed to get access of the data. Data encryption is very expensive technique, it takes time to encrypt and decrypt the data. Deciding the security approach for data security without understanding the security needs of the data is a technically not a valid approach. It is a basic requirement that one should understand the security level of data before applying data encryption security approach. To discover the data security level of the data, the authors used machine learning approach in the cloud. In this paper, a data classification approach is proposed for the cloud and is implemented in a virtual machine named as Master Virtual Machine (Vmm). Other Vms are the slave virtual machines which will receive from Vmm the classified information for further processing in cloud. In this study the authors used three (3) virtual machines, one master Vmm and two slaves Vms. The master Vmm is responsible for finding the classes of the data based on its confidentiality level. The data is classified into two classes, confidential (sensitive) and non-confidential (non-sensitive/public) data using K-NN algorithm. After classification phase, the security phase (encryption phase) shall encrypt only the confidential (sensitive) data. The confidentiality based data classification is using K-NN in cloud virtual environment as the method to encrypt efficiently the only confidential data. The proposed approach is efficient and memory space friendly and these are the major findings of this work.
Conference papers on the topic "Non-Centralized algorithms"
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Iman Taheri, Seyed, Lucas Lima Rodrigues, Mauricio B. C. Salles, and Alfeu Joãozinho Sguarezi Filho. "A day-ahead hybrid optimization algorithm for finding the dispatch schedule of VPP in a distribution system." In Simpósio Brasileiro de Sistemas Elétricos - SBSE2020. sbabra, 2020. http://dx.doi.org/10.48011/sbse.v1i1.2476.
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Distributed renewable generations such as photovoltaic units are electricity generators for installing close to the loads on the distribution system. In this paper, the dispatch function of a non-centralized Virtual Power Plant (VPP) with having a photovoltaic unit in each bus is considered to optimize. This dispatch function is assigned based on the predicted load shape of the next day. A new day-ahead hybrid optimization algorithm is presented to optimize the dispatch function. The proposed algorithm implements a new hybrid combination of Particle Swarm Optimization (PSO) and Genetic Optimization (GA) algorithms simultaneously to benefit both algorithms’ advantages. The objective function is the optimization of the voltage deviation of the VPP. The suggested algorithm is executed on a 13-bus-radial IEEE standard VPP system using MATLAB software coupled with open-source software called Open-DSS. The results show the importance of the proposed algorithm to optimize the voltage deviation of the VPP. The superiority of the proposed algorithm is related to the accuracy and calculation velocity in comparison with the other tested evolutionary algorithms. The Distribution System Operator could map and move towards its full benefits of the increasing integration of DGs with a strategic placement that could keen prosumers on integrating these actions.
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Panetta, Chandler J., Osama N. Ennasr, and Xiaobo Tan. "Distributed Particle Filter With Online Model Learning for Localization Using Time-Difference-of-Arrival (TDOA) Measurements." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3305.
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Abstract The problem of localizing a moving target arises in various forms in wireless sensor networks. Deploying multiple sensing receivers and using the time-difference-of-arrival (TDOA) of the target’s emitted signal is widely considered an effective localization technique. Traditionally, TDOA-based algorithms adopt a centralized approach where all measurements are sent to a predefined reference node for position estimation. More recently, distributed TDOA-based localization algorithms have been shown to improve the robustness of these estimates. For target models governed by highly stochastic processes, the method of nonlinear filtering and state estimation must be carefully considered. In this work, a distributed TDOA-based particle filter algorithm is proposed for localizing a moving target modeled by a discrete-time correlated random walk (DCRW). We present a method for using data collected by the particle filter to estimate the unknown probability distributions of the target’s movement model, and then apply the distribution estimates to recursively update the particle filter’s propagation model. The performance of the distributed approach is evaluated through numerical simulation, and we show the benefit of using a particle filter with online model learning by comparing it with the non-adaptive approach.
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Garg, Ankit, Aman Sharma, Saurabh Rajvanshi, Abhinav Suman, Bhargab Goswami, Mahendra Prasad Yadav, DKJ Narayana, and Rajesh Tiwary. "Optimization of Gas Injection Network Using Genetic Algorithm: A Solution for Intermittent Gas Lift Wells." In SPE Canadian Energy Technology Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/218028-ms.
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Abstract Multiple intermittent gas lift (IGL) wells are typically connected to a centralized high-pressure gas source, which can result in significant fluctuations in gas injection header pressure and subsequent liquid surges in the well fluid header when gas injection is initiated simultaneously in multiple wells. To address the challenge of gas injection interference among intermittent gas lift wells, we propose a mathematical model that utilizes genetic algorithm to optimize the staggering of time cycles, with the goal of achieving minimal interference. Genetic algorithms approach provides an effective optimization technique for addressing the time cycle staggering in intermittent gas lift wells. The algorithm involves creating a population of potential solutions, representing each solution as a set of genes or chromosome. In the context of this model, the gas injection time slots for each well are encoded as chromosomes. The developed model utilizes input gas injection time cycles, to compute the best possible time slots for each well. By leveraging the principles of natural selection and evolution, the model iteratively computes the best possible time slots for each well, continuously improving the solutions until convergence is reached. This approach minimizes gas injection interference and enhances the efficiency of gas lift operations. The current field practice involves manually staggering the time cycle slots to minimize interference among wells, which becomes impractical with increased well and time slot numbers. Our developed model based on genetic algorithm optimization approach offers an automated and efficient solution for time cycle staggering in intermittent gas lift wells. Despite the NP-hard (non-deterministic polynomial-time hardness) nature of the problem, genetic algorithms provide an effective means of generating near-optimal solutions within a reasonable computational time. By minimizing gas injection interference, this optimization technique enhances the overall efficiency of gas lift operations, preventing production losses. Application of the developed model in the onshore oil field of ONGC demonstrated a significant reduction in gas injection header pressure fluctuations which improved the overall performance of the gas lift system. In this study effect of manually staggered gas injection time cycle, on gas injection network pressure fluctuations is also analysed. The population of wells employing intermittent gas lift mode is progressively growing as oil fields undergo browning. This advancement in optimization methodology holds great promise for the oil and gas industry, facilitating the optimization of gas injection time cycle slots leading to reduced pressure fluctuations and improved production efficiency.
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Neagu, Laurentiumarian, Teodormihai Cotet, Mihai Dascalu, Stefan Trausanmatu, Laura Badescu, and Eugen Simion. "SEMANTIC AUTHOR RECOMMENDATIONS BASED ON THEIR BIOGRAPHY FROM THE GENERAL ROMANIAN DICTIONARY OF LITERATURE." In eLSE 2019. Carol I National Defence University Publishing House, 2019. http://dx.doi.org/10.12753/2066-026x-19-022.
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The Romanian Language Dictionary is a centralized text repository which contains detailed biographies of all Romanian authors and can be used to perform various subsequent analyses. The aim of this paper is to introduce a novel method to recommend authors based on their biography from the Romanian Language Dictionary. Starting from multiple PDF input files made available by the "G. C?linescu" Institute of Literary History and Theory, we extracted relevant information on Romanian authors which was indexed into Elasticsearch, a non-relational database optimized for full-text indexing and search. The relevant information considers author's full name, their pseudonym (if any), year of birth and of death (if applicable), brief description (including studies, cities they lived in, important people they met, brief history), writings, critical references of others, etc. The indexed information is easily accessible through a RESTful API and provides a powerful starting point which may contribute to future Romanian cultural findings. Based on this consistent database, our aim is to create an interactive map showing all Romanian literature contributors, enabling the identification of similarities and differences between them based on specific features (e.g., similar writing styles, time periods, or similar text descriptions in terms of semantic models). In order to have a clearer image on how authors relate one to another, we employed the k-Means and agglomerative clustering algorithms from the Scikit-learn machine learning library. The results depict the distribution of Romanian authors throughout history and enable the identification of correlations between them based on the emerging clusters. This paper is a proof of concept that makes use of only the first volume of the Romanian Language Dictionary and represents the first step for follow-up analyses performed using the indexed dictionary.
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Rocha, Celso, Paulo Radatz, Carlos Almeida, and Nelson Kagan. "OPF-based Active Network Management Strategy for Distribution Networks with High Penetration of Distributed Generation." In Simpósio Brasileiro de Sistemas Elétricos - SBSE2020. sbabra, 2020. http://dx.doi.org/10.48011/sbse.v1i1.2490.
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This paper presents a near real-time strategy for Active Network Management (ANM) considering distribution networks with high penetration of Distributed Generation (DG). It is built upon a centralized framework and availability of a broad communication infrastructure. Generation curtailment level of Medium Voltage (MV), residential and commercial-scale Photovoltaic (PV) systems are considered as control variables to manage voltage and asset loading levels in MV and Low-Voltage (LV) distribution networks through a three-phase unbalanced Non-Linear (NL) Optimum Power Flow (OPF) algorithm. The effectiveness of the strategy in maintaining the regulatory operational levels, its robustness and the effect of the processing and communication delays are assessed by simulating a real Brazilian network with 788 control elements.
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Rangasamy, Kotteeswaran, Rangam Vijaya Ramraj, and G. Sivagurunathan. "Performance Assessment Of Conventional And Cuckoo Search Algorithm Based Centralized PID Controller For Multivariable Non-square System With RHP Zeros." In 2020 First IEEE International Conference on Measurement, Instrumentation, Control and Automation (ICMICA). IEEE, 2020. http://dx.doi.org/10.1109/icmica48462.2020.9242858.
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Nestinger, Stephen S., Harry H. Cheng, and Bo Chen. "Flexible Dynamic Task Allocation in Cooperative Mission-Based Robotic Systems." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87627.
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Mission-based multi-robot systems (MRS) require the ability to quickly adapt to different missions while maintaining the innate advantages of cooperation and fault tolerance. This requires a flexible architecture capable of adapting to real-time changes in the system while dynamically assigning tasks to available drones. The provided mobile agent-based dynamic task allocation architecture enables non-centralized methods for allocating tasks to a heterogeneous system of mobile robots. The architecture utilizes three types of intelligent software agents including a mobile task agent, stationary control agent, and a mobile behavior agent. A mobile task agent is used to automatically collect the next available task and communicates with the on-board stationary control agent in order to complete the desired task. A mobile behavior agent is used to automatically gather the robot specific behaviors necessary to execute the low-level reactive control system required for the task. A case study involving border patrolling demonstrates the feasibility of the dynamic task allocation architecture. A simple yet effective wall-following behavior algorithm is given.
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Fong, K. F., T. T. Chow, and V. I. Hanby. "Development of Optimal Design of Solar Water Heating System by Using Evolutionary Algorithm." In ASME 2005 International Solar Energy Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/isec2005-76189.
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There are growing initiatives to promote renewable energy in Hong Kong, particularly for solar energy. In order to encourage wider application of centralized solar water heating system for high-rise residential buildings, it is important to pursue an optimal design to get significant energy saving potential. In this regard, system optimization would be useful, as it can relate to a number of design variables of the solar water heating system such as the tilt angle and surface azimuth of the solar collectors, the storage capacity of the hot water calorifier, and the flow rate of the circulation pump set for the solar collectors. The objective function is to maximize the year-round energy saving by using the solar heating instead of conventional domestic electric heating. For the methodology of optimization, evolutionary programming, one of the paradigms of evolutionary algorithm, was applied. This has been proven to be effective for optimization problems with a non-linear and multi-dimensional nature. To generate values for the objective function, a TRNSYS plant simulation model was developed and coupled with the optimization algorithm. From the optimization results, it is suggested that the solar collectors can be installed onto the external shading devices as an integrated architectural feature, since the optimal tilt angle is 21° and relatively flat. The optimal surface azimuth is southwest 16° instead of due south. For the engineering design, both the optimal values of calorifier storage capacity and pump flow rate show that the calculations from normal design practice may not achieve an optimal performance. Therefore, an effective methodology of optimization and simulation is essential to generate an optimal design in a holistic approach.