10 results
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2. Optimal relay nodes placement with game theory optimization for Wireless Sensor Networks.
- Author
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Radha, Subramanyam, Bala, G. Josemin, Rajkumar, Nalluri Prophess, Indumathi, G., and Nagabushanam, Perattur
- Subjects
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WIRELESS sensor networks , *GAME theory , *MATHEMATICAL optimization , *SENSOR networks , *UNDERGROUND pipelines , *FOREST monitoring , *ENERGY consumption , *LINEAR network coding - Abstract
Wireless Sensor Networks (WSN) play a major role in the wide variety of applications like underground pipeline and leaks monitoring, temperature distribution monitoring in industrial cyber systems, military, forest life monitoring, and environmental and geographical monitoring. Sensors are widely used in these different applications. The number of sensors and the application concerned mainly decides the energy consumption, network lifetime. In this process relay nodes may help the sensors as backbone to connect with sink node or base stations. In this paper, we introduce a new approach for relay node selection in WSN to minimize the energy consumption of the network. It uses channel aware relay selection technique using game theory optimization and act as a virtual backbone in connecting to the base station. However, the relay nodes are varied to check the optimal number of relays required for the small, medium and large number of nodes deployed in the network. Simulations are carried out using Network Simulator NS-2.35 and network is analyzed in wide variety of scenarios. Results show that the proposed relay node selection algorithm reduces energy consumption, improves lifetime, throughput of the network. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. Towards swarm optimization techniques for power communication systems and smart grid environments.
- Author
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Liu, Yongchao, Zhang, Tao, Li, Wenfang, Cheng, Tingting, and Zhang, Yaping
- Subjects
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TELECOMMUNICATION systems , *SMART power grids , *MATHEMATICAL optimization , *GRIDS (Cartography) , *SMART devices , *SWARM intelligence - Abstract
Today, continuous technical and emerging advances between power communication systems and smart grids and applying swarm intelligence have increased for data sharing and analytics in our life. On the other side, Internet of things (IoT) has important key role to establish constructive interactions between smart devices and smart grid and power communication applications. For enhancing data transformation and improvements of multi-objective Quality of Service (QoS) factors, Swarm Optimization Techniques (SOT) are applied simultaneously in a cooperative smart environment to solve NP-hard problems. This paper provides a comprehensive analysis to address a new technical taxonomy and categorization of existing SOT-based smart grid applications in power communication systems in the IoT. Also, existing service and resource management case studies on smart grids and power communication systems are briefly analyzed and discussed. Existing evaluation factors on smart grid applications using SOT are represented. Possible advantages and weaknesses of each category are discussed with respect to new challenges and open research directions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. Load balancing strategy in software defined network by improved whale optimization algorithm.
- Author
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Darade, Santosh Ashokrao and Akkalakshmi, M.
- Subjects
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SOFTWARE-defined networking , *MATHEMATICAL optimization , *ALGORITHMS , *WHALES , *TIME management , *HIGH performance computing - Abstract
From the recent study, it is observed that even though cloud computing grants the greatest performance in the case of storage, computing, and networking services, the Internet of Things (IoT) still suffers from high processing latency, awareness of location, and least mobility support. To address these issues, this paper integrates fog computing and Software-Defined Networking (SDN). Importantly, fog computing does the extension of computing and storing to the network edge that could minimize the latency along with mobility support. Further, this paper aims to incorporate a new optimization strategy to address the "Load balancing" problem in terms of latency minimization. A new Thresholded-Whale Optimization Algorithm (T-WOA) is introduced for the optimal selection of load distribution coefficient (time allocation for doing a task). Finally, the performance of the proposed model is compared with other conventional models concerning latency. The simulation results prove that the SDN based T-WOA algorithm could efficiently minimize the latency and improve the Quality of Service (QoS) in Software Defined Cloud/Fog architecture. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
5. Trust-based secure routing in IoT network based on rider foraging optimization algorithm.
- Author
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Amit Vijay, Kore and Manoj Ranjan, Mishra
- Subjects
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MATHEMATICAL optimization , *INTERNET of things , *ROUTING algorithms , *ENERGY consumption - Abstract
Due to the mobility and frequent topology changes, routing the data poses a challenging issue in the Internet of Things (IoT). Security poses a fundamental requirement factor in most IoT applications particularly, secure routing among the IoT nodes. The aim of this paper is to develop secure routing in IoT with minimal delay, minimal energy consumption and high throughput. Thus, a new algorithm, named Rider Foraging Optimization algorithm (RFO) is designed by the incorporation of standard Rider Optimization algorithm (ROA) and Bacterial Foraging optimization algorithm (BFO) that render an optimal solution, which is the optimal path for transmitting the information. Initially, the trust of the nodes is evaluated and the cluster head (CH) is chosen based on Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol to securely transmit the data. Then, the proposed RFO algorithm selects the optimal path using the fitness factors, such as trust, energy, and delay constraints. The results revealed that the energy consumption of the nodes is reduced and the lifetime of the network is enhanced through the implementation of the proposed RFO, which attained a maximal throughput of 1, and minimal delay and energy consumption of 0.1169 and 0.0002, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. Design of filter coefficients for optimal factored truncated cascade FIR filter using optimization algorithm.
- Author
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Srivatsan, K.
- Subjects
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FINITE impulse response filters , *MATHEMATICAL optimization , *SIGNAL filtering , *ALGORITHMS , *TELECOMMUNICATION systems , *EARTHQUAKE magnitude - Abstract
Signal filtering acts as one of the basic requirement of communication networks for the removal of unwanted features from the signal. The design of appropriate digital filter requires the selection of optimal filter coefficients for the generation of desired frequency response with reduced hardware complexity. This paper proposes a hybrid optimization algorithm named as Brain Storm- Grey Wolf Optimizer (BSGWO) algorithm for the selection of filter coefficients in the design of factored truncated cascade FIR filter. The proposed algorithm is the hybridization of the optimization algorithms, namely Brain Storm Optimization (BSO) and Grey Wolf Optimizer (GWO). The input signal is interpolated initially for the formation of an intermediate signal using the FIR filter. Then, the factored truncated cascade filter is developed for the interpolation of the signal. After designing the filter coefficients, the optimal selection of the filter coefficients is performed using the proposed BSGWO algorithm. The original filter is developed with the use of the least square estimation and the new filter is developed using the proposed algorithm that tunes the filter coefficients. The performance of the proposed system is analyzed using the metrics, such as fitness, Mean Absolute Error (MAE), magnitude, and the number of components. The proposed method produces minimum fitness, MAE, magnitude and number of components of 0.05, 0.0155, − 96.0 dB and 3372, respectively that shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
7. QoS and QoE aware multi objective resource allocation algorithm for cloud gaming.
- Author
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Désiré, Koné Kigninman, Dhib, Eya, Tabbane, Nabil, and Asseu, Olivier
- Subjects
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RESOURCE allocation , *ALGORITHMS , *COMPUTER systems , *MATHEMATICAL optimization , *VIDEO games , *SEARCH algorithms , *DISTRIBUTED algorithms - Abstract
Cloud gaming is an innovative model that congregates video games. The user may have different Quality-of-Experience (QoE), which is a term used to measure a user's level of satisfaction and enjoyment for a particular service. To guarantee general satisfaction for all users with limited cloud resources, it becomes a major issue in the cloud. This paper leverages a game theory in the cloud gaming model with resource optimization to discover optimal solutions to resolve resource allocation. The Rider-based harmony search algorithm (Rider-based HSA), which is the combination of Rider optimization algorithm (ROA) and Harmony search algorithm (HSA), is proposed for resource allocation to improve the cloud computing system's efficiency. The fitness function is newly devised considering certain QoE parameters, which involves fairness index, Quantified experience of players (QE), and Mean Opinion Score (MOS). The proposed Rider-based HSA showed better performance compared to Potential game-based optimization algorithm, Proactive resource allocation algorithm, QoE-aware resource allocation algorithm, Distributed algorithm, and Yusen Li et al., with maximal fairness of 0.999, maximal MOS of 0.873, and maximal QE of 1. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
8. Flow scheduling strategies for minimizing flow completion times in data center networks.
- Author
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Hu, Chao, Liu, Bo, Xing, Changyou, Ding, Ke, Xu, Bo, Wei, Xianglin, and Zhang, Xiaoming
- Subjects
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DATA libraries , *COMPUTER scheduling , *MATHEMATICAL models , *COMPUTER networks , *MATHEMATICAL optimization , *PROBABILITY theory - Abstract
Minimizing the flow completion time (FCT) is widely considered as an important optimization goal in designing data center networks. However, existing schemes either rely on the precondition that the size and deadline of each flow is known in advance, or require modifying the switch hardware, which is hard to implement in practice. In this paper, we present a flexible and dynamic flow scheduling strategy, namely, Maximal Completion Probability First (MCPF), to reduce the FCT. This strategy is based on the estimated probabilities of each flow to finish the transmission in a period time, and these flows which have higher completion probabilities are assigned with higher priority. Meanwhile, switches perform flow scheduling according to these priorities. We employ a queueing theory based mathematical model to analyze the average FCT of MCPF, and compare it with other two flow scheduling strategies. We also introduce the challenges and the solutions to implement MCPF in realistic networks. Finally, we evaluate the performance of MCPF in Mininet. The analysis and experimental results show that MCPF could effectively reduce the FCT. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
9. SNOT-WiFi: Sensor network-optimized training for wireless fingerprinting.
- Author
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Balzano, Walter, Murano, Aniello, and Vitale, Fabio
- Subjects
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HUMAN fingerprints , *WIRELESS Internet , *UBIQUITOUS computing , *GLOBAL Positioning System , *WIRELESS sensor networks , *MATHEMATICAL optimization - Abstract
Ubiquitous computing and contextual services have growing importance in nowadays literature due to the current availability of smart personal devices like smartphones. It is becoming increasingly important to have a reliable way to localize users in any possible scenario. In outdoor it's possible to use GNSS based systems like GPS or GLONASS, but for indoor scenarios a low-energy and affordable positioning system is still under heavy research. In this setting, wireless-based techniques like WiFi fingerprinting allow exploiting of currently available infrastructures to allow reasonable positioning accuracy while keeping power consumption at a minimum. WiFi fingerprinting systems operate in two phases: one training phase, in which signals are collected with regards to different spots in the interesting area, and one usage or tracking phase in which recorded data are used to localize users in space. The usage phase is effective and reliable, but the training phase can be very time consuming for large areas and it has to be repeated over time in order to maintain localization accuracy, in case of network structure changes or to adapt to environmental changes (like humidity, pressure or temperature). In this paper we propose a novel framework which makes use of an appropriate Wireless Sensor Network (WSN) which allows continuous training over time, in order to achieve real-time updating of the fingerprints database with no human intervention. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
10. An algorithm to detect complexes in PPI network based on harmony search clustering optimization.
- Author
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Le Chen, Linquan Xie, Shuxin Yang, Ying Cao, Ge Huang, and Xiaorong Li
- Subjects
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PROTEIN-protein interactions , *INTERMOLECULAR interactions , *MATHEMATICAL optimization , *ALGORITHMS , *MATHEMATICAL complexes - Abstract
The detection of complexes in protein-protein interaction (PPI) network bears great significance in predicting unknown protein and providing theoretical basis for various researches on a wide array of diseases. In this paper, a PPI network complexes detection algorithm based on Harmony Search Clustering Optimization Model (HMS-CD) is being proposed. Compared with the traditional harmony search algorithm, dynamic parameter of HMCR and BW is introduced to improve the search strategy. The method to find a set of nodes with a larger aggregation coefficient in PPI network is regarded as the objective function of the proposed algorithm. The experimental result on a real dataset like yeast PPI network shows that the proposed algorithm achieves better detection accuracy than traditional harmony search (HMS) algorithm and typical MCODE algorithm, and it can detect complexes in PPI network more effectively. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
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