Mobile edge computing (MEC) improves resource-limited mobile devices by transferring demanding computational activities to edge servers and cloud infrastructure, thereby mitigating issues related to user mobility and ...
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Mobile edge computing (MEC) improves resource-limited mobile devices by transferring demanding computational activities to edge servers and cloud infrastructure, thereby mitigating issues related to user mobility and network fluctuations. This paper introduces Flexible Mobility-Based Task Offloading (FlexiMRO), a mobility-oriented, fault-tolerant framework for adaptive task offloading in MEC and cloud settings. The suggested methodology incorporates a long short-term memory model to forecast user trajectories and network states with high transmission precision, alongside a deep reinforcement learning strategy utilizing Q-Deep networks for optimal job distribution at the edge and cloud layers. A random forest classifier guarantees fault tolerance by accurately predicting server failures and facilitating the reallocation of ongoing jobs. Comprehensive simulations illustrate the superiority of FlexiMRO compared to current methodologies, achieving a latency reduction of up to 70%, an enhancement in energy efficiency of 60%, and a failure rate of 80% in contrast to 70%. FlexiMRO offers a scalable and adaptable solution for 5G and IoT applications by minimizing latency, energy consumption, and quality of experience, efficiently utilizing edge and cloud computing to guarantee uninterrupted service delivery in dynamic MEC environments.
A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability w...
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A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial *** at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training *** it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality *** results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.
Multiplexing technology serves as an effective approach to increase both information storage and transmission ***,when exploring multiplexing methods across various dimensions,the polarization dimension encounters lim...
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Multiplexing technology serves as an effective approach to increase both information storage and transmission ***,when exploring multiplexing methods across various dimensions,the polarization dimension encounters limitations stemming from the finite orthogonal *** that only two mutually orthogonal polarizations are identifiable on the basic Poincarésphere,this poses a hindrance to polarization *** overcome this challenge,we propose a construction method for the optical polarized orthogonal matrix(OPOM),which is not constrained by the number of orthogonal ***,we experimentally validate its application in high-dimensional multiplexing of polarization *** explore polarization holography technology,capable of recording amplitude,phase,and polarization,for the purpose of recording and selective reconstruction of polarization *** research reveals that,despite identical polarization states,multiple images can be independently manipulated within distinct polarization channels through orthogonal polarization combinations,owing to the orthogonal selectivity among information.
The world of malware is shifting towards using encrypted traffic. While encryption improves the privacy of users, it brings challenges in the fields of QoS, QoE, and cybersecurity. Recent state-of-the-art Deep-Learnin...
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Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. A...
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In federated learning (FL), distributed users collaboratively train a neural network model under the coordination of a central server. However, during the training process, clients often exhibit time-varying availabil...
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ISBN:
(数字)9798350386059
ISBN:
(纸本)9798350386066
In federated learning (FL), distributed users collaboratively train a neural network model under the coordination of a central server. However, during the training process, clients often exhibit time-varying availability and have non-independent and non-identically distributed (non-IID) datasets. This results in system-induced bias, as models trained by the available clients do not accurately represent the entire population, which includes both available and unavailable clients. To address this bias, we propose a pricing-based incentive mechanism to encourage clients to adjust their availability. First, we model the strategic interaction among a large number of FL clients as a non-cooperative game under an arbitrary pricing scheme. We demonstrate that this game is a potential game, and its equilibrium can be found by solving an optimization problem. Second, based on equilibrium analysis, we derive an optimal pricing scheme for scenarios with a large client population. For general scenarios with any number of clients, we propose a bi-level optimization algorithm that utilizes Particle Swarm Optimization (PSO) to determine the optimal pricing scheme. This algorithm can effectively handles the intricate correlation between the equilibrium and pricing scheme. Our experimental results, based on real-world client availability datasets, highlight the effectiveness of our proposed incentive mechanism in mitigating system-induced bias, with improvements of up to 99.5% compared to the uniform pricing benchmark. Furthermore, this mechanism enhances the FL convergence rate by up to 3.43 times.
datascienceresearch is now transforming the world of data and information technology into a new crucial paradigm. Many academic researchers have gotten interested in this matter. The goal of this study is to provide...
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Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food ***,using the existing remote sensing-based crop yield models to accurately repr...
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Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food ***,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited ***,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing *** approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and ***,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the *** IPPs were determined using statistical yield data of reference years(2010–2015)over the *** findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),*** data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–*** addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city ***,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.
Cities are complex systems that develop under complicated interactions among their human and environmental *** generates substantial outcomes and opportunities while raising challenges including congestion,air polluti...
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Cities are complex systems that develop under complicated interactions among their human and environmental *** generates substantial outcomes and opportunities while raising challenges including congestion,air pollution,inequality,etc.,calling for efficient and reasonable solutions to sustainable ***,booming technologies generate large-scale data of complex cities,providing a chance to propose data-driven solutions for sustainable urban *** paper provides a comprehensive overview of data-driven urban sustainability *** this review article,we conceptualize MetaCity,a general framework for optimizing resource usage and allocation problems in complex cities with data-driven *** this framework,we decompose specific urban sustainable goals,e.g.,efficiency and resilience,review practical urban problems under these goals,and explore the probability of using data-driven technologies as potential solutions to the challenge of *** the basis of extensive urban data,we integrate urban problem discovery,operation of urban systems simulation,and complex decision-making problem solving into an entire cohesive framework to achieve sustainable development goals by optimizing resource allocation problems in complex cities.
Energy management is a crucial component of modern smart grid systems to ensure balance between supply-demand chains of power. The integration of complex structures for monitoring as well as existing techniques has en...
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