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Journal articles on the topic 'Time-Varying capacity networks'
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Sas, Bart, Elena Bernal-Mor, Kathleen Spaey, Vicent Pla, Chris Blondia, and Jorge Martinez-Bauset. "Modelling the time-varying cell capacity in LTE networks." Telecommunication Systems 55, no. 2 (August 8, 2013): 299–313. http://dx.doi.org/10.1007/s11235-013-9782-2.
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KUMARI, SUCHI, and ANURAG SINGH. "TIME-VARYING NETWORK MODELING AND ITS OPTIMAL ROUTING STRATEGY." Advances in Complex Systems 21, no. 02 (March 2018): 1850006. http://dx.doi.org/10.1142/s0219525918500066.
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Since all the existing real world networks are evolving, the study of traffic dynamics is a challenging task. Avoidance of traffic congestion, system utility maximization and enhancement of network capacity are prominent issues. Network capacity may be improved either by optimizing network topology or enhancing in routing approach. In this context, we propose and design a model of the time-varying data communication networks (TVCN) based on the dynamics of inflowing links. Traffic congestion can be avoided by using a suitable centrality measure, especially betweenness and Eigen vector centralities. If the nodes coming in user’s route are most betweenness central, then that route will be highly congested. Eigen vector centrality is used to find the influence of a node on others. If a node is most influential, then it will be highly congested and considered as least reputed. For that reason, routes are chosen such that the sum of the centralities of the nodes coming in user’s route should be minimum. Furthermore, Kelly’s optimization formulation for a rate allocation problem is used for obtaining optimal rates of distinct users at different time instants and it is found that the user’s path with lowest betweenness centrality and highest reputation will always give maximum rate at the stable point.
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Bhadra, Sandeep, Yingdong Lu, and Mark S. Squillante. "Optimal capacity planning in stochastic loss networks with time-varying workloads." ACM SIGMETRICS Performance Evaluation Review 35, no. 1 (June 12, 2007): 227–38. http://dx.doi.org/10.1145/1269899.1254909.
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Abrantes, F., Joao Taveira Araújo, and M. Ricardo. "Explicit Congestion Control Algorithms for Time Varying Capacity Media." IEEE Transactions on Mobile Computing 10, no. 1 (January 2011): 81–93. http://dx.doi.org/10.1109/tmc.2010.143.
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Amiri, Ali, and Hasan Pirkul. "Routing and capacity assignment in backbone communication networks under time varying traffic conditions." European Journal of Operational Research 117, no. 1 (August 1999): 15–29. http://dx.doi.org/10.1016/s0377-2217(98)00162-3.
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Supittayapornpong, Sucha, and Poompat Saengudomlert. "Joint Flow Control, Routing and Medium Access Control in Random Access Multi-Hop Wireless Networks with Time Varying Link Capacities." ECTI Transactions on Electrical Engineering, Electronics, and Communications 8, no. 1 (August 1, 2009): 22–31. http://dx.doi.org/10.37936/ecti-eec.201081.171988.
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This work extends the existing static framework for joint flow control, routing and medium access control (MAC) in random access multi-hop wireless networks to a dynamic framework where link capacities vary over time. The overall problem is formulated as a long term network utility maximization (NUM) problem (instead of the existing static NUM problem) that accounts for link capacity variation. This dynamic formulation is more realistic than the static one, and is one step closer to practical networks. Under the stationary and ergodic assumptions on the link capacity variation, the problem is decomposed to form a distributed algorithm. The algorithm samples current link capacities while it is iteratively and locally updating flow rates and link transmission probabilities. Simulation results demonstrate the ability of the algorithm to sustain the optimal average data rates despite the link capacity variation.
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Huang, Ping, Xiao-Long Chen, Ming Tang, and Shi-Min Cai. "Coupled Dynamic Model of Resource Diffusion and Epidemic Spreading in Time-Varying Multiplex Networks." Complexity 2021 (March 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/6629105.
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In the real world, individual resources are crucial for patients when epidemics outbreak. Thus, the coupled dynamics of resource diffusion and epidemic spreading have been widely investigated when the recovery of diseases significantly depends on the resources from neighbors in static social networks. However, the social relationships of individuals are time-varying, which affects such coupled dynamics. For that, we propose a coupled resource-epidemic (RNR-SIS) dynamic model (coupled model for short) on a time-varying multiplex network to synchronously simulate the resource diffusion and epidemic spreading in dynamic social networks. The equilibrium analysis of the coupled model is conducted in a general scenario where the resource generation varies between susceptible and infected states and the recovery rate changes between resourceful and noresource states. By using the microscopic Markov chain approach and Monte Carlo simulations, we determine a probabilistic framework of the intralayer and interlayer dynamic processes of the coupled model and obtain the outbreak threshold of epidemic spreading. Meanwhile, the experimental results show the trivially asymmetric interactions between resource diffusion and epidemic spreading. They also indicate that the stronger activity heterogeneity and the larger contact capacity of individuals in the resource layer can more greatly promote resource diffusion, effectively suppressing epidemic spreading. However, these two individual characters in the epidemic layer can cause more resource depletion, which greatly promotes epidemic spreading. Furthermore, we also find that the contact capacity finitely impacts the coupled dynamics of resource diffusion and epidemic spreading.
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Xu, Yao, Renren Wang, Hongqian Lu, Xingxing Song, Yahan Deng, and Wuneng Zhou. "Adaptive Event-Triggered Synchronization of Networked Neural Networks with Time-Varying Delay Subject to Actuator Saturation." Complexity 2021 (July 7, 2021): 1–14. http://dx.doi.org/10.1155/2021/9957624.
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This paper discusses the adaptive event-triggered synchronization problem of a class of neural networks (NNs) with time-varying delay and actuator saturation. First, in view of the limited communication channel capacity of the network system and unnecessary data transmission in the NCSs, an adaptive event-triggered scheme (AETS) is introduced to reduce the network load and improve network utilization. Second, under the AETS, the synchronization error model of the delayed master-slave synchronization system is constructed with actuator saturation. Third, based on Lyapunov–Krasovskii functional (LKF), a new sufficient criterion to guarantee the asymptotic stability of the synchronization error system is derived. Moreover, by solving the stability criterion expressed in the form of a set of linear matrix inequalities (LMIs), some necessary parameters of the system are obtained. At last, two examples are expressed to demonstrate the feasibility of this method.
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Shao, Junyi, Shuai Zhang, Weiqiang Sun, and Weisheng Hu. "Dimensioning access link capacity for time-varying traffic with mixed packet streams and circuit connections." Journal of Optical Communications and Networking 13, no. 11 (August 20, 2021): 276. http://dx.doi.org/10.1364/jocn.432651.
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Kennedy, Okokpujie, Emmanuel Chukwu, Olamilekan Shobayo, Etinosa Noma-Osaghae, Imhade Okokpujie, and Modupe Odusami. "Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (February 1, 2019): 359. http://dx.doi.org/10.11591/ijece.v9i1.pp359-368.
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This paper demonstrates the robustness of active queue management techniques to varying load, link capacity and propagation delay in a wireless environment. The performances of four standard controllers used in Transmission Control Protocol/Active Queue Management (TCP/AQM) systems were compared. The active queue management controllers were the Fixed-Parameter Proportional Integral (PI), Random Early Detection (RED), Self-Tuning Regulator (STR) and the Model Predictive Control (MPC). The robustness of the congestion control algorithm of each technique was documented by simulating the varying conditions using MATLAB® and Simulink® software. From the results obtained, the MPC controller gives the best result in terms of response time and controllability in a wireless network with varying link capacity and propagation delay. Thus, the MPC controller is the best bet when adaptive algorithms are to be employed in a wireless network environment. The MPC controller can also be recommended for heterogeneous networks where the network load cannot be estimated.
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Karimi, Hamid Reza, Neil A. Duffie, and Sergey Dashkovskiy. "Local Capacity $H_{\infty}$ Control for Production Networks of Autonomous Work Systems With Time-Varying Delays." IEEE Transactions on Automation Science and Engineering 7, no. 4 (October 2010): 849–57. http://dx.doi.org/10.1109/tase.2010.2046735.
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Islam, Md Tariqul, M. Jahangir Hossain, Md Ahasan Habib, and Muhammad Ahsan Zamee. "Adaptive Hosting Capacity Forecasting in Distribution Networks with Distributed Energy Resources." Energies 18, no. 2 (January 9, 2025): 263. https://doi.org/10.3390/en18020263.
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The sustainable integration of distributed energy resources (DER) into distribution networks requires accurate forecasting of hosting capacity. The network and DER variables alone do not capture the full range of external influences on DER integration. Traditional models often overlook the dynamic impacts of these exogenous factors, leading to suboptimal predictions. This study introduces a Sensitivity-Enhanced Recurrent Neural Network (SERNN) model, featuring a sensitivity gate within the neural network’s memory cell architecture to enhance responsiveness to time-varying variables. The sensitivity gate dynamically adjusts the model’s response based on external conditions, allowing for improved capture of input variability and temporal characteristics of the distribution network and DER. Additionally, a feedback mechanism within the model provides inputs from previous cell states into the forget gate, allowing for refined control over input selection and enhancing forecasting precision. Through case studies, the model demonstrates superior accuracy in hosting capacity predictions compared to baseline models like LSTM, ConvLSTM, Bidirectional LSTM, Stacked LSTM, and GRU. Study shows that the SERNN achieves a mean absolute error (MAE) of 0.2030, a root mean square error (RMSE) of 0.3884 and an R-squared value of 0.9854, outperforming the best baseline model by 48 per cent in MAE and 71 per cent in RMSE. Additionally, Feature engineering enhances the model’s performance, improving the R-squared value from 0.9145 to 0.9854. The sensitivity gate also impacts the model’s performance, lowering MAE to 0.2030 compared to 0.2283 without the sensitivity gate, and increasing the R-squared value from 0.9152 to 0.9854. Incorporating exogenous factors such as the time of day as a sensitivity gate input, further improves responsiveness, making the model more adaptable to real-world conditions. This advanced SERNN model offers a reliable framework for distribution network operators, supporting intelligent planning and proactive DER management. Ultimately, it provides a significant step forward in hosting capacity analysis, enabling more efficient and sustainable DER integration within next-generation distribution networks.
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Андрій Мазарчук and Ганна Більовська. "УРАХУВАННЯ СПЕЦИФІКИ ТОРГОВЕЛЬНИХ МЕРЕЖ У МОДЕЛЯХ ТРАНСПОРТНОЇ ЗАДАЧІ." International Journal of Innovative Technologies in Economy, no. 8(20) (November 30, 2018): 26–31. http://dx.doi.org/10.31435/rsglobal_ijite/30112018/6211.
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The article presents the results of the study of specific features of retail networks and ways of taking into account these features in the formation of a comprehensive model of management of product transportation in the network. The proposed comprehensive model takes into account such features of the retail network as multiproduct situation, indistinguishability of suppliers and consumers, availability of transit points, cost of storage at the transit point, capacity of warehouses and their load, transportation time and shelf life, priority of outlets and types of products, restrictions on capacity of transport. The proposed model allows solving multiproduct transport problems for trading networks of varying complexity, enabling decision makers to change the complexity of the model.
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Wang, Huan, Hai-Feng Zhang, Pei-Can Zhu, and Chuang Ma. "Interplay of simplicial awareness contagion and epidemic spreading on time-varying multiplex networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 8 (August 2022): 083110. http://dx.doi.org/10.1063/5.0099183.
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There has been growing interest in exploring the dynamical interplay of epidemic spreading and awareness diffusion within the multiplex network framework. Recent studies have demonstrated that pairwise interactions are not enough to characterize social contagion processes, but the complex mechanisms of influence and reinforcement should be considered. Meanwhile, the physical social interaction of individuals is not static but time-varying. Therefore, we propose a novel sUAU-tSIS model to characterize the interplay of simplicial awareness contagion and epidemic spreading on time-varying multiplex networks, in which one layer with 2-simplicial complexes is considered the virtual information layer to address the complex contagion mechanisms in awareness diffusion and the other layer with time-varying and memory effects is treated as the physical contact layer to mimic the temporal interaction pattern among population. The microscopic Markov chain approach based theoretical analysis is developed, and the epidemic threshold is also derived. The experimental results show that our theoretical method is in good agreement with the Monte Carlo simulations. Specifically, we find that the synergistic reinforcement mechanism coming from the group interactions promotes the diffusion of awareness, leading to the suppression of the spreading of epidemics. Furthermore, our results illustrate that the contact capacity of individuals, activity heterogeneity, and memory strength also play important roles in the two dynamics; interestingly, a crossover phenomenon can be observed when investigating the effects of activity heterogeneity and memory strength.
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Iacob, Stefan, and Joni Dambre. "Exploiting Signal Propagation Delays to Match Task Memory Requirements in Reservoir Computing." Biomimetics 9, no. 6 (June 14, 2024): 355. http://dx.doi.org/10.3390/biomimetics9060355.
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Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far, it is still unclear why distance-based delay networks (DDNs) perform better than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have a greater total linear memory capacity, with the same amount of non-linear processing power.
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Li, Jin, Hongping Zhang, Huasheng Liu, and Shiyan Wang. "Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network." Sustainability 16, no. 2 (January 11, 2024): 647. http://dx.doi.org/10.3390/su16020647.
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Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.
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Cen, Yi, Mingliu Liu, Deshi Li, Kaitao Meng, and Huihui Xu. "Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks." Sensors 21, no. 6 (March 23, 2021): 2252. http://dx.doi.org/10.3390/s21062252.
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The communication channel in underwater acoustic sensor networks (UASNs) is time-varying due to the dynamic environmental factors, such as ocean current, wind speed, and temperature profile. Generally, these phenomena occur with a certain regularity, resulting in a similar variation pattern inherited in the communication channels. Based on these observations, the energy efficiency of data transmission can be improved by controlling the modulation method, coding rate, and transmission power according to the channel dynamics. Given the limited computational capacity and energy in underwater nodes, we propose a double-scale adaptive transmission mechanism for the UASNs, where the transmission configuration will be determined by the predicted channel states adaptively. In particular, the historical channel state series will first be decomposed into large-scale and small-scale series and then be predicted by a novel k-nearest neighbor search algorithm with sliding window. Next, an energy-efficient transmission algorithm is designed to solve the problem of long-term modulation and coding optimization. In particular, a quantitative model is constructed to describe the relationship between data transmission and the buffer threshold used in this mechanism, which can then analyze the influence of buffer threshold under different channel states or data arrival rates theoretically. Finally, numerical simulations are conducted to verify the proposed schemes, and results show that they can achieve good performance in terms of channel prediction and energy consumption with moderate buffer length.
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Lian, Xie, Xiaolong Hu, Liangsheng Shi, Jinhua Shao, Jiang Bian, and Yuanlai Cui. "Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning." Water 16, no. 6 (March 20, 2024): 896. http://dx.doi.org/10.3390/w16060896.
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The parameters of the GR4J-CemaNeige coupling model (GR4neige) are typically treated as constants. However, the maximum capacity of the production store (parX1) exhibits time-varying characteristics due to climate variability and vegetation coverage change. This study employed differentiable parameter learning (dPL) to identify the time-varying parX1 in the GR4neige across 671 catchments within the United States. We built two types of dPL, including static and dynamic parameter networks, to assess the advantages of the time-varying parameter. In the dynamic parameter network, we evaluated the impact of potential evapotranspiration (PET), precipitation (P), temperature (T), soil moisture (SM), and normalized difference vegetation index (NDVI) datasets on the performance of dPL. We then compared dPL with the empirical functional method (fm). The results demonstrated that the dynamic parameter network outperformed the static parameter network in streamflow estimation. There were differences in streamflow estimation among the dynamic parameter network driven by various input features. In humid catchments, simultaneously incorporating all five factors, including PET, P, T, SM, and the NDVI, achieved optimal streamflow simulation accuracy. In arid catchments, it was preferable to introduce PET, T, and the NDVI separately for improved performance. dPL significantly outperformed the empirical fm in estimating streamflow and uncalibrated intermediate variables, like evapotranspiration (ET). Both the derived parX1 from dPL and the empirical fm exhibited significant spatiotemporal variation across 671 catchments. Notably, compared to parX1 obtained through the empirical fm, parX1 derived from dPL exhibited a distinct spatial clustering pattern. This study highlights the potential of dPL in enhancing model accuracy and contributes to understanding the spatiotemporal variation characteristics of parX1 under the influence of climate factors, soil conditions, and vegetation change.
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Sheng, Jiayin, Xinjie Guan, Fuliang Yang, and Xili Wan. "An Accelerated Maximum Flow Algorithm with Prediction Enhancement in Dynamic LEO Networks." Sensors 25, no. 8 (April 17, 2025): 2555. https://doi.org/10.3390/s25082555.
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Efficient data transmission in low Earth orbit (LEO) satellite networks is critical for supporting real-time global communication, Earth observation, and numerous data-intensive space missions. A fundamental challenge in these networks involves solving the maximum flow problem, which determines the optimal data throughput across highly dynamic topologies with limited onboard energy and data processing capability. Traditional algorithms often fall short in these environments due to their high computational costs and inability to adapt to frequent topological changes or fluctuating link capacities. This paper introduces an accelerated maximum flow algorithm specifically designed for dynamic LEO networks, leveraging a prediction-enhanced approach to improve both speed and adaptability. The proposed algorithm integrates a novel energy-time expanded graph (e-TEG) framework, which jointly models satellite-specific constraints including time-varying inter-satellite visibility, limited onboard processing capacities, and dynamic link capacities. In addition, a learning-augmented warm-start strategy is introduced to enhance the Ford–Fulkerson algorithm. It generates near-optimal initial flows based on historical network states, which reduces the number of augmentation steps required and accelerates computation under dynamic conditions. Theoretical analyses confirm the correctness and time efficiency of the proposed approach. Evaluation results validate that the prediction-enhanced approach achieves up to a 32.2% reduction in computation time compared to conventional methods, particularly under varying storage capacity and network topologies. These results demonstrate the algorithm’s potential to support high-throughput, efficient data transmission in future satellite communication systems.
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Yang, Xu, Hai Fang, Yuan Gao, Xingjie Wang, Kan Wang, and Zheng Liu. "Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks." Sensors 23, no. 24 (December 17, 2023): 9885. http://dx.doi.org/10.3390/s23249885.
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Traditional low earth orbit (LEO) satellite networks are typically independent of terrestrial networks, which develop relatively slowly due to the on-board capacity limitation. By integrating emerging mobile edge computing (MEC) with LEO satellite networks to form the business-oriented “end-edge-cloud” multi-level computing architecture, some computing-sensitive tasks can be offloaded by ground terminals to satellites, thereby satisfying more tasks in the network. How to make computation offloading and resource allocation decisions in LEO satellite edge networks, nevertheless, indeed poses challenges in tracking network dynamics and handling sophisticated actions. For the discrete-continuous hybrid action space and time-varying networks, this work aims to use the parameterized deep Q-network (P-DQN) for the joint computation offloading and resource allocation. First, the characteristics of time-varying channels are modeled, and then both communication and computation models under three different offloading decisions are constructed. Second, the constraints on task offloading decisions, on remaining available computing resources, and on the power control of LEO satellites as well as the cloud server are formulated, followed by the maximization problem of satisfied task number over the long run. Third, using the parameterized action Markov decision process (PAMDP) and P-DQN, the joint computing offloading, resource allocation, and power control are made in real time, to accommodate dynamics in LEO satellite edge networks and dispose of the discrete-continuous hybrid action space. Simulation results show that the proposed P-DQN method could approach the optimal control, and outperforms other reinforcement learning (RL) methods for merely either discrete or continuous action space, in terms of the long-term rate of satisfied tasks.
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Babaei, Farshad, Amin Safari, Meisam Farrokhifar, Mahmoud Ayish Younis, and Anas Quteishat. "Participation of Electric Vehicles in a Delay-Dependent Stability Analysis of LFC Considering Demand Response Control." Electronics 11, no. 22 (November 10, 2022): 3682. http://dx.doi.org/10.3390/electronics11223682.
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Today, time-varying delays may result from a communication network’s engagement in frequency control services. These delays have an impact on the effectiveness of the load frequency control (LFC) system, which can occasionally lead to power system instability. The electric vehicle (EV) can be used as a beneficial source for the LFC system with the development of demand-side response due to its vehicle-to-grid capacity. Although demand response control has certain advantages for the power system, communication networks used in LFC systems result in time delays that reduce the stability of the LFC schemes. A stability study of an LFC system, comprising an EV aggregator with two additive time-varying delays, is demonstrated in this work. An enhanced Lyapunov–Krasovskii functional (LKF), which incorporates time-varying delays using the linear matrix inequality approach, is used to perform a delay-dependent stability analysis of the LFC to determine the stability zone and criterion. In conclusion, the efficiency of the proposed stability criterion is validated by making use of the thorough simulation findings.
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Su, Huanyin, Wencong Tao, and Xinlei Hu. "A Line Planning Approach for High-Speed Rail Networks with Time-Dependent Demand and Capacity Constraints." Mathematical Problems in Engineering 2019 (March 17, 2019): 1–18. http://dx.doi.org/10.1155/2019/7509586.
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In high-speed rail networks, trains are operated with high speeds and high frequencies, which can satisfy passenger demand with different expected departure times. Given time-dependent demand, this paper proposes a line planning approach with capacity constraints for high-speed rail networks. In this paper, a bilevel optimization model is formulated and the constraints include track section capacity per unit time, train seat capacity, and the gap between the number of starting trains and that of ending trains at a station. In the upper level, the objective is to minimize train operational cost and passenger travel cost, and the decision variables include the line of each train, carriage composition of each train, train stop patterns, train start times, and train arrival and departure times at stops in the line plan. In the lower level, a schedule-based passenger assignment method, which assigns time-varying demand on trains with seat capacity constraints by simulating the ticket-booking process, is used to evaluate the line plan obtained in the upper level. A simulated annealing algorithm is developed to solve the model in which some strategies are designed to search for neighborhood solutions, including reducing train carriages, deleting trains, adding trains, increasing train carriages, and adjusting train start times. Finally, an application to the Chinese high-speed rail network is presented. The numerical results show that (i) the average time deviations between the expected departure times and the actual boarding times of passengers are within 30 min, (ii) the unserved passengers are less than 200, and (iii) the average load factors of trains are about 70%. Hence, line plan solutions meet time-dependent demand well and satisfy the capacity constraints for high-speed rail networks.
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Şahin, Gökhan, and Murat Azizoğlu. "Optical layer survivability for single and multiple service classes." Journal of High Speed Networks 10, no. 2 (January 1, 2001): 91–108. https://doi.org/10.3233/hsn-2001-197.
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This paper considers capacity provisioning for service and restoration in a WDM optical network which provides lightpaths to higher layer networks. An optical network will likely serve several client networks with different protection requirements from the optical layer. In this paper, a framework is developed for jointly assigning wavelengths to service and restoration paths for all failures, for traffic patterns with single or multiple classes of protection. For various restoration methods, the problem is reduced to a vertex coloring problem in a graph. We present routing and wavelength assignment algorithms for service and restoration with varying capacity/restoration time tradeoffs, and evaluate their performance through simulations. We consider three different kinds of traffic patterns in terms of protection requirement: 100% protected traffic, mixed protected/unprotected traffic, and mixed protected/low‐priority traffic. We quantify the capacity cost of protection in mixed traffic patterns as a function of the proportion ρ of the protected traffic in the mix, and identify the range of ρ where protection can be provided at low capacity penalties. This is important for assessing the economical feasibility of providing protection to a class of connections at the expense of reducing the amount of traffic that could be served without protection guarantees.
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Gong, Faming, Xingfang Zhao, Chengze Du, Kaiwen Zheng, Zhuang Shi, and Hao Wang. "Pressure and Temperature Prediction of Oil Pipeline Networks Based on a Mechanism-Data Hybrid Driven Method." Information 15, no. 11 (November 5, 2024): 709. http://dx.doi.org/10.3390/info15110709.
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To ensure the operational safety of oil transportation stations, it is crucial to predict the impact of pressure and temperature before crude oil enters the pipeline network. Accurate predictions enable the assessment of the pipeline’s load-bearing capacity and the prevention of potential safety incidents. Most existing studies primarily focus on describing and modeling the mechanisms of the oil flow process. However, monitoring data can be skewed by factors such as instrument aging and pipeline friction, leading to inaccurate predictions when relying solely on mechanistic or data-driven approaches. To address these limitations, this paper proposes a Temporal-Spatial Three-stream Temporal Convolutional Network (TS-TTCN) model that integrates mechanistic knowledge with data-driven methods. Building upon Temporal Convolutional Networks (TCN), the TS-TTCN model synthesizes mechanistic insights into the oil transport process to establish a hybrid driving mechanism. In the temporal dimension, it incorporates real-time operating parameters and applies temporal convolution techniques to capture the time-series characteristics of the oil transportation pipeline network. In the spatial dimension, it constructs a directed topological map based on the pipeline network’s node structure to characterize spatial features. Data analysis and experimental results show that the Three-stream Temporal Convolutional Network (TTCN) model, which uses a Tanh activation function, achieves an error rate below 5%. By analyzing and validating real-time data from the Dongying oil transportation station, the proposed hybrid model proves to be more stable, reliable, and accurate under varying operating conditions.
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Almeida, Dilini, Jagadeesh Pasupuleti, Shangari K. Raveendran, and M. Reyasudin Basir Khan. "Monte Carlo analysis for solar PV impact assessment in MV distribution networks." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (July 1, 2021): 23. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp23-31.
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The rapid penetration of solar photovoltaic (PV) systems in distribution networks has imposed various implications on network operations. Therefore, it is imperative to consider the stochastic nature of PV generation and load demand to address the operational challenges in future PV-rich distribution networks. This paper proposes a Monte Carlo based probabilistic framework for assessing the impact of PV penetration on medium voltage (MV) distribution networks. The uncertainties associated with PV installation capacity and its location, as well as the time-varying nature of PV generation and load demand were considered for the implementation of the probabilistic framework. A case study was performed for a typical MV distribution network in Malaysia, demonstrating the effectiveness of Monte Carlo analysis in evaluating the potential PV impacts in the future. A total of 1000 Monte Carlo simulations were conducted to accurately identify the influence of PV penetration on voltage profiles and network losses. Besides, several key metrics were used to quantify the technical performance of the distribution network. The results revealed that the worst repercussion of high solar PV penetration on typical Malaysian MV distribution networks is the violation of the upper voltage statutory limit, which is likely to occur beyond 70% penetration level.
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Taboada, Ianire, and Fidel Liberal. "A Novel Scheduling Index Rule Proposal for QoE Maximization in Wireless Networks." Abstract and Applied Analysis 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/647157.
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This paper deals with the resource allocation problem aimed at maximizing users’ perception of quality in wireless channels with time-varying capacity. First of all, we model the subjective quality-aware scheduling problem in the framework of Markovian decision processes. Then, given that the obtaining of the optimal solution of this model is unachievable, we propose a simple scheduling index rule with closed-form expression by using a methodology based on Whittle approach. Finally, we analyze the performance of the achieved scheduling proposal in several relevant scenarios, concluding that it outperforms the most popular existing resource allocation strategies.
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Gowrishankar, Gowrishankar, Bhargavi Gaurav Deshpande, Dhiraj Singh, Awakash Mishra, Zeeshan Ahmad Lone, and Bharat Bhushan. "Enhancing Energy Efficiency in Heterogeneous Cyber Security Networks Using Deep Q-Networks Data Routing." Journal of Cybersecurity and Information Management 14, no. 1 (2024): 160–78. http://dx.doi.org/10.54216/jcim.140111.
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Since heterogeneous wireless sensor networks consist of sensor nodes of varying capacity and energy-constrained, effective routing techniques are essential to ensure the proper functioning of the systems. Most traditional routing techniques fail to dynamically adjust to varying network conditions, leading to ineffective use of energy and poor performance. Therefore, deep Q-Networks implementation of reinforcement learning provides a beneficial approach to the problem due to adaptive routing decisions depending on the environmental signals and systems’ performance. Therefore, the suggested approach integrates Deep Q-Network into the data routing framework for different Wireless Sensor Networks to improve energy-efficiency and ensure data delivery. The DQN agent is designed to pick routing functions that maximize total rewards which depend on energy consumption, packet delivery, and network stability. Hence, the decentralized learning allows each sensor node to develop its routing policy based on the local environment under the interactions with their neighbors. Therefore, the approach promotes the ability to adapt and learn, crucial for changing network conditions. Thus, extensive simulation was conducted to assess the applicability of the DQN-based routing for different WSNs, proving the significant reducing of energy consumption compared to traditional routing approaches with an average of 25% regardless of the network formation and traffic conditions . This approach also demonstrates lower packet loss of 15%, revealing enhanced data transfer reliability . In particular, the existing on demand routing protocols, only forward the request that arrives first from each route discovery process. The attacker exploits this property of the operation of route discovery. The network lifetime was extended by 30% showing growing energy efficiency for a long-term run. In general, the integration of Deep Q-Networks into data routing provides an excellent opportunity to improve energy-efficiency in different types of wireless sensor networks. Hence, the proposed approach effectively optimizes the routing solutions in real-time, using adaptive lenience, also showing enhancing data delivery, and improving the systems’ lifetime. Hence, the presented results prove the capability of reinforcement learning methods to address the growing challenges of WSNs and leave space for further research in autonomous WSN improvement.
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Szu, Yu-Chin. "A Fuzzy Dropper for Proportional Loss Rate Differentiation under Wireless Network with a Multi-State Channel." Mathematical Problems in Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/827137.
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The proportional loss rate differentiation (PLD) model was proposed to provide controllable and predictable loss rate for different classes of wired network connections. However, these algorithms cannot be directly applied to wireless networks, because of the location-dependent and time-varying wireless channel capacity. This paper proposes a novel packet dropper for fuzzy controlling of the proportional loss rate differentiation in a wireless network with multistate channel. The proposed dropper, fuzzy proportional loss rate dropper (FPLR), prefers to drop the small packets destined to a poor condition channel to improve the network performance. The loss rate debts of the poor channel will be compensated later to keep PLD. From simulation results, FPLR does achieve accurate loss rate proportion, lower queuing delay and loss rate, and higher throughput, compared with other methods in the wireless environment.
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Dechouniotis, Dimitrios, Nikolaos Athanasopoulos, Aris Leivadeas, Nathalie Mitton, Raphael Jungers, and Symeon Papavassiliou. "Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective." Sensors 20, no. 8 (April 13, 2020): 2191. http://dx.doi.org/10.3390/s20082191.
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The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.
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Rabet, Iliar, Shunmunga Priyan Selvaraju, Hossein Fotouhi, Mário Alves, Maryam Vahabi, Ali Balador, and Mats Björkman. "SDMob: SDN-Based Mobility Management for IoT Networks." Journal of Sensor and Actuator Networks 11, no. 1 (January 21, 2022): 8. http://dx.doi.org/10.3390/jsan11010008.
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Internet-of-Things (IoT) applications are envisaged to evolve to support mobility of devices while providing quality of service in the system. To keep the connectivity of the constrained nodes upon topological changes, it is of vital importance to enhance the standard protocol stack, including the Routing Protocol for Lossy Low-power Networks (RPL), with accurate and real-time control decisions. We argue that devising a centralized mobility management solution based on a lightweight Software Defined Networking (SDN) controller provides seamless handoff with reasonable communication overhead. A centralized controller can exploit its global view of the network, computation capacity, and flexibility, to predict and significantly improve the responsiveness of the network. This approach requires the controller to be fed with the required input and to get involved in the distributed operation of the standard RPL. We present SDMob, which is a lightweight SDN-based mobility management architecture that integrates an external controller within a constrained IoT network. SDMob lifts the burden of computation-intensive filtering algorithms away from the resource-constrained nodes to achieve seamless handoffs upon nodes’ mobility. The current work extends our previous work, by supporting multiple mobile nodes, networks with a high density of anchors, and varying hop-distance from the controller, as well as harsh and realistic mobility patterns. Through analytical modeling and simulations, we show that SDMob outperforms the baseline RPL and the state-of-the-art ARMOR in terms of packet delivery ratio and end-to-end delay, with an adjustable and tolerable overhead. With SDMob, the network provides close to 100% packet delivery ratio (PDR) for a limited number of mobile nodes, and maintains sub-meter accuracy in localization under random mobility patterns and varying network topologies.
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Leontyev, А. S., and D. V. Zhmatov. "Study of the probabilistic and temporal characteristics of wireless networks using the CSMA/CA access method." Russian Technological Journal 12, no. 2 (April 10, 2024): 67–76. http://dx.doi.org/10.32362/2500-316x-2024-12-2-67-76.
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Objectives. The aim of this study is to develop analytical methods to evaluate the probabilistic and temporal characteristics and performance of wireless networks using the CSMA/CA access method. These methods enable the process of selecting rational operating modes to be automated and the impact of collisions in networks implementing the 802.11 protocols to be reduced.Methods. The methods employed herein include reliability theory, theory of random processes, queuing theory, and the Laplace–Stieltjes transform.Results. A problem statement is presented and developed, along with an analytical method for evaluating the probabilistic and temporal characteristics and performance of wireless networks using the CSMA/CA access method. This method considers time constraints on information transmission, thus expanding the applicability of previously proposed analytical approaches for studying Ethernet local area networks. The analysis of networks that use the CSMA/CA access method was carried out. An original mathematical model was developed that allows evaluating various characteristics of packet transmission processes in wireless networks under time constraints on the transmission. These characteristics include latency, packet transfer time, node load, and network performance. A software package was developed to simplify the analysis and evaluation of various operation modes of wireless networks using the CSMA/CA access method.Conclusions. We demonstrate the need for developing nested analytical models describing packet transmission processes in wireless networks under time constraints on link-layer transmission. This implies the development of more complex models for more exact description of packet transmission processes in such networks. The software package developed herein enables the various options for the functioning of the network to be studied and analytical calculations to be performed. Calculations were carried out, in order to assess the probabilistic and temporal characteristics of packet transmission processes and the wireless network performance. The research involved varying the number of workstations and the intensity of packet flows entering the network nodes under the time constraint on packet transmission. The application of the developed mathematical models will be useful in creating and optimizing wireless networks such as Wi-Fi networks, networks based on the IEEE 802.11 standard, and other data transmission systems using the CSMA/CA access method. Such models and the analysis based on them will be useful in optimizing network performance, adjusting parameters, as well as selecting the capacity and configuration of wireless networks.
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Ivanovich Vatin, Nikolai, and Rama Sundari. "Securing electric transportation networks: A machine learning-driven cyber threat detection." MATEC Web of Conferences 392 (2024): 01184. http://dx.doi.org/10.1051/matecconf/202439201184.
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The study examines the cybersecurity environment of electric transportation networks using a machine learning-based methodology. It analyzes the behaviors of electric vehicles, charging patterns, cyber threat occurrences, and the performance of machine learning models. An analysis of electric vehicle (EV) data shows that there are differences in battery capacity and distances covered, suggesting the presence of possible weaknesses across different cars. Cyber threat logs provide a comprehensive view of the various levels of threat severity and the time it takes to discover them, illustrating the ever-changing nature of cyber threats in the network. Machine learning models have varying performance; ML003 and ML005 exhibit excellent accuracy and precision in threat identification, whilst ML002 shows significantly lower metrics. These results highlight the need of implementing flexible cybersecurity solutions to handle different electric vehicle behaviors and effectively reduce cyber risks. This research emphasizes the need of using proactive threat detection tactics in order to effectively address high-severity attacks. It also highlights the need for ongoing improvement of machine learning models to strengthen network security. This study enhances our comprehension of cybersecurity obstacles in electric transportation networks, highlighting the crucial significance of machine learning-based analysis in strengthening network resilience against ever-changing cyber threats.
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Srivastava, Vivek, and Ravi Shankar Pandey. "A QoS Based Formal Model for Software Defined Network." International Journal of Sensors, Wireless Communications and Control 10, no. 3 (November 2, 2020): 395–401. http://dx.doi.org/10.2174/2210327909666190506145959.
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Background & Objective: Software-Defined Networks (SDN) decouple the responsibility of data plane, control plane and aggregates responsibilities at the controller. The controller manages all the requests generated from distributed switches to get the optimal path for sending data from source to destination using load balancing algorithms. The guarantee of packet reachability is a major challenge in real time scenario of a SDN which depends on components of network infrastructure as switches, a central controller, channel capacity and server load. The success of this aggregation and packet reachability demand is a high Quality of Service (QoS) requirement in terms of throughput, delay and packet loss due to high traffic volume and network size. This QoS has two perspectives one is required other is a computation of real QoS value. Methods: In this paper, we have presented the QoS based formal model of SDN to compute and to investigate the role of the real QoS value. This formal model includes QoS on the basis of packet movement hop by hop which is a real-time QoS. The hop by hop packet movement reliability has been computed using channel capacity and server load which is an abstraction of throughput, delay, and packet loss. The effect of channel capacity and server load can be varying using different values of the weight factor. We have also considered an equal role of channel capacity and server load to compute reliability. This QoS helps to the controller to match with required QoS to decide the better path. Conclusion: Our results finds the reliable path based on channel capacity and server load of the network. Also, results showed that the reliability of the network and controller which are based on the reliability of the packet delivery between two nodes.
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Sun, Jing, and Huiyi Yan. "RUL Prediction of Lithium-Ion Batteries based on Combined Network Model Considering Partial Charge and Discharge Data." Journal of The Electrochemical Society 171, no. 12 (December 3, 2024): 120522. https://doi.org/10.1149/1945-7111/ad9cc7.
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Lithium-ion batteries are widely used in new energy vehicles, but capacity regeneration and fluctuations during aging affect the accuracy of remaining useful life (RUL) prediction. Complete charge/discharge data are often unavailable during actual usage. To address these issues, this paper proposes a combined model for RUL prediction using partial charge/discharge data. Five health indicators are extracted from the voltage vs time curve and processed using variational mode decomposition to remove outliers and noise, improving the correlation between HIs and battery capacity. Spearman’s correlation coefficient verifies the relationship between HIs and capacity. The Kolmogorov-Arnold Networks-Structured State Space model (KAN-S4) is then developed, capturing spatial correlations and long-term degradation patterns. Experimental validation using data from our laboratory and the University of Maryland's CALCE center shows that the KAN-S4 model achieves accurate RUL predictions, even under complex conditions like capacity regeneration and rapid decline. The model demonstrates strong robustness and generalization across varying usage scenarios.
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Santos, Brena, André Soares, Tuan-Anh Nguyen, Dug-Ki Min, Jae-Woo Lee, and Francisco-Airton Silva. "IoT Sensor Networks in Smart Buildings: A Performance Assessment Using Queuing Models." Sensors 21, no. 16 (August 23, 2021): 5660. http://dx.doi.org/10.3390/s21165660.
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Smart buildings in big cities are now equipped with an internet of things (IoT) infrastructure to constantly monitor different aspects of people’s daily lives via IoT devices and sensor networks. The malfunction and low quality of service (QoS) of such devices and networks can severely cause property damage and perhaps loss of life. Therefore, it is important to quantify different metrics related to the operational performance of the systems that make up such computational architecture even in advance of the building construction. Previous studies used analytical models considering different aspects to assess the performance of building monitoring systems. However, some critical points are still missing in the literature, such as (i) analyzing the capacity of computational resources adequate to the data demand, (ii) representing the number of cores per machine, and (iii) the clustering of sensors by location. This work proposes a queuing network based message exchange architecture to evaluate the performance of an intelligent building infrastructure associated with multiple processing layers: edge and fog. We consider an architecture of a building that has several floors and several rooms in each of them, where all rooms are equipped with sensors and an edge device. A comprehensive sensitivity analysis of the model was performed using the Design of Experiments (DoE) method to identify bottlenecks in the proposal. A series of case studies were conducted based on the DoE results. The DoE results allowed us to conclude, for example, that the number of cores can have more impact on the response time than the number of nodes. Simulations of scenarios defined through DoE allow observing the behavior of the following metrics: average response time, resource utilization rate, flow rate, discard rate, and the number of messages in the system. Three scenarios were explored: (i) scenario A (varying the number of cores), (ii) scenario B (varying the number of fog nodes), and (iii) scenario C (varying the nodes and cores simultaneously). Depending on the number of resources (nodes or cores), the system can become so overloaded that no new requests are supported. The queuing network based message exchange architecture and the analyses carried out can help system designers optimize their computational architectures before building construction.
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Negara, Ridha M., Nana R. Syambas, Eueung Mulyana, Rashid M. Fajri, and Mochamad S. Budiana. "CacheCraft: A Topology-Aware PageRank Centrality Algorithm for Cache Optimization in Named Data Networking." Emerging Science Journal 9, no. 2 (April 1, 2025): 659–76. https://doi.org/10.28991/esj-2025-09-02-09.
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This study introduces CacheCraft, a novel approach for heterogeneous Content Store (CS) capacity allocation in Named Data Networking (NDN). Traditional NDN allocates CS capacity uniformly across routers, assuming equal storage requirements for all nodes. However, user content preferences and traffic patterns vary significantly, necessitating a more tailored allocation strategy. Additionally, the complexity of network topologies exacerbates the challenge, as static and homogeneous CS allocations lead to inefficiencies, increased latency, and reduced cache effectiveness in dynamic and dense networks. CacheCraft addresses these challenges by leveraging the PageRank algorithm to calculate the centrality of each node in the network. This centrality value determines the proportion of CS capacity assigned to each node, optimizing storage for nodes with higher traffic and strategic importance. The use of PageRank ensures scalable and reliable centrality computation, even in complex topologies. The performance of CacheCraft is validated across diverse network scenarios, including topologies of varying complexity, using metrics such as Cache Hit Ratio (CHR), average latency, and time complexity. Experimental results demonstrate that CacheCraft achieves an average improvement of 7.8% in CHR and a 5.6 ms reduction in latency compared to state-of-the-art methods. Moreover, CacheCraft maintains algorithmic computational efficiency, making it suitable for real-world deployment in complex and dynamic NDN environments. These findings highlight CacheCraft as a robust and scalable solution for optimizing NDN performance through adaptive and efficient CS capacity allocation. Doi: 10.28991/ESJ-2025-09-02-09 Full Text: PDF
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Zador, Anthony M., and Barak A. Pearlmutter. "VC Dimension of an Integrate-and-Fire Neuron Model." Neural Computation 8, no. 3 (April 1996): 611–24. http://dx.doi.org/10.1162/neco.1996.8.3.611.
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We compute the VC dimension of a leaky integrate-and-fire neuron model. The VC dimension quantifies the ability of a function class to partition an input pattern space, and can be considered a measure of computational capacity. In this case, the function class is the class of integrate-and-fire models generated by varying the integration time constant T and the threshold θ, the input space they partition is the space of continuous-time signals, and the binary partition is specified by whether or not the model reaches threshold at some specified time. We show that the VC dimension diverges only logarithmically with the input signal bandwidth N. We also extend this approach to arbitrary passive dendritic trees. The main contributions of this work are (1) it offers a novel treatment of computational capacity of this class of dynamic system; and (2) it provides a framework for analyzing the computational capabilities of the dynamic systems defined by networks of spiking neurons.
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Chen, Yu-Chen, Shi-Xuan Mi, Ya-Ping Tian, Xiao-Bo Hu, Qi-Yao Yuan, Khian-Hooi Chew, and Rui-Pin Chen. "Adaptive Vectorial Restoration from Dynamic Speckle Patterns Through Biological Scattering Media Based on Deep Learning." Sensors 25, no. 6 (March 14, 2025): 1803. https://doi.org/10.3390/s25061803.
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Imaging technologies based on vector optical fields hold significant potential in the biomedical field, particularly for non-invasive scattering imaging of anisotropic biological tissues. However, the dynamic and anisotropic nature of biological tissues poses severe challenges to the propagation and reconstruction of vector optical fields due to light scattering. To address this, we propose a deep learning-based polarization-resolved restoration method aimed at achieving the efficient and accurate imaging reconstruction from speckle patterns generated after passing through anisotropic and dynamic time-varying biological scattering media. By innovatively leveraging the two orthogonal polarization components of vector optical fields, our approach significantly enhances the robustness of imaging reconstruction in dynamic and anisotropic biological scattering media, benefiting from the additional information dimension of vectorial optical fields and the powerful learning capacity of a deep neural network. For the first time, a hybrid network model is designed that integrates convolutional neural networks (CNN) with a Transformer architecture for capturing local and global features of a speckle image, enabling adaptive vectorial restoration of dynamically time-varying speckle patterns. The experimental results demonstrate that the model exhibits excellent robustness and generalization capabilities in reconstructing the two orthogonal polarization components from dynamic speckle patterns behind anisotropic biological media. This study not only provides an efficient solution for scattering imaging of dynamic anisotropic biological tissues but also advances the application of vector optical fields in dynamic scattering environments through the integration of deep learning and optical technologies.
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Bjørnå, Hilde, and Nils Aarsæther. "Networking for Development in the North: Power, Trust, and Local Democracy." Environment and Planning C: Government and Policy 28, no. 2 (January 1, 2010): 304–17. http://dx.doi.org/10.1068/c0942.
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This paper addresses modes and effects of local government involvement in development projects. In particular, it examines public–private interactions with regard to networking and power relations. The study is based on four development projects involving local authorities, located in two neighbouring municipalities in northern Norway. In these we find that most relationships are balanced and that a networking mode underpins the capacity of ‘getting things done’ or ‘power to’. ‘Power over’ relations, however, are observed over time, related to critical events in the implementation of the projects, and demanding explanations that exceed network analysis approaches. Rather than being classified as a distinct type of governance, networking and networks should be regarded as elements of varying importance in processes that also involve hierarchical and market-based inputs. In the processes studied, we found municipal leaders capable of resuming control by staging metagoverning activities.
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Amaxilatis, Dimitrios, and Ioannis Chatzigiannakis. "Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks." Journal of Sensor and Actuator Networks 7, no. 4 (November 27, 2018): 51. http://dx.doi.org/10.3390/jsan7040051.
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A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications.
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Huang, Bo, Changhe Liu, Minghui Hu, Lan Li, Guoqing Jin, and Huiqian Yang. "Joint Estimation of SOC and Available Capacity of Power Lithium-Ion Battery." Electronics 11, no. 1 (January 4, 2022): 151. http://dx.doi.org/10.3390/electronics11010151.
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Temperature has an important effect on the battery model. A dual-polarization equivalent circuit model considering temperature is established to quantify the effect of temperature, and the initial parameters of the model are identified through experiments. To solve the defect of preset noise, the H-infinity filter algorithm is used to replace the traditional extended Kalman filter algorithm, without assuming that the process noise and measurement noise obey Gaussian distribution. To eliminate the influence of battery aging on SOC estimation, and considering the different time-varying characteristics of the battery states and parameters, the dual time scale double H-infinity filter is used to jointly estimate the revised SOC and available capacity. The simulation results at two temperatures show that, compared with the single time scale, the double time scale double H-infinity filter reduces the simulation time by nearly 90% under the premise that the accuracy is almost unchanged, which proves that the proposed joint estimation algorithm has the dual advantages of high precision and high efficiency.
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Hong, Yige, Qiaomin Xie, and Weina Wang. "Near-Optimal Stochastic Bin-Packing in Large Service Systems with Time-Varying Item Sizes." Proceedings of the ACM on Measurement and Analysis of Computing Systems 7, no. 3 (December 7, 2023): 1–46. http://dx.doi.org/10.1145/3626779.
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In modern computing systems, jobs' resource requirements often vary over time. Accounting for this temporal variability during job scheduling is essential for meeting performance goals. However, theoretical understanding on how to schedule jobs with time-varying resource requirements is limited. Motivated by this gap, we propose a new setting of the stochastic bin-packing problem in service systems that allows for time-varying job resource requirements, also referred to as 'item sizes' in traditional bin-packing terms. In this setting, a job or 'item' must be dispatched to a server or 'bin' upon arrival. Its resource requirement may vary over time while in service, following a Markovian assumption. Once the job's service is complete, it departs from the system. Our goal is to minimize the expected number of active servers, or 'non-empty bins', in steady state. Under our problem formulation, we develop a job dispatch policy, named Join-Reqesting-Server (JRS). Broadly, JRS lets each server independently evaluate its current job configuration and decide whether to accept additional jobs, balancing the competing objectives of maximizing throughput and minimizing the risk of resource capacity overruns. The JRS dispatcher then utilizes these individual evaluations to decide which server to dispatch each arriving job to. The theoretical performance guarantee of JRS is in the asymptotic regime where the job arrival rate scales large linearly with respect to a scaling factor r. We show that JRS achieves an additive optimality gap of O(√r) in the objective value, where the optimal objective value is Θ(r). When specialized to constant job resource requirements, our result improves upon the state-of-the-art o(r) optimality gap. Our technical approach highlights a novel policy conversion framework that reduces the policy design problem into a single-server problem.
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Anzo-Hernández, Andrés, Ernesto Zambrano-Serrano, Miguel Angel Platas-Garza, and Christos Volos. "Dynamic Analysis and FPGA Implementation of Fractional-Order Hopfield Networks with Memristive Synapse." Fractal and Fractional 8, no. 11 (October 24, 2024): 628. http://dx.doi.org/10.3390/fractalfract8110628.
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Memristors have become important components in artificial synapses due to their ability to emulate the information transmission and memory functions of biological synapses. Unlike their biological counterparts, which adjust synaptic weights, memristor-based artificial synapses operate by altering conductance or resistance, making them useful for enhancing the processing capacity and storage capabilities of neural networks. When integrated into systems like Hopfield neural networks, memristors enable the study of complex dynamic behaviors, such as chaos and multistability. Moreover, fractional calculus is significant for their ability to model memory effects, enabling more accurate simulations of complex systems. Fractional-order Hopfield networks, in particular, exhibit chaotic and multistable behaviors not found in integer-order models. By combining memristors with fractional-order Hopfield neural networks, these systems offer the possibility of investigating different dynamic phenomena in artificial neural networks. This study investigates the dynamical behavior of a fractional-order Hopfield neural network (HNN) incorporating a memristor with a piecewise segment function in one of its synapses, highlighting the impact of fractional-order derivatives and memristive synapses on the stability, robustness, and dynamic complexity of the system. Using a network of four neurons as a case study, it is demonstrated that the memristive fractional-order HNN exhibits multistability, coexisting chaotic attractors, and coexisting limit cycles. Through spectral entropy analysis, the regions in the initial condition space that display varying degrees of complexity are mapped, highlighting those areas where the chaotic series approach a pseudo-random sequence of numbers. Finally, the proposed fractional-order memristive HNN is implemented on a Field-Programmable Gate Array (FPGA), demonstrating the feasibility of real-time hardware realization.
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Song, Kaiqiang, Fengzhi Cui, and Jie Jiang. "An Efficient Lightweight Neural Network for Remote Sensing Image Change Detection." Remote Sensing 13, no. 24 (December 18, 2021): 5152. http://dx.doi.org/10.3390/rs13245152.
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Remote sensing (RS) image change detection (CD) is a critical technique of detecting land surface changes in earth observation. Deep learning (DL)-based approaches have gained popularity and have made remarkable progress in change detection. The recent advances in DL-based methods mainly focus on enhancing the feature representation ability for performance improvement. However, deeper networks incorporated with attention-based or multiscale context-based modules involve a large number of network parameters and require more inference time. In this paper, we first proposed an effective network called 3M-CDNet that requires about 3.12 M parameters for accuracy improvement. Furthermore, a lightweight variant called 1M-CDNet, which only requires about 1.26 M parameters, was proposed for computation efficiency with the limitation of computing power. 3M-CDNet and 1M-CDNet have the same backbone network architecture but different classifiers. Specifically, the application of deformable convolutions (DConv) in the lightweight backbone made the model gain a good geometric transformation modeling capacity for change detection. The two-level feature fusion strategy was applied to improve the feature representation. In addition, the classifier that has a plain design to facilitate the inference speed applied dropout regularization to improve generalization ability. Online data augmentation (DA) was also applied to alleviate overfitting during model training. Extensive experiments have been conducted on several public datasets for performance evaluation. Ablation studies have proved the effectiveness of the core components. Experiment results demonstrate that the proposed networks achieved performance improvements compared with the state-of-the-art methods. Specifically, 3M-CDNet achieved the best F1-score on two datasets, i.e., LEVIR-CD (0.9161) and Season-Varying (0.9749). Compared with existing methods, 1M-CDNet achieved a higher F1-score, i.e., LEVIR-CD (0.9118) and Season-Varying (0.9680). In addition, the runtime of 1M-CDNet is superior to most, which exhibits a better trade-off between accuracy and efficiency.
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Li, Jinna, Peng Zeng, Xuejun Zong, Meng Zheng, and Xiaoling Zhang. "Joint Design of Transmission Rate and Control for Wireless Sensor Networked Control Systems." Journal of Electrical and Computer Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/158404.
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This paper is concerned with transmission rate and control codesign of wireless sensor networked control systems (WSNCS) with time-varying delay. Jointly designing transmission rate and control is an attractive paradigm for WSNCS, since the control performance of WSNCS is highly sensitive to resource-constrained communication networks. The main idea of devised scheme is searching an optimal event-triggered transmission condition on the premise that wireless link capacity constraint is satisfied and stability of systems is guaranteed. The main aim of devised scheme is to greatly optimize control performance of WSNCS. First, two wireless network architectures characterized by multihop and star topology are put forward. Secondly, a model of WSNCS with event-triggered transmission mechanism is constructed. It is followed by stability analysis of WSNCS to obtain the asymptotical stability condition of systems. And then a search algorithm is presented for transmission rate and control codesign. Finally, numerical examples are given to illustrate the effectiveness of the proposed method.
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Luo, Zhaohui, and Minghui Liwang. "Intelligent Caching for Mobile Video Streaming in Vehicular Networks with Deep Reinforcement Learning." Applied Sciences 12, no. 23 (November 23, 2022): 11942. http://dx.doi.org/10.3390/app122311942.
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Caching-enabled multi-access edge computing (MEC) has attracted wide attention to support future intelligent vehicular networks, especially for delivering high-definition videos in the internet of vehicles with limited backhaul capacity. However, factors such as the constrained storage capacity of MEC servers and the mobility of vehicles pose challenges to caching reliability, particularly for supporting multiple bitrate video streaming caching while achieving considerable quality of experience (QoE). Motivated by the above challenges, in this paper, we propose an intelligent caching strategy that takes into account vehicle mobility, time-varying content popularity, and backhaul capability to improve the QoE of vehicle users effectively. First, based on the mobile video mean opinion score (MV-MOS), we designed an average download percentage (ADP) weighted QoE evaluation model. Then, the video content caching problem is formulated as a Markov decision process (MDP) to maximize the ADP weighted MV-MOS. Owing to the prior knowledge of video content popularity and channel state information that may not be available at the road side unit in practical scenarios, we propose a deep reinforcement learning (DRL)-based caching strategy to solve the problem while achieving a maximum ADP weighted MV-MOS. To accelerate its convergence speed, we further integrate the prioritized experience replay, dueling, and double deep Q-network technologies, which improve the performance of DRL algorithm. Numerical results demonstrate that the proposed DRL-based caching strategy significantly improves QoE, and achieves better video delivery reliability compared to existing non-learning approaches.
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Shabbir, Amna, Safdar Rizvi, Muhammad Mansoor Alam, and Mazliham Mohd Su’ud. "Optimized energy management and small cell activation in ultra-dense networks through a data-driven approach." PeerJ Computer Science 10 (December 12, 2024): e2475. https://doi.org/10.7717/peerj-cs.2475.
Full textAbstract:
With the exponential expansion of the wireless industry, the demand for improved data network throughput, capacity, and coverage has become critical. Heterogeneous ultra-dense networks (UDNs) have emerged as a promising solution to meet these escalating requirements for high data rates and capacity. However, effectively deploying and managing small cells within UDNs presents significant challenges, particularly amidst varying traffic loads and the necessity for efficient resource utilization to minimize energy consumption, especially in environments with high interference levels. Inadequate deployment of small cells can lead to excessive interference, resulting in suboptimal profitability and inefficient energy resource utilization. Addressing these challenges demands innovative approaches such as data-driven deployment strategies and efficient energy efficient resource (EER) management for small cells. Leveraging data-driven methodologies, operators can optimize small cell deployment locations and configurations based on real-time traffic patterns and environmental conditions, thereby maximizing network performance while minimizing energy consumption. This research investigates the effectiveness of a data-driven mechanism in enhancing the average achievable data rate of small cells within Heterogeneous UDNs. Our proposed approach Data Driven Opportunistic Sleep Strategy (D-DOSS) employs stochastic geometry based mathematical model for the heterogeneous networks (HetNets) wireless network will assess the impact of strategic small cell deployment on network performance in respect of energy savings. The results from Monte Carlo simulations reveal that D-DOSS outperforms traditional strategies by improving energy efficiency (EE) by 20% and achieving a 15% higher average data rate. Additionally, D-DOSS achieves a coverage probability of 50% at a signal-to-interference-plus-noise ratio (SINR) threshold of 5 dB, significantly better than random sleep mode (RSM) and load aware sleep (LAS) strategies. Overall, our findings underscore the significance of data-driven deployment and management strategies in optimizing the performance of HetNets UDNs. By embracing such approaches, wireless operators can meet the escalating demands for high-speed data transmission while achieving greater EE and sustainability in wireless network operations.
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Shi, Dapai, Jingyuan Zhao, Zhenghong Wang, Heng Zhao, Junbin Wang, Yubo Lian, and Andrew F. Burke. "Spatial-Temporal Self-Attention Transformer Networks for Battery State of Charge Estimation." Electronics 12, no. 12 (June 8, 2023): 2598. http://dx.doi.org/10.3390/electronics12122598.
Full textAbstract:
Over the past ten years, breakthroughs in battery technology have dramatically propelled the evolution of electric vehicle (EV) technologies. For EV applications, accurately estimating the state-of-charge (SOC) is critical for ensuring safe operation and prolonging the lifespan of batteries, particularly under complex loading scenarios. Despite progress in this area, modeling and forecasting the evaluation of multiphysics and multiscale electrochemical systems under realistic conditions using first-principles and atomistic calculations remains challenging. This study proposes a solution by designing a specialized Transformer-based network architecture, called Bidirectional Encoder Representations from Transformers for Batteries (BERTtery), which only uses time-resolved battery data (i.e., current, voltage, and temperature) as an input to estimate SOC. To enhance the Transformer model’s generalization, it was trained and tested under a wide range of working conditions, including diverse aging conditions (ranging from 100% to 80% of the nominal capacity) and varying temperature windows (from 35 °C to −5 °C). To ensure the model’s effectiveness, a rigorous test of its performance was conducted at the pack level, which allows for the translation of cell-level predictions into real-life problems with hundreds of cells in-series conditions possible. The best models achieve a root mean square error (RMSE) of less than 0.5 test error and approximately 0.1% average percentage error (APE), with maximum absolute errors (MAE) of 2% on the test dataset, accurately estimating SOC under dynamic operating and aging conditions with widely varying operational profiles. These results demonstrate the power of the self-attention Transformer-based model to predict the behavior of complex multiphysics and multiscale battery systems.
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De Falco, Pasquale, and Pietro Varilone. "Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks." Applied Sciences 11, no. 8 (April 16, 2021): 3574. http://dx.doi.org/10.3390/app11083574.
Full textAbstract:
Modern power systems are subject to waveform distortions that include spectral components (supraharmonics) in the range of 2–150 kHz. Due to the lack of regulation in this range and since supraharmonics may follow time-varying patterns, the operators can take advantage of the statistical characterization of supraharmonics, e.g., for determining convenient power quality limits or to analyze the residual capacity of networks toward further installations of power electronic converters. This paper studies the statistical characterization of supraharmonics in low-voltage distribution networks, considering both the overall supraharmonic distortion (through the characterization of the total supraharmonic distortion index) and individual supraharmonic components. Several probability distributions are proposed and compared, also considering multimodal distributions that can fit more general scenarios in which the supraharmonic emissions follow regime patterns. The outcome of numerical experiments based on publicly available data collected at actual low-voltage distribution networks suggests that multimodal distributions are useful in characterizing supraharmonics in most cases, with acceptable goodness of fitting even in the presence of stair-shaped empirical distributions. This paper can serve as a starting point for the development of probabilistic power system analysis tools accounting for supraharmonic emissions and for the convergence toward standardization in the 2–150 kHz range.
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Grindrod, P., and T. E. Lee. "On strongly connected networks with excitable-refractory dynamics and delayed coupling." Royal Society Open Science 4, no. 4 (April 2017): 160912. http://dx.doi.org/10.1098/rsos.160912.
Full textAbstract:
We consider a directed graph model for the human brain’s neural architecture that is based on small scale, directed, strongly connected sub-graphs (SCGs) of neurons, that are connected together by a sparser mesoscopic network. We assume transmission delays within neuron-to-neuron stimulation, and that individual neurons have an excitable-refractory dynamic, with single firing ‘spikes’ occurring on a much faster time scale than that of the transmission delays. We demonstrate numerically that the SCGs typically have attractors that are equivalent to continual winding maps over relatively low-dimensional tori, thus representing a limit on the range of distinct behaviour. For a discrete formulation, we conduct a large-scale survey of SCGs of varying size, but with the same local structure. We demonstrate that there may be benefits (increased processing capacity and efficiency) in brains having evolved to have a larger number of small irreducible sub-graphs, rather than few, large irreducible sub-graphs. The network of SCGs could be thought of as an architecture that has evolved to create decisions in the light of partial or early incoming information. Hence the applicability of the proposed paradigm to underpinning human cognition.