Energy Internet has become the current international frontier and national demand, and is the key means to achieve cross domain integration and collaborative sharing of energy resources. Regional integrated energy system (RIES) is an important form to tap the complementary capacity of different energy sources and promote energy Internet technology. Energy Internet has a three-tier architecture of energy layer, information layer and value layer. Based on the study of energy conversion mechanism, network interconnection demand, energy efficiency and energy quality efficiency of energy system, the energy conversion path evaluation index for energy layer, the network interworking ability evaluation index for information layer and the comprehensive energy efficiency evaluation index for value layer are designed respectively in this paper, then the evaluation index body of regional comprehensive energy system is founded and verified by an example. The proposed evaluation index system can be used for quantitative analysis of the operation and design of RIES, and guide the relevant research and construction work of investors, design and planning units and system operators.
The article consists of a PowerPoint presentation on the Customized Application for Mobile Enhanced Logic (CAMEL) . The specific areas/topics discussed include: CAMEL Phase 1 overview, functions, architecture, protocol, and CAMEL Phase 2.
In this paper, we consider an Internet of Things (IoT) system where the employed unmanned aerial vehicle (UAV) carries edge computing server to perform data collection and execution for multiple IoT nodes (INs). For such a network, UAV trajectory and uplink transmission power optimization are integrated to minimize the Age of Information (AoI) of the results returned to all ground INs subject to energy consumption limitations. Due to the non-convex nature of the formulated problem, it is divided into two subproblems, which are respectively solved by Lagrangian dual and convex optimization methods, and block coordinate descent method is applied to solve the overall problem. Simulation results show that the proposed algorithm achieves the lowest average AoI of all INs compared with other schemes. The results also reveal the relationship between the average AoI and the number of INs and advantages of the proposed scheme.
Vehicular edge intelligence (VEI) is a promising paradigm for enabling future intelligent transportation systems by accommodating artificial intelligence (AI) at the vehicular edge computing (VEC) system. Federated learning (FL) stands as one of the fundamental technologies facilitating collaborative model training locally and aggregation, while safeguarding the privacy of vehicle data in VEI. However, traditional FL faces challenges in adapting to vehicle heterogeneity, training large models on resource-constrained vehicles, and remaining susceptible to model weight privacy leakage. Meanwhile, split learning (SL) is proposed as a promising collaborative learning framework which can mitigate the risk of model wights leakage, and release the training workload on vehicles. SL sequentially trains a model between a vehicle and an edge cloud (EC) by dividing the entire model into a vehicle-side model and an EC-side model at a given cut layer. In this work, we combine the advantages of SL and FL to develop an Adaptive Split Federated Learning scheme for Vehicular Edge Computing (ASFV). The ASFV scheme adaptively splits the model and parallelizes the training process, taking into account mobile vehicle selection and resource allocation. Our extensive simulations, conducted on non-independent and identically distributed data, demonstrate that the proposed ASFV solution significantly reduces training latency compared to existing benchmarks, while adapting to network dynamics and vehicles' mobility.
Energy efficiency (EE) is among the main considerations in the design of modern wireless networks. In this paper, we investigate the EE enhancement for asymmetric analog network coding (ANC) protocol of a two-way relay system based on statistical channel information. A power allocation problem is formulated as the system EE maximization problem with objective function quantified by Goodbit-per-Energy (GPE). Importantly, the EE optimization problem may not be convex and can be categorized into a nonlinear fractional programming problem. Therefore, to solve the problem, a nonlinear fractional programming based algorithm is proposed and closed-form solution is obtained, providing valuable insights into practical system designs. Simulation results highlight the effect of the proposed power allocation.
Abstract $$H_{\infty }$$ H∞ state estimation is addressed for continuous-time neural networks in the paper. The norm-bounded uncertainties are considered in communication neural networks. For the considered neural networks with uncertainties, a reduced-order $$H_{\infty }$$ H∞ state estimator is designed, which makes that the error dynamics is exponentially stable and has weighted $$H_{\infty }$$ H∞ performance index by Lyapunov function method. Moreover, it is also given the devised method of the reduced-order $$H_{\infty }$$ H∞ state estimator. Then, considering that sampling the output y ( t ) of the neural network at every moment will result in waste of excess resources, the event-triggered sampling strategy is used to solve the oversampling problem. In addition, a devised method is also given for the event-triggered reduced-order $$H_{\infty }$$ H∞ state estimator. Finally, by the well-known Tunnel Diode Circuit example, it shows that a lower order state estimator can be designed under the premise of maintaining the same weighted $$H_{\infty }$$ H∞ performance index, and using the event-triggered sampling method can reduce the computational and time costs and save communication resources.
Vehicular ad hoc network (VANET) is able to facilitate data exchange among vehicles and provides diverse data services. Intuitively, end-to-end backlog and delay bounds are considered significant metrics to evaluate the quality of service in VANETs. In order to analyze how the multi-hop transmission impacts the delay performance, we model the multi-hop service process into a virtualized single service in a min-plus convolution form. To obtain multi-hop end-to-end backlog and delay bound, we consider the stochastic network traffic characteristics and the highly dynamic channel environment under the static priority, first in first out, and earliest deadline first scheduling policies by applying the martingale theory. The IEEE 802.11p enhanced distributed channel access mechanism is also adopted to analyze the access performance in the MAC sub-layer. With three kinds of real wireless data traces, i.e., VoIP, gaming, and UDP, we verify our algorithm by considering the double Nakagami-m fading channel model among vehicles. From the simulation results, we can see that the supermartingale end-to-end backlog and delay bound are remarkably tight to the real simulation results when compared with the existing standard bounds. The effect of the number of vehicles on the highway on the end-to-end backlog and delay performance is also investigated.
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