UAV-Aided Data and Energy Integrated Network: System Design and Prototype Development

Terminal devices deployed in outdoor environments are facing a thorny problem of power supply.Data and energy integrated network(DEIN)is a promising technology to solve the problem,which simultaneously transfers data and energy through radio frequency signals.State-of-the-art researches mostly focus on theoretical aspects.By contrast,we provide a complete design and implementation of a fully functioning DEIN system with the support of an unmanned aerial vehicle(UAV).The UAV can be dispatched to areas of interest to remotely recharge batteryless terminals,while collecting essential information from them.Then,the UAV uploads the information to remote base stations.Our system verifies the feasibility of the DEIN in practical applications.

Research on the Trusted Energy-Saving Transmission of Data Center Network

According to the high operating costs and a large number of energy waste in the current data center network architectures, we propose a kind of trusted flow preemption scheduling combining the energy-saving routing mechanism based on typical data center network architecture. The mechanism can make the network flow in its exclusive network link bandwidth and transmission path, which can improve the link utilization and the use of the network energy efficiency. Meanwhile, we apply trusted computing to guarantee the high security, high performance and high fault-tolerant routing forwarding service, which helps improving the average completion time of network flow.

Create Your Own Data and Energy Integrated Communication Network:A Brief Tutorial and a Prototype System

In order to satisfy the ever-increasing energy appetite of the massive battery-powered and batteryless communication devices,radio frequency(RF)signals have been relied upon for transferring wireless power to them.The joint coordination of wireless power transfer(WPT)and wireless information transfer(WIT)yields simultaneous wireless information and power transfer(SWIPT)as well as data and energy integrated communication network(DEIN).However,as a promising technique,few efforts are invested in the hardware implementation of DEIN.In order to make DEIN a reality,this paper focuses on hardware implementation of a DEIN.It firstly provides a brief tutorial on SWIPT,while summarising the latest hardware design of WPT transceiver and the existing commercial solutions.Then,a prototype design in DEIN with full protocol stack is elaborated,followed by its performance evaluation.

Integration and development of energy and information network in the Pan-Arctic region

The Global Energy Interconnection is an important strategic approach used to achieve efficient worldwide energy allocation.The idea of developing integrated power,information,and transportation networks provides increased power interconnection functionality and meaning,helps condense forces,and accelerates the integration of global infrastructure.Correspondingly,it is envisaged that it will become the trend of industrial technological development in the future.In consideration of the current trend of integrated development,this study evaluates a possible plan of coordinated development of fiber-optic and power networks in the Pan-Arctic region.Firstly,the backbone network architecture of Global Energy Interconnection is introduced and the importance of the Arctic energy backbone network is confirmed.The energy consumption and developmental trend of global data centers are then analyzed.Subsequently,the global network traffic is predicted and analyzed by means of a polynomial regression model.Finally,in combination with the current construction of fiber-optic networks in the Pan-Arctic region,the advantages of the integration of the fiber-optic and power networks in this region are clarified in justification of the decision for the development of a Global Energy Interconnection scheme.

Towards Practical Implementation of Data and Energy Integrated Networks

With the rapid development of the mobile internet and the massive deployment of the Internet of Things, mobile devices, including both the consumer electronics and the sensors, become hungrier for the energy than ever before. Conventional cable based charging largely restrict the movement of the mobile devices. Wireless charging hence emerges as an essential technique for enabling our ultimate goal of charging anytime and anywhere. By efficiently exploiting the legacy of the existing communication infrastructure, we propose a nov- el data and energy integrated network （DEIN） in order to re- alise the radio frequency （RF） based wireless charging with- out degrading the information transmission. In this treatise, we focus on the implementation of the DEIN in both the theoretical and practical aspects, concerning the transceiver architecture design and the rectifier circuit design. Furthermore, we also present a Wi-Fi based testbed for demonstrating the availability of the RF based wireless charging.

NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT

A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git.

Deep convolutional Ritz method: parametric PDE surrogates without labeled data

The parametric surrogate models for partial differential equations(PDEs)are a necessary component for many applications in computational sciences,and the convolutional neural networks(CNNs)have proven to be an excellent tool to generate these surrogates when parametric fields are present.CNNs are commonly trained on labeled data based on one-to-one sets of parameter-input and PDE-output fields.Recently,residual-based deep convolutional physics-informed neural network(DCPINN)solvers for parametric PDEs have been proposed to build surrogates without the need for labeled data.These allow for the generation of surrogates without an expensive offline-phase.In this work,we present an alternative formulation termed deep convolutional Ritz method(DCRM)as a parametric PDE solver.The approach is based on the minimization of energy functionals,which lowers the order of the differential operators compared to residualbased methods.Based on studies involving the Poisson equation with a spatially parameterized source term and boundary conditions,we find that CNNs trained on labeled data outperform DCPINNs in convergence speed and generalization abilities.The surrogates generated from the DCRM,however,converge significantly faster than their DCPINN counterparts,and prove to generalize faster and better than the surrogates obtained from both CNNs trained on labeled data and DCPINNs.This hints that the DCRM could make PDE solution surrogates trained without labeled data possibly.

Energy internet or comprehensive energy network?

What needs to be developed from the concept of"Smart Grid"is that:when renewable energy sources are absolutely prevailing in power generation,distributed power generation and distributed energy storage systems are widespread across the grid,and electric vehicle charging loads are prevailing in power load demands,how can the power grid support electric power as a core secondary energy source,undertake the role of a bridge between primary energy and end-use energy,and achieve the coordination and the optimization in macro energy perspective;how to guarantee the security of both macro energy and environment as well as the reliability of electricity.If a new term is needed,it should be Comprehensive Energy Network,not Energy Internet.

Security Analysis of a Park-level Agricultural Energy Network Considering Agrometeorology and Energy Meteorology

China’s industrial manufacturing industry is well developed,but its agriculture is primitive.The only way to solve this problem is to improve through modern agriculture.The cross integration of new energy development and modern agriculture is becoming more and more critical.However,the research on the interaction between the meteorological disaster of facility agriculture and the power supply security of the integrated energy supply system has not formed a systematic theoretical system,which challenges the collaborative security of the facility agriculture and energy system.In this paper,energy meteorology and agrometeorology are considered and modeled,and the static security of a park-level agricultural energy network is simulated and analyzed under different weather conditions.

Energy Sharing and Frequency Regulation in Energy Network via Mixed H_(2)/H_(∞) Control with Markovian Jump

In this paper,the problem of mixed optimization for energy sharing and frequency regulation in a typical energy network scenario where energy routers(ERs)interconnected AC microgrids(MGs)is investigated.Continuous-time Markov chains are introduced to describe the switching paths in the power dynamics of MGs.Such that the modelling of considered energy network system could be closer to the real-world engineering practice.Advanced parameter estimation techniques are integrated into the proposed method to achieve better modelling accuracy and controlling performance.Based on the parameters of MG power dynamics,the mixed H_(2)/H_(∞) controllers are obtained via stochastic control theory.The feasibility and efficacy of the proposed approach are evaluated in numerical examples.

QoS-guaranteed energy saving routing strategy using SDN central control for backbone networks

This paper targets on energy saving and considers network responding time for backbone networks. The objective is to find idle links as many as possible which can be put into sleep mode under the situation of quality of service(QoS)-guaranteed. This paper proposes a software defined network(SDN) based routing strategy which is especially aimed at QoS-guaranteed energy saving for backbone networks. Under SDN structure, the topology change of network can be detected directly by network controller. And network can be managed more easily and effectively. Based on the open shortest path first(OSPF) protocol, network topology can be changed as little as possible in our strategy. So, the network reconfiguring time caused by the energy saving strategy will be decreased dramatically. This makes the strategy more feasible. Then the backbone network energy optimizing problem is transformed to the maximum clique problem(MCP). And, this paper designed a routing strategy called backbone networks energy saving strategy(BNESS) for energy saving in backbone networks. Simulation result shows that our proposed strategy can save energy and QoS can be guaranteed.

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect distributed renewable energy power generation,and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy.Energy systems with high penetration of distributed renewable energy involve the high-dimensional,nonlinear dynamics of large-scale complex systems,and the optimal solution of the uncertainty model is a difficult problem.From the perspective of statistical machine learning,the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

Multi-radio access based bandwidth allocation strategy for video communication in heterogeneous wireless networks

In order to make full use of the radio resource of heterogeneous wireless networks（HWNs） and promote the quality of service（Qo S） of multi-homing users for video communication, a bandwidth allocation algorithm based on multi-radio access is proposed in this paper. The proposed algorithm adopts an improved distributed common radio resource management（DCRRM） model which can reduce the signaling overhead sufficiently. This scheme can be divided into two phases. In the first phase, candidate network set of each user is obtained according to the received signal strength（RSS）. And the simple additive weighted（SAW） method is employed to determine the active network set. In the second phase, the utility optimization problem is formulated by linear combining of the video communication satisfaction model, cost model and energy efficiency model. And finding the optimal bandwidth allocation scheme with Lagrange multiplier method and Karush-Kuhn-Tucker（KKT） conditions. Simulation results show that the proposed algorithm promotes the network load performances and guarantees that users obtain the best joint utility under current situation.

Coordinated adaptive beamformer over distributed antenna network

The spatial diversity of distributed network demands the individual filter to accommodate the topology of interference environment. In this paper, a type of distributed adaptive beamformer is proposed to mitigate interference over coordinated antenna arrays network. The proposed approach is formulated as generalized sidelobe canceller （GSC） structure to facilitate the convex combination of neighboring nodes＇ weights, and then it is solved by unconstrained least mean square （LMS） algorithm due to simplicity. Numerical results show that the robustness and convergence rate of antenna arrays network can be significantly improved in strong interference scenario. And they also clearly illustrate that mixing vector is optimized adaptively and adjusted according to the spatial diversity of the distributed nodes which are placed in different power of received signals to interference ratio （SIR） environments.

Energy aware hierarchical cluster-based routing protocol for WSNs

Wireless sensor networks（WSNs） are emerging as essential and popular ways of providing pervasive computing environments for various applications. Unbalanced energy consumption is an inherent problem in WSNs, characterized by multi-hop routing and a many-to-one traffic pattern. This uneven energy dissipation can significantly reduce network lifetime. In multi-hop sensor networks, information obtained by the monitoring nodes need to be routed to the sinks, the energy consumption rate per unit information transmission depends on the choice of the next hop node. In an energy-aware routing approach, most proposed algorithms aim at minimizing the total energy consumption or maximizing network lifetime. In this paper, we propose a novel energy aware hierarchical cluster-based（NEAHC） routing protocol with two goals： minimizing the total energy consumption and ensuring fairness of energy consumption between nodes. We model the relay node choosing problem as a nonlinear programming problem and use the property of convex function to find the optimal solution. We also evaluate the proposed algorithm via simulations at the end of this paper.

A Novel Routing Strategy to Provide Source Location Privacy in Wireless Sensor Networks

In order to secure the source location privacy when information is sent back to the base station in wireless sensor network, we propose a novel routing strategy which routes the packets to the base station through three stages： directional random routing, h-hop routing in the annular region and the shortest path routing. These stages provide two fold protections to prevent the source location from being tracked down by the adversary. The analysis and simulation results show that proposed scheme, besides providing longer safety period, can significantly reduce energy consumption compared with two baseline schemes.

BLE Mesh： A Practical Mesh Networking Development Framework for Public Safety Communications

Owing to advanced storage and communication capabilities today, smart devices have become the basic interface between individuals and their surrounding environment. In particular, massive devices connect to one other directly in a proximity area, thereby enabling abundant Proximity Services（Pro Se）, which can be classified into two categories： public safety communication and social discovery. However, two challenges impede the quick development and deployment of Pro Se applications. From the viewpoint of networking, no multi-hop connectivity functionality component can be directly operated on commercially off-the-shelf devices, and from the programming viewpoint, an easily reusable development framework is lacking for developers with minimal knowledge of the underlying communication technologies and connectivity. Considering these two issues, this paper makes a twofold contribution. First, a multi-hop mesh networking based on Bluetooth Low Energy（BLE） is implemented,in which a proactive routing mechanism with link-quality（i.e., received signal strength indication） assistance is designed. Second, a Pro Se development framework called BLE Mesh is designed and implemented, which can provide significant benefits for application developers, framework maintenance professionals, and end users. Rich application programming interfaces can help developers to build Pro Se apps easily and quickly. Dependency inversion principle and template method pattern allow modules in BLE Mesh to be loosely coupled and easy to maintain and update. Callback mechanism enables modules to work smoothly together and automation processes such as registration, node discovery, and messaging are employed to offer nearly zero-configuration for end users.Finally, based on the designed Pro Se development kit, a public safety communications app called Quote Send App is built to distribute emergency information in close area without Internet access. The process illustrates the easy usability of BLE Mesh to develop Pro Se apps.

A conceptual methodology for simultaneous optimization of water and heat with non-isothermal mixing

A new conceptual methodology is proposed to simultaneously integrate water allocation and energy networks with non-isothermal mixing. This method employs a simultaneous model and includes two design steps. In the first step, the water allocation network （WAN）, which could achieve the targets of saving water and energy, is obtained by taking account the temperature factor into the design procedure. The optimized targets of both freshwater and energy are reached at this step which ensures this approach is a simultaneous one. In the second step, based on the obtained WAN, the whole water allocation and heat exchange network （WAHEN） is combined with the non-isothermal mixing to reduce the number of heat exchangers. The thus obtained WAHEN can achieve three optimization targets （minimization of water, energy and the number of heat exchangers）. Furthermore, the effectivity of our method has been demonstrated by solving two literature examples.

Toward accurate and efficient dynamic computational strategy for heterogeneous catalysis:Temperature-dependent thermodynamics and kinetics for the chemisorbed on-surface CO

Computational tools on top of first principle calculations have played an indispensable role in revealing the molecular details,thermodynamics,and kinetics in catalytic reactions.Here we proposed a highly efficient dynamic strategy for the calculation of thermodynamic and kinetic properties in heterogeneous catalysis on the basis of efficient potential energy surface(PES)and MD simulations.Taking CO adsorbate on Ru(0001)surface as the illustrative model system,we demonstrated the PES-based MD can efficiently generate reliable two-dimensional potential-of-mean-force(PMF)surfaces in a wide range of temperatures,and thus temperature-dependent thermodynamic properties can be obtained in a comprehensive investigation on the whole PMF surface.Moreover,MD offers an effective way to describe the surface kinetics such as adsorbate on-surface movement,which goes beyond the most popular static approach based on free energy barrier and transition state theory(TST).We further revealed that the dynamic strategy significantly improves the predictions of both thermodynamic and kinetic properties as compared to the popular ideal statistic mechanics approaches such as harmonic analysis and TST.It is expected that this accurate yet efficient dynamic strategy can be powerful in understanding mechanisms and reactivity of a catalytic surface system,and further guides the rational design of heterogeneous catalysts.