Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)devices.To ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus process.According to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious attacks.Additionally,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network security.Simulation results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

Efficient Digital Twin Placement for Blockchain-Empowered Wireless Computing Power Network

As an open network architecture,Wireless Computing PowerNetworks(WCPN)pose newchallenges for achieving efficient and secure resource management in networks,because of issues such as insecure communication channels and untrusted device terminals.Blockchain,as a shared,immutable distributed ledger,provides a secure resource management solution for WCPN.However,integrating blockchain into WCPN faces challenges like device heterogeneity,monitoring communication states,and dynamic network nature.Whereas Digital Twins(DT)can accurately maintain digital models of physical entities through real-time data updates and self-learning,enabling continuous optimization of WCPN,improving synchronization performance,ensuring real-time accuracy,and supporting smooth operation of WCPN services.In this paper,we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models.We adopt an enumeration-based optimal placement algorithm(EOPA)and an improved simulated annealing-based near-optimal placement algorithm(ISAPA)to achieve minimum average DT synchronization latency under the constraint of DT error.Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency.

Federated learning based QoS-aware caching decisions in fog-enabled internet of things networks

Quality of Service(QoS)in the 6G application scenario is an important issue with the premise of the massive data transmission.Edge caching based on the fog computing network is considered as a potential solution to effectively reduce the content fetch delay for latency-sensitive services of Internet of Things(IoT)devices.Considering the time-varying scenario,the machine learning techniques could further reduce the content fetch delay by optimizing the caching decisions.In this paper,to minimize the content fetch delay and ensure the QoS of the network,a Device-to-Device(D2D)assisted fog computing network architecture is introduced,which supports federated learning and QoS-aware caching decisions based on time-varying user preferences.To release the network congestion and the risk of the user privacy leakage,federated learning,is enabled in the D2D-assisted fog computing network.Specifically,it has been observed that federated learning yields suboptimal results according to the Non-Independent Identical Distribution(Non-IID)of local users data.To address this issue,a distributed cluster-based user preference estimation algorithm is proposed to optimize the content caching placement,improve the cache hit rate,the content fetch delay and the convergence rate,which can effectively mitigate the impact of the Non-IID data set by clustering.The simulation results show that the proposed algorithm provides a considerable performance improvement with better learning results compared with the existing algorithms.

Computing Power Network:The Architecture of Convergence of Computing and Networking towards 6G Requirement

In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.

Reputation-based joint optimization of user satisfaction and resource utilization in a computing force network

Under the development of computing and network convergence,considering the computing and network resources of multiple providers as a whole in a computing force network(CFN)has gradually become a new trend.However,since each computing and network resource provider(CNRP)considers only its own interest and competes with other CNRPs,introducing multiple CNRPs will result in a lack of trust and difficulty in unified scheduling.In addition,concurrent users have different requirements,so there is an urgent need to study how to optimally match users and CNRPs on a many-to-many basis,to improve user satisfaction and ensure the utilization of limited resources.In this paper,we adopt a reputation model based on the beta distribution function to measure the credibility of CNRPs and propose a performance-based reputation update model.Then,we formalize the problem into a constrained multi-objective optimization problem and find feasible solutions using a modified fast and elitist non-dominated sorting genetic algorithm(NSGA-II).We conduct extensive simulations to evaluate the proposed algorithm.Simulation results demonstrate that the proposed model and the problem formulation are valid,and the NSGA-II is effective and can find the Pareto set of CFN,which increases user satisfaction and resource utilization.Moreover,a set of solutions provided by the Pareto set give us more choices of the many-to-many matching of users and CNRPs according to the actual situation.

Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks

Fueled by the explosive growth of ultra-low-latency and real-time applications with specific computing and network performance requirements,the computing force network(CFN)has become a hot research subject.The primary CFN challenge is to leverage network resources and computing resources.Although recent advances in deep reinforcement learning(DRL)have brought significant improvement in network optimization,these methods still suffer from topology changes and fail to generalize for those topologies not seen in training.This paper proposes a graph neural network(GNN)based DRL framework to accommodate network trafic and computing resources jointly and efficiently.By taking advantage of the generalization capability in GNN,the proposed method can operate over variable topologies and obtain higher performance than the other DRL methods.

Communication efficiency optimization of federated learning for computing and network convergence of 6G networks

Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models.However,factors such as network topology and computing power of devices can affect its training or communication process in complex network environments.Computing and network convergence(CNC)of sixth-generation(6G)networks,a new network architecture and paradigm with computing-measurable,perceptible,distributable,dispatchable,and manageable capabilities,can effectively support federated learning training and improve its communication efficiency.By guiding the participating devices'training in federated learning based on business requirements,resource load,network conditions,and computing power of devices,CNC can reach this goal.In this paper,to improve the communication eficiency of federated learning in complex networks,we study the communication eficiency optimization methods of federated learning for CNC of 6G networks that give decisions on the training process for different network conditions and computing power of participating devices.The simulations address two architectures that exist for devices in federated learning and arrange devices to participate in training based on arithmetic power while achieving optimization of communication efficiency in the process of transferring model parameters.The results show that the methods we proposed can cope well with complex network situations,effectively balance the delay distribution of participating devices for local training,improve the communication eficiency during the transfer of model parameters,and improve the resource utilization in the network.

Numerical simulation of neuronal spike patterns in a retinal network model

This study utilized a neuronal compartment model and NEURON software to study the effects of external light stimulation on retinal photoreceptors and spike patterns of neurons in a retinal network Following light stimulation of different shapes and sizes, changes in the spike features of ganglion cells indicated that different shapes of light stimulation elicited different retinal responses. By manipulating the shape of light stimulation, we investigated the effects of the large number of electrical synapses existing between retinal neurons. Model simulation and analysis suggested that interplexiform cells play an important role in visual signal information processing in the retina, and the findings indicated that our constructed retinal network model was reliable and feasible. In addition, the simulation results demonstrated that ganglion cells exhibited a variety of spike patterns under different light stimulation sizes and different stimulation shapes, which reflect the functions of the retina in signal transmission and processing.

Prediction based dynamic resource allocation method for edge computing first networking

Aiming at the factory with high-complex and multi-terminal in the industrial Internet of things(IIoT),a hierarchical edge networking collaboration(HENC)framework based on the cloud-edge collaboration and computing first networking(CFN)is proposed to improve the capability of task processing with fixed computing resources on the edge effectively.To optimize the delay and energy consumption in HENC,a multi-objective optimization(MOO)problem is formulated.Furthermore,to improve the efficiency and reliability of the system,a resource prediction model based on ridge regression(RR)is proposed to forecast the task size of the next time slot,and an emergency-aware(EA)computing resource allocation algorithm is proposed to reallocate tasks in edge CFN.Based on the simulation result,the EA algorithm is superior to the greedy resource allocation in time delay,energy consumption,quality of service(QoS)especially with limited computing resources.

Credibility protection for resource sharing and collaborating in non-center network computing environments

Non-center network computing environments have some unique characteristics, such as instability, heterogeneity, autonomy, distribution and openness, which bring serious issues of security and reliability. This article proposes a brand-new credibility protection mechanism for resource sharing and collaboration in non-center network computing environments. First, the three-dimensional hierarchical classified topology （3DHCT） is proposed, which provides a basic framework for realizations of the identity credibility, the behavior credibility and the capability credibility. Next, the agent technology is utilized to construct the credibility protection model. This article also proposes a new comprehensive credibility evaluation algorithm with simple, efficient, quantitative and able to meet the requirements of evaluating behavior credibility and the capability credibility evaluation as well. The Dempster-Shafer theory of evidence and the combination rule are used to achieve the evaluation of the capability credibility. The behavior credibility is evaluated with the current and historical performance of nodes for providers and consumers to realize more accurate prediction. Based on the non-center network computing simulation test platform, simulation is been conducted to test the performance and validity of the proposed algorithms. Experiment and analysis show that the proposed algorithms are suitable for large-scale, dynamic network computing environments, and able to maintain the credibility for networks without relying on central node, make a non-center network gradually evolve into an orderly, stable and reliable computing environment efficiently.

Process of Petri Nets Extension

To describe the dynamic semantics for the network computing, the concept on process is presented Based on the semantic model with variable, resource and relation. Accordingly, the formal definition of process and the mapping rules from the specification of Petri nets extension to process are discussed in detail respectively. Based on the collective concepts of process, the specification of dynamic semantics also is constructed as a net system. Finally, to illustrate process intuitively, an example is specified completely.

Architecture and Key Technology of Distributed Intelligent Open Systems

High-speed large-bandwidth networks and growth in rich internet applications has brought unprecedented pressure to bear on telecom operators. Consequently, operators need to play to the advantages of their networks, make good use of their large customer bases, and expand their business resources in service, platform, and interface. Network and customer resources should be integrated in order to create new business ecosystems. This paper describes new threats and challenges facing telecom operators and analyzes how leading operators are handling transformation in terms of operations and business model. A new concept called distributed intelligent open system （DIOS）—a public computing communication network—is proposed. The architecture and key technologies of DIOS is discussed in detail.

Special Section of Tsinghua Science and Technology on Wireless Computing and Networking

The publication of Tsinghua Science and Technology was started in 1996. Since then, it has been an international academic journal sponsored by Tsinghua University and published bimonthly. This journal aims at presenting the state-of-art scientific achievements in computer science, and other IT fields, and is currently indexed by Ei and other abstracting indices. From year 2013, the journal will be available for open access through IEEE Xplore Digital Library. This year＇s special section on Wireless Computing and Networking of Tsinghua Science and Technology is devoted to gather and present new research that address the challenges in the broad areas of Wireless Networks, Sensor Networks, Wireless Computing and Communication. While Wireless Networks have great potential to provide heterogeneous access and services for ubiquitous users, the demanding communication environment of wireless networks imposes challenges to many interesting research topics, such as channel estimation, communication protocol design, resource management, system design and so on. In Wireless Network research, it is unavoidable to wrestle unique problems such as non-uniform spectrum allocation, various radio resource management policies, economic concerns, the scarcity of radio resources, and user mobility. This Special Section therefore aims to publish high quality, original, unpublished research papers in the broad area of Wireless Computing and Networking, and thus presents a platform for scientists and scholars to share their observations and research results in the field. Specific topics for this special section include but are not limited to：

AN OBJECT-ORIENTED DISTRIBUTED MULTIMEDIA EDITOR

Open Editor is an Object-Oriented multimedia editor,which runs in the network distributed environment.To add audio media into multimedia application,an audio server based on Client/Server paradigm is designed.In this paper,we first give an overview of Open Editor,then an in-depth discussion of the implementation techniques of audio functions is presented.

Optimum Tactics of Parallel Multi-Grid Algorithm with Virtual Boundary Forecast Method Running on a Local Network with the PVM Platform

In this paper, an optimum tactic of multi-grid parallel algorithmwith virtual boundary forecast method is disscussed, and a two-stage implementationis presented. The numerical results of solving a non-linear heat transfer equationshow that the optimum implementation is much better than the non-optimum one.

Computational Approaches for Prioritizing Candidate Disease Genes Based on PPI Networks

With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction（PPI） networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.

Collaborative Mobile Charging and Coverage

Wireless energy charging using mobile vehicles has been a viable research topic recently in the area of wireless networks and mobile computing. This paper gives a short survey of recent research conducted in our research group in the area of collaborative mobile charging. In collaborative mobile charging, multiple mobile chargers work together to accomplish a given set of ob jectives. These ob jectives include charging sensors at different frequencies with a minimum number of mobile chargers and reaching the farthest sensor for a given set of mobile chargers, subject to various constraints, including speed and energy limits of mobile chargers. Through the process of problem formulation, solution construction, and future work extension for problems related to collaborative mobile charging and coverage, we present three principles for good practice in conducting research. These principles can potentially be used for assisting graduate students in selecting a research problem for a term project, which can eventually be expanded to a thesis/dissertation topic.

Integration of IoT and DRAGON-lab in cloud environment

Distributed research ＆ academic gigabits open network lab （DRAGON-lab） is the only test-bed for research purpose related to next generation interact （NGI） which based on the confederation network using three layers cloud structure. As an essential part of NGI, the research related to the Internet of things （loT） devices should be applied on the DRAGON-lab platform. This paper proposes an approach to converging the IoT devices to confederation network by integrating each layer of cloud structure between DRAGON-lab and IoT. This research activity extends the use of DRAGON-lab, makes the IoT and wireless sensor network （WSN） devices working well in the confederation network. It was also a foundational research for three layers cloud structure, which could be reused for other related research. Finally, it finishes the preparation work for IoT internet protocol version 6 （IPv6） devices research in DRAGON-Jab, which is essential for the NGI.

Sensorless Sensing with WiFi

Can WiFi signals be used for sensing purpose？ The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world＇s largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications.

Bayes-Based ARP Attack Detection Algorithm for Cloud Centers

To address the issue of internal network security, Software-Defined Network（SDN） technology has been introduced to large-scale cloud centers because it not only improves network performance but also deals with network attacks. To prevent man-in-the-middle and denial of service attacks caused by an address resolution protocol bug in an SDN-based cloud center, this study proposed a Bayes-based algorithm to calculate the probability of a host being an attacker and further presented a detection model based on the algorithm. Experiments were conducted to validate this method.