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Background: Trigeminal neuralgia (TN) is a neuropathic pain that occurs in branches of the trigeminal nerve. MicroRNAs (miRNAs) have been considered key mediators of neuropathic pain. This study was aimed to elucidate the pathophysiological function and mechanisms of miR‐223‐3p in mouse models of TN. Methods: Infraorbital nerve chronic constriction injury (CCI‐ION) was applied in male C57BL/6J mice to establish mouse models of TN. Pain responses were assessed utilizing Von Frey method. The expression of miR‐223‐3p, MKNK2, and MAPK/ERK pathway protein in trigeminal ganglions (TGs) of CCI‐ION mice was measured using RT‐qPCR and Western blotting. The concentrations of inflammatory cytokines were evaluated using Western blotting. The relationship between miR‐223‐3p and MKNK2 was tested by a luciferase reporter assay. Results: We found that miR‐223‐3p was downregulated, while MKNK2 was upregulated in TGs of CCI‐ION mice. MiR‐223‐3p overexpression by an intracerebroventricular injection of Lv‐miR‐223‐3p attenuated trigeminal neuropathic pain in CCI‐ION mice, as well as reduced the protein levels of pro‐inflammatory cytokines in TGs of CCI‐ION mice. MKNK2 was verified to be targeted by miR‐223‐3p. Additionally, miR‐223‐3p overexpression decreased the phosphorylation levels of ERK1/2, JNK, and p38 protein in TGs of CCI‐ION mice to inhibit MAPK/ERK signaling. Conclusions: Overall, miR‐223‐3p attenuates the development of TN by targeting MKNK2 to suppress MAPK/ERK signaling. [ABSTRACT FROM AUTHOR]

The construction of smart campuses can help realize the modernization of higher education. However, the subjects and mechanisms are easily ignored in the construction of smart campuses in colleges and universities. The purpose of this paper is to construct an evolutionary game model based on the complex network structure characteristics of smart campus subjects and the dynamic relationship of collaborative games. Taking the scale-free network as the carrier, the decision-making behavior among subjects is explained. Then, the importance of subject coordination and the effectiveness of the mechanisms of leadership organization, special funds, assessment rewards, and punishments are analyzed through the simulation platform, i.e., Simpy. The results show that (1) moderate subject collaboration helps to avoid the phenomenon of getting something for nothing, (2) appropriate leadership and organization mechanisms can promote the construction process, (3) special fund mechanisms can ensure sustainable development, and (4) assessment reward and punishment mechanisms can increase the popularity of achievements. The proposed method enriches relevant theories of smart campus research and provides a reference for decision-making in smart campus construction. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things, trust has become a limited factor in the integration of heterogeneous IoT networks. In this regard, we use the combination of blockchain technology and SDN/NFV to build a heterogeneous IoT network resource management model based on the consortium chain. In order to solve the efficiency problem caused by the full amount of data on the chain, we deploy light nodes and full nodes for the consortium chain. At the same time, we use the idea of identification to realize the separation of identification and resource information, build the application mode of on-chain identification and off-chain information, and realize resources endogenous trust management. We also propose a practical Byzantine fault-tolerant consensus mechanism based on reputation value to save consensus costs and improve efficiency. Combined with artificial intelligence technology, we introduce deep reinforcement learning for service function chain orchestration, and design a service function chain orchestration algorithm based on Asynchronous Advantage Actor-Critic to optimize orchestration costs. The final simulation results show that the consensus algorithm and service function chain orchestration algorithm we designed have good performance in terms of cost saving and efficiency improvement. [ABSTRACT FROM AUTHOR]

In-network computing indicates the convergence of computing and network, which can be further considered as computing-based networking and network-enabled computing, from which comes the edge in-network computing paradigm. In this newly proposed paradigm, the computing, caching, and communication resources are deployed on in-transit edge nodes, such as routers or switches; hence, services are provided with less delay and energy consumption by the deep aggregation of edge-side resources. However, open challenges still exist in dealing with heterogeneous resource sharing, scheduling, and ensuring credibility of service processing. Given this background, this article aims to address the edge-side heterogeneous resource trust management in multi-domain scenarios. A multi-domain heterogeneous resource trust management architecture is first constructed to achieve trusted resource sharing and transaction. Then, under the proposed architecture, a smart-contract-based resource sharing mechanism is formulated, including an incentive mechanism based on end-side node contribution. Furthermore, a differential-evolution-enabled containerized microservice orchestration algorithm is finally designed to minimize operation delay and load imbalance during the container deployment. The application of this platform in a highly elastic power grid scenario has been tested as an example. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is evolving from a stovepipe architecture to a collaborative and shared converged architecture. In the future, it will become a network development trend to construct a network-based virtual operating platform by virtualizing the IoT infrastructure to support different IoT services. The trust issues encountered during resource aggregation in heterogeneous networks and state measurements limit the applicability of the virtual network operating platform. In this article, we propose a framework for an endogenous trusted network to support virtual network operation, provide shared services externally, allocate resources internally, and implement artificial-intelligence-driven customization services for trusted virtual resources for the IoT. We use the consortium blockchain to establish the endogenous trusted framework for heterogeneous networks through software-defined networking and network function virtualization. In this framework, we design a mechanism for on-chain and off-chain collaborative resource allocation. The framework integrates time prediction algorithms and resource allocation algorithms to predict resource demand and allocate resources to achieve customization and dynamic adjustment. The application of this framework in specific scenarios is described using application examples. [ABSTRACT FROM AUTHOR]

Blockchain technology is developing rapidly and has been applied in various aspects, among which there are broad prospects in Internet of Things (IoT). However, IoT mobile devices are restricted in communication and computation due to mobility and portability, so that they can’t afford the high computing cost for blockchain mining process. To solve it, the free resources displayed on non-mining-devices and edge cloud are selected to construct collaborative mining network(CMN) to execute mining tasks for mobile blockchain. Miners can offload their mining tasks to non-mining-devices within a CMN or the edge cloud when there are insufficient resources. Considering competition for resource of non-mining-devices, resource allocation problem in a CMN is formulated as a double auction game, among which Bayes-Nash Equilibrium (BNE) is analyzed to figure out the optimal auction price. When offloading to edge cloud, Stackelberg game is adopted to model interactions between edge cloud operator and different CMNs to obtain the optimal resource price and devices’ resource demands. The mechanism realizes improving the mining utility in mining networks while ensuring the maximum profit of edge cloud operator. Finally, profits of mining networks are compared with an existing mode which only considers offloading to edge cloud. Under the proposed mechanism, mining networks obtain 6.86% more profits on average. [ABSTRACT FROM AUTHOR]

In order to meet the requirement of internet of things (IoT) and 5G, the cloud radio access network is a paradigm which converges all base stations computational resources into a cloud baseband unit (BBU) pool, while the distributed radio frequency signals are collected by remote radio head (RRH). A precondition for centralized processing in the BBU pool is an interconnection fronthaul network with high capacity and low delay. However, it has become more complex and frequent in the interaction between RRH and BBU and resource scheduling among BBUs in cloud. Cloud radio over fiber network has been proposed in our previous work already. In order to overcome the complexity and latency, in this paper, we first present a novel cross stratum resources protection (CSRP) architecture in fog-computing-based radio over fiber networks (F-RoFN) for 5G services. Additionally, a cross stratum protection (CSP) scheme considering the network survivability is introduced in the proposed architecture. The CSRP with CSP scheme can effectively pull the remote processing resource locally to implement the cooperative radio resource management, enhance the responsiveness and resilience to the dynamic end-to-end 5G service demands, and globally optimize optical network, wireless and fog resources. The feasibility and efficiency of the proposed architecture with CSP scheme are verified on our software defined networking testbed in terms of service latency, transmission success rate, resource occupation rate and blocking probability. [ABSTRACT FROM AUTHOR]

To adapt the 5G communication, the cloud radio access network is a paradigm introduced by operators which aggregates all base stations computational resources into a cloud BBU pool. The interaction between RRH and BBU or resource schedule among BBUs in cloud have become more frequent and complex with the development of system scale and user requirement. It can promote the networking demand among RRHs and BBUs, and force to form elastic optical fiber switching and networking. In such network, multiple stratum resources of radio, optical and BBU processing unit have interweaved with each other. In this paper, we propose a novel multiple stratum optimization (MSO) architecture for cloud-based radio over optical fiber networks (C-RoFN) with software defined networking. Additionally, a global evaluation strategy (GES) is introduced in the proposed architecture. MSO can enhance the responsiveness to end-to-end user demands and globally optimize radio frequency, optical spectrum and BBU processing resources effectively to maximize radio coverage. The feasibility and efficiency of the proposed architecture with GES strategy are experimentally verified on OpenFlow-enabled testbed in terms of resource occupation and path provisioning latency. [ABSTRACT FROM AUTHOR]

As an emerging distributed machine learning (ML) method, federated learning (FL) can protect data privacy through collaborative learning of artificial intelligence (AI) models across a large number of devices. However, inefficiency and vulnerability to poisoning attacks have slowed FL performance. Therefore, a blockchain-based asynchronous federated learning (BAFL) framework is proposed to ensure the security and efficiency required by FL. The blockchain ensures that the model data cannot be tampered with while asynchronous learning speeds up global aggregation. A novel entropy weight method is used to evaluate the participating rank and proportion of the local model trained in BAFL of the devices. The energy consumption and local model update efficiency are balanced by adjusting the local training and communication delay and optimizing the block generation rate. The extensive evaluation results show that the proposed BAFL framework has higher efficiency and higher performance for preventing poisoning attacks than other distributed ML methods. [ABSTRACT FROM AUTHOR]

With the rapid development of smart city and 5C, user demand for Internet services has increased exponentially. Through collaborative content sharing, the storage limitation of a single edge server (ES) can be broken. However, when mobile users need to download the whole content through multiple regions, independently deciding the caching content for ESs in different regions may result in redundant caching. Furthermore, frequent switching of communication connection during user movement also causes retransmission delay. As a revolutionary approach in the artificial intelligence field, deep reinforcement learning (DRL) has earned great success in solving high-dimensional and network resource management related problems. Therefore, we integrate collaborative caching and DRL to build an intelligent edge caching framework, so as to realize collaborative caching between cloud and ESs. In this caching framework, a fed-erated-machine-learning-based user behavior prediction model is first designed to characterize the content preference and movement trajectory of mobile users. Next, to achieve efficient resource aggregation of ESs, a user-behavior-aware dynamic collaborative caching domain (DCCD) construction and management mechanism is devised to divide ESs into clusters, select cluster heads, and set the re-clustering rules. Then a DRL-based content caching and delivery algorithm is presented to decide the caching content of ESs in a DCCD from a global perspective and plan the transmission path for users, which reduces redundant content and transmission delay. Especially when a user request cannot be satisfied by the current DCCD, a cross-domain content delivery strategy is presented to allow ESs in other DCCDs to provide and forward content to the user, avoiding the traffic pressure and delay caused by requesting services from cloud. The simulation results show that the proposed collaborative caching framework can improve user satisfaction in terms of content hit rate and service delay. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV)-Assisted becomes promising prospects for the development of 6G non-terrestrial networks (NTN) due to their low cost, low latency, flexible coverage, and support for emergency response. UAVs utilize close-range multi-point cooperative computing to provide computational, storage, and communication resources for 6G NTN. However, insufficient resources and variable business requirements can lead to node overload or task timeout. Therefore, selecting collaborative nodes and optimizing data transmission paths between non-terrestrial nodes has become a formidable challenge. Due to the capacity constraints, power limitations, time-varying communication links, and increasing terminal demands, existing algorithms face difficulties in meeting the requirements of cooperative computing for non-terrestrial nodes. To solve the issue, this paper proposes a multi-task cooperative computing mechanism that focuses on node selection and path optimization. Furthermore, node selection and path optimization algorithm based on multi-agent proximal policy optimization (NSPO-MAPPO) is designed to optimize the latency-aware multi-task cooperative computing objective, thereby improving computational task execution efficiency and service quality. Experimental comparisons with other algorithms demonstrate the effectiveness of our method in minimizing latency, enhancing the task load balancing degree, and ensuring stable and reliable cooperative computing services for non-terrestrial nodes. [ABSTRACT FROM AUTHOR]

At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm. [ABSTRACT FROM AUTHOR]

Blockchain technologies can enable secure computing environments among mistrusting parties. Permissioned blockchains are particularly enlightened by companies, enterprises, and government agencies due to their efficiency, customizability, and governance-friendly features. Obviously, seamlessly fusing blockchain and cloud computing can significantly benefit permissioned blockchains; nevertheless, most blockchains implemented on clouds are originally designed for loosely-coupled networks where nodes communicate asynchronously, failing to take advantages of the closely-coupled nature of cloud servers. In this paper, we propose an innovative cloud-oriented blockchain – CloudChain, which is a modularized three-layer system composed of the network layer, consensus layer, and blockchain layer. CloudChain is based on a shared-memory model where nodes communicate synchronously by direct memory accesses. We realize the shared-memory model with the Remote Direct Memory Access technology, based on which we propose a shared-memory consensus algorithm to ensure presistence and liveness, the two crucial blockchain security properties countering Byzantine nodes. We also implement a CloudChain prototype based on a RoCEv2-based testbed to experimentally validate our design, and the results verify the feasibility and efficiency of CloudChain. [ABSTRACT FROM AUTHOR]

To meet the execution requirements of delay-sensitive services in vehicular edge computing (VEC) networks, vehicular services need to be offloaded to edge computing nodes. For complex, large-scale services, the services need to be migrated if the services are not completed before the vehicles leave the coverage of edge computing nodes. Trust and resource matching between areas thus become major problems. This paper studies the decision model of vehicular service offloading and migration. First, software-defined network (SDN) technology is introduced into the traditional network architecture, and a two-layer distributed SDN-controlled VEC network architecture is designed, which is divided into a domain control layer and an area control layer. In this framework, we use the consortium blockchain as a carrier to share network topology information between SDN controllers to prevent information leakage. We then established a service offloading and migration optimization problem model to minimize service execution delay, reduce energy consumption and maximize the throughput of the blockchain system. We describe the problem model as a Markov Decision Process (MDP), introduce a deep reinforcement learning (DRL) algorithm named asynchronous advantage actor-critic (A3C) and design a dynamic service offloading and migration algorithm (DSOMA) based on A3C to solve the problem. Simulation results show that DSOMA can increase the throughput of the blockchain system, and DSOMA is superior to the deep Q-learning (DQN) algorithm and greedy offloading algorithm in reducing service execution delay and system energy consumption. [ABSTRACT FROM AUTHOR]

Objective: The aim of the present study was to summarize the clinical efficacy of three‐dimensional (3D) printing technology combined with the Masquelet technique in the treatment of calcaneal defects. Methods: From January 2018 to April 2019, 3D printing combined with induced masquelet technology was used to treat four patients with calcaneal defects, including two men and two women. The patients were aged 22–52 years old, with an average age of 36 years. There were two cases of traffic accident injuries, there was one case of a fall from height, and there was one case of crush injury. CT scans were used to reconstruct the bilateral calcaneus, mirror technology was used to construct the bone defect area, and Materialise 3‐matic software was used to design the calcaneus shaper mold and 3D print the mold. During the operation, the mold was used to shape the bone cement and fill the bone defect. In the second stage, the bone cement was removed and autologous bone was implanted to repair the bone defect. All patients were followed up to observe the effect. Results: All four patients were followed up for 14 months (range, 10–18 months). There were three cases of infectious bone defects: two cases of Escherichia coli and one case of Pseudomonas aeruginosa. The 3D printed mold was used to shape the bone cement. During the operation, it was found to have a high degree of matching with the defect area of calcaneus. There is no need to adjust it again, and the wound healed well after the first stage. In the second stage of surgery, it was found that the induced membrane formed was complete and of appropriate size; the bone cement was easily removed during the operation. The fracture healing time was 3–6 months, with an average of 4 months. At the last follow up, there was no pain and the patients walked with full weight bearing. The Maryland score was 94 points (range, 88–98 points); three cases were excellent and one case was good. The AOFAS score ranged from 86 to 98, with an average of 92.8 points; three cases were excellent and one case was good. Conclusion: Three‐dimensional printing technology combined with induced membrane technology is an effective approach for treating calcaneal bone defects. [ABSTRACT FROM AUTHOR]

Smart gird, integrated power network with communication network, has brought an innovation of traditional power for future green energy. Optical fiber technology and synchronous digital hierarchy (SDH) technology is widely used in smart grid communication transmission network. It is a challenge to reduce impact of the availability of smart grid communication services caused by random failures and random time to repair. Firstly, we create a service channel violation risk degree (SCVRD) model to precisely track the violation risk change of communication service channel. It is denoted by the probability of service channel cumulative failure duration exceeding the prescribed duration. Secondly, a service channel violation risk degree routing mechanism is proposed to improve the availability of communication service. At last, the simulation is implemented with MATLAB and network data in one province are used as data instance. The simulation results show that the average service channel failure rate of availability-aware routing based on statistics (AAR-OS) algorithm and risk-aware provisioning algorithm are reduced by 15% and 6%, respectively. [ABSTRACT FROM AUTHOR]

Smart grid has brought an innovation of traditional power. Smart grid, which couples communication network with power network, tends to cascade because of the heterogeneous coupling. Some research works have focused on external network features to mitigate the cascade, such as vulnerability and reliability. These studies, however, put less attention to an internal fault and fail to grasp cascading details. In this paper, we propose a pairwise scheme to investigate fault propagation in an interdependent network. The interdependent network model is built to characterize the fault cascade. Then, we leverage fault pair and analyze the necessary condition of the cascade. To alleviate cascading, we resort to find the minimized node set with constraints from a communication perspective. Further, the heuristic DCDR algorithm is proposed to hold the necessary redundant node set to relieve cascade. Extensive experiments not only illustrate the effectiveness of our approach on networks with different parameters, but also highlight some important observations which may give more insight to interdependent networks. [ABSTRACT FROM AUTHOR]

• An edge-terminal collaborative mining task processing framework for microgrid is proposed. • A delay-and-throughput-based blockchain computing task offloading model is constructed. • Optimize the offloading decision, transmission power, as well as the generating frequency and size of blocks. In the regional distribution network, microgrid is often used to build local energy system to realize regional autonomy in the process of power generation, transmission, and consumption. Applying blockchain technology in microgrid can meet the needs of security and privacy in energy transactions, and can conduct secure point-to-point transactions between anonymous entities. However, blockchain nodes will generate numerous computing-intensive tasks in the process of mining, and cause high delay in energy transaction. Therefore, we take advantage of mobile edge computing (MEC) technology and propose an edge-terminal collaborative mining task processing framework to increase the computing ability of the blockchain system. This framework includes three working modes: local computing, user collaboration and edge node collaboration. Particularly, the trust value of collaborative user nodes is considered to avoid security threats caused by malicious nodes. Furthermore, we establish a delay-and-throughput-based blockchain computing task offloading model, and use asynchronous advantage actor-critic (A3C) algorithm to jointly optimize offloading decision, transmission power allocation, block interval and size configuration. Simulation results show that, compared with Only-MEC and Fixed-BlockSize algorithms, the proposed algorithm can reduce the average delay by 1.7% and 2.5%, and improve the average transaction throughput by 12.1% and 28.5% respectively. [ABSTRACT FROM AUTHOR]