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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]

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]

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]

• 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]