As the great prevalence of various Internet of Things (IoT) terminals, how to solve the problem of isolated information among different IoT platforms attracts attention from both academia and industry. It is necessary to establish a trusted access system to achieve secure authentication and collaborative sharing. Therefore, this paper proposes a distributed and trusted authentication system based on Blockchain and edge computing, aiming to improve authentication efficiency. This system consists of Physical network layer, Blockchain edge layer and Blockchain network layer. Through the Blockchain network, an optimized practical Byzantine fault tolerance (PBFT) consensus algorithm is designed to construct a consortium Blockchain for storing authentication data and logs. It guarantees trusted authentication and achieves activity traceability of terminals. Furthermore, edge computing is applied in Blockchain edge nodes, to provide name resolution and edge authentication service based on smart contracts. Meanwhile, an asymmetric cryptographic algorithm is designed, to prevent connection between nodes and terminals from being attacked. And a caching strategy based on edge computing is proposed to improve hit ratio. Our proposed authentication mechanism is evaluated with respect to communication and computation costs. Simulation results show that the caching strategy outperforms existing edge computing strategies by 6%-12% in terms of average delay, and 8%-14% in hit ratio.

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.

Blockchain 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.

Nowadays, the widely used Internet of Things (IoT) mobile devices (MDs) generate huge volumes of data, which needs analyzing and extracting accurate information in real time by compute-intensive deep learning (DL) inference tasks. Due to its multilayer structure, the deep neural network (DNN) is appropriate for mobile edge computing (MEC) environment, and the DL tasks can be offloaded to DNN partitions deployed in MEC servers (MECSs) for speed-up inference. In this paper, we first assume the arrival process of DL tasks as Poisson distribution and develop a tandem queueing model to evaluate the end-to-end (E2E) inference delay of DL tasks in multiple DNN partitions. To minimize the E2E delay, we develop a joint optimization problem model of partition deployment and resource allocation in MECSs (JPDRA). Since the JPDRA is a Mixed Integer Nonlinear Programming (MINLP) problem, we decompose the original problem into a computing resource allocation (CRA) problem with fixed partition deployment decision and a DNN partition deployment (DPD) problem that optimizes the optimal-delay function related to CRA problem. Next, we design a CRA algorithm based on Markov approximation and a low-complexity DPD algorithm to obtain the near-optimal solution in the polynomial time. The simulation results demonstrate that the proposed algorithms are more efficient and can reduce the average E2E delay by 25.7% with better convergence performance.

Private and public networks sharing resources for IoT network through Network Function Virtualization (NFV) and Software-Defined-Networking (SDN) forms a heterogeneous cloud-edge environment. However, the heterogeneous cloud-edge network faces trust and adaptation issues in resource allocation. To address these two problems, we introduce consortium blockchain and Deep Reinforcement Learning (DRL) to construct the trusted and auto-adjust Service Function Chain (SFC) orchestration architecture. In the architecture, this paper integrates consortium blockchain into the distributed SFC orchestration model to realize trusted resource sharing. In addition, for realizing auto-adjusted service provision, the paper designs a Dynamic Hierarchical SFC Orchestration Algorithm (DHSOA) based on DRL to minimize orchestration cost and improve the QoS. Moreover, considering dynamics of network entities, the paper proposes a time-slotted model to support dynamic service migration which adapts to high-mobility IoT network. Simulation results show that DHSOA has better performance than link-state routing algorithm and deep Q-network placement algorithm not only in cost saving of 15.8% and 10.1% but also in time saving of 22.0% and 10.0%.

The vision of next-generation TV is to support media services to achieve sharing of cross-domain experience, and the eMBB scenario of the 5G network is one of its important driving forces. Considering the bandwidth and resource requirements of different services, such as unicast and multicast services of multimedia TV broadcasting, rationally allocating resources while providing high-quality services and realizing green energy savings of base stations is one of the challenges. This paper is aimed at the resource allocation for TV multimedia service in the 5G wireless cloud network (C-RAN) scenario, which can support unicast services for cellular users and multicast services for broadcast services simultaneously, and it proposes the corresponding slice resources allocation architecture based on the concept of a self-organizing network. The management architecture first builds the functions and processes of the corresponding autonomous resource management. Based on the multidimensional data, an effective deep learning model named LSTM (long short-term memory) is used to construct the dynamic traffic model of the multicast service in space-time, which provides a basis for further network resource allocation. Based on the prediction results and the condition of satisfying the changing requirements of users, the corresponding optimization model is constructed with the goal of minimizing the energy usage of the RRHs (remote radio heads) and taking the QoS constraints of the users into account. A deep reinforcement learning (DRL) framework combined with a convex optimization method are then used to complete the users' bandwidth and power resource allocation. The experimental results show that the proposed method can not only predict the multicast service requirement accurately but also effectively improve the energy efficiency of the network under targeted QoS requirements along with time variations.