Recent advances in the industrial Internet of things (IIoT) provide plenty of opportunities for various industries. To address the security and efficiency issues of the massive IIoT data, blockchain is widely considered as a promising solution to enable data storing/processing/sharing in a secure and efficient way. To meet the high throughput requirement, this paper proposes a novel deep reinforcement learning (DRL)-based performance optimization framework for blockchain-enabled IIoT systems, the goals of which are threefold: 1) providing a methodology for evaluating the system from the aspects of scalability, decentralization, latency, and security; 2) improving the scalability of the underlying blockchain without affecting the system's decentralization, latency, and security; and 3) designing a modulable blockchain for IIoT systems, where the block producers, consensus algorithm, block size, and block interval can be selected/adjusted using the DRL technique. Simulations results show that our proposed framework can effectively improve the performance of blockchain-enabled IIoT systems and well adapt to the dynamics of the IIoT.

Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach

Edge computing provides a promising paradigm to support the implementation of Industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this article, we consider the optimization of channel selection that is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection framework with service reliability awareness, energy awareness, backlog awareness, and conflict awareness, by leveraging the combined power of machine learning, Lyapunov optimization, and matching theory. We provide rigorous theoretical analysis, and prove that the proposed framework can achieve guaranteed performance with a bounded deviation from the optimal performance with global state information (GSI) based on only local and causal information. Finally, simulations are conducted under both single-MTD and multi-MTD scenarios to verify the effectiveness and reliability of the proposed framework.

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Learning-Based Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT

Edge intelligence is a key enabler for IIoT as it offers smart cloud services in close proximity to the production environment with low latency and less cost. The need for ubiquitous communication, computing, and caching resources in 5G beyond will lead to a growing demand to integrate heterogeneous resources into the edge network. Furthermore, distributed edge services can make resource transactions vulnerable to malicious nodes. Ensuring secure edge services under complex industrial networks is a big challenge. In this article, we present an edge intelligence and blockchain empowered IIoT framework, which achieves flexible and secure edge service management. Then we propose a cross-domain sharing inspired edge resource scheduling scheme and design a credit-differentiated edge transaction approval mechanism. Numerical results indicate that the proposed schemes bring significant improvement in both edge service cost and service capacities.

Edge Intelligence and Blockchain Empowered 5G Beyond for the Industrial Internet of Things

Automatic modulation classification (AMC) is an promising technology for non-cooperative communication systems in both military and civilian scenarios. Recently, deep learning (DL) based AMC methods have been proposed with outstanding performances. However, both high computing cost and large model sizes are the biggest hinders for deployment of the conventional DL based methods, particularly in the application of internet-of-things (IoT) networks and unmanned aerial vehicle (UAV)-aided systems. In this correspondence, a novel DL based lightweight AMC (LightAMC) method is proposed with smaller model sizes and faster computational speed. We first introduce a scaling factor for each neuron in convolutional neural network (CNN) and enforce scaling factors sparsity via compressive sensing. It can give an assist to screen out redundant neurons and then these neurons are pruned. Experimental results show that the proposed LightAMC method can effectively reduce model sizes and accelerate computation with the slight performance loss.

LightAMC: Lightweight Automatic Modulation Classification via Deep Learning and Compressive Sensing

Blockchain for smart cities: A review of architectures, integration trends and future research directions

Machine-to-machine communication with autonomous data acquisition and exchange plays a key role in realizing the “control”-oriented tactile Internet applications such as industrial automation. In this paper, we develop a two-stage access control and resource allocation algorithm. In the first stage, we propose a contract-based incentive mechanism to motivate some delay-tolerant machine-type communication devices to postpone their access demands in exchange for higher access opportunities. In the second stage, a long-term cross-layer online resource allocation approach is proposed based on Lyapunov optimization, which jointly optimizes rate control, power allocation, and channel selection without prior knowledge of channel states. Particularly, the joint power allocation and channel selection problem is formulated as a two-dimensional matching problem, and solved by a pricing-based stable matching approach. Finally, the performance of the proposed algorithm is verified under various simulation scenarios.

Access Control and Resource Allocation for M2M Communications in Industrial Automation

By leveraging the charging and discharging capabilities of Internet of electric vehicles (IoEV), demand response (DR) can be implemented in smart cities to enable intelligent energy scheduling and trading. However, IoEV-based DR confronts many challenges, such as a lack of incentive mechanism, privacy leakage, and security threats. This motivates us to develop a distributed, privacy-preserved, and incentive-compatible DR mechanism for IoEV. Specifically, we propose a consortium blockchain-enabled secure energy trading framework for electric vehicles (EVs) with moderate cost. To incentivize more EVs to participate in DR, a contract theory-based incentive mechanism is proposed, in which various contract items are tailored for the unique characteristics of EV types. The contract optimization problem falls into the category of difference of convex programing, and is solved by using the iterative convex–concave procedure algorithm. Furthermore, we consider the scenario where the statistical knowledge of the EV type is unknown. In such a case, we demonstrate how to derive the probability distribution of the EV type by exploring computational intelligence-based state of charge estimation techniques, e.g., Gaussian process regression. Finally, the security and efficiency performance of the proposed scheme is analyzed and validated.

Blockchain and Computational Intelligence Inspired Incentive-Compatible Demand Response in Internet of Electric Vehicles

In this paper, we develop a two-stage access control and resource allocation algorithm for machine-to-machine (M2M) communications in smart grid. In the first stage, we propose a contract-based incentive mechanism to motivate some delay-tolerant machine-type communication (MTC) devices to postpone their access demands. In the second stage, a long-term cross-layer online resource allocation approach is proposed based on Lyapunov optimization, which jointly optimizes rate control, power allocation, and channel selection. Particularly, the joint power allocation and channel selection problem is solved by a pricing-based stable matching approach. Finally, the performance of the proposed algorithm is verified.

Access Control and Resource Allocation for M2M Communications in Smart Grid

Spatial diversity detection using single-photodetector coherent receiver array for free-space optical communications

Orthogonal frequency division multiplexing (OFDM) technology is presented for use in free-space optical (FSO) communications accompanied by the spatial diversity reception. Using quadrature phase shift keying (QPSK) modulation formats, the OFDM signals show robustness to support high spectral efficiency and compatibility with the spatial diversity reception to improve receiver sensitivity. Compared with the single-carrier QPSK signal, the OFDM-QPSK signal with 64 sub-carriers can reduce the BER from 2.87 × 10−3 to 2.98 × 10−4 at the SNR of 6 dB. Using a two-aperture spatial diversity reception with OFDM, the BER can be reduced from 2.45 × 10−3 of a single receiver to 6.10 × 10−4 under moderate turbulence conditions. Under strong turbulence, the BER of the single receiver is 2.14 × 10−2. It can be improved to 1.16 × 10−3 by using four-aperture receivers, and even 6.87 × 10−4 by using six-aperture receivers. The optimized aperture number should be selected according to channel conditions.

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Yufei Guo

Papers

Access Control and Resource Allocation for M2M Communications in Industrial Automation

Blockchain and Computational Intelligence Inspired Incentive-Compatible Demand Response in Internet of Electric Vehicles

Access Control and Resource Allocation for M2M Communications in Smart Grid

Spatial diversity detection using single-photodetector coherent receiver array for free-space optical communications

Performance Improvement of the Free-Space Optical Communication Link Using Spatial Diversity Reception-Assisted OFDM Signals