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Small cell (SC) and device-to-device (D2D) communications can fulfill high-speed wireless communication in indoor industrial Internet-of-Things (IIoT) services and cell-edge devices. However, controlling interference is crucial for optimizing resource sharing (RS). To address this, we present an adaptive interference avoidance and mode selection (MS) framework that incorporates MS, channel gain factor (CGF), and power-allocation (PA) techniques to reduce reuse interference and increase the data rate of IIoT applications for 5G D2D-enabled SC networks. Our proposed approach employs a two-phase RS algorithm that minimizes the system's computational complexity while maximizing the network sum rate. First, we adaptively determine the D2D user mode for each cell based on the D2D pair channel gain ratios of the cellular and reuse mode. We compute the CGF for each cell with a D2D pair in reuse mode (RM) to select the reuse partner. Then we determine the optimal distributed power for the D2D users and IoT-user equipment using the Lagrangian dual decomposition method to maximize the network sum rate while limiting the interference power. The simulation results indicate that our proposed approach can maximize system throughput and signal-to-interference plus noise ratio, reducing signaling overhead compared to other algorithms.

The ever-increasing requirements of demand response dynamics, competition among different stakeholders, and information privacy protection intensify the challenge of the optimal operation of microgrids. To tackle the above problems, this article proposes a three-stage optimization strategy with a deep reinforcement learning (DRL)-based distributed privacy optimization. In the upper layer of the model, the rule-based deep deterministic policy gradient (DDPG) algorithm is proposed to optimize the load migration problem with demand response, which enhances dynamic characteristics with the interaction between electricity prices and consumer behavior. Due to the competition among different stakeholders and the information privacy requirement in the middle layer of the model, a potential game-based distributed privacy optimization algorithm is improved to seek Nash equilibriums (NEs) with encoded exchange information by a distributed privacy-preserving optimization algorithm, which can ensure the convergence as well as protect privacy information of each stakeholder. In the lower layer of the model of each stakeholder, economic cost and emission rate are both taken as operation objectives, and a gradient descent-based multiobjective optimization method is employed to approach this objective. The simulation results confirm that the proposed three-stage optimization strategy can be a viable and efficient way for the optimal operation of microgrids.

The world is facing a series of new societal and industrial challenges, such as continuously increasing costs in food and energy supply and a short supply of skilled labor. To solve these new challenges, operation, information, and communication technology is entering a new era with more focus targeted to cost reduction and energy savings. In this article, how innovative technologies including virtualization, low-code development, digital twins, and industrial agents can support and impact digital and green transitions is analyzed. In addition, how these technologies will help the security and sustainability of future industrial automation systems is also discussed.

The ever-increasing false data injection (FDI) attack on the demand side brings great challenges to the energy management of interconnected microgrids. To address those aspects, this article proposes a resilient optimal defensive strategy with the distributed deep reinforcement learning (DRL) approach. To evaluate the FDI attack on demand response (DR), an online evaluation approach with the recursive least-square (RLS) method is proposed to evaluate the extent of supply security or voltage stability of the microgrids system is affected by the FDI attack. On the basis of evaluated security confidence, a distributed actor network learning approach is proposed to deduce optimal network weight, which can generate an optimal defensive scheme to ensure the economic and security issue of the microgrids system. From the methodology’s view, it can also enhance the autonomy of each microgrid as well as accelerate DRL efficiency. According to those simulation results, it can reveal that the proposed method can evaluate FDI attack impact well and an improved distributed DRL approach can be a viable and promising way for the optimal defense of microgrids against the FDI attack on the demand side.

Positioning with ultrawideband (UWB) is prominent among industrial localization systems, due to its high-range resolution attributes and lower cost. However, one notable challenge with UWB positioning in industrial environments is the prevalent presence of nonline-of-sight (NLOS) components or signals that degrade localization performance drastically. Coupled with this, industrial settings tend to constantly change making NLOS signals challenging to characterize each time these changes occur. Recently, promising approaches have been proposed to identify NLOS components, however their performances are limited to the specific environment where measurements were performed. Their performance cannot be extended to other unknown environments due to the distribution divergence problem, owing to differences in environments captured by the channel impulse response (CIR) waveforms. This therefore requires laborious processes of data collection and training environment-specific models for NLOS identification. In this article, we propose a robust transfer learning-based NLOS identification approach, which harnesses transition information via cross-domain mappings from both source and target domains, to construct representative homogeneous features of both domains. The representative homogeneous features capture discriminative information of both domains, while reducing the distribution divergence between the domains, making it easy to classify LOS and NLOS components from both environments together. To test the robustness of our approach, we perform extensive simulations with CIR data collected from two distinct environments—“hard NLOS” (characterized by high relative permittivity of surrounding objects, e.g., thick concrete walls, metallic objects, etc.) and “soft NLOS” (characterized by low relative permittivity of surrounding objects, e.g., plasterboard walls). Our proposed approach is not just effective in transferring knowledge between distinct environments, but significantly outperforms state-of-the-art works to NLOS identification in UWB positioning networks, while reducing the laborious process of data collection in the target domain.

The integration of large-scale distributed generators into active distribution networks (ADNs) will aggravate voltage fluctuations, which can affect the secure operation of power grids seriously. In this article, we investigate a cooperated voltage regulation problem of ADNs. Specifically, we first formulate a two-timescale voltage regulation problem considering the coordination of various hybrid devices while reducing the power loss of the whole ADNs. Given that the aforementioned problem is challenging to solve directly, we reformulate it as bilevel Markov games. Then, we propose a hierarchical multi-agent attention-based deep reinforcement learning algorithm to solve them. To be specific, the upper level Markov game is solved by a discrete multi-actor-attention-critic (MAAC) algorithm, and the lower level Markov game is solved by a continuous MAAC algorithm. In addition, the two-timescale coordination between upper level and lower level agents is implemented through the information exchange of rewards during the training process. Simulation results show that the proposed algorithm has good effectiveness, robustness, and scalability in voltage regulation.

Internet-of-Things (IoT)-enabled wireless body area networks (WBANs) are resource-constrained in nature (energy, bandwidth, and time-slot resources); hence, their performance in healthcare monitoring often deteriorates as the number of active IoT devices sharing the network increases. Consequently, improving the network efficiency of IoT-enabled WBAN systems is essential for improving healthcare monitoring. Hence, we propose an energy-efficient multichannel hybrid medium access control (MAC) (MC-HYMAC) protocol that combines the benefits of the carrier sense multiple access with collision avoidance (CSMA/CA) and time division multiple access (TDMA) protocols to improve the overall performance of IoT-enabled WBAN systems. We also proposed an adaptive power control scheme, time-slot management scheme, channel utilization mechanism, and dynamic back-off time policy to improve the overall network efficiency. In addition, we applied a finite-state discrete-time Markov model to determine the traffic arrival pattern and analyze the transition states of biomedical devices to facilitate optimal decision-making for enhanced overall performance of the network. Standard metrics, such as energy efficiency, throughput, delay, packet drop ratio, and network lifetime, were used to evaluate and compare the existing MAC protocols.

Time-based ranging approaches have long been used for indoor positioning due to their reliability and accuracy. Two-way ranging (TWR) with ultrawideband (UWB) has often been utilized for indoor positioning systems (IPSs) in industrial settings due to the ranging accuracy offered by the temporal and spatial resolution of UWB even in challenging non-line-of-sight (NLOS) and reflective environments. With the adoption of the IEEE 802.11mc Standard, Wi-Fi now offers a fine time measurement (FTM) capability that supports the Wi-Fi round trip time (RTT) protocol for time-of-flight (ToF) ranging. This offers an improved ranging accuracy over previous non-time-based Wi-Fi ranging approaches and is considered a potential alternative to UWB TWR. In this article, we introduce UWB TWR and Wi-Fi FTM and discuss their use in real-time IPSs. We include a quantitative comparison of ranging performance in LOS and NLOS ranging measurements to give an initial indication of accuracy in industrial settings. Recommendations for researchers and IPS designers on which technology type to deploy for LOS and NLOS conditions as well as general guidelines for accurate ranging and localization for both technologies are also discussed.

Due to the existence of different stakeholders, it makes competitive game characteristic in hybrid energy systems (HESs). Combined with the high-dimensional complexity and output uncertainty of distributed energy resources, the optimal operation of HESs can be a more challenging problem. Here, this article proposes a potential game-based two-layered hierarchical optimization strategy to deal with this problem. With consideration of its high-dimensional complexity, a two-layered hierarchical HES model is created, consisting of an upper-level and a lower-level model. For properly solving competitive relationships among different stakeholders in the upper-level model, a multiagent system for stakeholders is created and a potential game is employed with a distributed primal–dual perturbed algorithm, and its convergence and optimality have been both proved. Moreover, an uncertainty and robustness analysis is done with coordination between lower and upper models, which deduces a feasible robust uncertainty interval in the lower-level model. For better dealing with the lower-level model, a gradient descent-based multiobjective differential evolution (GD-MODE) algorithm is utilized to optimize the economic cost and emission issue simultaneously, producing a set of Pareto-optimal schemes. Combined with simulation results, it is proven that the proposed method can reduce computational complexity as well as properly deal with uncertainty problems for the optimal operation of HESs.