In the era of intelligent systems, the real-time perception task becomes essential to enable emerging applications, such as assistive technology for visually impaired individuals, autonomous navigation in robotics, mission control, and smart agriculture, among others. As artificial intelligence (AI) continues to advance, deploying lightweight AI models on low-power Internet of Things (IoT) devices remains a critical challenge due to computational and power limitations. The goal of this research is to investigate and develop a system model of real-time AI-driven perception tasks for low-power IoT devices. Therefore, a system model is designed to solve the perception task in real-time. The significance of this research lies in enabling lightweight AI models to operate efficiently on resource-constrained devices, making obstacle detection accessible and cost-effective for IoT applications. In particular, this work investigates an obstacle detection system as a perception task by leveraging a You Only Look Once (YOLO)-based machine learning (ML) model on a low-cost and low-power computing platform such as the Raspberry Pi 4B. The results demonstrate a promising average accuracy level of 84.8% with a maximum of 95.6%, while the end-to-end post-processing time is 1.7ms to complete the considered perception task.

The use of terahertz (THz) communications with massive multiple input multiple output (MIMO) systems in 6G can potentially provide high data rates and low latency communications. However, accurate channel estimation in THz frequencies presents significant challenges due to factors such as high propagation losses, sensitivity to environmental obstructions, and strong atmospheric absorption. These challenges are particularly pronounced in urban environments, where traditional channel estimation methods often fail to deliver reliable results, particularly in complex non-line-of-sight (NLoS) scenarios. This paper introduces a novel vision-based channel estimation technique that integrates causal reasoning into urban THz communication systems. The proposed method combines computer vision algorithms with variational causal dynamics (VCD) to analyze real-time images of the urban environment, allowing for a deeper understanding of the physical factors that influence THz signal propagation. By capturing the complex, dynamic interactions between physical objects (such as buildings, trees, and vehicles) and the transmitted signals, the model can predict the channel with up to twice the accuracy of conventional methods. This model improves estimation accuracy and demonstrates superior generalization performance. Hence, it can provide reliable predictions even in previously unseen urban environments. The effectiveness of the proposed method is particularly evident in NLoS conditions, where it significantly outperforms traditional methods such as by accounting for indirect signal paths, such as reflections and diffractions. Simulation results confirm that the proposed vision-based approach surpasses conventional artificial intelligence (AI)-based estimation techniques in accuracy and robustness, showing a substantial improvement across various dynamic urban scenarios. This framework provides a promising solution for enabling resilient THz communication, offering scalability and practicality for future 6G deployments in diverse urban landscapes.

This paper proposes an AI framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)-assisted base station (BS)-enabled wireless network. The AI-driven framework aims to achieve optimized power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO BS for serving the users. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the communication signal-to-interference-plus-noise ratio (SINRc) of the received signals and beam-pattern gains to improve the sensing SINR of reflected echo signals, which in turn maximizes the achievable rate of users. A novel AI-driven framework is presented to tackle the formulated NP-hard problem that divides it into two problems: a sensing problem and a power allocation problem. The sensing problem is solved by employing a variational autoencoder (VAE)-based mechanism that obtains the sensing information leveraging AoS, which is used for the location update. Subsequently, a deep deterministic policy gradient-based deep reinforcement learning scheme is devised to allocate the desired power by activating the required grids based on the sensing information achieved with the VAE-based mechanism. Simulation results demonstrate the superior performance of the proposed AI framework compared to advantage actor-critic and deep Q-network-based methods, achieving a cumulative average SINRcimprovement of 8.5 dB and 10.27 dB, and a cumulative average achievable rate improvement of 21.59 bps/Hz and 4.22 bps/Hz, respectively. Therefore, our proposed AI-driven framework guarantees efficient power allocation for holographic beamforming through ISAC schemes leveraging AoS.

The emergence of unmanned aerial vehicles (UAVs) in wireless communication has opened up new prospects for improving network performance and user experience. This study aims to create a smart supply chain that enables collaborative communication, navigation, and caching in multi-UAV wireless networks. To achieve this, a user-UAV clustering strategy using the Whale Optimization Algorithm (WOA) optimizes resource distribution and network management. Additionally, Multi-Agent Deep Deterministic Policy Gradients (MADDPG) jointly optimize UAV trajectory planning, bandwidth allocation, and caching decisions, enabling UAVs to enhance trajectory, allocate resources, and manage cached content efficiently. Experimental results show a 10% reduction in average response time and a 12% decrease in system energy consumption, demonstrating the efficiency of the proposed approach in improving data delivery and extending UAV lifespan for sustainable network operations.

Intelligent poisoning attacks will pose fundamental challenges for sixth-generation (6G) wireless network security due to the massive deployment of native AI in radio units as well as in core networks. Network metrics and parameters, which are inherently uncertain, can become susceptible to intelligent poisoning through native generative AI (GenAI) mechanisms. In this paper, GenAI-driven intelligent attacks in wireless networks are investigated in order to understand their impact and severity by using uncertainty-informed root cause analysis. Then, a new approach for mitigating GenAI-driven attacks is proposed through the use of trustworthy service aggregation. First, a joint decision problem is formulated for generating intelligent adversarial attacks, understanding uncertain attack severity, and mitigating them in wireless networks. Second, a novel evidencetheoretic trustworthy AI (ET-TAI) framework is developed to address the formulated problem by understanding the root-cause of the native GenAI-driven intelligent attack and establishing defense in wireless networks. In particular, the proposed ET-TAI framework enables a narrow GenAI scheme that is designed to penetrate intelligent adversarial attacks in wireless networks’ metrics and parameters. Then a Dempster–Shafer-based mechanism that is deployed to capture the uncertain behavior of those intelligent attacks through prior evidence to quantify the trust for further mitigation. Extensive experimental analysis shows the proposed ET-TAI framework’s efficacy in understanding the trust in GenAI-driven intelligent poisoning attacks on network parameters and metrics by quantifying root causes and mitigating rates. Results show that the GenAI can penetrate intelligent poisoning attacks with high reconstruction capabilities of 95% for downlink services.

Operational resiliency becomes a key requirement to prevent critical recall of distributed energy resources (DER) in Cyber-Physical Energy Systems (CPES). Thus, CPES requires methods and metrics to observe and understand the potential faults of unwanted cyber-physical events. This paper introduces a new Native-AI-driven predictive maintenance framework to ensure the operational resiliency of CPES. First, a system model is designed to capture operational states such as normal, service mode, and faults by observing the operational behavior of DERs. Second, a predictive maintenance framework is proposed and developed by introducing a dual-method feature selection mechanism while the features of critical recall states are selected by tailored Gini and permutation-based importance metrics. Third, the predictive model is designed by leveraging tree-based models that utilize cyclical temporal encoding and lag features, while a Long Short-Term Memory (LSTM) family models employ sequence learning with normalized temporal and angular representations. Finally, the proposed framework can achieve up to 99.97% Critical Recall detection accuracy with an average of 82.5%. As a result, this work advances AI-driven predictive maintenance by reducing downtime by 15–30% in simulated scenarios.

Understanding the potential of generative AI (GenAI)-based attacks on the power grid is a fundamental challenge that must be addressed in order to protect the power grid by realizing and validating risk in new attack vectors. In this paper, a novel zero trust framework for a power grid supply chain (PGSC) is proposed. This framework facilitates early detection of potential GenAI-driven attack vectors (e.g., replay and protocol-type attacks), assessment of tail risk-based stability measures, and mitigation of such threats. First, a new zero trust system model of PGSC is designed and formulated as a zero-trust problem that seeks to guarantee for a stable PGSC by realizing and defending against GenAI-driven cyber attacks. Second, in which a domain-specific generative adversarial networks (GAN)-based attack generation mechanism is developed to create a new vulnerability cyberspace for further understanding that threat. Third, tail-based risk realization metrics are developed and implemented for quantifying the extreme risk of a potential attack while leveraging a trust measurement approach for continuous validation. Fourth, an ensemble learning-based bootstrap aggregation scheme is devised to detect the attacks that are generating synthetic identities with convincing user and distributed energy resources device profiles. Experimental results show the efficacy of the proposed zero trust framework that achieves an accuracy of 95.7% on attack vector generation, a risk measure of 9.61% for a 95% stable PGSC, and a 99% confidence in defense against GenAI-driven attack.

In the context of the widespread use of Radio Frequency (RF) communication networks as in electronic warfare, ensuring security and optimization has become increasingly important. This study investigates methods for detecting attacks and determining optimal routes within RF networks. In this paper, we investigate a novel framework that can proactively detect attacks in RF-based Electronic Warfare (EW), find the signal blockage to install anti-jammer and recommend an optimal path for mission success. First, we propose a logistic regression-based machine learning (ML) mechanism to train a model to differentiate between attack signals vs normal communications. Second, we devise a state–action–reward–state–action (SARSA)-based reinforcement learning (RL) scheme to find an end-to-end path for reaching to RF-enabled mission target. Third, we have A* to restore connectivity by deploying anti-jammers in optimal places. Finally, we have used a real-world Electronic Warfare (EW) dataset to evaluate the proposed framework. Our experiment shows that the proposed logistic regression produces reasonably accurate attack detection, with 98% correct classification. Further, we have achieved higher accuracy in path planning with our RL agent, where normalized Euclidean distance error between 0.1 to 0.3 as compared to the optimal distance. These results showcase the feasibility of integrating machine learning and graph theory to enhance the security and optimization of RF networks.

Urban air Mobility (UAM) has been conceived as a new form of transportation. UAM ultimately aims to operate unmanned, so it needs to select its trajectory and periodically send its status to the base station (BS). As an status indicator, the age of information (AoI) signifies the freshness of the information, and it is crucial for applications like real-time control systems. In this article, we address two main challenges: optimizing the UAM’s trajectory and updating the AoI between the UAM and the BS. We formulate an algorithm to maximize the energy efficiency of each UAM’s trajectory and jointly minimize the AoI cycle. As a complicated and non-convex problem, we approach proximal policy optimization (PPO) as our solution in this paper. Experiment results show that our proposed method outperformed the direct trajectory baseline in similar energy efficiency but achieved 46% increased efficiency in average AoI.

The semantic communication system has become one of the promising communication technologies to support high data-intensive artificial intelligence (AI) applications and services such as meta-verse, 3D maps, and so on for achieving low communication overhead. Unlike traditional communication system, accurate channel estimation is a vital issue in semantic wireless communication since a semantic transmitter is required to send the core meaning of a message rather than an entire bit streams for the receiver. Thus, designing a semantic communication framework is challenging due to the dependencies of the semantic encoder and decoder over the orthogonal frequency division multiplexing (OFDM) setting. Therefore, first, this work designs a holistic semantic communication system model that is composed of a semantic encoder, a 3GPP-defined cluster delay line (CDL) wireless channel model, and a semantic decoder for AI services. Second, this paper proposes a semantic communication framework for AI services, 1) a masked autoencoder (MAE)-based channel estimation, and 2) a hierarchical reinforcement learning (HRL)-based pilot allocation method. Third, the proposed semantic communication framework is trained in an end-to-end manner, combined with OFDM layers, considering the image reconstruction and the performance of vision AI applications. Finally, the proposed MAE-based channel estimation and HRL-based pilot allocation RL agent are integrated into the semantic communication framework. Finally, Experimental results show that the proposed semantic framework demonstrates up to a 21.25% performance improvement in image segmentation tasks. Furthermore, the proposed channel estimator and pilot allocator also show higher channel estimation accuracy compared to existing channel estimators and pilot allocation methods.

Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution among edge clients can impair the local model performance. The existing single prototype-based strategy represents a class by using the mean of the feature space. However, feature spaces are usually not clustered, and a single prototype may not represent a class well. Motivated by this, this article proposes a multiprototype federated contrastive learning approach (MP-FedCL) which demonstrates the effectiveness of using a multiprototype strategy over a single-prototype under non-IID settings, including both label and feature skewness. Specifically, a multiprototype computation strategy based on k-means is first proposed to capture different embedding representations for each class space, using multiple prototypes$(k$centroids) to represent a class in the embedding space. In each global round, the computed multiple prototypes and their respective model parameters are sent to the edge server for aggregation into a global prototype pool, which is then sent back to all clients to guide their local training. Finally, local training for each client minimizes their own supervised learning tasks and learns from shared prototypes in the global prototype pool through supervised contrastive learning, which encourages them to learn knowledge related to their own class from others and reduces the absorption of unrelated knowledge in each global iteration. Experimental results on MNIST, Digit-5, Office-10, and DomainNet show that our method outperforms multiple baselines, with an average test accuracy improvement of about 4.6% and 10.4% under feature and label non-IID distributions, respectively.

This article introduces a novelattentive drivingframework in intelligent transportation systems (ITS) to investigate the influence of cognitive behavior on distracting driving activities that lead to inattention while driving. Therefore, this work proposes a holistic computational and communication framework that can monitor on-compartment real-time multimodal sensory observation such as physiological, camera, and environmental inputs while capable of distraction detection and emotion recognition for driver's mood stabilization. In particular, this work develops a capsule network for distraction detection, a 1-D convolutional neural network for emotion recognition, an a priori algorithm for sequential context fusion, and a Bayesian network for recommending auditory stimulus content for driver mood stabilization and audio-visual safety messages for road safety. Further, an asynchronous client control scheme has developed to overcome the challenges of multitime scale sensory observations and communicate among the multimodel sensory hubs. Finally, a prototype is developed and tested in a simulation environment. The quantitative analysis results show that the proposed framework can successfully detect around 89% and 87% of distractive activities and the affective state of a driver, respectively. Finally, based on experimental results, the proposed system demonstrates the capability to sustain a driver's attention for approximately 97% of the time, with a confidence level of 95%.

The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensing-communication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communication users.

The future sixth-generation (6G) wireless communication networks are expected to provide massive connectivity with lower power requirements for generating the desired beamforming. Therefore, holographic MIMO assisted integrated sensing and communication framework is proposed that ensures lower power requirements to activate the minimum number of grids from the holographic grid array (HGA) for the effective beamforming. An optimization problem is formulated that maximizes the signal to noise-interference ratio (SNIR) of the users which in turn maximizes the utility function for sensing (UFS) considering the beampattern gains, distances, and sensing-communication loss. A novel artificial intelligence (AI) framework is proposed to solve the formulated problem which is a NP-hard problem. First, a variational autoencoder (VAE) based scheme is developed to solve the challenges of determining the exact location of the users and complete data distribution. Then, a sequential neural network-based mechanism is devised to allocate the communication resources to the heterogeneous users for the desired beamforming based on the results obtained from VAE. Finally, simulation results demonstrate that the proposed algorithms confirm 23% power savings compared to long short-term memory (LSTM) method to perform effective beamforming for serving the users.

Federated learning (FL) allows local clients to train a global model by cooperating with a server while ensuring that their raw data is not revealed. However, most existing works usually choose clients randomly, regardless of their capabilities and contributions to training. Additionally, FL client selection mechanisms concentrate on a significant challenge associated with system or statistical heterogeneity. This paper tries to manage both the system and statistical heterogeneity of distributed clients in the networks. First, to manage the system heterogeneity, an optimization objective is first proposed to maximize the number of clients with similar capabilities such as storage, computational, and communication capabilities. Then, a network framework with a logical layer is proposed to logically group similar clients by checking their capabilities. Finally, to manage the statistical heterogeneity among clients, a novel Contribution-based Dynamic Federated training strategy, called CDFed, is designed to dynamically adjust the probability of clients being chosen based on Shapley values in each global round. Experimental results on two baseline datasets: MNIST and FMNIST, demonstrate that our proposal has a faster convergence rate, about 50%, and a higher average test accuracy, at least 1%, than baselines in most cases.

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. Thus, a reliable XAI twin system becomes essential to discretizing the physical behavior of the Internet of Everything (IoE) and identifying the reasons behind that behavior for enabling ZSM. To address the challenges of extensible, modular, and stateless management functions in ZSM, a novel neuro-symbolic XAI twin framework is proposed that to enable trustworthy ZSM for a wireless IoE. The proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. The physical space of the XAI twin executes a neural-network-driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation, such as uplink, downlink, and service provisioning. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multiarm bandit-based learning problem is proposed for reducing the gap between the expected explained score and the current obtained score of the proposed neuro-symbolic XAI twin. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation.

Unmanned aerial vehicles (UAVs) nowadays are developing rapidly for various applications such as UAV taxis and delivery drones. However, the limited battery energy restricts the flight distance of the UAVs. Thus, urban prosumers equipped with drone recharge stations are introduced to provide charging services for the UAVs. In this article, first, a day-ahead energy scheduling problem for UAV charging-enabled urban prosumers is studied, where the objective is to maximize the overall energy satisfaction of the prosumers with ensuring the Quality of Service (QoS) of the charged UAVs. Specifically, to deal with the considered problem, we decompose it into two stages: 1) the day-ahead energy requirement data prediction stage and 2) energy scheduling stage per prosumer. Thus, second, a joint method based on hierarchical federated learning (HFL) on long short-term memory (LSTM) architecture (HFL-LSTM) and stochastic game-based multi-agent double deep$Q$-learning (MADDQN) with community agent-independent approach is proposed. In particular, the HFL-LSTM approach is leveraged to forecast each prosumer’s energy requirement data without centralized collecting local prosumers’ data such that to protect data privacy. Then, the stochastic game is adopted to analyze the formulated problem, aiming to find the Nash equilibrium (NE) strategy. Afterward, MADDQN with a community agent-independent method is utilized to achieve the best energy scheduling strategy per prosumer. Finally, the experimental results demonstrate the superiority of the proposed joint method that can achieve the lowest mean squared error with the value of 0.0152 and the highest energy satisfaction$(36388)$achieved by the NE policy compared with the benchmarks.

Prosumers nowadays are capable of consuming and generating renewable energy along with providing charging services for public electric vehicles (EVs) through EV support equipment (EVSE). However, the energy demand of prosumers and EVs as well as the renewable energy generation of prosumers have uncertain nature, which causes difficulty for each prosumer to purchase the proper energy at a lower price in advance. Thus, it is paramount important to do energy procurement prediction (EPP) for each prosumer. Nevertheless, submitting data from each prosumer to a centralized server for EPP will result in communication delay and need to consume a huge amount of network bandwidth and energy. Therefore, in this paper, a clustering-based serverless edge computing-assisted federated learning (FL) approach is proposed for EPP, where the objective is to minimize the Huber loss between the predicted and the real value per prosumer. In particular, firstly, normalized Laplacian-based spectral clustering is leveraged to group the prosumers with a similar energy procurement pattern to solve the problem of biased energy procurement forecast caused by updating the model among all the clients. Secondly, long short-term memory (LSTM) in the federated learning setting is utilized to train the global model of each clustered group, where the model aggregation occurs in the serverless edge computing ability-enhanced local edge server with the best performance. The evaluation results demonstrate the proposed method can achieve the lowest Huber loss compared with the baseline methods.

One of the core envisions of the sixth-generation (6G) wireless networks is to accumulate artificial intelligence (AI) for autonomous controlling of the Internet of Everything (IoE). Particularly, the quality of IoE services delivery must be maintained by analyzing contextual metrics of IoE such as people, data, process, and things. However, the challenges incorporate when the AI model conceives a lake of interpretation and intuition to the network service provider. Therefore, this paper provides an explainable artificial intelligence (XAI) framework for quality-aware IoE service delivery that enables both intelligence and interpretation. First, a problem of quality-aware IoE service delivery is formulated by taking into account network dynamics and contextual metrics of IoE, where the objective is to maximize the channel quality index (CQI) of each IoE service user. Second, a regression problem is devised to solve the formulated problem, where explainable coefficients of the contextual matrices are estimated by Shapley value interpretation. Third, the XAI-enabled quality-aware IoE service delivery algorithm is implemented by employing ensemble-based regression models for ensuring the interpretation of contextual relationships among the matrices to reconfigure network parameters. Finally, the experiment results show that the uplink improvement rate becomes 42.43% and 16.32% for the AdaBoost and Extra Trees, respectively, while the downlink improvement rate reaches up to 28.57% and 14.29%. However, the AdaBoost-based approach cannot maintain the CQI of IoE service users. Therefore, the proposed Extra Trees-based regression model shows significant performance gain for mitigating the trade-off between accuracy and interpretability than other baselines.

In this article, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an electrical vehicle (EV) charging facility for human-driven connected vehicles (CVs) and AVs. The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. Therefore, the scheduling policy of each EVSE must be adaptively accumulated the irrational charging request to satisfy the charging demand of both CVs and autonomous vehicles (AVs). To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO. Thus, we devise a rational reward maximization problem to adapt the irrational behavior by CVs in a data-informed manner. We propose a novel risk adversarial multiagent learning system (RAMALS) for CAV-CI to solve the formulated RDSS problem. In RAMALS, the DSO acts as a centralized risk adversarial agent (RAA) for informing the laxity risk to each EVSE. Subsequently, each EVSE plays the role of a self-learner agent to adaptively schedule its own EV sessions by coping advice from RAA. The experiment results show that the proposed RAMALS affords around 46.6% improvement in charging rate, about 28.6% improvement in the EVSE’s active charging time, and at least 33.3% more energy utilization, as compared to a currently deployed ACN EVSE system, and other baselines.

Recently, the deployment of electric vehicles supply equipment (EVSE) and its market is expanding rapidly to support the massive penetration of electric vehicles (EVs). However, to accomplish an effective EV charging mechanism for urban prosumer communities, it is imperative to tackle the challenges of distinct energy generation among the communities, dependency of the total purchasable energy price of each EV based on the distance between EV and EVSE, and extreme uncertainty among the energy demand and generation. Therefore, in this paper, the problem of EV charging of urban prosumer communities is studied. In particular, a joint optimization problem is proposed to maximize both the social welfare and EV charging achieved rate of the considered urban prosumer communities. Consequently, the formulated problem is decomposed into 1) truthful double auction problem for determining the unit price and winners by maximizing social welfare, and 2) EV auction losers charging problem for improving EVs charging achieved rate by purchasing energy from the power grid. Then the breakeven-based double auction (BDA) mechanism is proposed to find the unit price and EV winners’ for charging. Sequentially, a multi-agent deep reinforcement learning-based asynchronous advantage actor-critic algorithm with a long short-term memory layer (A3C-LSTM) is adopted to achieve the optimal grid energy buying decision for ensuring the charging of the losers. Finally, the experimental results demonstrate the efficacy of the proposed model that can increase the number of EV charging up to 57.31%, and prosumer communities have gained 86.04% of their income compared to baseline methods.

The recent flourish of diversified distributed energy resources (DERs) such as generators, consumers, and prosumers brings indispensable cybersecurity challenges for the smart grid controller. Therefore, to assure a resilient smart grid operation, in this paper, we study the problem of continuous-time consensus policy-based DERs control mechanism for the smart grid controller. In particular, we propose an intelligent grid shepherd for the smart grid controller in the power grid framework. That can autonomously detect the abnormal behavior of the received status message from each DER and apply control decisions into the smart grid controller. To do this, first, we propose a continuous-time Markov decision process problem by formulating a resilient control system for the intelligent grid shepherd. Second, we design a data-informed policy-based model-free reinforcement learning framework to find the optimal consensus policy for each DER control decision (i.e., remain connected with the main grid or disconnected). Thus, we devise a distributed energy resources control algorithm for the intelligent grid shepherd. Particularly, we design an advantage actor-critic scheme under the continuous-time domain with the shared neural network mechanism. Finally, experimental results show the efficiency of the proposed intelligent grid shepherd in terms of accuracy and robustness towards a resilient DERs control.

Ultra-reliable low-latency communication (uRLLC) and enhanced mobile broadband (eMBB) are two influential services of the emerging 5G cellular network. Latency and reliability are major concerns for uRLLC applications, whereas eMBB services claim for the maximum data rates. Owing to the trade-off among latency, reliability and spectral efficiency, sharing of radio resources between eMBB and uRLLC services, heads to a challenging scheduling dilemma. In this paper, we study the co-scheduling problem of eMBB and uRLLC traffic based upon the puncturing technique. Precisely, we formulate an optimization problem aiming to maximize the minimum expected achieved rate (MEAR) of eMBB user equipment (UE) while fulfilling the provisions of the uRLLC traffic. We decompose the original problem into two sub-problems, namely scheduling problem of eMBB UEs and uRLLC UEs while prevailing objective unchanged. Radio resources are scheduled among the eMBB UEs on a time slot basis, whereas it is handled for uRLLC UEs on a mini-slot basis. Moreover, for resolving the scheduling issue of eMBB UEs, we use penalty successive upper bound minimization (PSUM) based algorithm, whereas the optimal transportation model (TM) is adopted for solving the same problem of uRLLC UEs. Furthermore, a heuristic algorithm is also provided to solve the first sub-problem with lower complexity. Finally, the significance of the proposed approach over other baseline approaches is established through numerical analysis in terms of the MEAR and fairness scores of the eMBB UEs.

In this paper, we design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed to improve the data freshness and connectivity to the Internet of Things (IoT) devices. First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy. We also incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. Second, we propose an agile deep reinforcement learning with experience replay model to solve the formulated problem concerning the contextual constraints for the UAV-BS navigation. Moreover, the proposed approach is well-suited for solving the problem, since the state space of the problem is extremely large and finding the best trajectory policy with useful contextual features is too complex for the UAV-BSs. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time. Finally, the simulation results illustrate the proposed approach is 3.6% and 3.13% more energy efficient than those of the greedy and baseline deep Q Network (DQN) approaches.

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. Especially, the risk of energy shortfall is involved with uncertainties in both energy consumption and generation. In this article, we study a risk-aware energy scheduling problem for a microgrid-powered MEC network. First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the expected residual of scheduled energy for the MEC networks and we show this problem is an NP-hard problem. Second, we analyze our formulated problem using a multi-agent stochastic game that ensures the joint policy Nash equilibrium, and show the convergence of the proposed model. Third, we derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This method mitigates the curse of dimensionality of the state space and chooses the best policy among the agents for the proposed problem. Finally, the experimental results establish a significant performance gain by considering CVaR for high accuracy energy scheduling of the proposed model than both the single and random agent models.

The stringent requirements of mobile edge computing (MEC) applications and functions fathom the high capacity and dense deployment of MEC hosts to the upcoming wireless networks. However, operating such high capacity MEC hosts can significantly increase energy consumption. Thus, a base station (BS) unit can act as a self-powered BS. In this article, an effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. First, a two-stage linear stochastic programming problem is formulated with the goal of minimizing the total energy consumption cost of the system while fulfilling the energy demand. Second, a semi-distributed data-driven solution is proposed by developing a novel multi-agent meta-reinforcement learning (MAMRL) framework to solve the formulated problem. In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent. Sequentially, the meta-agent optimizes (i.e., exploits) the energy dispatch decision by accepting only the observations from each local agent with its own state information. Meanwhile, each BS agent estimates its own energy dispatch policy by applying the learned parameters from meta-agent. Finally, the proposed MAMRL framework is benchmarked by analyzing deterministic, asymmetric, and stochastic environments in terms of non-renewable energy usages, energy cost, and accuracy. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost (with 95.8% prediction accuracy), compared to other baseline methods.

The ongoing development of edge computing in fifth-generation (5G) networks promises to provide an artificial intelligence-as-a-service (AIaaS) for meeting the stringent requirements of everything as a service (XaaS) in the edge of the networks. Therefore, the concept of edge-artificial intelligence (edge-AI) is not only evolving but also emergent enabler toward AI service fulfillment. In this paper, we investigate an AI-based service aggregation problem for a mobile agent in AIaaS-enabled edge computing. First, we propose an optimization problem for the mobile agent and the objective is to maximize the AI service fulfillment achieved rate while satisfying the computational, memory, and delay requirements. Thus, we show that this optimization problem is NP-hard. Second, we compel the formulated problem in a community discovery problem and derive a solution by executing a data-driven approach. To do this, we incorporate density-based spatial clustering of applications with noise (DBSCAN) and flow control algorithm, and propose a low computational complexity algorithm for AI service aggregation of the mobile agent. Finally, numerical analysis shows the proposed model can perform better over other baseline methods in terms of deprived AI services, server utilization, and complexity analysis.

The nature of multi-access edge computing (MEC) is to deal with heterogeneous computational tasks near to the end users, which induces the volatile energy consumption for the MEC network. As an energy supplier, a microgrid is able to enable seamless energy flow from renewable and non- renewable sources. In particular, the risk of energy demand and supply is increased due to nondeterministic nature of both energy consumption and generation. In this paper, we impose a risk- sensitive energy profiling problem for a microgrid-enabled MEC network, where we first formulate an optimization problem by considering Conditional Value-at-Risk (CVaR). Hence, the formulated problem can determine the risk of expected energy shortfall by coordinating with the uncertainties of both demand and supply, and we show this problem is NP-hard. Second, we design a multi-agent system that can determine a risk- sensitive energy profiling by coping with an optimal scheduling policy among the agents. Third, we devise the solution by applying a multi-agent deep reinforcement learning (MADRL) based on asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This approach mitigates the curse of dimensionality for state space and also, can admit the best energy profile policy among the agents. Finally, the experimental results establish the significant performance gain of the proposed model than that a single agent solution and achieves a high accuracy energy profiling with respect to risk constraint.

The computational tasks at multiaccess edge computing (MEC) are unpredictable in nature, which raises uneven energy demand for MEC networks. Thus, to handle this problem, microgrid has the potentiality to provides seamless energy supply from its energy sources (i.e., renewable, nonrenewable, and storage). However, supplying energy from the microgrid faces challenges due to the high uncertainty and irregularity of the renewable energy generation over the time horizon. Therefore, in this paper, we study about the microgrid-enabled MEC networks' energy supply plan, where we first formulate an optimization problem and the objective is to minimize the energy consumption of microgrid-enabled MEC networks. The problem is a mixed integer nonlinear optimization with computational and latency constraints for tasks fulfillment, and also coupled with the dependencies of uncertainty for both energy consumption and generation. Therefore, we show that the problem is an NP-hard problem. As a result, second, we decompose our formulated problem into two subproblems: 1) energy-efficient tasks assignment problem for MEC into community discovery problem and 2) energy supply plan problem into Markov decision process. Third, we apply a low complexity density-based spatial clustering of applications with noise to solve the first subproblem for each base station distributedly. Sequentially, we use the output of the first subproblem as a input for solving the second subproblem, where we apply a model-based deep reinforcement learning. Finally, the simulation results demonstrate the significant performance gain of the proposed model with a high accuracy energy supply plan.

Edge-of-Things (EoT)-based healthcare services are forthcoming patient-care amenities related to autonomic and persuasive healthcare, where an EoT broker usually works as a middleman between the Healthcare Service Consumers (HSC) and Computing Service Providers (CSP). The computing service providers are the edge computing service providers (ECSP) and cloud computing service provider (CCSP). Sensor observations from a patient’s body area networks (BAN) and patients’ medical and genetic historical data are very sensitive and have a high degree of interdependency. It follows that EoT based patient monitoring systems or applications are tightly coupled and require obstinate synchronization. Therefore, this paper proposes a portfolio optimization solution for the selection of virtual machines (VMs) of edge and/or cloud computing service providers. The dynamic pricing for an EoT computation service is considered by the EoT broker for optimal VM provisioning in an EoT environment. The proposed portfolio optimization solution is compared with the traditional certainty equivalent approach. As the portfolio optimization is a centralized solution approach, this paper also proposes an alternating direction method of multipliers (ADMM) based distributed provisioning method for the healthcare data in the EoT computing environment. A comparative study shows the cost-effective provisioning for the healthcare data through portfolio optimization and ADMM methods over the traditional certainty equivalent and greedy approach, respectively.

The increased number of nonlinear residential loads in today's typical home and power electronics based distributed generation (DG) systems is a growing concern for the utility companies due to the power quality issues. However, properly controlled DG-grid interfacing converters are able to improve the distribution system power quality. Thus increased number of DG systems can effectively be utilized to address the power quality concern raised by increased nonlinear residential loads. This paper is mainly focused on the distribution system harmonic control through the DG-grid interfacing converters. An in-depth analysis and comparison of different compensation schemes based on the virtual harmonic impedance concept are carried out. The analysis results are verified by simulation of a test residential distribution system.