Research

High penetration of inverter-based resources (IBRs) in islanded microgrids lowers inertia and makes fast frequency response (FFR) sensitive to feeder reconfiguration, which the fixed droop control, topology blind droop loop cannot track. When IBRs are aggregated into virtual power plants (VPPs) that bid FFR reserve into the ancillary service market, the committed reserve is only as deliverable as the active feeder can route, so the VPP's real-time execution layer must stay topology aware as the distribution system operator reconfigures the...read more

The rapid growth of Electric Vehicles (EVs) increases stress on distribution networks while intensifying pricing competition among Electric Vehicle Charging Station (EVCS) operators. Existing studies often treat pricing and EV-to-EVCS assignment separately, without accounting for grid limits and realistic market behavior. This paper proposes a framework combining a price competition game among EVCS operators with a many-to-one matching mechanism to jointly assign EVs to stations and set prices under distribution network constraints...read more

This paper proposes a bi-level framework for the optimal siting and sizing of Electric Vehicle Charging Stations (EVCS) integrated with photovoltaic, wind turbine, and battery energy storage systems. The lower level solves a Markov decision process for the intraday dispatch of each EVCS, while the upper level jointly optimizes the site, EVCS type, and Volt-VAR dispatch using a Reinforcement Learning-based Hybrid Parameters and Mutation Strategies Differential Evolution (RL-HPSDE)...read moreœ

The increasing penetration of inverter-interfaced resources has significantly reduced effective inertia in islanded microgrids, causing pronounced frequency deviations. Existing control methods lack topology-awareness and adaptive coordination. This paper proposes a graph neural network (GNN)-enhanced multi-agent reinforcement learning (MARL) framework that represents the electrical network as a graph and enables coordinated electric vehicle charging station (EVCS) control. Evaluated on a CIGRE MV benchmark, the proposed approach demonstrates superior frequency stability compared to conventional droop control...read more

Electric vehicle charging stations equipped with photovoltaic generation and battery storage can provide demand-response flexibility to a distribution operator, but their contribution should preserve charging service. This paper proposes a three-layer virtual power plant (VPP) framework for point-of-common-coupling (PCC) import reduction in a campus charging network. The framework separates day-ahead reserve scheduling, real-time coordination, and station-level model predictive control (MPC) execution...read more

The rapid growth of electric vehicle (EV) adoption requires charging infrastructure that is both economically viable and environmentally sustainable. This study proposes an integrated optimal sizing framework for two distinct Electric Vehicle Charging Station (EVCS) configurations: a grid-connected system and a fully standalone system, each integrating Photovoltaic (PV) systems, small Wind Turbines (sWT), and Battery Energy Storage Systems (BESS)...read more

The increasing global demand for clean water necessitates innovative and sustainable wastewater treatment technologies. Triboelectric nanogenerators (TENGs), particularly those utilizing polymers, have emerged as a promising self-powered solution for environmental applications, offering the potential to drive wastewater treatment processes without external energy input. This review comprehensively examines the application of polymer-based TENGs for sustainable wastewater treatment. It begins by classifying TENGs and outlining common fabrication methods, followed by a detailed discussion of the principles governing TENG operation in environmental contexts...read more

Triboelectric nanogenerators (TENGs) are a transformative technology for harvesting mechanical energy, yet their practical output is often limited by the efficiency of charge collection and transport. While most research focuses on triboelectric layer pairings, this review asserts that the electrode is the pivotal component determining the final voltage outcome and overall device performance. This review article presents a comprehensive analysis of material and architectural trends in TENG electrodes, systematically evaluating their direct impact on voltage generation...read more

With declining costs of Battery Energy Storage Systems (BESS) and Renewable Energy (RE) sources such as Photovoltaics (PV) and Wind Turbines (WT), their integration into Electric Vehicle Charging Stations (EVCS) has become more viable. However, without optimal power distribution, unnecessary energy purchases from the grid can occur, reducing EVCS owner profits and increasing EV charging costs...read more

The falling prices of Battery Energy Storage Systems (BESS) and Renewable Energy (RE) technologies like Photovoltaics (PV) have made it much more practical to integrate them into Electric Vehicle Charging Stations (EVCS). However, using a simple bi-level strategy often fails to consider the interests of all stakeholders, including the Distribution System Operator (DSO), the charging station, and the vehicle owners, which results in financial losses for one of the groups...read more

This paper proposes a two-layer optimization framework for a grid-connected fast electric vehicle charging station (EVCS) integrated with a photovoltaic (PV) system and a battery energy storage system (BESS). The primary objective is to minimize operational costs while guaranteeing the specific energy requirements of each individual electric vehicle (EV) transaction. The first layer utilizes Mixed-Integer Linear Programming (MILP) to establish a 24-hour energy schedule in one-hour intervals based on forecasted data...read more

This study develops a techno-economic framework to evaluate the operational and business feasibility of Flexible Electric Vehicle Charging Stations (Flexible EVCS) powered by Photovoltaic (PV) systems and Battery Energy Storage Systems (BESS). The analysis considers standalone operation, aiming to maximize the lessee's profit while ensuring the lessor's payback period. Four representative leasing scenarios are examined: (i) short-term use for events, fairs, or emergency relief, (ii) short-term deployment for construction sites or seasonal fleets, (iii) medium-term services in temporary offices or...read more

The decreasing costs of Battery Energy Storage Systems (BESS) and Renewable Energy (RE) technologies, such as Photovoltaics (PV) have made their integration into Electric Vehicle Charging Stations (EVCS) increasingly feasible. Nonetheless, in the absence of optimal power allocation strategies, excessive reliance on grid electricity may occur, leading to higher charging costs for EV users and reduced profitability for EVCS operators...read more

The rapid growth of electric vehicle (EV) adoption requires charging infrastructure that is both economically viable and environmentally sustainable. This study proposes an integrated optimal sizing framework for two distinct Electric Vehicle Charging Station (EVCS) configurations: a grid-connected system and a fully standalone system, each integrating Photovoltaic (PV) systems, small Wind Turbines (sWT), and Battery Energy Storage Systems (BESS)...read more

With declining costs of Battery Energy Storage Systems (BESS) and Renewable Energy (RE) sources such as Photovoltaics (PV) and Wind Turbines (WT), their integration into Electric Vehicle Charging Stations (EVCS) has become more viable. However, without optimal power distribution, unnecessary energy purchases from the grid can occur, reducing EVCS owner profits and increasing EV charging costs...read more

Large-scale electric vehicle (EV) integration challenges distribution-grid operation when coordinated charging depends on reliable wireless control, because downlink failures prevent timely charging commands, trigger fallback behavior, and erode peak-load suppression. This paper proposes a communication-aware smart charging framework based on user-centric cell-free massive MIMO that links spectral efficiency, control feasibility, and charging execution...read more

The rapid growth of Electric Vehicles (EVs) increases stress on distribution networks while intensifying pricing competition among Electric Vehicle Charging Station (EVCS) operators. Existing studies often treat pricing and EV-to-EVCS assignment separately, without accounting for grid limits and realistic market behavior. This paper proposes a framework combining a price competition game among EVCS operators with a many-to-one matching mechanism to jointly assign EVs to stations and set prices under distribution network constraints...read more

This paper proposes a bi-level framework for the optimal siting and sizing of Electric Vehicle Charging Stations (EVCS) integrated with photovoltaic, wind turbine, and battery energy storage systems. The lower level solves a Markov decision process for the intraday dispatch of each EVCS, while the upper level jointly optimizes the site, EVCS type, and Volt-VAR dispatch using a Reinforcement Learning-based Hybrid Parameters and Mutation Strategies Differential Evolution (RL-HPSDE)...read more

Fast-charging stations with photovoltaic generation and battery storage must reconcile day-ahead schedules with real-time uncertainty. Comparisons between model predictive control and reinforcement learning use unequal decision-time information, and learned policies cannot guarantee feasibility. This paper asks whether a bounded learned correction preserves day-ahead economics at lower online cost. A degradation-aware mixed-integer linear program sets the day-ahead reference; a residual soft actor-critic policy corrects battery operation and dispatches connectors; a fixed-size feasibility projection enforces operating constraints outside the policy...read more

Electric vehicle (EV) charging load forecasting depends on session records transmitted from EV charging stations (EVCSs) to a cloud operator over partially secured channels, exposing the reconstructed load to false-data-injection (FDI) attacks on fields such as delivered energy, state-of-charge (SoC), and charging timestamps. Existing defenses either stop at a binary detection decision or pair statistical detectors with generic replacement, leaving corrupted fields uncorrected or physically inconsistent, so the downstream forecast is not reliably restored...read more

The rapid adoption of electric vehicles (EVs) is reshaping power systems and creating new challenges for short-term load forecasting at charging stations. EV charging demand is highly variable and nonlinear, driven by user behavior, mobility patterns, and temporal factors, which limits the effectiveness of traditional forecasting methods based on regression or classical time-series models...read more

The falling prices of Battery Energy Storage Systems (BESS) and Renewable Energy (RE) technologies like Photovoltaics (PV) have made it much more practical to integrate them into Electric Vehicle Charging Stations (EVCS). However, using a simple bi-level strategy often fails to consider the interests of all stakeholders, including the Distribution System Operator (DSO), the charging station, and the vehicle owners, which results in financial losses for one of the groups...read more

This paper proposes a two-layer optimization framework for a grid-connected fast electric vehicle charging station (EVCS) integrated with a photovoltaic (PV) system and a battery energy storage system (BESS). The primary objective is to minimize operational costs while guaranteeing the specific energy requirements of each individual electric vehicle (EV) transaction. The first layer utilizes Mixed-Integer Linear Programming (MILP) to establish a 24-hour energy schedule in one-hour intervals based on forecasted data...read more

Vietnam's rapidly growing electric vehicle (EV) market has created urgent demand for well-planned charging infrastructure. We present an agent-based simulation (ABM) framework, built in the GAMA platform, to evaluate charging-station placement strategies for Nha Trang, a mid-sized coastal city. Candidate sites are generated via Voronoi partitioning to ensure spatial coverage, and individual drivers are modeled with stochastic trip generation, road travel, and queuing dynamics...read more

The increasing adoption of electric vehicles (EVs) requires efficient charging station operation, particularly when integrating renewable energy and battery energy storage system (BESS). This paper presents a Mixed-Integer Linear Programming (MILP) framework for daily optimization of a solar-powered EV charging station with a battery energy storage system (BESS) and grid connection. The model maximizes the daily net profit of the charging station while respecting technical constraints, using EV demand data obtained from on-site surveys to ensure realistic applicability...read more

This study develops a techno-economic framework to evaluate the operational and business feasibility of Flexible Electric Vehicle Charging Stations (Flexible EVCS) powered by Photovoltaic (PV) systems and Battery Energy Storage Systems (BESS). The analysis considers standalone operation, aiming to maximize the lessee's profit while ensuring the lessor's payback period. Four representative leasing scenarios are examined: (i) short-term use for events, fairs, or emergency relief, (ii) short-term deployment for construction sites or seasonal fleets, (iii) medium-term services in temporary offices or...read more

The decreasing costs of Battery Energy Storage Systems (BESS) and Renewable Energy (RE) technologies, such as Photovoltaics (PV) have made their integration into Electric Vehicle Charging Stations (EVCS) increasingly feasible. Nonetheless, in the absence of optimal power allocation strategies, excessive reliance on grid electricity may occur, leading to higher charging costs for EV users and reduced profitability for EVCS operators...read more

Electric vehicle charging stations equipped with photovoltaic generation and battery storage can provide demand-response flexibility to a distribution operator, but their contribution should preserve charging service. This paper proposes a three-layer virtual power plant (VPP) framework for point-of-common-coupling (PCC) import reduction in a campus charging network. The framework separates day-ahead reserve scheduling, real-time coordination, and station-level model predictive control (MPC) execution...read more

This paper investigates receding-horizon model predictive control for coordinated electric vehicle charging under site power constraints and delayed command execution. The proposed framework combines peak-shaving-oriented charging coordination with a closed-loop command-holding execution model that captures sub-slot communication delay. Evaluation is conducted on a representative day extracted from a real charging-session dataset. The results show that the site congestion factor and the unmet-energy penalty strongly govern the trade-off between peak reduction and charging-service quality...read more

The rapid proliferation of electric vehicles (EVs) is placing unprecedented pressure on fixed charging infrastructure, particularly in regions with constrained power grids. Mobile charging robots have emerged as a versatile solution, offering scalable deployment and the ability to integrate renewable energy sources dynamically. This paper proposes a novel Auction-based Deep Reinforcement Learning (AD-DRL) framework for the efficient and sustainable coordination of mobile charging robots...read more

Simultaneous wireless information and power transfer (SWIPT) with intelligent reflecting surfaces (IRS) enhances wireless performance but requires solving discrete phase optimization under hardware constraints. This letter studies joint energy delivery and interference suppression in a dual-transmitter IRS-aided SWIPT system with 1-bit elements. A hybrid binary grey wolf-particle swarm optimization algorithm directly optimizes IRS phases in the discrete domain...read more

Wireless power transfer (WPT) systems are increasingly applied in electric vehicles (EV). The battery voltage of rate EV system is either 400 V and 800 V. Conventional approaches typically employ an additional DC-DC converter to regulate the output voltage, resulting in a two-stage power conversion architecture with increased complexity and power loss. To address these limitations, this paper proposes a novel WPT system architecture incorporating a split-source inverter (SSI)-based power conversion stage....read more

Single-stage AC–AC matrix converters eliminate the electrolytic DC-link capacitor of the conventional two-stage AC–DC–AC inductive power transfer (IPT), reducing component count and improving power density. Whether this simplification yields a genuine efficiency advantage across the SAE J2954 power spectrum has not been established in a unified, topology-agnostic framework. This review addresses that gap through a two-component conduction-loss model parameterized by a dimensionless loss factor...read more

The rapid adoption of electric vehicles (EVs) is placing increasing pressure on parking charging infrastructure, while mobile charging robots that deliver energy directly to parked vehicles provide a flexible alternative to fixed chargers. Scheduling a small robot fleet to serve many deadline-constrained EV requests under limited robot energy, charging-dock availability, and time-varying electricity prices is a dynamic combinatorial problem that is further coupled with robot localization and wireless communication conditions...read more

Diffusion planners model multimodal driving futures, but low-step sampling is fragile when one reverse process must both discover maneuver topology and refine continuous geometry. Vision-Informed Navigation via Anchored Diffusion (VINAD) factorizes these roles: route- and map-compatible anchors from the lane graph define maneuver basins, and a denoising diffusion implicit model student refines trajectories locally inside each basin. The evaluated student is obtained by progressive 50-25-12-6 distillation and always executes six reverse steps...read more

This paper presents a task allocation framework for managing a fleet of mobile robots tasked with charging electric vehicles at dynamic electricity prices. With peak and off-peak prices, the robots must optimize both their vehicle maintenance and self-recharging schedules to minimize energy costs. The proposed solution combines auction-based coordination with reinforcement learning, allowing each robot to make autonomous decisions based on real-time utility assessments...read more

In this paper, we present a deep reinforcement learning (DRL) policy for autonomous mobile robot navigation in cluttered environments. A key challenge in applying DRL to vision-based navigation is the high dimensionality of raw image observations, which often leads to poor sample efficiency and unstable training. To address this issue, we propose a representation learning module based on a variational autoencoder (VAE), trained on simulated depth images to compress visual input into a compact latent representation...read more

Simultaneous wireless information and power transfer (SWIPT) with intelligent reflecting surfaces (IRS) enhances wireless performance but requires solving discrete phase optimization under hardware constraints. This letter studies joint energy delivery and interference suppression in a dual-transmitter IRS-aided SWIPT system with 1-bit elements. A hybrid binary grey wolf-particle swarm optimization algorithm directly optimizes IRS phases in the discrete domain...read more

Interference-aware beamforming for millimeter wave multiple-input multiple-output (mmWave MIMO) systems is challenged by limited channel state information (CSI) availability and the non-convex, discrete constraints of analog phase-shifter architectures. To address these issues, this paper develops a hyperparameter optimization (HPO)-driven deep reinforcement learning (DRL) framework that enables measurement-based beam design without explicit CSI...read more

Uplink power control in user-centric cell-free massive MIMO involves a nonconvex spectral efficiency (SE) trade-off between user fairness and network throughput caused by the coupled signal-to-interference-plus-noise ratio structure. This paper proposes a weighted-sum SE scalarization that combines the worst-user SE and the average SE, through a single tunable weight w ∈ [0, 1] that continuously controls the fairness-throughput operating point under per-UE power constraints...read more

Electric vehicle charging stations (EVCSs) have become critical cyber-physical hubs. They tie together consumer-side Internet of Things devices, distribution-level smart grids, and payment settlement infrastructures, and they do so through a mix of protocols including ISO/IEC 15118, the Open Charge Point Protocol (OCPP), and the Open Charge Point Interface (OCPI). Conventional safeguards like transport layer security, public key infrastructure, and signature-based intrusion detection are still necessary, but on their own they no longer go far enough...read more

Large-scale electric vehicle (EV) integration challenges distribution-grid operation when coordinated charging depends on reliable wireless control, because downlink failures prevent timely charging commands, trigger fallback behavior, and erode peak-load suppression. This paper proposes a communication-aware smart charging framework based on user-centric cell-free massive MIMO that links spectral efficiency, control feasibility, and charging execution...read more

The rapid adoption of electric vehicles (EVs) is placing increasing pressure on parking charging infrastructure, while mobile charging robots that deliver energy directly to parked vehicles provide a flexible alternative to fixed chargers. Scheduling a small robot fleet to serve many deadline-constrained EV requests under limited robot energy, charging-dock availability, and time-varying electricity prices is a dynamic combinatorial problem that is further coupled with robot localization and wireless communication conditions...read more

Digital-twin (DT)-assisted smart charging of electric vehicles (EVs) depends on timely telemetry that must traverse a shared cellular uplink (UL). When the residual UL radio resources are scarce, not every EV can report its state in each period and the DT ages, degrading the charging decisions that rely on it. Allocating these resources to maximize communication throughput is shown not to maximize charging performance...read more

Modern electric vehicle charging stations (EVCSs) connect chargers, industrial controllers, local energy resources, edge gateways, and cloud platforms over heterogeneous protocols, including the Open Charge Point Protocol (OCPP) and Modbus Transmission Control Protocol (TCP). Most existing solutions address authentication, encryption, access control, or traffic filtering as separate mechanisms. This paper proposes a five-stage edge cloud security framework that unifies device registration, access token validation, attribute based access control (ABAC), AES-GCM secure tunneling, and cloud side traffic filtering into a single...read more

This paper investigates receding-horizon model predictive control for coordinated electric vehicle charging under site power constraints and delayed command execution. The proposed framework combines peak-shaving-oriented charging coordination with a closed-loop command-holding execution model that captures sub-slot communication delay. Evaluation is conducted on a representative day extracted from a real charging-session dataset. The results show that the site congestion factor and the unmet-energy penalty strongly govern the trade-off between peak reduction and charging-service quality...read more

The rapid proliferation of electric vehicles (EVs) is placing unprecedented pressure on fixed charging infrastructure, particularly in regions with constrained power grids. Mobile charging robots have emerged as a versatile solution, offering scalable deployment and the ability to integrate renewable energy sources dynamically. This paper proposes a novel Auction-based Deep Reinforcement Learning (AD-DRL) framework for the efficient and sustainable coordination of mobile charging robots...read more

High penetration of inverter-based resources (IBRs) in islanded microgrids lowers inertia and makes fast frequency response (FFR) sensitive to feeder reconfiguration, which the fixed droop control, topology blind droop loop cannot track. When IBRs are aggregated into virtual power plants (VPPs) that bid FFR reserve into the ancillary service market, the committed reserve is only as deliverable as the active feeder can route, so the VPP's real-time execution layer must stay topology aware as the distribution system operator reconfigures the...read more

Fast-charging stations with photovoltaic generation and battery storage must reconcile day-ahead schedules with real-time uncertainty. Comparisons between model predictive control and reinforcement learning use unequal decision-time information, and learned policies cannot guarantee feasibility. This paper asks whether a bounded learned correction preserves day-ahead economics at lower online cost. A degradation-aware mixed-integer linear program sets the day-ahead reference; a residual soft actor-critic policy corrects battery operation and dispatches connectors; a fixed-size feasibility projection enforces operating constraints outside the policy...read more

The rapid adoption of electric vehicles (EVs) is reshaping power systems and creating new challenges for short-term load forecasting at charging stations. EV charging demand is highly variable and nonlinear, driven by user behavior, mobility patterns, and temporal factors, which limits the effectiveness of traditional forecasting methods based on regression or classical time-series models...read more

The increasing penetration of inverter-interfaced resources has significantly reduced effective inertia in islanded microgrids, causing pronounced frequency deviations. Existing control methods lack topology-awareness and adaptive coordination. This paper proposes a graph neural network (GNN)-enhanced multi-agent reinforcement learning (MARL) framework that represents the electrical network as a graph and enables coordinated electric vehicle charging station (EVCS) control. Evaluated on a CIGRE MV benchmark, the proposed approach demonstrates superior frequency stability compared to conventional droop control...read more

This paper presents a task allocation framework for managing a fleet of mobile robots tasked with charging electric vehicles at dynamic electricity prices. With peak and off-peak prices, the robots must optimize both their vehicle maintenance and self-recharging schedules to minimize energy costs. The proposed solution combines auction-based coordination with reinforcement learning, allowing each robot to make autonomous decisions based on real-time utility assessments...read more

Vietnam's rapidly growing electric vehicle (EV) market has created urgent demand for well-planned charging infrastructure. We present an agent-based simulation (ABM) framework, built in the GAMA platform, to evaluate charging-station placement strategies for Nha Trang, a mid-sized coastal city. Candidate sites are generated via Voronoi partitioning to ensure spatial coverage, and individual drivers are modeled with stochastic trip generation, road travel, and queuing dynamics...read more

The increasing adoption of electric vehicles (EVs) requires efficient charging station operation, particularly when integrating renewable energy and battery energy storage system (BESS). This paper presents a Mixed-Integer Linear Programming (MILP) framework for daily optimization of a solar-powered EV charging station with a battery energy storage system (BESS) and grid connection. The model maximizes the daily net profit of the charging station while respecting technical constraints, using EV demand data obtained from on-site surveys to ensure realistic applicability...read more

Digital-twin (DT)-assisted smart charging of electric vehicles (EVs) depends on timely telemetry that must traverse a shared cellular uplink (UL). When the residual UL radio resources are scarce, not every EV can report its state in each period and the DT ages, degrading the charging decisions that rely on it. Allocating these resources to maximize communication throughput is shown not to maximize charging performance...read more

Photovoltaic generation and battery storage offer operational flexibility to electric vehicle charging stations, but coordinated dispatch must accommodate variable generation, charging demand, tariffs, and battery constraints. This paper presents a digital-twin-enabled energy-management framework linking physical assets, edge state synchronization, forecasting, and hierarchical optimization. Day-ahead forecasts drive mixed-integer linear scheduling over 24 hours, while model predictive control updates dispatch every minute over a tenminute horizon. A soft battery-energy reference couples the two layers. Evaluation uses a 31-day telemetry-calibrated closed-loop simulation; the physical VinUni platform remains observe-only without autonomous actuation...read more

Electric vehicle charging stations (EVCSs) have become critical cyber-physical hubs. They tie together consumer-side Internet of Things devices, distribution-level smart grids, and payment settlement infrastructures, and they do so through a mix of protocols including ISO/IEC 15118, the Open Charge Point Protocol (OCPP), and the Open Charge Point Interface (OCPI). Conventional safeguards like transport layer security, public key infrastructure, and signature-based intrusion detection are still necessary, but on their own they no longer go far enough...read more

Electric vehicle (EV) charging load forecasting depends on session records transmitted from EV charging stations (EVCSs) to a cloud operator over partially secured channels, exposing the reconstructed load to false-data-injection (FDI) attacks on fields such as delivered energy, state-of-charge (SoC), and charging timestamps. Existing defenses either stop at a binary detection decision or pair statistical detectors with generic replacement, leaving corrupted fields uncorrected or physically inconsistent, so the downstream forecast is not reliably restored...read more

The rise of quantum computing poses significant risks to classical cryptographic algorithms, especially those used in digital signatures within Virtual Private Networks (VPNs). This paper proposes a hybrid cryptographic framework for IPsec VPNs that combines traditional digital signatures with post-quantum cryptographic (PQC) techniques to enhance security against both conventional and quantum threats. Our method introduces a dual-signature scheme integrated into the IKEv2 protocol, verified using formal methods via the Tamarin Prover to ensure robustness...read more

Modern electric vehicle charging stations (EVCSs) connect chargers, industrial controllers, local energy resources, edge gateways, and cloud platforms over heterogeneous protocols, including the Open Charge Point Protocol (OCPP) and Modbus Transmission Control Protocol (TCP). Most existing solutions address authentication, encryption, access control, or traffic filtering as separate mechanisms. This paper proposes a five-stage edge cloud security framework that unifies device registration, access token validation, attribute based access control (ABAC), AES-GCM secure tunneling, and cloud side traffic filtering into a single...read more