World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE)
Title: World Journal of Future Technologies in Computer Science and Engineering (WJFTCSE) Publisher: Wissira research Lab Publisher Address: Carmel, United States- 46074 Publisher Email: editor@wissira.us Starting Year: 2025 Main Subject: Computer Science Scope: Future Technologies in Computer Science and Engineering Language: English Publication Format: Online (Open Access) Website: https://wjftcse.org Contact: +91 75359 16555
Zero Trust Architectures for Edge-Native AI Inference Systems
Edge‑Native AI Inference Systems (ENAIS) are rapidly proliferating across domains such as autonomous vehicles, smart manufacturing, healthcare diagnostics, and critical infrastructure monitoring. By bringing AI inference closer to data sources, ENAIS dramatically reduce latency, conserve bandwidth, and enable real‑time decision‑making. However, the shift from centralized cloud environments to widely distributed, resource‑constrained edge nodes introduces unique security challenges: traditional perimeter defenses become ineffective, attack surfaces multiply, and heterogeneity complicates policy enforcement. To address these concerns, this manuscript presents a comprehensive Zero Trust Architecture (ZTA) tailored for ENAIS. Building on the principle of “never trust, always verify,” our design integrates three core components: identity‑centric access controls, micro‑segmentation of inference pipelines, and continuous telemetry‑driven policy adaptation. We detail the architectural blueprint, describe its implementation within a simulation framework, and conduct a rigorous evaluation under realistic threat scenarios. Statistical analysis of the simulation data—covering metrics such as unauthorized access prevention, inference latency, and resource overhead—reveals that the proposed ZTA blocks over 98% of unauthorized actions while incurring only a modest 16.6% latency penalty and minimal CPU and network overhead. These results demonstrate that ZTA can substantially elevate the security posture of ENAIS without compromising real‑time performance requirements. We conclude with a discussion of deployment considerations, potential integration with federated learning, and directions for future work in securing next‑generation edge AI.
Zero Trust, Edge Computing, AI Inference, Micro Segmentation, Continuous Authentication
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
AI-Powered Threat Modeling for API Gateways in Microservices
In the rapidly evolving landscape of microservice architectures, API gateways occupy a pivotal role by acting as the primary ingress point for client requests, performing critical functions such as routing, protocol translation, policy enforcement, authentication, rate limiting, and observability. These gateways, however, also represent a concentrated attack surface and have become prime targets for adversaries seeking to exploit misconfigurations, logical flaws, and emergent vulnerabilities. Traditional threat modeling approaches—centered on manual decomposition of system components, application of taxonomies like STRIDE, and expert-driven mitigation planning—are time‑consuming, labor‑intensive, and struggle to keep pace with the dynamism of large‑scale microservice deployments. To address these challenges, we present an AI‑powered threat modeling framework specifically tailored for API gateways within microservices environments. Our approach integrates a three‑stage pipeline: automated architecture ingestion to construct detailed system graphs; generative AI to propose candidate threats and attack scenarios based on engineered STRIDE prompts; and predictive anomaly detection leveraging machine‑learning classifiers trained on historical gateway logs. We evaluated our framework across fifty real‑world deployments spanning finance, healthcare, and e‑commerce sectors. The results demonstrate a precision of 0.87 and recall of 0.82 against expert‑verified threat models, alongside a 45% reduction in analysis time (from 22 hours down to 12 minutes per model). Notably, the system identified eighteen previously unrecognized tampering and information‑disclosure scenarios, subsequently validated by security engineers.
AI Powered Threat Modeling, API Gateway Security, Microservices, Automated Vulnerability Analysis, Generative AI
Blockchain-Enabled Anomaly Detection for Secure CI/CD Pipelines
Blockchain technology has emerged as a transformative force in securing distributed systems, offering tamper‑evident, decentralized ledgers that ensure data integrity and transparency. As software development organizations increasingly adopt Continuous Integration and Continuous Deployment (CI/CD) pipelines to accelerate release cycles, they concurrently expose themselves to sophisticated threats such as supply‑chain attacks, insider tampering, and configuration drifts that traditional security measures struggle to detect in real time. This manuscript presents ChainSec‑CI, a novel framework that marries the immutability guarantees of a permissioned blockchain—specifically Hyperledger Fabric—with AI‑driven anomaly detection to secure CI/CD pipelines comprehensively. Within ChainSec‑CI, every critical pipeline event—ranging from source code commits and build artifacts to test executions and deployment actions—is recorded on‑chain via lightweight smart contracts, creating a verifiable, append‑only audit trail. To address the high‑volume, heterogeneous nature of pipeline metadata, we extract key features (e.g., stage durations, failure frequencies, configuration hash changes, and sequence anomalies) and feed these into an unsupervised isolation forest model, enabling the system to learn “normal” pipeline behavior without requiring labeled attack data. We developed a Jenkins–Hyperledger Fabric integration prototype and evaluated it using 10,000 synthetic pipeline runs containing both benign operations and injected malicious scenarios such as unauthorized config modifications, abnormally prolonged build or test steps, and out‑of‑order stage executions.
CI/CD Pipelines, Blockchain, Anomaly Detection, Hyperledger Fabric, Unsupervised Learning
Privacy-Aware Authentication in Cyber-Physical Industrial Systems
Industrial control environments—spanning manufacturing floors, power grids, and critical infrastructure—now operate as complex cyber‑physical industrial systems (CPIS) that integrate programmable logic controllers (PLCs), sensors, actuators, and supervisory networks. As CPIS increasingly interconnect with enterprise IT and cloud services, they face heightened risks of unauthorized access and privacy breaches. Traditional authentication schemes, often repurposed from IT networks, either impose excessive computational load on resource‑constrained devices or fail to conceal sensitive metadata that can reveal operational characteristics. To address these challenges, we propose a novel privacy‑aware authentication protocol optimized for CPIS. Leveraging elliptic‑curve cryptography (ECC) for lightweight public‑key operations and Schnorr‑style zero‑knowledge proofs (ZKPs) to obfuscate device identities, our scheme achieves mutual authentication in just two communication rounds. We implement the protocol on common industrial controllers (Siemens S7‑1200, Allen‑Bradley CompactLogix, WAGO PFC200) using the TinyCrypt ECC library and Java‑Bouncy Castle on the server side. Over 200 trials, our solution attains an average end‑to‑end latency of 150 ms (±20 ms), a privacy leakage score of 0.15 on a normalized entropy scale (0–1), a false acceptance rate of 0.5%, and a false rejection rate of 1.2%. Compared to representative ECC‑only and ECC+ZKP schemes, we reduce authentication latency by up to 25% and diminish metadata leakage by 40%, while preserving reliability under induced network jitter. We conclude by discussing deployment guidelines—such as hardware‑accelerated cryptographic modules—and outline future research directions toward mesh‑network scalability and post‑quantum resilience.
Privacy-Aware Authentication, Cyber-Physical Industrial Systems, Elliptic-Curve Cryptography, Zero-Knowledge Proofs, Industrial IoT Security
Context-Aware Threat Detection in Multi-Cloud AI Platforms
In contemporary enterprise environments, the strategic adoption of multi-cloud AI platforms has unlocked unprecedented capabilities for scalable, resilient, and cost-effective deployment of artificial intelligence (AI) workloads. However, this architectural evolution has simultaneously introduced a complex and expanding attack surface that defies traditional security paradigms. In particular, threat actors exploit the dynamic interplay between heterogeneous cloud services, AI model telemetry, and contextual environmental variables to orchestrate stealthy, high-impact attacks that often elude conventional detection mechanisms. This manuscript presents a comprehensive context-aware threat detection framework expressly designed for multi-cloud AI ecosystems. By ingesting and fusing telemetry from cloud infrastructure logs (e.g., AWS CloudWatch, Azure Monitor, Google Cloud Operations), AI model inference and training logs, and auxiliary context such as workload schedules and user behavior patterns, the proposed system constructs a unified, high-dimensional representation of the operational environment. A hybrid detection engine—comprising supervised gradient boosting machines (GBMs) and unsupervised deep autoencoders—leverages these fused features to identify anomalies and known malicious patterns in real time. Through extensive experimentation within a simulated multi-cloud AI deployment featuring open-source workloads (TensorFlow Serving, Kubeflow pipelines) and adversarial scenarios (stealthy lateral movement, privilege escalation, data exfiltration), the framework achieves a detection accuracy of 92.0%, a false positive rate reduction of 45% relative to context-agnostic baselines, and maintains average alert latency under 1.5 seconds.
Context-Aware Threat Detection, Multi-Cloud Security, AI Platform Monitoring, Behavioral Analytics, Adaptive Defenses
Quantum-Resistant AI Models for Intrusion Detection
As quantum computing transitions from theoretical constructs to practical implementations, classical cryptographic foundations—long the bedrock of cybersecurity—face unprecedented vulnerability. Shor’s algorithm and related quantum techniques threaten to render public-key schemes like RSA and ECC obsolete, exposing sensitive communications and stored data to adversarial compromise. In this context, Intrusion Detection Systems (IDS) that harness artificial intelligence (AI) must evolve to incorporate quantum-resistant mechanisms to secure both their operational logic and the data they process. This manuscript presents an end-to-end framework for a quantum-resistant AI-based IDS, integrating lattice-based public-key encryption (FrodoKEM) for feature confidentiality and Cheon-Kim-Kim-Song (CKKS) homomorphic encryption for privacy-preserving inference. We develop and optimize a deep neural network with encrypted weights and encrypted input vectors, enabling packet inspection and anomaly detection entirely in the encrypted domain. Experiments on UNSW-NB15 and CIC-IDS2017 datasets demonstrate that our quantum-resistant IDS achieves an average detection accuracy of 97.2%, a true positive rate of 96.0%, and a false positive rate of 3.5%, closely matching plaintext performance while incurring a total latency overhead of approximately 25% (14.8 ms encrypted inference vs. 12.1 ms plaintext, plus 2.9 ms encryption/decryption). We analyze computational and memory trade-offs, explore key-management strategies compliant with NIST PQC guidelines, and discuss scalability considerations for real-world deployment. Our findings confirm that integrating post-quantum cryptography into AI-driven IDS can future-proof network security infrastructures against both classical and quantum adversaries, with acceptable performance overhead for enterprise environments.
Quantum-Resistant, Post-Quantum Cryptography, Machine Learning, Intrusion Detection, Homomorphic Encryption
Multi-Agent Drone Swarms for Agricultural Intelligence Networks
In modern precision agriculture, the use of Unmanned Aerial Vehicles (UAVs) has revolutionized the capabilities for real‑time monitoring of crop health, soil moisture, pest infestations, and resource allocation. However, single‑drone systems face inherent limitations in flight endurance, payload capacity, and operational redundancy, which constrain their effectiveness over expansive agricultural tracts. This study introduces and validates a multi‑agent drone swarm approach—comprising ten coordinated quadcopters—designed to function as an Agricultural Intelligence Network (AIN). Each drone is equipped with high‑resolution RGB and multispectral cameras, onboard processing units for in‑flight data analysis, and mesh‑network communications enabling peer‑to‑peer coordination without reliance on a central controller. We implemented a consensus‑based waypoint allocation algorithm alongside a U‑Net convolutional neural network for automated detection of pests and weeds. Field trials were conducted on a 50‑hectare maize plot characterized by heterogeneous health zones. Performance metrics included coverage rate (ha/min), detection accuracy (%), mission duration (min), and communication latency (ms). Statistical analysis using independent‑samples t‑tests (α = .05) across ten missions per configuration revealed that the swarm system achieved a 36% higher coverage rate and a 22.2% absolute improvement in detection accuracy compared to single‑drone missions, while reducing mission duration by 35%. Communication latency increased modestly but remained operationally acceptable. These results confirm that decentralized multi‑agent swarms significantly enhance both the efficiency and quality of agricultural surveillance. The paper discusses implications for large‑scale precision farming, addresses environmental and regulatory constraints, and outlines future research directions to further optimize swarm scalability and energy management.
Multi Agent Drone Swarm, Precision Agriculture, Decentralized Coordination, Target Detection, Consensus Algorithms
Smart Grippers for Predictive Object Recognition in Warehousing Robots
Smart grippers that integrate predictive object recognition capabilities have emerged as pivotal components in the next generation of autonomous warehousing robots. By anticipating both the identity and grasp affordances of items prior to contact, these systems can significantly reduce mispicks, optimize cycle times, and enhance overall throughput. This paper presents an end-to-end framework combining a soft, sensorized gripper design with a parallel deep learning architecture that fuses visual and tactile modalities. The hardware includes an Ecoflex-based soft gripper embedded with triboelectric nanogenerator (TENG) sensors—both pressure (P‑TENG) and bend (B‑TENG)—that provide rich tactile feedback. On the algorithmic side, we deploy a dual-network pipeline: a YOLOv5 model for rapid object detection and classification, and a GG‑CNN for pixel-wise grasp quality estimation, both trained and fine-tuned on a custom warehouse dataset. A lightweight convolutional network processes the high‑frequency tactile signals to infer object geometric categories. These streams converge in a Bayesian fusion module that dynamically selects optimal grasp parameters (finger spread, approach angle, and force threshold) before actuation. In a 1,000‑trial evaluation across ten item classes in a mock warehouse environment, our system achieved a predictive recognition accuracy of 94.3% and a grasp success rate of 92.1%, reducing mispicks by 37% and improving average pick cycle time by 18% compared to vision‑only baselines. Under challenging conditions—low lighting (< 100 lux) and partial occlusions—the fused approach maintained an 89.7% success rate, whereas vision‑only performance dropped to 78.4%.
Smart Gripper, Predictive Object Recognition, Warehousing Robots, Deep Learning, Sensor Fusion
Energy Harvesting in Micro-Robots for Self-Sustainability
Energy harvesting has emerged as a transformative approach for powering micro‑robotic systems autonomously, obviating the need for bulky batteries and frequent recharging. By converting ambient mechanical, solar, or electromagnetic energy into electrical power, micro‑robots can maintain continuous operation in diverse environments. This study provides an in‑depth comparison of three energy‑harvesting modalities—piezoelectric, photovoltaic, and radio‑frequency (RF)—implemented on identically sized micro‑robot prototypes. We designed and fabricated three sets of 20 × 20 × 10 mm³ micro‑robots, each equipped with either a PZT cantilever, a monocrystalline silicon solar cell, or a dual‑band RF rectenna. Under controlled laboratory conditions—120 Hz, 1 g vibration for piezoelectric trials; 500 lux illumination for photovoltaic trials; and a calibrated 2 W ERP RF field inside a shielded chamber—each harvester’s output was recorded over ten 60‑second runs. Using a precision data‑logging system sampling at 1 kHz, we integrated harvested charge to compute energy yields per trial. A one‑way ANOVA (α=0.05) tested differences among modalities, revealing significant performance disparities: piezoelectric (mean = 120 µJ, SD = 15), photovoltaic (mean = 85 µJ, SD = 12), and RF (mean = 60 µJ, SD = 10). Post‑hoc Tukey tests confirmed that all pairwise differences were statistically significant (p<0.01). These results underscore the critical role of environmental compatibility in harvester selection and motivate hybrid configurations to balance strengths and weaknesses. We discuss implications for micro‑robot design, propose guidelines for selecting and integrating harvesters based on mission profiles, and outline future research paths, including long‑duration field deployments and multimodal integration to achieve true self‑sustainability.
Energy Harvesting, Micro Robots, Self Sustainability, Piezoelectric, Photovoltaic, RF
Underwater Robotic Systems with Real-Time Acoustic AI Mapping
Underwater robotic exploration has been revolutionized by advances in acoustic sensing and artificial intelligence (AI), enabling autonomous underwater vehicles (AUVs) to navigate and map complex seabed environments without reliance on optical systems. Traditional optical approaches fail under turbid conditions and at depth, where light attenuation is severe, whereas acoustic sensors—such as multi‑beam and side‑scan sonar—provide robust, range‐based measurements regardless of visibility. However, raw sonar data are plagued by speckle noise, multipath reflections, and environmental interference, which complicate feature extraction and mapping. To address these challenges, we present Underwater Robotic Systems with Real‑Time Acoustic AI Mapping, a comprehensive framework that integrates a synchronized multi‑modal acoustic sensor suite with onboard deep learning models for semantic feature extraction and simultaneous localization and mapping (SLAM). Our system hardware combines a multi‑beam sonar for broad bathymetric coverage, side‑scan sonar for high-resolution imagery, an inertial measurement unit (IMU) for attitude estimation, and a depth sensor for accurate pressure-based altitude readings. Sensor streams are synchronized using Precision Time Protocol (PTP) to ensure consistent timestamp alignment. At the core of our software pipeline lies a convolutional neural network (CNN) based on the U‑Net architecture, trained to segment seabed structures—such as ridges, pipelines, and wreckage—in acoustic intensity images. The network was trained on a curated dataset of over 5,000 labeled sonar returns, augmented with simulated noise profiles replicating real‑world interference patterns.
Underwater Robotics, Acoustic Mapping, Deep Learning, SLAM, Autonomous Underwater Vehicle
AI-Enhanced Remote Diagnosis in Robotic Maintenance Systems
The ever‑increasing sophistication of robotic maintenance systems in manufacturing and critical‑infrastructure domains has outpaced the capabilities of traditional diagnostic regimes. Conventional approaches—predicated on scheduled inspections, manual symptom recognition, and simple threshold alarms—prove insufficient when confronted with heterogeneous fleets of high‑degree‑of‑freedom manipulators, mobile platforms, and collaborative robots operating under continuous, high‑load conditions. Such systems demand a diagnostic paradigm that not only anticipates failures before catastrophic breakdowns but also adapts dynamically to evolving operating contexts. This manuscript presents an AI‑enhanced remote diagnosis framework tailored for modern robotic maintenance, integrating multi‑modal sensor fusion, edge‑based preprocessing, and cloud‑native machine learning analytics. We detail the system architecture—comprising onboard accelerometers, thermistors, and motor‑current monitors; an MQTT‑based communication layer; and a scalable Spark‑Streaming/XGBoost analytics pipeline—and describe the data‑generation and labeling processes used for model training. A comprehensive simulation in ROS‑Gazebo involving six UR10 arms executing representative pick‑and‑place tasks evaluates the framework’s performance. Statistical analysis on an unseen test set reveals a 90.4% diagnostic accuracy—15.2% higher than a baseline threshold method—alongside a 33.6% reduction in mean time to detection. Through a 72‑hour virtual operation, end‑to‑end latencies averaged 120 ms, demonstrating real‑time feasibility. False‑alarm rates fell from 8.7% to 2.3%, and false negatives from 5.5% to 3.1%, confirming robust sensitivity and specificity. The proposed system therefore offers a scalable, adaptive solution for minimizing unscheduled downtime and maintenance costs in Industry 4.0 deployments. Future work will incorporate motion‑context awareness and real‑world field trials to further refine diagnostic precision and operational resilience.
AI-Enhanced Diagnosis, Robotic Maintenance, Predictive Maintenance, Sensor Fusion, Remote Monitoring
Blockchain-Based Robot Identity and Coordination in Multi-Agent Environments
Blockchain technology, with its decentralized ledger and smart‑contract capabilities, offers a robust framework for addressing critical challenges in multi‑agent robotic systems—namely, trustless identity management, secure peer authentication, and fault‑tolerant task coordination. In traditional architectures, centralized controllers enforce policies and mediate interactions, creating single points of failure, scalability bottlenecks, and opportunities for malicious interference. By contrast, a permissioned blockchain network distributes authority across participating robots, enabling each agent to verify the provenance of messages, enforce consensus‑driven agreements, and record immutable logs of interactions. This study presents a comprehensive examination of blockchain‑based robot identity and coordination in multi‑agent environments. We systematically review existing decentralized identity (DID) schemes and smart‑contract protocols, analyze their performance through statistical comparisons with centralized baselines, and implement a Hyperledger Fabric prototype across ten heterogeneous mobile robots. Our experiments demonstrate that on‑chain identity registration and automated smart‑contract–driven task allocation reduce coordination latency by 35%, improve task completion rates by 9.8%, and shorten fault recovery times by 20%, albeit with a 17% increase in energy consumption due to cryptographic operations.
Blockchain, Robot Identity, Multi Agent Coordination, Decentralized Control, Smart Contracts
AI-Based Terrain Adaptation Algorithms for Walking Robots
Legged robots capable of traversing unstructured and unpredictable environments are increasingly vital for applications such as disaster response, planetary exploration, precision agriculture, and inspection of hazardous zones. Traditional model-based control strategies require precise system identification and assume known, quasi-static terrain profiles; as a result, their performance degrades sharply when confronted with unforeseen surface irregularities, variable compliance, or dynamic obstacles (Zhao & Li, 2020). In contrast, AI-based terrain adaptation algorithms leverage data-driven learning to endow walking robots with the ability to perceive, interpret, and respond to diverse terrain features in real time. This manuscript presents a unified framework integrating deep reinforcement learning (DRL), sensor fusion, and hybrid model-based/data-driven control for bipeds, quadrupeds, and hexapods. We detail the system architecture—comprising a perception module (LIDAR, vision, IMU, and force sensing), a DRL policy trained via Proximal Policy Optimization (PPO), and an impedance-based safety layer—that enables robust foothold selection, gait modulation, and reflexive fallback behaviors. Experimental validation in high-fidelity MuJoCo simulations across flat ground, variable slopes, deformable sand patches, and discrete obstacle fields demonstrates that our hybrid controller reduces slip events by 28% and energy consumption by 12%, while increasing average locomotion speed by 15% compared to standard model predictive control (MPC) baselines. We conclude by discussing practical considerations for real-world deployment, including domain randomization, computational latency, and sensor calibration, and outline future directions for extending terrain taxonomy and minimizing sim-to-real gaps (Kumar et al., 2021; Patel & Singh, 2021).
Terrain Adaptation, Walking Robots, Deep Reinforcement Learning, Sensor Fusion, Hierarchical Control
Cyber-Physical Co-Simulation Frameworks for Autonomous Vehicles
The integration of cyber‑physical co‑simulation frameworks is increasingly recognized as a foundational technique for advancing autonomous vehicle (AV) research and development. By tightly coupling traffic microsimulation, high‑fidelity vehicle dynamics, and realistic network emulation, such frameworks enable researchers and engineers to evaluate perception, planning, and control algorithms under conditions that closely mimic real‑world operations. This manuscript presents an enhanced exploration of a novel co‑simulation architecture built on ROS 2, orchestrating SUMO for urban traffic scenarios, CARLA for detailed vehicle and sensor modeling, and NS‑3 for end‑to‑end communication emulation. We detail the architectural components, synchronization mechanisms, and data‑exchange protocols that ensure sub‑50 ms round‑trip latencies and near‑real‑time execution. A comprehensive statistical analysis over 100 randomized scenarios—including latency, real‑time factor, CPU utilization, and memory footprint—is provided, with performance metrics rigorously profiled and confidence intervals computed. Validation against single‑simulator baselines demonstrates trajectory divergence under 0.05 m over 1 km, confirming high modeling fidelity. Finally, we discuss extensibility features, including plug‑and‑play modules for threat injection and hardware‑in‑the‑loop (HIL) integration, and identify future research avenues such as standardized FMI‑ROS interfaces and multi‑agent V2X coordination. Through this work, we contribute a robust, open‑source co‑simulation platform that bridges critical gaps in AV systems validation and paves the way for more resilient, scalable, and secure autonomous driving solutions.
Autonomous Vehicles, Co Simulation, Cyber Physical Systems, Latency, Scalability
Next-Gen Drone Swarm Coordination via Federated Learning
The rapid evolution of unmanned aerial vehicle (UAV) technology has ushered in an era in which cooperative drone swarms can execute complex, large‑scale missions across diverse civilian, commercial, and defense sectors. Traditional centralized coordination frameworks, however, struggle to meet the demands of these dynamic, resource‑constrained environments due to their limited scalability, single points of failure, and privacy vulnerabilities. This manuscript introduces Next‑Gen Drone Swarm Coordination via Federated Learning (DSC‑FL), a hierarchical, privacy‑preserving architecture that enables real‑time, collaborative decision‑making among heterogeneous UAVs. By dynamically clustering drones according to proximity and mission role, DSC‑FL balances local compute, communication overhead, and model accuracy. Within each cluster, drones perform local model training on sensor and state data, encrypt gradient updates via secure aggregation protocols, and transmit only aggregated model deltas to higher‑level aggregators—minimizing bandwidth consumption and protecting raw data. A consensus‑based flight‑control algorithm fuses federated model predictions across neighbors to adapt formations, avoid obstacles, and respond to environmental disturbances. We evaluate DSC‑FL in a high‑fidelity simulation featuring urban canyons, wind gusts, intermittent link failures, and adversarial perturbations. Our results demonstrate a 25% improvement in formation‑maintenance accuracy and a 30% reduction in communication bandwidth compared to centralized and peer‑to‑peer baselines, while maintaining resilience against gradient‑inversion attacks. These findings establish DSC‑FL as a scalable, secure, and adaptable solution for next‑generation drone swarm deployments.
Drone Swarm Coordination, Federated Learning, UAV Clusters, Privacy Preserving, Distributed Training
Tactile-Sensor Embedded Robotic Skins for Fine-Grained Dexterity
The integration of tactile sensors into robotic skins represents a transformative milestone in the advancement of dexterous robotic manipulation. Over the past decade, the convergence of flexible electronics, microfabrication techniques, and soft-material engineering has enabled the development of tactile-sensor embedded skins that closely mimic the human sense of touch. These skins incorporate dense arrays of taxels—individual tactile sensing units— that can detect normal forces, shear forces, and even subtle texture variations. By distributing these sensing elements beneath compliant elastomeric layers, robotic systems gain the ability to perform adaptive grasping, prevent object slippage, and interact safely with delicate or unpredictable environments. In this work, we present a comprehensive framework for the design, fabrication, calibration, and evaluation of a multi-modal robotic skin featuring both capacitive and optical sensing modalities. We detail the materials selection process, manufacturing steps, electronics integration, and data acquisition architecture. Calibration protocols establish the relationships between applied forces and sensor outputs, ensuring accuracy across a broad dynamic range. Experimental evaluations demonstrate sub-gram force resolution, spatial discrimination below half a millimeter, and reliable texture classification with over 95% accuracy. Durability testing confirms sustained performance after extensive mechanical cycling. We conclude by discussing how tactile feedback enhances closed-loop control in manipulation tasks, outlining current limitations— such as power consumption and data bandwidth—and proposing future research directions, including on-skin preprocessing, machine-learning–driven perception, and large-area scalable fabrication.
Tactile-Sensor Embedded Robotic Skins, Fine-Grained Dexterity, Multi-Modal Taxe, Compliant Robotics
On-Chip AI Processing in Neuromorphic Robot Controllers
On-chip artificial intelligence (AI) processing implemented within neuromorphic robot controllers represents a paradigm shift in autonomous robotics, enabling ultralow-latency sensorimotor loops, drastic reductions in energy consumption, and adaptive behavior through biologically inspired spiking neural networks (SNNs). This manuscript delivers an in-depth exploration of on-chip AI for neuromorphic controllers, structured as follows: an expansive introduction to the motivation and context; a comprehensive literature review tracing the evolution of neuromorphic hardware and its robotic applications; a detailed Statistical Analysis section (including one comparative table) benchmarking neuromorphic versus conventional controllers on latency, power, and accuracy; a rigorous methodology outlining network architectures, simulation environments, and measurement protocols; extensive results highlighting significant performance gains; a substantive conclusion synthesizing findings; and a thorough scope and limitations discussion identifying future research directions. Through simulation-based experiments using Intel’s Loihi chip and an ARM Cortex-M4 microcontroller baseline, we demonstrate that on-chip neuromorphic processing reduces control-loop latency by approximately 68%, lowers power consumption by 83%, and modestly enhances inference accuracy. We contextualize these gains within real-world robotics, discuss implementation challenges such as hardware variability and integration complexity, and propose paths for incorporating on-chip learning. This expanded treatment underscores the transformative potential of neuromorphic AI for real-time, energy-constrained autonomy in robotics.
On-Chip AI Processing, Neuromorphic Controllers, Spiking Neural Networks, Robotic Control, Energy Efficiency
Autonomous Firefighting Robots Using Reinforcement Learning
Autonomous firefighting robots represent a transformative approach to mitigating risk and enhancing effectiveness during fire‐fighting operations in hazardous environments. This study presents a comprehensive investigation into the design, development, and evaluation of a mobile firefighting robot driven by deep reinforcement learning (DRL). By integrating advanced perception modules—comprising LiDAR‐based simultaneous localization and mapping (SLAM) for precise environment mapping and a fused RGB‑thermal imaging pipeline for robust fire detection—with a suppression subsystem featuring a water‑mist nozzle, the robot is equipped to autonomously navigate cluttered indoor spaces, identify fire sources, and deploy extinguishing actions. We employ a Deep Q‑Network (DQN) augmented with prioritized experience replay to learn optimal navigation and suppression policies in a simulation environment reflecting realistic fire dynamics, including dynamic obstacles and variable fire intensities. Training is conducted over 300,000 time steps, with the reward structure carefully shaped to balance exploration, navigation efficiency, hazard approach, and collision avoidance. The resulting policy achieves a navigation success rate of 92% and extinguishing success of 88% across fifty test episodes, yielding a 35% reduction in average time‑to‑extinguish compared to a heuristic baseline employing A* planning and threshold‑based thermal detection. Statistical analysis via two‑sample t‑tests confirms the significance of performance gains (p < 0.001), and qualitative failure‐case examination highlights areas for improvement in smoke occlusion handling.
Autonomous Firefighting Robots, Deep Reinforcement Learning, Deep Q Network, LiDAR Mapping, Thermal Imaging
Biologically Inspired Navigation Models for Autonomous Swarms
Biologically inspired navigation models have emerged as a powerful paradigm for guiding autonomous robotic swarms in complex, dynamic environments. By emulating collective behaviors observed in natural systems—such as flocking birds, schooling fish, and foraging insects—these models enable large groups of simple agents to achieve robust, scalable navigation without centralized control. In this study, we present a unified framework that integrates four canonical bio-inspired strategies—Boids flocking rules, ant colony optimization (ACO), particle swarm optimization (PSO), and artificial bee colony (ABC)—into a comparative simulation environment. Furthermore, we propose a novel hybrid model combining local interaction rules with stigmergic pheromone signaling to leverage the complementary strengths of these approaches. Our simulation platform, built on ROS and Gazebo, facilitates controlled experiments across a range of obstacle densities, communication constraints, and swarm sizes. We perform rigorous statistical analyses (one-way ANOVA and Tukey’s HSD) to evaluate path efficiency, collision rates, and energy consumption over 30 trials per model. The hybrid model consistently outperforms individual strategies, achieving up to 14% improvement in path efficiency, a 44% reduction in collisions, and 15% lower energy usage. Detailed simulation studies further reveal the hybrid model’s resilience under high obstacle density and communication loss, as well as near-linear scalability up to 100 agents. These findings demonstrate that integrating local rule-based navigation with stigmergic communication yields a versatile, fault-tolerant solution for real-world swarm applications. Future work will validate these insights on physical robot platforms and explore adaptive parameter tuning via machine learning.
Bio-Inspired Navigation, Swarm Robotics, Boids, Ant Colony Optimization, Particle Swarm Optimization
Robotic Surgery Assisted by Haptic Feedback and Edge AI
Robotic surgery has revolutionized the landscape of minimally invasive procedures by offering unparalleled precision, dexterity, and visualization. Yet, one of its most significant limitations remains the absence of tactile sensation, forcing surgeons to rely exclusively on visual cues when manipulating delicate tissues and anatomical structures. This sensory gap can contribute to inadvertent damage, reduced procedural efficiency, and increased cognitive workload. Concurrently, the rise of cloud-based AI for surgical assistance brings powerful analytical capabilities but suffers from unpredictable network latency and data privacy concerns. Edge artificial intelligence (AI)—deploying machine learning models on local computing nodes near the point of care—promises to overcome these challenges by enabling low-latency inference and preserving sensitive data within the surgical suite. Integrating force-reflective haptic feedback with edge AI thus offers a compelling dual-modality approach: restoring the surgeon’s sense of touch while providing real-time, intelligent data processing. In this study, we present a comprehensive evaluation of a prototype robotic surgical platform enhanced with bilateral haptic interfaces and edge-deployed AI algorithms. Our system employs high-resolution force sensors at the robotic end effectors and a lightweight convolutional neural network (CNN) optimized for sub-10 ms inference on an NVIDIA Jetson Xavier NX. A randomized crossover trial was conducted with thirty board-certified laparoscopic surgeons performing three standardized tasks—incision accuracy, knot tying, and object transfer—on anatomically realistic tissue phantoms. Performance metrics included task completion time, force fidelity (measured as the correlation between commanded and rendered forces), positional accuracy, and system latency, alongside subjective workload assessed via the NASA-TLX instrument.
Robotic Surgery, Haptic Feedback, Edge AI, Latency, Surgical Accuracy
Self-Reconfiguring Modular Robots for Deep-Sea Exploration
Self‑reconfiguring modular robots (SRMRs) represent a paradigm shift in underwater robotics, offering unprecedented adaptability, resilience, and efficiency for deep‑sea exploration missions. This manuscript provides an in‑depth analysis of an SRMR system engineered to operate under extreme hydrostatic pressures (up to 150 MPa), subzero temperatures (down to –2 °C), and complex, unstructured seabed terrains. We detail a novel modular architecture comprising hermetically sealed aluminum alloy modules filled with dielectric oil, interconnected via magnetic latches and spring‑loaded electrical contacts. Each module houses a pressure‑tolerant brushless DC actuator and onboard microcontroller, enabling fully distributed decision‑making. A hybrid control framework integrates a centralized mission planner—responsible for global pathing and morphology directives—with module‑level behavior controllers that handle local reconfiguration, collision avoidance, and energy management. High‑fidelity simulations in Gazebo, augmented with hydrodynamic plugins to emulate abyssal currents (up to 1 m/s) and terrain types (sand, silt, and rocky outcrops), validate the system’s capabilities. Key performance metrics include reconfiguration time (12.3 ± 2.1 s between chain and wheel morphologies), locomotion efficiency (0.85 m/J in wheel mode, a 35% improvement over chain mode at 0.63 m/J), fault tolerance (92% waypoint completion under a 10% random module failure rate), and long‑duration uptime (>90% operational over 48 h). Results demonstrate that SRMRs can dynamically optimize their morphology to balance speed, stability, and energy consumption, negotiating obstacles via legged configurations when needed. We discuss the practical implications for seabed mapping, sample collection, and infrastructure inspection, highlighting areas for future work such as real‑world prototype testing, advanced energy storage, and enhanced acoustic communication strategies.
Self Reconfiguring Modular Robots, Deep Sea Exploration, Adaptive Morphology, Distributed Control, Underwater Robotics
Decentralized DNS Models for Secure, AI-Backed Content Delivery Networks
The Domain Name System (DNS) is the foundational naming infrastructure of the Internet, translating human‐readable domain names into machine‐readable IP addresses. Despite its critical role, the traditional DNS architecture—characterized by hierarchical, centralized name servers—suffers from inherent vulnerabilities that undermine the security, availability, and performance of downstream services such as Content Delivery Networks (CDNs). Central points of failure can be exploited by Distributed Denial of Service (DDoS) attackers, and cache poisoning incidents can redirect legitimate traffic to malicious endpoints. In response, decentralized DNS models, leveraging blockchain and peer‑to‑peer (P2P) distributed hash tables (DHTs), have emerged to eliminate single points of failure, provide data immutability, and resist censorship. However, decentralization alone does not fully address performance optimization or intelligent threat mitigation. To close this gap, we propose an integrated framework that couples decentralized DNS architectures with artificial intelligence (AI) modules for real‑time anomaly detection and dynamic caching strategies. We implement two decentralized DNS prototypes—a blockchain‑based system using Ethereum Name Service principles and a Kademlia‑inspired P2P DHT system—and embed AI components: a random forest classifier for DNS traffic anomaly detection and a deep Q‑network agent for cache pre‑fetching. Through a comprehensive ns‑3 simulation of a global CDN spanning 50 edge servers and realistic Internet traffic patterns (including Alexa Top 1 Million requests and periodic high‑volume DDoS bursts), we collect metrics on DNS lookup latency, DDoS mitigation efficacy, and cache hit ratios. Statistical analysis over 1,000 trials with 95% confidence intervals reveals that our AI‑backed decentralized models reduce average DNS lookup latency by 25–29%, improve DDoS mitigation from 40% to over 90%, and enhance cache hit ratios by 12–15% compared to the centralized DNS baseline. These quantifiable benefits affirm the viability of combining decentralization with AI to secure and accelerate CDN operations. We conclude by discussing practical deployment considerations, such as blockchain transaction throughput, peer authentication mechanisms, and cross‑domain interoperability, and outline future research directions in federated learning, economic incentive designs, and quantum‑resilience for next‑generation DNS systems.
Decentralized DNS, Blockchain DNS, Peer-to-Peer DNS, AI-Driven CDN, Security, Performance
Event-Driven Cloud-Native ML Pipelines in Continuous Intelligence Systems
Continuous intelligence (CI) systems represent the fusion of real‑time analytics, automated decision‑making, and feedback loops that continuously refine operational processes. At the core of these systems lie event‑driven, cloud‑native machine learning (ML) pipelines that respond instantaneously to incoming data, performing preprocessing, inference, and result dissemination without human intervention. In this work, we present an end‑to‑end design and empirical evaluation of such a pipeline. We begin by motivating the need for event‑driven architectures in CI, highlighting latency, throughput, and scalability requirements in domains ranging from fraud detection to IoT monitoring. We then describe our cloud‑native deployment, leveraging managed Kubernetes with Knative Eventing for serverless event routing, Apache Kafka on Confluent Cloud as the streaming backbone, and KServe for scalable model serving. Our implementation also integrates Kubeflow pipelines for periodic retraining and OpenTelemetry/Prometheus for observability. To assess performance, we simulate a continuous stream of synthetic sensor events at rates up to 5,000 events per second, measuring metrics such as end‑to‑end latency (ingestion to inference to sink), throughput, resource utilization, autoscaling behavior, and fault‑recovery time under injected failures. Experimental results demonstrate median latencies under 150 milliseconds, linear throughput scaling with cluster size, and recovery times below 10 seconds with fewer than 0.5% lost events. Observability data reveals how feature‑drift alerts and confidence‑based routing enhance model reliability in production.
Continuous Intelligence, Event Driven Architecture, Cloud Native, Machine Learning Pipelines, Serverless, Kubernetes
Secure Function-as-a-Service Platforms Using Trusted Execution Environments
Function‑as‑a‑Service (FaaS) represents a paradigm shift in cloud computing, enabling developers to deploy individual functions—discrete units of computation—in an event‑driven, fully managed environment. By abstracting away server provisioning and scaling concerns, FaaS empowers rapid development cycles and cost‑efficient execution based solely on actual resource consumption. However, the very characteristics that make FaaS attractive—multi‑tenancy, ephemeral execution, and opaque provider control—also introduce significant security and privacy challenges. Specifically, untrusted infrastructures and potentially malicious insiders can exploit shared kernels, memory channels, and management APIs to tamper with function code, exfiltrate sensitive data, or compromise integrity guarantees. Trusted Execution Environments (TEEs), such as Intel SGX and ARM TrustZone, supply hardware‑enforced isolation and cryptographic memory protection within secure enclaves, thereby mitigating these risks. This manuscript undertakes a comprehensive exploration of Secure FaaS platforms that leverage TEEs. We begin with a systematic analysis of existing designs—surveying enclave‑based FaaS prototypes, snapshot‑driven optimization techniques, and attestation infrastructures—highlighting their security properties and performance trade‑offs. Building on these insights, we propose a reference architecture that integrates rapid enclave instantiation (via snapshot pools), a heterogeneous‑TEE scheduler, and an immutable, blockchain‑backed resource ledger for accountability. We then describe our prototype implementation atop Apache OpenWhisk: featuring an in‑enclave WebAssembly runtime, CRIU‑based snapshot manager, and a gRPC‑driven attestation broker. Through extensive experiments—covering cold‑start latency, warm‑start throughput, scaling behavior, and measurement accuracy under adversarial tampering—we demonstrate that Secure FaaS can deliver end‑to‑end confidentiality and integrity with modest overhead (average latency increase ≤ 10%) while preserving the elasticity and pay‑per‑use economics of conventional serverless platforms. We conclude by discussing deployment considerations, developer tooling requirements, and avenues for future research in confidential, accountable serverless computing.
Function as a Service, Trusted Execution Environment, Intel SGX, Serverless Security, Confidential Computing
AI-Enhanced Network Slicing Orchestration in Telco Edge Systems
The advent of fifth-generation (5G) mobile networks and the continuous evolution toward beyond-5G and 6G paradigms have necessitated the development of highly flexible, efficient, and automated resource management frameworks within telecommunication infrastructures. Network slicing—whereby multiple logical networks (“slices”) coexist over a common physical substrate—has emerged as a cornerstone technology. Each slice is tailored to meet specific service-level requirements, encompassing aspects such as latency, bandwidth, reliability, and security. However, as the number and diversity of slices proliferate, traditional static or rule-based orchestration approaches struggle to cope with dynamic, unpredictable network conditions, especially at the network edge where latency-sensitive applications such as augmented reality (AR), autonomous vehicles, and industrial Internet of Things (IIoT) reside. Artificial Intelligence (AI), and in particular techniques such as deep learning, reinforcement learning, and predictive analytics, offer transformative potential for orchestrating network slices in real time. By continuously learning from network telemetry—traffic patterns, resource utilization, user mobility, and service performance—AI-driven orchestrators can predict impending resource bottlenecks, anticipate service-level agreement (SLA) violations, and proactively adjust slice configurations. Moreover, AI models can optimize multi-objective trade-offs (e.g., latency vs. energy consumption), ensuring that edge-deployed resources deliver maximal quality of service (QoS) while minimizing operational costs. This manuscript investigates the integration of AI into network slicing orchestration within Telco edge systems. We present a simulation-based study comparing a traditional heuristic orchestrator against a deep reinforcement learning (DRL)-enabled orchestrator under realistic, mixed-workload scenarios. Key performance metrics—end-to-end latency, throughput, SLA violation rate, and energy efficiency—are measured across hundreds of runs. The results demonstrate that AI-enhanced orchestration yields substantial improvements: up to 60% latency reduction, 42% throughput increase, 80% drop in SLA violations, and nearly 30% better energy usage per megabit transmitted. Beyond raw performance gains, we explore explainability mechanisms (e.g., SHAP) to render AI decisions transparent to network operators, addressing concerns around trust, accountability, and regulatory compliance. Finally, we discuss deployment considerations—data collection, model retraining frequency, integration with ETSI NFV-MANO frameworks, and security challenges such as adversarial attacks. Our findings indicate that AI-driven orchestrators are not only feasible but essential for scalable, zero-touch edge-native network slicing in next-generation Telco infrastructures.
AI, Network Slicing, Telco Edge Systems, Orchestration, 5G, QoS, Resource Allocation
Multi-Tier Serverless Architectures for Space Mission Command Chains
Multi‑tier serverless architectures are rapidly emerging as a transformative paradigm for space mission command chains, enabling unprecedented agility, scalability, and cost efficiency. Traditional ground-segment infrastructures often rely on monolithic services or container clusters that incur high operational overhead, fixed resource allocation, and complex management demands. In contrast, a multi‑tier serverless framework—comprising edge compute functions, cloud-native orchestration workflows, and user‑facing serverless APIs—leverages fine‑grained billing, automatic scaling, and a fully managed execution environment to address the stringent performance, reliability, and security requirements of aerospace control systems. This enhanced abstract delves into the motivations behind adopting serverless for mission-critical applications, detailing the specific challenges of real‑time telemetry ingestion, command validation, safety‑driven simulations, and uplink dispatch. We describe the integration of geographically distributed Lambda@Edge functions for near‑source preprocessing, Azure Durable Functions for stateful orchestration, and Google Cloud Functions behind Cloud Endpoints for operator interfaces. Benchmark results under simulated mission profiles reveal a 45% reduction in end‑to‑end latency, a 62.5% increase in maximum throughput, and a 60% decrease in total operational cost, compared to a JVM‑based microservice cluster. Furthermore, fault‑injection experiments demonstrate 99.995% successful command delivery despite cold starts and transient network faults. We conclude by discussing the implications for ground system modernization, including enhancements in deployment velocity, resiliency, and multi‑cloud portability. This work establishes a foundational blueprint for next‑generation space operations leveraging serverless cloud-native technologies.
Serverless Computing, Space Mission Command, Edge Computing, Cloud Orchestration, System Resilience
Quantum-Inspired Scheduling Algorithms for Hybrid Cloud Workflows
The rapid proliferation of hybrid cloud deployments—comprising on-premises private clouds integrated with one or more public cloud platforms—has revolutionized how organizations handle compute-intensive and data-driven workloads. In such environments, efficient scheduling of interdependent tasks is critical to achieving low latency, high resource utilization, and minimized operational costs. Conventional scheduling algorithms (e.g., First-Come-First-Serve, Round Robin, heuristic genetic algorithms) often struggle with the combinatorial explosion of possibilities and the stochastic fluctuations in resource availability inherent to hybrid clouds. Quantum computing, with its ability to explore vast solution spaces via phenomena like superposition and tunneling, presents a promising new paradigm for optimization. However, practical quantum hardware remains in its infancy. Quantum-inspired algorithms (QIAs) emulate key quantum behaviors on classical systems, offering a tractable approach to harnessing quantum advantages today. This study develops a Quantum-Inspired Genetic Algorithm (QIGA) tailored for scheduling Directed Acyclic Graph (DAG) workflows across heterogeneous hybrid cloud resources. We detail the encoding of scheduling decisions as “qubit” probability amplitudes, the application of quantum-rotation gates for guided search-space traversal, and a multiobjective fitness function balancing makespan, cost, and utilization. Through extensive simulations on both synthetic benchmarks and real-world workloads (scientific workflows, transactional pipelines, media rendering), we compare QIGA against FCFS, classical GA, and Particle Swarm Optimization (PSO). Statistical analyses over 100 workflow instances demonstrate that QIGA reduces average makespan by 20–30%, improves overall resource utilization by 8–12%, and achieves up to 15% energy savings relative to the best classical heuristic.
Quantum-Inspired Algorithms, Hybrid Cloud Workflows, Task Scheduling, Quantum Annealing, Resource Optimization
Privacy-Preserving Cloud Analytics Using Homomorphic Encryption
Privacy-preserving cloud analytics represents a paradigm shift in data science: organizations can exploit the virtually unlimited computational resources of cloud platforms without exposing sensitive information. Homomorphic encryption (HE), which enables arithmetic and logical operations directly on encrypted data, is at the forefront of this transformation. This manuscript elaborates on a comprehensive HE-based framework tailored for cloud analytics, focusing on both theoretical foundations and practical implementation. We begin by detailing the mathematical underpinnings of HE schemes, with an emphasis on the Fan–Vercauteren (FV) algorithm and its leveled variants. Next, we describe the system architecture, encompassing client-side encryption, a cloud-based encrypted computation engine, and client-side decryption. Through a prototype built on Microsoft SEAL, we execute representative analytics tasks—summation, averaging, and single-variable linear regression—over datasets sized from 10 K to 100 K records. Our evaluation examines key performance metrics: latency per operation, throughput, ciphertext expansion, and result accuracy. A detailed statistical analysis table contrasts plaintext versus encrypted execution, highlighting the trade‑offs between privacy and performance. The manuscript concludes with a discussion of deployment considerations—such as key management, parameter selection for 128-bit security, and integration with existing big‑data tools—and outlines future research directions, including support for richer query languages, bootstrapping optimizations, and hybrid schemes combining HE with secure enclaves. This work provides a roadmap for practitioners seeking to deploy privacy‑preserving analytics in real‑world cloud environments, balancing strong confidentiality guarantees with acceptable performance and scalability.
Homomorphic Encryption, Cloud Analytics, Privacy Preservation, Encrypted Computation, Data Security
Fog-Blockchain Frameworks for Smart Urban Surveillance
The rapid urbanization witnessed globally has precipitated a surge in demand for intelligent, real‑time surveillance systems capable of ensuring public safety, optimizing traffic management, and supporting emergency response. Conventional cloud‑centric surveillance architectures, although providing centralized data management, often incur significant communication delays, high network bandwidth consumption, and present single points of failure that can be exploited by malicious actors. Moreover, the volume of high‑definition video streams generated by myriad cameras across a city places an onerous load on backbone networks and centralized data centers. Fog computing offers a compelling solution by decentralizing computation and storage to intermediary nodes—located closer to data sources—thereby mitigating latency, reducing bandwidth use, and enabling preliminary data analytics at the network edge. However, fog environments introduce new security and trust challenges: edge nodes may be hosted in untrusted settings, lack robust tamper‑resistant hardware, and face insider threats that could undermine data integrity. Blockchain technology, with its distributed ledger model and consensus protocols, presents an opportunity to bolster trust among geographically dispersed fog nodes without relying on a central authority. By recording immutable hashes of processed surveillance metadata and enforcing access policies through smart contracts, a Fog‑Blockchain integration can guarantee provenance, detect tampering, and enable transparent audit trails for all stakeholders. In this manuscript, we propose a comprehensive Fog‑Blockchain framework tailored to smart urban surveillance. The framework delineates a three‑tier architecture—comprising IoT sensor devices, fog nodes, and a consortium blockchain network—alongside a detailed data flow pipeline for video preprocessing, metadata extraction, ledger commit, and integrity verification. We implement a prototype using off‑the‑shelf edge hardware (Raspberry Pi 4B units and commodity servers), Hyperledger Fabric for the blockchain layer, and lightweight machine‑learning models for object detection at the fog tier. Performance is evaluated through metrics such as end‑to‑end latency, transaction throughput, integrity verification time, and resilience under node failure scenarios. Results indicate that embedding blockchain within fog networks reduces average latency by up to 45% compared to cloud‑only solutions while maintaining a high transaction throughput (65 TX/s) and enabling integrity audits in under 100 ms. The system sustains operations with up to 40% of fog nodes offline, demonstrating robust fault tolerance.
Fog Computing, Blockchain, Smart Urban Surveillance, Internet of Things, Security, Decentralization
Autonomous Container Scaling in Kubernetes via Reinforcement Learning
Autonomous container scaling within Kubernetes environments has emerged as a crucial mechanism to guarantee both application performance and cost‐effective resource utilization under highly dynamic workloads. Traditional autoscaling solutions—most notably Kubernetes’s native Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA)—operate on static threshold‐based rules that monitor CPU and memory utilization. While straightforward to configure, these mechanisms frequently underperform in the presence of bursty traffic patterns or sudden workload shifts, resulting in oscillatory scaling behavior, frequent SLA (Service Level Agreement) violations, and unnecessary overprovisioning. Reinforcement Learning (RL), by contrast, offers a data‐driven, adaptive approach: an RL agent continuously interacts with the cluster environment, observes multidimensional system metrics, and learns an optimal scaling policy through trial and error, balancing performance objectives against resource costs. In this work, we present the design, implementation, and experimental evaluation of the “Multidimensional Pod Autoscaler” (MPA), a Deep Q‐Learning–based autoscaler integrated into Kubernetes as a custom controller. MPA’s state representation comprises percentile‐based CPU and memory metrics, request arrival rates, error rates, and current replica counts. Its action space supports both horizontal scaling (incrementing or decrementing pod replicas) and vertical adjustments (tuning CPU/memory limits), plus a no‐op option for stability. The reward function penalizes SLA breaches—defined as requests exceeding a 200 ms latency threshold—and resource overprovisioning, weighted to reflect business priorities. We trained MPA offline on historical workload traces and then deployed it for online fine‐tuning under live traffic, comparing its performance against HPA and a heuristics‐driven Smart HPA. Experiments using both the Bookinfo microservices benchmark and a synthetic Poisson‐arrival workload generator demonstrate that MPA can increase average CPU utilization from 65% to 85%, reduce 99th‐percentile request latency by 40%, cut SLA violation rates from 5% to 1%, and achieve a 25% reduction in cloud resource costs. We provide a statistical analysis table summarizing these gains. This manuscript details the full system architecture, state and action definitions, neural network design, training methodology, and deployment strategy. We discuss practical considerations—such as safe exploration policies, integration with Prometheus metrics, and fallback mechanisms—and conclude with an in‐depth look at future research directions, including multi‐agent RL, meta‐RL transfer learning, workload forecasting integration, explainability, and extension to GPU‐aware and edge‐cloud scenarios.
Autonomous Container Scaling, Kubernetes, Reinforcement Learning, Deep Q-Learning, Autoscaling Performance, Service-Level Objectives