International Journal of Advanced Research in Computer Science and Engineering (IJARCSE)
Title: International Journal of Advanced Research in Computer Science and Engineering (IJARCSE) Publisher: Wissira Research Lab Publisher Address: Carmel, United States- 46074 Publisher Email: editor@wissira.us Copyright: Remain Journal Starting Year: 2025 Main Subject: Computer Science Scope: Future Technologies in Computer Science and Engineering Language: English Publication Format: Online (Open Access) Website: https://ijarcse.org Contact: +91 75359 16555
Articles are published under the Creative Commons Attribution NonCommercial 4.0 License (CC BY NC 4.0), allowing others to distribute, remix, adapt, and build upon the work for non-commercial purposes while crediting the original author.
Image Segmentation Techniques for Brain Tumor Localization
Accurate localization of brain tumors from magnetic resonance imaging (MRI) is critical for diagnosis, surgical planning, radiotherapy contouring, and longitudinal monitoring. This manuscript reviews and operationalizes state-of-the-art image segmentation techniques for brain tumor localization, spanning classical image processing pipelines to modern deep neural architectures that fuse convolution and attention. We analyze practical challenges—heterogeneous tumor phenotypes across patients and scanners, small and imbalanced targets (e.g., enhancing tumor), intensity non-standardization, and domain shift—and translate them into design choices for robust systems. Building on these insights, we develop a unified pipeline comprising multi-modal MRI preprocessing (N4 bias correction, skull stripping, z-score standardization, and rigid co-registration), a model zoo of 3D U-Net variants (vanilla, attention, UNet++), and a transformer-augmented architecture (Swin-UNETR). We combine Dice and boundary-aware losses, strong 3D augmentations, and uncertainty-aware post-processing with connected-component filtering and a 3D CRF. A five-fold cross-validated simulation on multi-modal MRI demonstrates that transformer-augmented and nested skip-connection models improve Dice and Hausdorff distance over a 3D U-Net baseline, with statistically significant gains particularly for enhancing tumor. We further probe robustness to artifacts, label noise, and domain shift via intensity perturbations and style transfer. The results suggest that (i) multi-modal fusion and hierarchical features are indispensable, (ii) small-lesion sensitivity benefits from attention and boundary losses, and (iii) simple two-model ensembles deliver consistent, clinically meaningful improvements while preserving inference efficiency. Limitations and avenues for deployment—calibration, active learning, and test-time adaptation—are discussed.
Brain tumor segmentation; MRI; U-Net; Transformer; Attention; Dice coefficient; Hausdorff distance; Deep learning; Multi-modal fusion; Uncertainty
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AI-Powered Vehicle Counting and Classification in Smart Cities
The rapid growth of urban traffic has outpaced the capabilities of traditional loop sensors and manual surveys, creating an urgent need for scalable, low-latency, and cost-effective traffic intelligence. This manuscript presents an end-to-end, AI-powered system for vehicle counting and classification designed for smart-city deployments. The pipeline integrates single-shot object detection with multi-object tracking to produce de-duplicated counts and fine-grained classes across heterogeneous camera views. The proposed method emphasizes practical constraints: camera placement variability, day–night domain shifts, adverse weather, heavy occlusions, and compute limits on edge devices. We fuse a one-stage detector (for bounding-box localization and coarse class labels) with an appearance-embedding tracker to maintain identities through occlusions and support virtual line-crossing logic that yields robust counts. An optional attribute head refines classes (e.g., car, bus, truck, two-wheeler, auto-rickshaw) using shape priors and aspect ratios. We also introduce normalization techniques (perspective-aware regions of interest, homography-based scale cues, and temporal smoothing) that stabilize predictions under viewpoint changes. Simulation-based evaluations (CARLA + SUMO) emulate dense intersections with configurable lighting and weather, producing 100k labeled frames across five junction archetypes. The system attains high detection accuracy (mAP@0.5 = 0.81), strong tracking (IDF1 = 0.78), and reliable counts (overall MAE = 2.3 vehicles/minute lane-crossing) at 25–30 FPS on an NVIDIA Jetson-class edge device via INT8 quantization. A statistical analysis demonstrates consistent performance across classes and time-of-day, with night-time recall improved by temporal voting. The results suggest the approach is deployable at city scale, enabling real-time traffic planning, adaptive signal control, and safety analytics with modest infrastructure upgrades.
smart cities; vehicle counting; vehicle classification; multi-object tracking; deep learning; edge AI; traffic analytics; domain shift; INT8 quantization; homography
Facial Recognition System Using 3D Morphable Models
Facial recognition in unconstrained environments remains challenging due to large pose variation, non-uniform illumination, partial occlusion, and expression dynamics. This manuscript presents a full-stack facial recognition system centered on 3D Morphable Models (3DMMs) to canonicalize facial geometry and appearance before identity embedding and matching. We formulate the 3DMM with separate identity and expression subspaces and estimate per-subject shape, texture, camera, and illumination via a robust differentiable fitting pipeline that combines photometric, landmark, and regularization losses with occlusion-aware weighting. After fitting, we generate pose- and light-normalized canonical representations—UV texture maps and neutralized meshes—that feed a margin-based deep embedding network trained for identity discrimination. A score-level fusion of 3D geometric similarity and 2D appearance embeddings yields improved robustness under extreme head rotations (±60°), directional lighting, and synthetic occlusions. A comprehensive statistical analysis reports verification True Accept Rate at 1% False Accept Rate (TAR@FAR=1%), Equal Error Rate (EER), and Rank-1 identification accuracy with 95% confidence intervals derived by stratified bootstrap; significance against a strong 2D baseline is measured via McNemar’s test. In simulated experiments on a multi-pose, multi-illumination benchmark (≈2,000 identities, ≈10,000 probe images), the proposed 3DMM-based pipeline improves Rank-1 by 3.6–5.8 percentage points, halves EER, and raises TAR@FAR=1% particularly for profile views and occluded faces. We discuss system design, ablations, runtime considerations, limitations (ageing, heavy occlusion >40%, cross-sensor shift), and ethical concerns, and outline future extensions including self-supervised 3D pretraining and photorealistic data generation for long-tail conditions.
3D morphable model; facial recognition; pose invariance; illumination normalization; occlusion robustness; differentiable rendering; identity embedding
Real-Time Sign Language Detection Using YOLOv5 and CNN
Real-time sign language understanding can expand access to education, healthcare, and public services for Deaf and hard-of-hearing communities, but it remains technically challenging due to fast hand motion, self-occlusion, variable lighting, diverse signing styles, and the need to operate at edge-device frame rates. This manuscript presents a practical, end-to-end pipeline that combines YOLOv5 for fast, robust hand-and-face localization with a lightweight convolutional neural network (CNN) for isolated sign classification. The detector narrows attention to semantically relevant regions, while the classifier focuses on pose, shape, and finger configuration. For dynamic signs, we extend the classifier with a short sliding-window encoder that aggregates evidence across 8–16 frames without sacrificing latency. The workflow supports online augmentation, temporal smoothing, and confidence-aware post-processing to stabilize predictions in live streams. We describe dataset preparation, annotation strategies, loss functions, hyperparameters, and deployment considerations (CPU/GPU and embedded). A simulation study using public ASL-style alphabets and custom capture clips evaluates accuracy, mAP, macro-F1, and latency/FPS; ablations quantify the contribution of region-of-interest (ROI) cropping, color jitter, and temporal aggregation. Results indicate that coupling YOLOv5 with a compact CNN improves classification accuracy by ~10 percentage points over whole-frame baselines while meeting real-time constraints on commodity GPUs and modern edge SoCs. We discuss typical failure modes (similar hand shapes, signer variance, background clutter) and outline opportunities in continuous (sentence-level) recognition, signer adaptation, and multilingual coverage. The proposed design balances accuracy, speed, and simplicity, making it suitable for assistive kiosks, classroom captioning, and mobile translation aids.
YOLOv5; convolutional neural networks; sign language recognition; real-time detection; human–computer interaction; edge AI; computer vision
Deep Learning-Based Noise Removal from Low-Resolution Medical Images
Medical images acquired under low-dose, short-exposure, or portable settings often suffer from strong noise and reduced spatial resolution, which complicate diagnosis and downstream computer-aided tasks. Traditional denoising requires accurate noise models or clean targets and tends to over-smooth subtle anatomical structures. This manuscript presents a comprehensive methodology for deep learning–based noise removal tailored to low-resolution (LR) medical images. We formulate the problem as joint noise suppression and detail preservation under modality-aware noise (Poisson–Gaussian for CT, Rician for MRI, and multiplicative speckle for ultrasound). We propose DUAL-NET, a dual-branch, multi-scale architecture that couples a noise-estimation branch (blind-spot + confidence-guided attention) with a structure-restoration branch (residual UNet with hybrid self-/cross-attention). The training scheme blends supervised learning on synthetically corrupted data with self-supervised fine-tuning (Noise2Self/Noise2Void-style masking) on real clinical LR scans. A rigorous evaluation plan uses PSNR, SSIM, RMSE, and downstream task fidelity (segmentation Dice), accompanied by paired statistical testing. In simulation studies across MRI, CT, and ultrasound subsets, DUAL-NET improved PSNR by ~2.1–3.8 dB and SSIM by 0.015–0.042 over strong classical (BM3D) and CNN baselines (DnCNN), while preserving edges needed for delineating lesions and vessels. We analyze failure cases, robustness to noise mis-specification, and computational overhead, and outline pathways for deployment under privacy, reproducibility, and A/B validation constraints. The results indicate that modern denoising with uncertainty-aware attention and self-supervised adaptation can materially enhance clinical image quality even when only low-resolution, noisy data are available.
medical image denoising; low resolution; self-supervised learning; blind-spot networks; Poisson–Gaussian noise; Rician noise; speckle noise; UNet; attention; Noise2Void
Adaptive Threshold Algorithms for Real-Time Flood Detection Using IoT Sensors
Real-time flood detection demands algorithms that react quickly to hydrologic change while remaining robust to sensor noise, seasonal drift, and connectivity constraints typical of Internet-of-Things (IoT) deployments. Classical static thresholds (e.g., a fixed water-level cutoff) are simple but brittle: they generate false alarms during monsoon build-up and miss fast-rising flash floods when the baseline regime shifts. This manuscript proposes and evaluates an adaptive, multi-criteria thresholding framework that runs on resource-constrained edge nodes and scales to catchment-wide networks. The core idea is to couple Exponentially Weighted Quantiles (EWQ) for dynamic baselines with robust dispersion measures (MAD), rate-of-rise checks, and change-point logic (CUSUM/Page-Hinkley) and then fuse them into a single Risk Index with hysteresis and upstream context. We describe an implementable algorithm using O(1) memory updates and percentile tracking via the P² algorithm, suitable for LoRaWAN/NB-IoT sensors. A simulation study with synthetic hyetographs and a unit-hydrograph routing model across 20 virtual stations compares the proposed method to static thresholds, moving-average dynamic thresholds, and standalone CUSUM. Results show a median detection latency reduction of 38–55% versus baselines, a false alarm rate below 0.2/day in noisy conditions, and improved F1-scores (0.89 vs. 0.71–0.83). We also quantify energy and bandwidth savings from edge filtering and event-driven reporting. The paper concludes with deployment considerations, limitations (e.g., extreme outliers, sensor drift beyond calibration), and practical guidance for tuning in monsoon-dominated basins.
flood detection, adaptive threshold, IoT sensors, EWMA/EWQ quantiles, MAD, CUSUM, LoRaWAN, NB-IoT, edge computing, hydrologic networks
Sensor Fusion-Based Navigation Systems for Autonomous Delivery Robots
Autonomous delivery robots must navigate sidewalks, corridors, and mixed indoor–outdoor campuses while maintaining accuracy, safety, and efficiency under imperfect sensing. Single-sensor pipelines (e.g., wheel odometry or vision alone) degrade under wheel slip, poor lighting, occlusions, and multipath. This manuscript presents a sensor-fusion navigation architecture that integrates inertial measurement units (IMUs), wheel encoders, cameras, LiDAR, and optional ultra-wideband (UWB) anchors to achieve robust localization and motion planning in dynamic environments. We detail a modular stack: (1) time-synchronized preprocessing and calibration, (2) multi-rate odometry (wheel–IMU EKF, visual–inertial odometry, LiDAR–inertial odometry), (3) factor-graph smoothing with loop closures and UWB priors, (4) semantic mapping that separates static structure from dynamic obstacles, (5) dual-horizon planning with D*-Lite globally and model-predictive control (MPC) locally, and (6) a safety supervisor enforcing stop/slowdown under uncertainty spikes. A simulation campaign across campus-sidewalk and urban-alley scenes (varying lighting, ground friction, and pedestrian density) compares four configurations: baseline wheel–IMU EKF, VIO-aided EKF, LiDAR-inertial odometry, and full multimodal factor-graph fusion including UWB. The fused system reduces absolute trajectory error by ~82% and collision rate by ~89% relative to the baseline, while adding <16 ms average fusion latency. We report statistically significant gains (ANOVA, Tukey HSD, p < 0.01) in success rate, path efficiency, and energy per kilometer. Results suggest that tightly-coupled, uncertainty-aware fusion—combined with semantic dynamics handling—yields navigation resilience suitable for last-meter delivery. We conclude with deployment guidance and open problems in long-term calibration drift, low-texture scenes, and learning-enhanced fusion.
autonomous delivery robots; sensor fusion; SLAM; factor graphs; visual–inertial odometry; LiDAR–inertial odometry; UWB; model-predictive control; dynamic obstacle avoidance; semantic mapping
Middleware Framework for Interoperability in Heterogeneous IoT Networks
The rapid proliferation of Internet of Things (IoT) devices has resulted in the emergence of highly heterogeneous networks composed of diverse communication protocols, hardware architectures, and application domains. This heterogeneity, while beneficial for specialized use cases, poses significant interoperability challenges that hinder seamless data exchange, service orchestration, and cross-platform integration. Middleware frameworks serve as an essential architectural layer that abstracts heterogeneity and enables uniform communication between disparate IoT systems. This paper presents a comprehensive study on the design, implementation, and evaluation of a middleware framework for interoperability in heterogeneous IoT networks. The proposed framework incorporates a layered design with protocol translation, semantic data modeling, and dynamic service discovery to ensure interoperability across devices using MQTT, CoAP, HTTP, ZigBee, LoRaWAN, and BLE. We review existing literature, outline a detailed methodology, and conduct simulation experiments in an NS-3 and iFogSim environment to analyze latency, throughput, and interoperability success rate. Statistical analysis demonstrates that the proposed middleware achieves a 23–38% improvement in cross-platform message success rate compared to existing middleware solutions, with minimal latency overhead. The findings confirm that middleware-driven interoperability can significantly enhance the scalability and reliability of heterogeneous IoT networks, particularly in smart city, healthcare, and industrial applications.
IoT interoperability, middleware framework, heterogeneous networks, protocol translation, semantic data modeling, service discovery, cross-platform communication
Anomaly Detection in Smart Home IoT Devices Using Unsupervised Learning
Smart homes concentrate dozens of heterogeneous Internet-of-Things (IoT) devices—thermostats, cameras, door locks, voice assistants, motion sensors—into a single, always-connected environment. This heterogeneity, coupled with weak device security and intermittent connectivity, widens the attack surface and increases the chance of silent malfunctions. Traditional intrusion detection systems struggle here because labeled attack data are scarce, device firmware and behavior change rapidly, and “normal” routines vary widely across households. This manuscript investigates unsupervised learning methods for anomaly detection in smart homes, focusing on network-centric and device-telemetry signals. We design a modular pipeline that (i) ingests raw network flows and device logs, (ii) normalizes and featurizes time-series windows, (iii) learns baseline behavior using Isolation Forest, One-Class Support Vector Machines (OC-SVM), and deep autoencoder variants (feed-forward and LSTM), and (iv) raises alerts using statistically principled thresholds (Median Absolute Deviation and Extreme Value Theory). A simulation study blends real-world-like benign traffic with injected anomalies (port scans, command-and-control beacons, ARP spoofing, rogue firmware updates, packet floods, and sensor drift/freeze faults). We evaluate using AUROC/AUPRC, F1 at a threshold chosen on a small validation subset, precision@k for triage, false-positive rate at fixed true-positive rate, and detection delay. Results show that temporal autoencoders (LSTM-AE) excel on slow-drift operational faults and bursty command sequences, while Isolation Forest offers robust, interpretable baselines with low computational cost suitable for deployment on home gateways. OC-SVM performs competitively on stationary features but is sensitive to scaling and concept drift. We discuss threshold calibration, household personalization, privacy-preserving deployment, and human-in-the-loop feedback. The study suggests a hybrid approach—Isolation Forest for coarse screening, followed by LSTM-AE for fine-grained confirmation—can reduce false positives by ~25% at comparable recall, making unsupervised anomaly detection practical for resource-constrained smart homes.
Smart home, IoT security, anomaly detection, unsupervised learning, autoencoder, Isolation Forest, OC-SVM, time series, network telemetry, edge computing
Energy Harvesting Models in Urban Environmental IoT Deployments
Urban Environmental Internet of Things (E-IoT) deployments—such as air-quality monitors, noise meters, parking sensors, and smart streetlights—are often limited by battery maintenance and grid dependence. Energy harvesting (EH) can transform these constraints by enabling “energy-neutral” operation where nodes harvest, store, and intelligently spend energy to match workload demands. This manuscript develops a unified, deployment-oriented view of EH models for cities, covering solar (outdoor/indoor), wind microturbines, thermoelectric gradients on building facades and manholes, vibration/piezo sources along roads/bridges, and ambient RF from cellular/Wi-Fi infrastructure. We formulate source-specific power models, storage dynamics, and power-management policies, and integrate them into a cross-layer methodology for scheduling, duty-cycling, and link adaptation. A simulation study for a 1 km² downtown district (500 nodes) compares a conventional battery-only baseline with a hybrid EH design using supercapacitors and model-predictive scheduling. Results indicate that 86% of nodes achieve energy neutrality over 120 simulated days, packet delivery ratio (PDR) increases by 9.7% on average, and expected battery replacements fall by 92%. Statistical testing confirms significant improvements in PDR and lifetime while maintaining application-level latency constraints. Sensitivity analyses show robustness to seasonal irradiance, wind variability, and RF density. The work provides a practical blueprint—models, parameters, and algorithms—for planners seeking to scale urban E-IoT with minimal maintenance and improved sustainability.
Energy harvesting, urban IoT, solar PV, thermoelectric, piezoelectric vibration, ambient RF, supercapacitor storage, duty cycling, energy neutrality, smart city
Security Challenges in IoT-Blockchain Integrated Ecosystems
The convergence of Internet-of-Things (IoT) infrastructures with blockchain platforms promises verifiable data provenance, tamper-evident logging, and decentralized coordination across untrusted devices. Yet, the integration itself creates new security exposures at the seams between constrained edge devices, resource-heavy distributed ledgers, and the middleware that binds them. This manuscript analyzes the multi-layer attack surface of IoT–blockchain systems and demonstrates, via a simulation-driven study, how design choices—permissioning model, consensus algorithm, key management, smart-contract engineering, and off-chain/on-chain partitioning—affect risk. We first synthesize the dominant threats: physical compromise of endpoints; identity spoofing and Sybil amplification; side-channel leakage through traffic metadata; gateway bottlenecks susceptible to denial-of-service; oracle and cross-chain manipulation; smart-contract logic and reentrancy bugs; and privacy/regulatory conflicts tied to immutability. We then propose a methodology for evaluating security posture using a layered reference architecture and a logit-based statistical model that estimates the probability of successful attacks under different controls. In a discrete-event simulation of 5,000 heterogeneous IoT nodes bridged to (a) a permissioned PBFT network and (b) a public PoA sidechain, we observe that enabling hardware roots-of-trust, edge-rate-limiting, and formally verified smart contracts reduces estimated attack success odds by 61–78% (scenario-dependent) while incurring modest latency overhead (<18% median) and marginal energy costs at the edge (<6%). The results emphasize that “blockchain” does not neutralize classical IoT threats; rather, it can amplify them if identity, oracles, and gateways are weak. We conclude with a prioritized control portfolio and engineering guidelines to harden real-world deployments without sacrificing the performance envelope needed for time-sensitive IoT workloads.
IoT security; blockchain; smart contracts; PBFT; PoA; Sybil; oracles; edge computing; privacy; formal verification
IoT-Enabled Smart Waste Management Systems Using RFID and Sensors
Rapid urbanization has stressed municipal solid waste management (SWM) systems, causing missed pickups, overflowing bins, high fuel consumption, and significant greenhouse gas emissions. This manuscript proposes and evaluates an IoT-enabled smart waste management system that integrates RFID-tagged containers with multi-sensor smart-bin nodes (ultrasonic fill-level, load-cell weight, tilt, temperature/gas) connected via low-power wide-area networks to edge gateways and a cloud analytics stack. Unique EPC-based RFID identities ensure asset traceability and bin–vehicle event logging, while sensors provide near–real-time fill-state telemetry. An analytics pipeline forecasts fill trajectories and dynamically solves a capacitated vehicle routing problem with time windows (VRPTW), producing on-demand collection routes that minimize distance and overflow risk. We describe a modular architecture covering hardware design, energy management, device firmware, networking, data models, and security-by-design measures. A simulation study of a mid-size city ward (≈2 km × 2 km, 250 bins, three trucks) and an emulated 12-week operational log demonstrate reductions in route distance (≈31%), fuel use (≈32%), overflow incidents (≈62%), and collection cycle time (≈33%), alongside a packet delivery ratio above 97% and multi-year battery life projections under adaptive duty-cycling. Statistical analysis (paired tests, effect sizes) confirms improvements across key KPIs. The results suggest that combining RFID provenance with sensor-driven optimization materially improves operational efficiency, service reliability, and environmental outcomes, while creating transparent audit trails for compliance and performance-based contracting. We conclude with deployment guidance and limitations relating to connectivity heterogeneity, sensor drift, and behavior change requirements.
smart waste management, IoT, RFID, sensors, LoRaWAN, route optimization, predictive analytics, VRPTW, edge computing, sustainability
ML-Based Predictive Maintenance in Industrial IoT Networks
Industrial Internet of Things (IIoT) deployments are transforming asset-intensive sectors by instrumenting machines with dense sensor networks, enabling real-time monitoring and data-driven maintenance. Yet, many plants still rely on fixed-interval or reactive maintenance, which increases downtime, spare-parts waste, and safety risk. This manuscript presents an end-to-end, ML-based predictive maintenance (PdM) framework designed specifically for IIoT networks operating under realistic bandwidth, latency, and reliability constraints. The proposed architecture combines edge analytics for fast anomaly screening, fog-node feature aggregation for context fusion, and cloud orchestration for model lifecycle management. The learning stack integrates supervised classification for failure prediction windows, regression for remaining useful life (RUL) estimation, and unsupervised anomaly detection for new or rare failure modes. We first review PdM literature across signal processing, feature learning, and networked systems considerations, highlighting common pitfalls such as label sparsity, class imbalance, data drift, and domain shift across sites. We then outline a methodology covering data acquisition (vibration, acoustic, current, temperature, pressure), multi-rate synchronization, feature engineering (time/frequency/cepstral), automated model selection (tree ensembles, temporal deep learning), and cost-aware thresholding. For statistical validation, we define a plant-realistic simulation comprising 240 virtual rotating assets and compressors producing multi-modal streaming data over MQTT/OPC UA with injected degradation processes and intermittent network loss. Results show that a hybrid model (LSTM sequence encoder + XGBoost decision head) improves failure F1-score by 28.7 percentage points over a threshold baseline, reduces RUL error by 61.2%, and lowers mean alarm lead time variance, while keeping inference latency within a 20 ms budget via edge batching. A one-way ANOVA on model F1-scores confirms significant differences (p < 0.001), with Tukey HSD indicating the hybrid’s superiority over random forests (p = 0.003) and a modest but significant gain over a pure LSTM (p = 0.047). We conclude with deployment guidance on edge-cloud partitioning, active learning for label scarcity, condition-based work-order integration in CMMS/ERP, and governance for model risk management in safety-critical environments.
predictive maintenance; Industrial IoT; edge analytics; remaining useful life; anomaly detection; LSTM; XGBoost; MQTT; OPC UA; time-series modeling
Real-Time Air Quality Monitoring Using Edge IoT Gateways
Reliable, high-resolution air quality information is essential for urban planning, public health alerts, and environmental governance. Yet conventional monitoring networks—based on a few expensive reference-grade analyzers—lack spatial granularity, while large fleets of low-cost sensors suffer from drift, cross-sensitivity, connectivity constraints, and cloud-processing latency. This manuscript proposes and evaluates an edge-centric architecture for real-time air quality monitoring that fuses low-cost sensing with gateway-level analytics. Each edge IoT gateway locally aggregates streams from particulate (PM2.5/PM10) and gas sensors (NO₂, O₃, CO, VOCs), performs multi-variate calibration with temperature/humidity compensation, filters anomalies, computes short-term Air Quality Index (AQI) values, compresses and prioritizes data, and synchronizes with cloud services for model updates and long-term storage. We detail the hardware/software stack, communication protocols (LoRaWAN/NB-IoT/MQTT), security hardening (secure boot, mTLS), and power-aware scheduling. A field deployment with 60 nodes over 25 km² and a discrete-event simulation demonstrate that edge analytics reduce mean absolute error for PM2.5 from 8.7 to 3.2 µg/m³ (vs. a co-located reference monitor), cut p95 latency from 14.8 s to 2.6 s, and shrink uplink volume by 62% without sacrificing coverage. Statistical analysis shows improved R², lower bias, higher uptime, and reduced packet loss compared to cloud-only baselines. The results validate edge gateways as a cost-effective and scalable substrate for real-time environmental intelligence, with implications for hyperlocal mapping, personal exposure analytics, and municipal alerting systems.
Edge computing; IoT gateways; air quality; low-cost sensors; calibration; AQI; LoRaWAN; NB-IoT; real-time analytics
Low-Power Routing Algorithms for WSNs in Agricultural IoT Systems
Agricultural Internet-of-Things (IoT) deployments rely on Wireless Sensor Networks (WSNs) to monitor soil moisture, temperature, humidity, leaf wetness, and micro-climate conditions across large, heterogeneous fields. Because nodes are battery-powered and often difficult to access, routing must maximize network lifetime while preserving data fidelity for decisions such as irrigation scheduling and disease prevention. This manuscript evaluates low-power routing strategies for agricultural WSNs and proposes AELR (Adaptive Energy & Link-quality Routing), a lightweight cross-layer approach that blends residual-energy awareness, link-quality metrics, geographic progress, and application-level sampling dynamics. Using a first-order radio energy model and realistic traffic patterns (periodic telemetry with event-driven bursts tied to soil-moisture thresholds), we simulate baseline protocols (LEACH, HEED, and RPL-OF0) against AELR on 100–300 nodes over a 500×500 m farm plot. AELR introduces (i) score-based next-hop selection with hysteresis to prevent route flapping, (ii) adaptive cluster rotation driven by energy skew, (iii) duty-cycling coordinated with sampling phases, and (iv) in-network delta aggregation to compress slowly varying signals. Results show improvements in time-to-first-node-death (FND), packet delivery ratio (PDR), and energy-per-useful-bit, with statistically significant gains over baselines under both uniform and hot-spot traffic. The study demonstrates that modest cross-layer cues—particularly coupling routing with application thresholds—yield meaningful lifetime extensions without expensive computation or GPS hardware. We discuss complexity, implementation notes for Contiki-NG/RIOT, limitations (e.g., mobility and sparse anchors), and directions for field trials with mobile sinks and energy harvesting.
Agricultural IoT; Wireless Sensor Networks; Energy-Efficient Routing; LEACH; HEED; RPL; Duty Cycling; Soil-Moisture Sensing; Link Quality; In-Network Aggregation
AI-Enabled VM Migration Strategies in Cloud Infrastructure
Live migration of virtual machines (VMs) is a foundational mechanism for elasticity, high availability, and energy-aware consolidation in cloud datacenters. Yet, traditional threshold-based or rule-driven policies struggle with nonstationary workloads, heterogeneous hosts, and multi-objective trade-offs among service-level objectives (SLOs), energy, and migration overhead. This manuscript proposes and evaluates AI-enabled migration strategies that combine (i) predictive analytics for host-overload/underload detection and migration cost estimation, and (ii) decision-making via reinforcement learning (RL) to schedule and route migrations under uncertainty. We present a modular pipeline: feature engineering from per-host/per-VM telemetry; supervised forecasting of near-future resource pressure; a learned migration-cost model; and an RL policy that selects migration actions using a reward shaping that balances SLO violations, energy consumption, and migration time. A simulation study with realistic bursty traces and heterogenous hosts compares a baseline heuristic, a supervised-learning policy, and a Proximal Policy Optimization (PPO) RL agent. Results indicate that the AI-enabled strategies reduce SLO violation rate by 31–54% and energy consumption by 12–23% relative to the baseline, while maintaining low downtime and bounded network overhead. A statistical analysis (one-way ANOVA with post-hoc tests) confirms improvements are significant at α=0.05 for primary outcomes. We discuss design choices (e.g., reward coefficients, safe-action filters), operational safeguards (e.g., blacklisting hot pages, rate-limiting), and limitations (trace bias, simulator fidelity). The study demonstrates that AI-driven migration unifies prediction and control to adapt to workload dynamics, providing a principled path to greener, more reliable cloud infrastructure.
live migration, virtual machines, reinforcement learning, predictive analytics, cloud orchestration, energy-aware consolidation, SLA/SLO, datacenter optimization
Fog Computing for Edge AI Workloads in Smart Transportation Systems
Smart transportation systems (STS) increasingly rely on AI models that process high-rate data from roadside cameras, connected vehicles, and infrastructure sensors. Centralized cloud processing alone struggles to meet stringent real-time constraints for perception, prediction, and control—especially under volatile wireless bandwidth and bursty event loads. Edge computing helps by placing inference close to data sources, but limited resources on embedded devices create bottlenecks during peak demand and complicate model lifecycle management. This manuscript investigates fog computing as a middle-tier orchestration layer between edge and cloud to host elastic micro-datacenters at network aggregation points (e.g., traffic operations centers, base-station sites, and municipal fiber hubs). We propose a fog-native architecture that combines (i) latency-aware workload placement, (ii) deadline-driven scheduling with early-exit inference, (iii) adaptive model compression, and (iv) predictive offloading using traffic and radio context. We develop a city-scale simulation that couples a microscopic traffic simulator with a network emulator and a containerized AI serving stack. Workloads include object detection for incident response, trajectory forecasting for bus ETA, and signal-phase timing optimization. Compared with cloud-only and edge-only baselines, the proposed fog+edge approach reduces median end-to-end inference latency by 41–63%, cuts 95th-percentile tail latency by 52–68%, and increases deadline-meeting rate by 20–33 percentage points under rush-hour load. Bandwidth costs drop due to upstream feature compression, while energy per inference declines as fog nodes leverage right-sized GPUs/NPUs at higher utilization. A one-way ANOVA confirms statistically significant improvements across latency and deadline-meeting rate; post-hoc pairwise tests show fog+edge significantly outperforms both baselines (p < 0.01). We conclude with practical guidance for municipalities: deploy fog clusters at fiber aggregation rings, use admission control with soft deadlines, and combine model-aware caching with DAG-level prefetch to tame microbursts.
fog computing; edge AI; smart transportation; V2X; latency-aware scheduling; traffic optimization; micro-datacenter; container orchestration.
Cost-Performance Optimization in Hybrid Cloud Deployment Models
The hybrid cloud deployment model, integrating private and public cloud infrastructures, has become a cornerstone of modern IT strategy, enabling organizations to balance cost efficiency, performance, and regulatory compliance. However, optimizing this balance remains a multidimensional challenge involving workload placement, scaling policies, and dynamic cost models. This research presents an integrated approach combining predictive workload modeling, intelligent resource allocation, and simulation-based validation to address cost–performance trade-offs in hybrid environments. Drawing upon a comprehensive literature review, the study identifies key parameters influencing hybrid cloud economics, including compute cost variability, data egress fees, storage tiering, and network latency. The methodology leverages workload profiling, a dual cost–performance model, and CloudSim-based simulations to compare threshold-based and machine learning (ML)-driven scaling strategies. Statistical analysis of simulated workloads (n=120 test runs) shows that the ML-based scaling approach yields an average 27% cost reduction, 22.9% improvement in average response times, and 25.9% increase in resource utilization, with all results statistically significant at p < 0.05. Notably, SLA compliance improved by 4.1%, demonstrating that cost savings did not come at the expense of service quality. These findings contribute to both academic research and industry practice by providing a reproducible optimization framework. The results are particularly relevant for organizations experiencing unpredictable workloads, such as e-commerce platforms, financial analytics services, and computational research clusters. This work further highlights that hybrid cloud cost–performance optimization is not solely a technical exercise but also a strategic decision-making process requiring continuous monitoring and adaptive governance. Recommendations are provided for integrating the proposed approach into existing cloud management platforms and DevOps pipelines to ensure long-term sustainability and return on investment.
Hybrid Cloud, Cost Optimization, Performance Tuning, Workload Allocation, Cloud Simulation, Auto-Scaling, Cloud Economics
Distributed Load Testing for SaaS Applications in Cloud Environments
Software as a Service (SaaS) has transformed the software delivery paradigm, enabling organizations to offer applications via the internet without requiring local installation or maintenance. This model has rapidly evolved due to its flexibility, cost-effectiveness, and scalability. However, the dynamic nature of SaaS, especially when hosted in distributed cloud environments, introduces significant challenges for performance assurance. The diversity of geographical user locations, fluctuating workloads, and multi-tenant architecture create performance uncertainties that centralized testing models often fail to capture. Distributed load testing addresses this gap by deploying load generators across multiple cloud regions to emulate realistic user patterns, network latencies, and request volumes. Unlike traditional centralized load testing, this approach provides a more accurate representation of end-user experiences, enabling the identification of bottlenecks that could otherwise remain undetected. This manuscript expands on both the theoretical and practical dimensions of distributed load testing in SaaS environments. It first examines the architectural complexities of cloud-hosted SaaS, then presents a comprehensive literature review of state-of-the-art methods and tools. A hybrid methodology—integrating open-source testing frameworks like Apache JMeter, Locust, and k6 with cloud-native infrastructure from AWS, Azure, and Google Cloud—is proposed. The approach leverages elasticity for scalability and reduces operational costs by automating resource provisioning and decommissioning after tests.
Distributed Load Testing, SaaS, Cloud Computing, Performance Engineering, Scalability Testing, JMeter, k6, AWS, Azure, Response Time, Throughput.
Container-Based Virtual Labs for Scalable Cloud-Based Education
Container-based virtual laboratories leverage lightweight operating-system virtualization technologies—chiefly Docker containers orchestrated via Kubernetes—to provide flexible, reproducible, and cost-effective practical environments for cloud-based education. This study presents the design, deployment, and evaluation of a containerized lab framework tailored to undergraduate computer science curricula, comparing traditional VM-based labs with containerized solutions (with and without autoscaling). A total of 120 students across three cohorts completed identical eight-week modules on programming and networking. Key performance indicators included container startup latency, resource utilization, task completion time, and student satisfaction. Containers booted in an average of 4.2 s (SD = 0.8), versus VM boot times exceeding 90 s. Autoscaling maintained CPU utilization at 65% (SD = 10%), avoiding the peaks (78%, SD = 12%) seen in non-autoscaled setups. Students in the autoscaled container cohort completed assignments 20.9% faster (M = 25.4 min, SD = 4.2) than those using VMs (M = 32.1 min, SD = 6.5; t(78) = 7.45, p < 0.001) and reported higher satisfaction (M = 4.3/5). These findings demonstrate that container-based labs with dynamic scaling dramatically improve provisioning speed, resource efficiency, and learning outcomes, while reducing infrastructure overhead. The paper concludes with best-practice recommendations and discusses scope and limitations.
containerization, virtual labs, cloud education, Docker, Kubernetes, scalability
Auto-Scaling Algorithms in Serverless Cloud Environments
Serverless cloud computing, epitomized by Function-as-a-Service (FaaS) platforms, offers a revolutionary paradigm where developers focus solely on code logic while infrastructure concerns are fully abstracted by cloud providers. By enabling fine-grained resource billing based on actual execution duration and per-request consumption, serverless mitigates upfront capacity planning and minimizes idle infrastructure costs. However, inherent workload variability and abrupt request bursts introduce performance and cost challenges. Traditional reactive auto-scaling approaches, which provision additional function instances only after utilization thresholds are breached, often incur cold-start delays and transient latency spikes. Conversely, fully predictive algorithms, relying on historical time-series forecasting, can misestimate sudden demand changes, leading to under- or over-provisioning that either degrades user experience or elevates cost inefficiency. In this manuscript, we propose a novel Hybrid Predictive-Reactive (HPR) auto-scaling algorithm specifically tailored for serverless environments. The algorithm integrates lightweight single exponential smoothing for near-term workload forecasting with robust reactive threshold triggers, triggering proactive scale-outs when forecasts anticipate imminent capacity exhaustion and reactive adjustments when actual utilization deviates beyond safe bounds. Controlled experiments are conducted in an enhanced CloudSim-based simulation framework, employing synthetic sinusoidal patterns, randomized Poisson bursts, and an industry-standard real-world FaaS workload trace. Performance metrics such as average response time, scaling latency, CPU utilization, throughput, and cost per thousand requests are systematically evaluated. Compared against baseline reactive-only and predictive-only schemes, HPR reduces mean response time by over 20 %, lowers average scaling latency by 25 %, increases utilization by 8 %, and cuts cost by 8 % on average. These results underscore the effectiveness of combining predictive foresight with reactive safety nets in achieving both stringent Service Level Objectives (SLOs) and cost efficiency in serverless auto-scaling. Implications for practical deployment and avenues for integrating advanced machine-learning forecasts and dynamic threshold tuning are also discussed.
Auto-scaling; Serverless computing; Function-as-a-Service; Predictive scaling; Cloud performance
Secure Cloud Storage with Attribute-Based Encryption and Audit Logs
This manuscript presents a robust framework for secure cloud storage by integrating Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with immutable audit logs, thereby achieving both expressive, fine-grained access control and comprehensive accountability. Data owners define rich access policies—arbitrary Boolean formulas over user attributes—and encrypt files under these policies using CP-ABE. A semi-trusted Attribute Authority (AA) issues private attribute keys to users following credential verification. The cloud server, modeled as honest-but-curious, stores encrypted data and an append-only audit log: every access request (granted or denied) is recorded in tamper-evident fashion via hash chaining. We formally analyze confidentiality, collusion resistance, and log integrity under standard bilinear Diffie-Hellman assumptions. To evaluate practicality, we implement a prototype using Charm-Crypto and simulate a range of workloads: policy sizes (5–20 attributes), file sizes (100 KB–5 MB), and concurrent users. We measure encryption/decryption latency, storage overhead, and log-generation time. Results demonstrate that even complex policies (20 attributes) incur encryption times under 200 ms and decryption times under 150 ms; ciphertext expansion remains below 1% of file size, and each log append takes less than 50 ms. Concurrent logging sustains over 200 records/s without integrity loss. These findings confirm the scheme’s suitability for real-world deployments requiring stringent confidentiality and auditability, such as healthcare, finance, and government archives.
Attribute-Based Encryption; Cloud Storage Security; Audit Logs; Fine-Grained Access Control; Accountability; Performance Evaluation
QoS-Aware Multi-Tenant Container Orchestration Using Kubernetes
In modern cloud-native environments, container orchestration platforms such as Kubernetes play a pivotal role in managing microservices-based applications at scale. However, the advent of multi-tenancy—where multiple independent applications share the same physical cluster—introduces complex challenges in guaranteeing Quality of Service (QoS). Resource contention, noisy-neighbor interference, and unpredictable workload patterns can lead to performance degradation, SLA violations, and tenant dissatisfaction. This manuscript presents an enhanced QoSaware multi-tenant orchestration framework built on Kubernetes primitives, augmented by custom controllers and a scheduler plugin. We define a formal model for tenant Service Level Objectives (SLOs) via a SloPolicy Custom Resource Definition, encompassing latency percentiles, throughput requirements, and weighted resource shares. A QoS-SLO Controller reconciles policies into Kubernetes constructs—PriorityClasses, ResourceQuotas, and LimitRanges—while a Scheduler Plugin computes real-time QoS scores to guide scheduling and preemption decisions. A closed-loop feedback mechanism, leveraging Prometheus metrics, dynamically adjusts priorities and autoscaling parameters upon SLO deviations. Experimental evaluation on a heterogeneous microservices workload demonstrates that our framework reduces 99th-percentile latency by up to 45%, cuts SLA violation rates to under 5%, and improves fairness in resource allocation without sacrificing overall cluster utilization (maintained above 70%). These results underscore the viability of integrating declarative SLOs with adaptive scheduling for robust multi-tenant orchestration.
Kubernetes, Quality of Service, Multi-Tenancy, Container Orchestration, Resource Management
Energy-Efficient Resource Allocation in Green Cloud Infrastructure
Energy-efficient resource allocation in green cloud infrastructure is pivotal for reducing operational costs and environmental impact while maintaining Quality of Service (QoS). This manuscript investigates a novel heuristic-based allocation algorithm designed to minimize energy consumption across virtualized data centers powered partly by renewable energy sources. Performance is evaluated through simulation in a heterogeneous cloud environment, comparing the proposed approach against baseline and existing heuristic methods. Statistical analysis of energy usage, carbon emissions, resource utilization, and latency demonstrates significant improvements using the proposed algorithm. Results indicate up to 18% reduction in energy consumption and a 22% decrease in carbon footprint without compromising application performance. The study concludes with detailed recommendations for integrating renewable-aware scheduling policies, discusses practical deployment considerations such as integration with existing cloud orchestration frameworks, and highlights future research avenues, including adaptive learning mechanisms and incorporation of energy storage solutions.
Cloud computing; energy efficiency; resource allocation; green infrastructure; renewable energy; heuristic scheduling
AI-Based Dynamic Load Balancing in Cloud Data Centers
Cloud data centers underpin the digital infrastructure of contemporary organizations, hosting diverse applications ranging from e-commerce platforms to large-scale scientific computations. With fluctuating and often bursty request patterns, static or heuristic load balancing schemes struggle to maintain optimal resource utilization, low latency, and SLA compliance. This paper introduces an AI-driven dynamic load balancing framework based on a Deep Q-Network (DQN) agent that continuously observes multi-dimensional host states—CPU utilization, memory occupancy, queue lengths, and network I/O—and makes real-time decisions on request routing and VM migration. We implement the framework in CloudSim 5.0, modeling a mid-sized data center of 20 hosts and 200 VMs under both Poisson and heavy-tailed arrival distributions. Over 30 independent trials, our approach reduces average response time by 29% relative to Round Robin and 23% relative to Least Connections, boosts throughput by up to 33%, and raises CPU utilization from ~70% to ~83%. Rigorous statistical validation (one-way ANOVA, Tukey’s HSD, effect-size analysis) confirms the significance and robustness of these gains. In addition to performance improvements, the DQN agent exhibits rapid adaptation to workload surges, converging to stable policies within 600 training episodes. This study demonstrates that reinforcement learning can serve as a viable, generalizable strategy for real-time, multi-objective resource management in cloud environments, paving the way for self-optimizing data centers.
AI-based load balancing, dynamic resource allocation, cloud data centers, reinforcement learning, performance optimization
IoT Firmware Security Auditing Using Automated Vulnerability Scanning
The exponential growth of the Internet of Things (IoT) has led to an unprecedented proliferation of connected devices across consumer, industrial, and critical-infrastructure domains. Firmware—the embedded software that governs hardware behavior—is often overlooked yet constitutes a critical attack surface. Security flaws in firmware can enable large-scale botnets, persistent backdoors, data exfiltration, and unauthorized control of devices. Traditional manual auditing approaches are labor-intensive, error-prone, and struggle to keep pace with the rapid firmware release cycles adopted by vendors. In this manuscript, we present an automated vulnerability-scanning framework tailored for IoT firmware security auditing. Our pipeline integrates multi-stage analysis—firmware unpacking, static rule-based inspection, dynamic emulation, API fuzzing, and machine-aided correlation—into a cohesive workflow. Leveraging tools such as Binwalk, QEMU, AFL, and custom YARA rule sets, the framework identifies memory corruption issues, insecure configurations, outdated libraries, hardcoded credentials, and protocol-level flaws. Evaluated on 50 firmware images spanning routers, IP cameras, smart home hubs, and wearable gateways, the prototype achieved a 92% detection rate for known vulnerabilities, uncovered 37 novel security flaws, and reduced manual audit effort by 85%. Detailed performance metrics, false-positive statistics, and vendor-verified patch outcomes are discussed. Our results demonstrate that automated scanning significantly enhances coverage, repeatability, and efficiency of firmware security assessments, offering a scalable solution for device manufacturers, security researchers, and regulatory bodies.
IoT firmware security auditing; automated vulnerability scanning; static analysis; dynamic emulation; embedded device security
Comparative Analysis of Signature-Based and Anomaly-Based IDS
Intrusion Detection Systems (IDS) are critical components in modern network security architectures, providing continuous, real-time monitoring and alerting of malicious activities within enterprise and cloud environments. Two predominant paradigms exist: signature‐based IDS, which relies on precompiled patterns of known threats, and anomaly‐based IDS, which models baseline normal behavior to flag deviations that may indicate novel or zero‐day attacks. While signature‐based systems offer high reliability for recognized threats—with mature rule sets maintained by security communities—they struggle to detect previously unseen exploits. Conversely, anomaly‐based systems excel at uncovering novel attack vectors but often incur higher false alarm rates and processing overhead due to the complexity of behavioral modeling. In this manuscript, we present a comprehensive comparative analysis of these approaches, leveraging a controlled Mininet simulation populated with mixed legitimate traffic (HTTP, DNS, SSH) and a variety of attack vectors (DoS floods, port scans, buffer overflow exploits). We deployed Snort 2.9.15 as the signature‐based IDS and a Gaussian Mixture Model (GMM) implemented in Python’s scikit‐learn library as the anomaly‐based IDS. Over 30 independent experimental runs, we measured detection rate, false positive rate, and processing latency, and we applied two‐sample t‐tests—with checks for normality and effect‐size calculations—to evaluate statistical significance. Results reveal that signature‐based IDS achieved a detection rate of 98.5 ± 0.7 % and a low false positive rate of 1.2 ± 0.3 %, with mean latency of 15.4 ± 2.1 ms. The anomaly‐based IDS attained a 95.2 ± 1.3 % detection rate, 4.8 ± 0.9 % false positive rate, and 25.8 ± 3.4 ms latency, demonstrating superior adaptability to zero‐day threats at the cost of increased computational burden. Statistical tests confirm that differences in false positive rate and latency are highly significant (p < 0.001), with large effect sizes (Cohen’s d > 1.2). We discuss practical deployment considerations, including hybrid architectures, integration with SIEM platforms, and automated rule‐generation enhancements, to guide security practitioners toward optimal IDS strategies.
Intrusion Detection System, Signature‐Based IDS, Anomaly‐Based IDS, Comparative Analysis, Network Security
AI-Driven Malware Behaviour Classification and Detection Systems
The contemporary cybersecurity landscape is characterized by an incessant arms race between malicious actors designing increasingly sophisticated malware and defenders seeking to detect and mitigate these threats effectively. Traditional signature-based antivirus solutions, which rely on known patterns and static characteristics, are rendered largely impotent against novel, polymorphic, and metamorphic malware that can adapt their code to evade detection. In response, the industry has witnessed a paradigm shift toward behavior-based detection systems empowered by artificial intelligence (AI) and machine learning (ML). This manuscript delves into an AI-driven framework for dynamic malware behavior classification and real-time detection, detailing the processes of comprehensive feature extraction, rigorous statistical analysis, classifier training, and simulated deployment in enterprise environments. We curated a diverse dataset comprising 5,000 benign and 5,000 malicious Windows PE samples, executing each in a controlled sandbox environment to capture API call sequences, network traffic metrics, file system interactions, and registry modifications over a five-minute runtime. Features were aggregated into temporal and frequency-based descriptors, yielding a feature vector of 120 attributes per sample. Dimensionality reduction via Principal Component Analysis (PCA) preceded supervised learning using Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN) models. The RF classifier demonstrated superior performance with 97.8% accuracy, 96.5% precision, and 98.1% recall under four-fold cross-validation. Statistical testing (two-sample t-tests, Mann–Whitney U) corroborated the discriminative power of key dynamic features (p < 0.001). A simulation testbed, implemented in NS-3, emulated an enterprise network of 200 hosts engaging in regular productivity traffic interspersed with stealth and burst malware injection scenarios. The RF-based detection pipeline, deployed as a RESTful microservice, achieved an average detection latency of 120 ms, maintained CPU utilization under 65% during peak loads, and sustained a 99% detection rate with zero false positives in stealth mode. The proposed system thus offers a robust, adaptive solution for cybersecurity operations centers (SOCs) and Security Information and Event Management (SIEM) platforms, capable of countering emerging threats with minimal human intervention.
AI-driven malware behavior classification and detection systems machine learning dynamic analysis real-time detection
Zero Trust Security Architecture in Microservices-Based Web Applications
Zero Trust Security Architecture (ZTSA) has emerged as a fundamental paradigm for protecting distributed systems by eliminating implicit trust and enforcing continuous verification of every component. In this enhanced manuscript, we deepen our investigation of the design, implementation, and evaluation of a Zero Trust model tailored for microservices-based web applications by expanding each section to provide richer technical details, comprehensive discussion of underlying principles, and extended analysis of empirical results. We propose an architecture that leverages mutual Transport Layer Security (mTLS), fine-grained policy enforcement at the service mesh layer, and centralized identity and access management via OAuth-2.0/OpenID Connect. Our methodology comprises both statistical performance analysis—measuring latency, throughput, and resource utilization—and a detailed simulation study that injects realistic traffic patterns and adversarial behaviors into a representative microservices testbed. The statistical analysis reveals that the proposed ZTSA introduces an average authentication latency of 35 ms (σ=5 ms) and increases CPU utilization by 8%, while maintaining a false positive rate below 2%. The simulation demonstrates effective mitigation of lateral movement and unauthorized access, with over 95% of attack attempts thwarted. We conclude that implementing Zero Trust in microservices environments is both feasible and beneficial, delivering robust security guarantees with manageable performance overhead and providing organizations with actionable guidance on design, deployment, and ongoing operations.
Zero Trust; microservices; service mesh; mTLS; simulation; performance analysis
Lightweight Cryptographic Protocols for Wearable Health Devices
Wearable health devices have revolutionized personal healthcare by enabling continuous monitoring of physiological parameters with minimal user intervention. However, their constrained computational resources and limited energy budgets pose significant challenges for implementing robust security measures. This manuscript investigates the performance and security trade-offs of three representative lightweight cryptographic protocols—PRESENT (a lightweight block cipher), SPECK (a lightweight cipher optimized for software), and a 160-bit curve Elliptic Curve Cryptography (ECC-160) scheme—within the context of wearable health devices. We develop a simulation framework to evaluate key metrics such as execution time, memory footprint, and energy consumption under realistic workload scenarios. A statistical analysis table summarizes the comparative performance of these protocols. Through detailed simulation research, we demonstrate that while ECC-160 offers the highest security margin, SPECK and PRESENT deliver substantially lower energy usage and faster execution, making them more suitable for continuous, low-power health monitoring. Our results guide the selection of an appropriate cryptographic primitive based on application-specific requirements in wearable health systems, offering actionable recommendations for device manufacturers, firmware developers, and healthcare providers. By providing granular insights into how each protocol behaves under varying data transmission patterns, encryption frequencies, and environmental conditions, this work fills a critical knowledge gap and lays the groundwork for more secure and energy-efficient wearable health ecosystems.
Lightweight cryptography, wearable health devices, PRESENT, SPECK, ECC-160, energy efficiency, security trade-offs