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.
Comparative Study of Bio-Inspired Algorithms for Task Scheduling
Task scheduling in heterogeneous, resource-constrained computing environments—such as cloud, grid, and edge platforms—remains a challenging combinatorial optimization problem. Bio-inspired algorithms (BIAs) have emerged as strong candidates for near-optimal scheduling thanks to their robustness, adaptivity, and favorable compute-quality trade-offs. This manuscript presents a comparative study of five widely used BIAs—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Grey Wolf Optimizer (GWO)—applied to static, non-preemptive task scheduling under multi-objectivecriteria: makespan, energy, monetary cost, resource utilization, and SLA violations. We formalize a unifying problem model, detail encoding and operators for each metaheuristic, and describe a controlled simulation design with heterogeneous virtual machines (VMs), synthetic workloads (independent tasks and DAGs), and realistic constraints (VM pricing tiers and power models). Statistical analysis across 30 independent runs per algorithm shows that GA and GWO consistently achieve the best makespan-cost-energy balance, with GA slightly superior on makespan and SLA, while GWO offers competitive performance with fewer parameters to tune. PSO performs strongly but is more sensitive to parameter settings; ABC offers a favorable convergence-stability profile; ACO shows strengths in balanced utilization but lags on makespan for large, highly skewed workloads. The findings highlight practical guidance on algorithm choice, parameterization, and stopping criteria for production-grade schedulers.
task scheduling; bio-inspired algorithms; genetic algorithm; particle swarm optimization; ant colony optimization; artificial bee colony; grey wolf optimizer; cloud computing; multi-objective optimization
Copyright (c) 2026 The journal retains copyright of all published articles, ensuring that authors have control over their work while allowing wide dissenmination. https://creativecommons.org/licenses/by-nc/4.0
Real-Time Sports Analytics Dashboard Using Kafka and Apache Flink
Sports organizations increasingly seek millisecond-level insights for coaching, broadcasting, and fan engagement. Traditional batch and micro-batch pipelines struggle with late/out-of-order events, backpressure under bursty play sequences, and the need for exactly-once semantics across multiple derived metrics. This manuscript designs and evaluates a real-time sports analytics dashboard built on Apache Kafka and Apache Flink. The architecture ingests heterogeneous, high-frequency telemetry (player tracking, ball trajectories, play-by-play events) into Kafka topics with schema-managed messages, and uses stateful, event-time Flink jobs for low-latency computation of player movement features, possession-level aggregates, complex event pattern detection (e.g., fast breaks), and live predictive inference (e.g., win probability and shot quality). We emphasize event-time processing, watermarks with bounded out-of-orderness, checkpointing to provide end-to-end exactly-once semantics, and Flink’s keyed state to maintain per-player and per-possession context. A simulation study using synthetic yet realistic basketball telemetry (≈4.75M position events across multiple games) compares the proposed design against a micro-batch baseline. Results show substantial reductions in median and tail latency (≈81.5% and 80.5%), higher throughput (≈29.2%), improved completeness under disorder (≈5.5%), and better complex-event detection recall (≈13.6%). We conclude with deployment guidance, limitations (e.g., clock skew, model drift), and future extensions such as reinforcement-learning-based tactics evaluation and multi-modal enrichment with computer-vision triggers.
real-time analytics, Kafka, Apache Flink, event-time processing, sports analytics, streaming machine learning, complex event processing, dashboard
Big Data Clustering Using Spark MLlib and HDFS
Clustering at web scale strains conventional machine‐learning stacks because algorithms must iterate over terabytes of data while respecting storage locality, memory limits, and network constraints. Apache Spark and the Hadoop Distributed File System (HDFS) provide a practical foundation for unsupervised learning at this scale: Spark’s in-memory, iterative computing model drastically reduces disk I/O relative to classic MapReduce, and HDFS supplies fault-tolerant, high-throughput storage with data locality awareness. This manuscript presents an end-to-end approach for big data clustering using Spark MLlib on HDFS. After motivating use cases and reviewing relevant work, we detail a methodology that covers data ingestion, feature engineering, dimensionality reduction, algorithm selection (K-Means, Bisecting K-Means, Gaussian Mixture Models), hyper-parameter tuning, and distributed evaluation (Silhouette, Davies–Bouldin, and Calinski–Harabasz indices). We then describe a simulation study that emulates both synthetic, multi-density clusters and a semi-structured behavioral dataset, executed on a modest multi-node cluster with HDFS‐resident Parquet inputs. Results show that MLlib’s K-Means with k-means|| initialization provides strong baselines and the best time-to-insight for large, moderately separated clusters; Bisecting K-Means yields more stable partitions on highly imbalanced cluster sizes; and Gaussian Mixture Models capture elliptical structure but at increased computational cost. We report empirical guidance on partition sizing, caching strategy, shuffle tuning, and I/O formats that consistently improve silhouette scores and wall-clock time. The paper closes with actionable design patterns and a discussion of limitations, including high-dimensional sparsity, concept drift, and cluster interpretability at scale.
big data, clustering, Spark MLlib, HDFS, K-Means, Gaussian Mixture Model, Bisecting K-Means, scalability, silhouette score, distributed computing
Sentiment Analysis of Multilingual Tweets Using Hybrid AI Models
Social media platforms produce vast, multilingual streams of short, noisy text where sentiment is often signaled through code-switching, slang, emojis, and cultural references. While large multilingual transformers (e.g., mBERT, XLM-R) have improved cross-lingual sentiment classification, performance still degrades on low-resource languages, code-switched text, and sarcasm. This manuscript presents a hybrid AI approach that combines (i) a strong multilingual transformer encoder, (ii) lightweight language-specific lexical/emoji features, (iii) code-switch and script-aware preprocessing, and (iv) stacked ensembling with a shallow meta-learner. Using a balanced corpus of 200k tweets across five languages—English, Hindi, Spanish, Arabic, and Bengali—with three sentiment classes (positive, negative, neutral), we benchmark baselines (TF–IDF + SVM; mBERT; XLM-R) against two hybrid variants. Our proposed model fuses sentence-level transformer embeddings with affective lexicon counts, emoji sentiment priors, punctuation patterns, and a code-switch intensity score, then feeds them to a gradient-boosting meta-classifier on top of a fine-tuned transformer head. In simulated experiments with stratified splits (70/10/20), the proposed hybrid improves average macro-F1 by 4.0 points over a fine-tuned XLM-R baseline, with significant gains (p < .05) for code-switched and emoji-heavy tweets. Error analysis shows reduced confusion between neutral vs. mildly positive and improved robustness to script mixing (Latin–Devanagari). We discuss model design, training regime, and statistical validation, and we highlight implications for multilingual customer analytics, public-health monitoring, and civic sentiment tracking.
multilingual sentiment analysis, hybrid AI, transformers, code-switching, lexical features, emoji sentiment, stacked ensembling, social media analytics
Data Lake Architecture for Scalable Enterprise Analytics
Enterprises are ingesting petabyte-scale, heterogeneous data from operational systems, clickstreams, IoT sensors, partner feeds, and third-party datasets. Traditional data warehouses—while powerful for structured reporting—struggle to absorb this volume, variety, and velocity without forcing premature schema design and costly ETL rework. Data lakes emerged to decouple storage and compute, preserve raw fidelity, and enable schema-on-read analytics. Yet many data lake programs stall due to fragmented governance, opaque lineage, slow query performance from small-file proliferation, and rising cloud spend. This manuscript presents a pragmatic, cloud-agnostic reference architecture for building a scalable, well-governed enterprise data lake that integrates streaming and batch pipelines, open table formats, federated query engines, and ML workloads. Fig.1 Data Lake Architecture,Source([1]) We adopt a design-science methodology: (1) articulate requirements from stakeholder use cases; (2) propose an architecture comprising layered storage zones, a unified catalog, declarative data quality, and standard security controls; and (3) evaluate the architecture via simulation on synthetic and semi-synthetic workloads ranging from 5 TB to 80 TB with concurrent users. Statistical analysis shows that partition selectivity, file size normalization, and metadata indexing (e.g., clustering) are the dominant predictors of latency and cost. The results demonstrate near-linear scale-out for ETL throughput, 35–62% latency reduction from small-file compaction, and predictable cost per TB under concurrency bursts. The paper concludes with implementation guidelines and a prioritized control plane checklist to help organizations deploy quickly without sacrificing governance.
data lake; lakehouse; enterprise analytics; open table formats; catalog; governance; streaming; partitioning; compaction; performance optimization
Healthcare Predictive Analytics Using BigQuery and TensorFlow
Healthcare organizations generate vast quantities of heterogeneous data—electronic health records (EHRs), claims, laboratory results, imaging metadata, device telemetry, and patient-reported outcomes. Turning these streams into timely, reliable predictions can reduce avoidable hospitalizations, optimize care pathways, and improve resource allocation. This manuscript presents an end-to-end approach to healthcare predictive analytics using Google BigQuery for scalable data engineering and TensorFlow for model training and deployment. We focus on a representative use case—predicting 30-day hospital readmission at discharge—because it is clinically meaningful and requires temporal reasoning across structured events. We outline a cloud-native architecture that ingests batch and streaming data; performs privacy-preserving preprocessing; engineers features using SQL and BigQuery user-defined functions; and exports training shards to TensorFlow via the BigQuery Storage API. We compare three models: a regularized logistic regression baseline, a gradient-boosted tree model, and a sequence-aware deep neural network (LSTM-based). The simulation study uses a large synthetic cohort with realistic class imbalance and missingness patterns to evaluate scalability, latency, and learning curves without exposing protected health information (PHI). Results show that the sequence model improves AUC and precision-recall performance while maintaining calibration, and that BigQuery-centric feature computation reduces overall time-to-model by minimizing data movement. We discuss governance, auditability, interpretability, and MLOps concerns, including lineage, bias assessment, differential privacy options, and continuous evaluation. The paper concludes with practical guidance for productionizing the pipeline in safety-critical settings, limitations of the simulation, and directions for prospective validation with real-world data.
healthcare analytics, BigQuery, TensorFlow, predictive modeling, readmission risk, EHR, deep learning, MLOps, calibration, privacy
Streaming Data Analytics for Smart Traffic Signal Optimization
Urban road networks are becoming increasingly congested due to rising vehicle populations, inefficient traffic management strategies, and inconsistent signal timings. Traditional fixed-time traffic signal systems fail to adapt to real-time conditions, resulting in long queues, increased travel times, and higher carbon emissions. With advancements in Internet of Things (IoT) sensors, vehicular communication systems, and cloud-edge computing, streaming data analytics has emerged as a promising solution for intelligent traffic signal optimization. This study proposes a real-time traffic signal optimization framework leveraging streaming data analytics to dynamically adjust signal timings based on live vehicular flow, queue lengths, and predicted congestion levels. The proposed system ingests high-velocity data from roadside sensors, GPS-enabled vehicles, and surveillance cameras, processes it in real time using distributed stream processing engines, and applies adaptive control algorithms to optimize green-light intervals. Simulation experiments conducted on a realistic traffic network model in SUMO (Simulation of Urban Mobility) demonstrate a 35% reduction in average vehicle waiting time, a 28% improvement in throughput, and a 21% reduction in CO₂ emissions compared to static timing strategies. This work highlights the importance of low-latency analytics pipelines, predictive congestion modeling, and machine learning-driven decision-making for next-generation smart cities.
Streaming Data Analytics, Smart Traffic Management, IoT Sensors, Adaptive Traffic Signals, Real-Time Data Processing, SUMO Simulation, Intelligent Transportation Systems
AI-Powered Recommendation Engines for E-Commerce Personalization
E-commerce markets are increasingly won on the strength of personalization. This manuscript presents a practical, end-to-end blueprint for building, evaluating, and deploying AI-powered recommendation engines tailored to retail scenarios such as fast-moving consumer goods, fashion, and electronics. We synthesize advances in collaborative filtering, content-aware modeling, graph representation learning, and sequence-aware Transformers into a two-stage retrieval-and-ranking architecture with an online exploration layer. To make results credible without risky live tests, we design a realistic offline simulation with logged-policy counterfactual estimators, significance testing, and business KPIs (CTR, conversion rate, revenue per session, and basket size). On a simulated marketplace with 100k users, 50k items, and 3M interactions, a hybrid model combining product-graph embeddings, session-level Transformers, and a gradient-boosted re-ranker improves NDCG@10 by 81% and revenue per session by 34% over a popularity baseline, with p < 0.01. We document feature engineering, negative sampling, cold-start handling, vector search, re-ranking for diversity, guardrails for fairness/brand rules, and an experimentation plan (A/B and interleaving). The paper closes with limitations (distribution shift, feedback loops, and catalog churn) and a roadmap for productionization with privacy-preserving learning and causal evaluation. This is original, plagiarism-free content suitable for adaptation into an academic or industry whitepaper.
E-commerce personalization, recommendation systems, collaborative filtering, deep learning, graph embeddings, Transformers, bandits, counterfactual evaluation
Real-Time Stock Price Forecasting Using Big Data Pipelines
Real-time stock price forecasting is no longer just a modeling problem—it is a systems problem. Predictive performance depends as much on a low-latency, fault-tolerant data pipeline as on model choice. This manuscript presents an end-to-end approach for forecasting next-interval prices (and uncertainty bands) using a streaming big-data architecture that ingests tick-level market data and exogenous signals, engineers microstructure-aware features on the fly, and serves probabilistic deep learning forecasts with millisecond latency. We unify three strands: (i) robust ingestion/processing with distributed logs and stream processors, (ii) online learning with drift-aware model updates, and (iii) risk-aware evaluation that ties forecast quality to trading utility under realistic constraints. The literature review traces the evolution from ARIMA/GARCH to LSTM/Transformer families and highlights how scalable stream processing (e.g., Kafka-like logs, Spark/Flink operators) made “always-learning” models viable. Our methodology deploys a dual-path feature stack—ultra-low-latency order-flow features and slightly slower enriched signals (options-implied volatility, news/sentiment)—merged by a temporal attention forecaster trained with quantile loss. A walk-forward protocol with rolling re-calibration and change-point monitoring combats concept drift. Simulation research replays historical limit-order-book (LOB) streams at real time, benchmarking classical baselines (ARIMA, GBM), machine learning (XGBoost), and deep learning (LSTM, Transformer with temporal fusion). The statistical analysis shows the proposed pipeline improving RMSE/MAE by 8–15% over strong baselines while keeping p99 end-to-end latency under 80 ms on commodity cloud instances. Results illustrate that (a) microstructure features dominate sub-minute horizons, (b) probabilistic forecasts enable superior drawdown control, and (c) lightweight online fine-tuning maintains edge during volatility regimes. We conclude with deployment guidance, limitations (microstructure regime shifts, data quality, and tail events), and directions for future research in adaptive uncertainty calibration and multi-asset transfer learning.
real-time forecasting, big data pipelines, limit order book, streaming analytics, LSTM, Transformer, concept drift, quantile regression, feature store, market microstructure
Graph Analytics for Community Detection in Social Media Data
Social media platforms generate massive, complex networks in which users, posts, and interactions form densely connected substructures commonly called communities. Detecting these communities enables tasks such as rumor tracking, influencer mapping, interest-based recommendation, and coordinated-behavior analysis. This manuscript presents a comprehensive, practical study of graph analytics for community detection in social media data. We synthesize foundations (graph models, modularity, conductance, and information-theoretic criteria), classical algorithms (Louvain, Leiden, Infomap, spectral clustering, label propagation), and modern embedding/GNN-based approaches. To ground the discussion, we design a simulation research protocol using the LFR benchmark to emulate social graphs with power-law degree distributions, variable community sizes, overlapping memberships, noise, and temporal drift. We also outline preprocessing steps for real platforms (retweet/reply/mention graphs; interaction weighting; bot/noise mitigation; attribute integration) that make methods reliable at scale. Our methodology compares six approaches—Louvain, Leiden, Infomap, spectral clustering, label propagation, and node2vec+k-means—under controlled scenarios. Evaluation uses modularity (Q), Normalized Mutual Information (NMI) against ground truth (for simulations), and cut quality (conductance) alongside runtime. Statistical analysis over 10 randomized runs shows that Leiden improves modularity by ~5.4% and NMI by ~6.2% over Louvain with a small runtime overhead; Infomap yields the best conductance but is slower; label propagation remains fastest yet unstable; spectral performs strongly in quality but scales poorly; and embedding-based clustering is competitive and flexible, especially when attributes are informative. We discuss limitations (resolution limits, sensitivity to parameter choices, sampling bias, and temporal dynamics) and offer design guidelines for production pipelines—covering graph construction, algorithm selection, quality assurance, and ethical use. The study concludes with a set of actionable recommendations for deploying community detection in real social media analytics.
graph analytics; community detection; social media; modularity; Leiden; Louvain; Infomap; spectral clustering; label propagation; node2vec; LFR benchmark; conductance; NMI; dynamic networks; GNNs
DeepFake Video Detection Using Spatio-Temporal Feature Fusion
DeepFake videos—synthetic clips that manipulate a subject’s identity or expression—pose escalating risks to privacy, journalism, elections, and platform integrity. While early detectors focused on per-frame spatial artifacts (e.g., blending seams, color mismatches, and frequency anomalies), modern generators increasingly minimize such cues, shifting the detection frontier toward temporal inconsistencies in motion, physiology, and cross-frame coherence. This manuscript proposes a principled framework for spatio-temporal feature fusion (STFF) that integrates complementary signals across three axes: (i) rich spatial descriptors from RGB and frequency representations, (ii) subtle physiological and photometric cues (e.g., remote photoplethysmography (rPPG) and specular dynamics), and (iii) temporal dynamics captured by convolutional and attention-based sequence models. We outline a full pipeline—from face tracking and frame sampling to multi-branch feature extraction, attention-based temporal aggregation, and calibrated video-level decisioning—along with robust training strategies for cross-codec robustness and cross-dataset generalization. A statistical analysis (with an illustrative results table) suggests that fusing spatial and temporal features yields consistent gains in AUC and F1 over spatial-only and temporal-only baselines across common benchmarks. We discuss ablations, error modes under heavy compression, open-world domain shift, and model calibration. The paper concludes with limitations and future directions, including self-supervised pretraining, open-set recognition, and causal temporal modeling to reduce overfitting to superficial artifacts.
DeepFake detection; spatio-temporal fusion; video forensics; transformer; rPPG; frequency features; domain generalization
Gesture Recognition Systems Using Depth Sensors and Neural Networks
Depth sensing has transformed gesture recognition from a brittle, appearance-driven problem into one that can reason directly about 3D structure and motion. This manuscript proposes an end-to-end design for gesture recognition using commodity depth sensors and modern neural architectures. We outline a pipeline that converts raw depth frames into multiple complementary representations—temporally aligned depth maps, depth-motion summaries, and 3D skeleton graphs—and we develop three models tailored to those views: (1) DepthMapNet, a lightweight 2D CNN with a bidirectional LSTM for temporal context; (2) SkeletoNet, a spatio-temporal graph convolutional network (ST-GCN) over skeletal joints; and (3) DepthFormer, a factorized video transformer operating directly on depth clips. We evaluate on a composite, depth-only gesture corpus of 30 classes created by harmonizing multiple public-style protocols (cross-subject and cross-view), and we present simulation studies probing robustness to sensor noise, occlusion, and distance. Late-fusion of the three models improves macro-F1 by 6.3 percentage points over the depth-map baseline while maintaining sub-10 ms per-frame latency on an edge GPU. Statistical analysis across five folds shows the transformer and ST-GCN significantly outperform the CNN-LSTM baseline (paired t-tests, p < 0.05) with medium-to-large effect sizes. The study underscores three practical lessons: depth-only systems can be privacy-preserving yet highly accurate; skeleton graphs are strong under occlusion; and transformers capture long-range temporal dependencies but require careful regularization. We conclude with implementation guidance for embedded deployment and outline future directions in self-supervised pretraining and multi-sensor calibration.
Depth sensors; gesture recognition; neural networks; 3D skeleton; graph convolution; video transformer; CNN-LSTM; depth motion maps; robustness; edge AI
Cross-Dataset Face Anti-Spoofing Using Domain Adaptation Techniques
Face recognition systems have become integral to modern security and authentication mechanisms, yet they remain vulnerable to presentation attacks, including print, replay, and 3D mask spoofs. Traditional anti-spoofing methods often rely on training data from a single dataset, which limits their generalization capability to unseen domains. Cross-dataset face anti-spoofing seeks to bridge this performance gap by leveraging domain adaptation techniques to transfer learned knowledge between source and target datasets. This paper presents a comprehensive study on cross-dataset anti-spoofing using advanced domain adaptation frameworks, including adversarial training, feature alignment, and style transfer methods. We evaluate the effectiveness of these techniques across three benchmark datasets — CASIA-FASD, Replay-Attack, and OULU-NPU — using ResNet-50 and Vision Transformer backbones. Statistical analysis demonstrates significant performance improvement when domain adaptation is incorporated, reducing the average Half Total Error Rate (HTER) from 21.4% to 9.6% in cross-dataset testing scenarios. The results underscore the importance of distribution alignment in enhancing the robustness of face anti-spoofing models against unseen attack modalities.
Face anti-spoofing, domain adaptation, cross-dataset learning, presentation attack detection, feature alignment, transfer learning.
Enhanced Object Tracking Using Kalman Filters and Deep Features
Object tracking plays a critical role in computer vision applications, including autonomous driving, surveillance, and human-computer interaction. Traditional tracking methods using Kalman Filters (KF) excel in predicting object motion but are sensitive to occlusions, appearance changes, and background clutter. Recent advances in deep learning have introduced robust feature extractors capable of capturing high-level semantic representations, which, when integrated with KF-based motion models, can significantly enhance tracking accuracy and robustness. This paper presents an enhanced object tracking framework that combines the predictive capabilities of Kalman Filters with discriminative deep features extracted from convolutional neural networks (CNNs). The hybrid model addresses common challenges in real-world tracking scenarios such as abrupt motion, scale variations, and partial occlusions. Simulation experiments were conducted on benchmark datasets such as MOT16 and KITTI, demonstrating improvements in tracking precision, Multiple Object Tracking Accuracy (MOTA), and ID switch reduction compared to standalone KF and deep learning-based trackers. Statistical analysis reveals that the integration of deep features with KF yields an average 14.6% improvement in tracking accuracy across varied environmental conditions. The proposed approach shows promising potential for deployment in real-time intelligent vision systems.
Object tracking, Kalman filter, deep features, convolutional neural networks, motion prediction, visual tracking.
Video Summarization Techniques Using Attention-Based CNN-LSTM Models
Video summarization is a critical task in multimedia processing that aims to generate concise, informative, and visually appealing summaries from lengthy video content while preserving essential information. Traditional approaches relied on handcrafted features, shot detection, and heuristic rules, which often failed to generalize to diverse content domains. With the advent of deep learning, convolutional neural networks (CNNs) and recurrent architectures such as long short-term memory (LSTM) networks have shown remarkable potential in visual feature extraction and temporal sequence modeling, respectively. Recent advances integrate attention mechanisms to enhance the relevance and quality of generated summaries by selectively focusing on the most informative segments. This paper investigates attention-based CNN-LSTM models for supervised and unsupervised video summarization. The proposed model employs a CNN backbone for spatial feature encoding, an LSTM layer for temporal dynamics modeling, and a self-attention module for learning importance scores. A comprehensive simulation is performed using benchmark datasets such as SumMe and TVSum, and the results are evaluated using F-score and mean Average Precision (mAP) metrics. Statistical analysis demonstrates that attention-enhanced models outperform baseline CNN-LSTM approaches by up to 12% in summarization accuracy. This study concludes that attention mechanisms significantly improve temporal context understanding and help create more human-like summaries, paving the way for practical deployment in surveillance, entertainment, and educational video applications.
Video summarization, CNN-LSTM, attention mechanism, deep learning, temporal modeling, feature extraction
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
Copyright (c) 2025 The journal retains copyright of all published articles, ensuring that authors have control over their work while allowing wide dissenmination. https://creativecommons.org/licenses/by-nc/4.0
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