One Algorithm, Four Worlds: The Cross-Domain AI Map That Could Reshape Connected Systems

A Random Forest model that flags a botnet in one study quietly reappears in another as a wheat-yield estimator. A Long Short-Term Memory network trained to spot DDoS traffic in one paper resurfaces elsewhere forecasting soil moisture for cotton irrigation. This eerie recurrence is not coincidence, and it has now been mapped in detail. A team of researchers led by Bashir Olaniyi Sadiq of Kampala International University has published an open-access cross-domain review in Discover Artificial Intelligence that synthesizes prominent AI and machine learning algorithms across four application domains: IoT security, computer networks and ad hoc routing, image processing and computer vision, and smart agriculture. Drawing on a corpus of more than 200 systematically screened studies published between 2018 and 2026, the review argues that the same algorithmic families recur across these fields but are almost always discussed in isolation, leaving researchers without a defensible guide for choosing techniques in real connected environments.

The review’s central claim is that the value of an algorithm cannot be understood from its mathematical form alone. It must be interpreted in relation to data type, deployment environment, computational budget, interpretability requirements, and operational risk. To establish this, the authors adopted a PRISMA-guided screening workflow, searching major academic databases including Springer, IEEE Xplore, ScienceDirect, Google Scholar, MDPI, Frontiers, Elsevier, Wiley, and arXiv. Their Boolean search combined AI method terms with domain and application terms and functional task terms, ensuring that retrieved studies actually applied machine learning in one of the four target domains for a specific analytical purpose rather than merely mentioning it in passing. After title and abstract screening against predefined eligibility criteria, followed by full-text review, 220 studies met all criteria and were used as the bibliography, with 221 studies forming the corpus from which prominent algorithms were selected empirically rather than from a general list found in the AI literature.

The resulting taxonomy is organized by functional role rather than by domain. In IoT security, the most prevalent algorithmic functions are traffic classification, intrusion detection, anomaly discovery, botnet recognition, and lightweight edge inference. In network optimization, the functions shift to routing, load balancing, channel allocation, congestion mitigation, and policy adaptation. Image processing demands feature extraction, segmentation, compression, visualization, object recognition, and global-context modelling. Smart agriculture requires yield prediction, disease detection, crop recommendation, weather forecasting, soil monitoring, irrigation support, and farm-level decision support. This dual-axis structure, cross-referencing each algorithm by both model family and typical role across domains, is the review’s most distinctive contribution, and it reveals that algorithmic depth per domain is unequal: IoT security and image processing span both classical algorithms such as SVM, KNN, and Random Forest alongside deep architectures, while smart agriculture is more evenly spread across tree-based and regression methods.

In IoT security, the review finds that classical supervised algorithms remain important even though network-flow features are structured and labeled data are available. SVM, KNN, Naive Bayes, Random Forest, and XGBoost are routinely used to classify traffic as benign or malicious, but these models become problematic when new attack types arise or traffic distributions shift. Unsupervised and semi-supervised methods partially overcome these drawbacks. Autoencoders, which reconstruct normal traffic and flag high reconstruction errors as anomalies, have become the most popular unsupervised paradigm for IoT intrusion detection, particularly useful for zero-day and novel attack detection where labeled data are scarce. Isolation Forest efficiently detects outliers in high-dimensional data. The review also documents a clear architectural trend: CNN-based intrusion detection is moving from single networks to hybrids combining spatial feature extraction with sequential or attention-based techniques. Studies cited include a CNN-GRU model achieving high detection accuracy across IoT datasets and a CNN-BiLSTM-Transformer explicitly addressing long-sequence dependencies on CIC-IDS2017 and BoT-IoT benchmarks. Crucially, the review notes that higher model complexity does not provide proportional detection gains but does cause computational cost to rise disproportionately, a finding with direct implications for edge deployment on resource-constrained devices.

In computer networks and ad hoc routing, the review identifies reinforcement learning as the natural framework for sequential decision-making under dynamic conditions. Networks must deal with congestion, mobility, interference, link failure, changing traffic demand, and quality-of-service requirements, and traditional routing protocols based on fixed metrics or predetermined rules struggle to adapt. Q-Learning, SARSA, Deep Q-Networks, Actor-Critic methods, and Policy Gradient techniques learn routing or resource-allocation policies by interacting with a network environment. The review traces Q-Learning’s expansion into multi-objective distributed learning for IoT routing, Deep Q-Networks’ integration with the OLSR routing protocol for dynamic MANETs, and Soft Actor-Critic architectures within digital-twin-aided vehicular edge computing frameworks that jointly optimize task offloading, transmit power, and resource block allocation. Graph Neural Networks emerge as a crucial concept because communication networks are naturally graph-structured, with nodes, links, capacities, delays, and traffic demands being relational rather than tabular features. The review cautions that reinforcement-learning models can be difficult to stabilize before training converges, that exploration is risky in operational networks, and that deep models add latency and computational overhead, so AI routing must be compared against robust protocol baselines and tested on unseen topologies with safety mechanisms to prevent unstable or unfair routing decisions.

In image processing and computer vision, the review documents the shift from handcrafted features such as edges, texture descriptors, color histograms, and shape features to data-driven deep learning. Convolutional Neural Networks remain the go-to choice for computationally limited and real-time applications due to their spatial inductive bias and parameter efficiency, even as Vision Transformers become a new architectural contender. Vision Transformers treat image patches as sequences and use self-attention to capture long-range dependencies and global contextual relationships, yielding impressive results in medical image segmentation, remote sensing classification, and plant disease localization where global context is essential. The review highlights token sparsification approaches that remove redundant tokens while retaining discriminative information, lowering calculation and memory utilization for deployable transformer-based vision systems in resource-constrained settings like autonomous vehicles and drones. Autoencoders continue to serve as powerful representation learning tools for denoising, compression, and anomaly detection, while PCA and t-SNE remain valuable for dimensionality reduction, feature compression, and visualization of deep learning feature distributions. The review notes that PCA can compress transformer-based visual representations by over an order of magnitude with very little drop in classification accuracy.

In smart agriculture, the review emphasizes that agricultural systems are affected by weather, soil type, crop variety, disease pressure, pest activity, water availability, farm practice, and seasonal variability, making them fundamentally different from purely digital domains. Models must combine heterogeneous data from field sensors, weather stations, satellite images, UAV images, soil analysis, and farm bookkeeping. Classical machine learning remains relevant because many agricultural datasets are structured and of moderate size: Decision Trees formulate interpretable rules for crop decision-making, Random Forest and XGBoost capture non-linear relationships between weather, soil, vegetation indices, and management variables for yield prediction, and Logistic Regression provides transparent probabilistic baselines for disease-risk decision support. Deep learning is particularly beneficial where agricultural information is visual or sequential, with CNNs used extensively for plant disease detection, weed recognition, and remote-sensing interpretation, and LSTMs applied to weather forecasting, crop growth monitoring, and irrigation scheduling. The review stresses that good performance on images from one region does not assure good performance in another because of differences in light, cultivar, disease expression, background, or camera quality, necessitating field validation and explainability linked to agronomic variables.

Across all four domains, the review identifies recurring cross-cutting gaps that collectively reveal where current AI adoption falls short. Dataset realism is the first: lab traffic, known attack scripts, curated images, and location-specific agricultural datasets rarely capture deployment diversity, so benchmark performance must be clearly differentiated from deployment readiness. Model drift is the second: IoT devices evolve, attackers evolve, farms experience seasonal and climate changes, and network traffic shifts with applications, yet static train-test validation is insufficient to assess reliability over time, demanding online learning, periodic retraining, drift detection, and human-in-the-loop review. Explainability is the third: security analysts must understand what traffic caused an alert, network operators should know why a learned routing policy altered traffic paths, and agronomists require explanations connected to visible symptoms, weather variables, or soil conditions. Resource-aware deployment is the fourth: deep models and reinforcement-learning policies can be costly on IoT edges, farm sensors, and mobile devices, so pruning, quantization, knowledge distillation, and edge-cloud partitioning deserve greater attention. The review argues that a slightly less accurate but faster, more stable, explainable, and energy-efficient model may be better in real deployments.

The review concludes with a practical algorithm-selection framework that begins with the shape of the data rather than model popularity. When data is well-structured, tabular, and not too large, Decision Trees, Random Forests, Gradient Boosting, XGBoost, Logistic Regression, and SVM are strong first-line options. For images, CNNs and Vision Transformers are suitable; for sequences, LSTMs, Transformers, and temporal CNNs; for graphs, Graph Neural Networks; for action-oriented sequential problems, reinforcement learning after simpler baselines have been achieved. Label availability is the second decision point: when labeled examples are plentiful, supervised learning is typically appropriate, but when labels are scarce or costly, unsupervised and semi-supervised methods such as autoencoders, Isolation Forest, DBSCAN, and variational autoencoders become appealing. Deployment context is the third: when interpretability is required, linear models and tree ensembles with feature-attribution techniques may be more suitable; when data ownership prevents centralization, federated learning might be appropriate; when immediate response is needed, latency and failure behaviours should be checked before adding model complexity. Validation design is the fourth: security systems demand adversarial robustness and false-positive cost analysis, routing requires topology variation and traffic bursts, computer vision needs external databases and real-world image variation, and smart agriculture requires geographic, seasonal, and crop-variety diversity. Without such validation, the ranking of algorithms may be misleading. The review’s ultimate message is that AI adoption in connected environments is additive rather than substitutive: classical supervised learning, ensemble learning, unsupervised anomaly detection, deep neural networks, reinforcement learning, graph learning, and federated learning all play special roles, and future research should focus on lightweight, robust, multimodal foundation models and explainable AI validated on realistic datasets across IoT, campus networks, smart agriculture, image processing, and ad hoc routing environments.

Subject of Research: Artificial Intelligence

Article Title: A cross-domain review of artificial intelligence techniques for iot security, computer networks, image processing and smart agriculture in connected environments

Article References: Sadiq, B. O., Buhari, M. D., Ibrahim, A., Edozie, E., & Shabiluh, A. (2026). A cross-domain review of artificial intelligence techniques for iot security, computer networks, image processing and smart agriculture in connected environments. Discover Artificial Intelligence, 6(1), Article 1535. https://doi.org/10.1007/s44163-026-02500-9

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02500-9

Keywords: cross-domain AI review, IoT security, computer networks, image processing, smart agriculture, machine learning, reinforcement learning, federated learning, connected environments, Discover Artificial Intelligence, cross-domain, review