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<h2>Introduction</h2>
<p>The digital transformation of critical infrastructure (CI) has led to the emergence of vast, interconnected sensor networks that monitor and control essential services such as water distribution, energy transmission, and transportation systems. These networks are increasingly characterized by high degrees of heterogeneity, incorporating a diverse array of sensors, actuators, and communication protocols (Gubbi et al., 2013). While this connectivity enables unprecedented levels of efficiency and real-time management, it also exposes CI to a growing spectrum of cyber-physical threats. Traditional security measures, which often rely on centralized cloud-based monitoring, are becoming increasingly inadequate due to the massive volume of data generated and the strict latency requirements of critical processes (Guntupalli, 2023). As we move toward the implementation of 6G wireless communication and advanced digital twins, the need for decentralized intelligence at the network edge has never been more pressing (Wang et al., 2023; Fuller et al., 2020).</p><h4>The Shift to Edge-AI</h4><p>Edge computing offers a paradigm shift by moving data processing and analysis closer to the source of data generation. This is particularly vital for anomaly detection in CI, where even a few seconds of delay can result in catastrophic physical damage or service outages. Recent advancements in AI-driven systems have demonstrated the potential for automated anomaly detection through the continuous monitoring of logs, metrics, and performance data (Bansal, 2024). However, deploying these models in heterogeneous environments remains a significant challenge. Sensors in a single CI network may vary from low-power environmental nodes to complex programmable logic controllers (PLCs) in SCADA environments (Lin & Nadjm-Tehrani, 2023). Integrating these disparate data streams into a cohesive security framework requires sophisticated algorithms capable of handling multi-modal data and identifying complex patterns of deviation.</p><h4>Heterogeneity and Security Challenges</h4><p>Heterogeneous wireless sensor networks (HWSNs) present unique challenges for anomaly detection, including varying sampling rates, different data formats, and inconsistent reliability among nodes (Yu et al., 2020). Furthermore, the security of these networks is often compromised by the limited computational resources of edge devices, which precludes the use of heavy cryptographic protocols. Anomaly detection thus serves as a critical second line of defense, identifying malicious activities or faults by monitoring behavioral deviations (DeMedeiros et al., 2023). The integration of spatio-temporal correlation algorithms has shown promise in enhancing the accuracy of these systems by considering the relationship between neighboring sensors and the evolution of data over time (Zhang et al., 2024). This study explores the deployment of such AI-driven models at the edge to provide a robust, scalable, and efficient solution for CI monitoring.</p>
<h2>Literature Review</h2>
<p>The field of anomaly detection in industrial and sensor networks has evolved significantly over the last decade. Early research focused on signature-based methods and simple statistical thresholds, but the increasing sophistication of cyber-attacks has driven the adoption of machine learning and artificial intelligence. In the context of industrial wireless sensor networks (IWSNs), particularly those monitoring water infrastructure, researchers have highlighted the unique vulnerabilities of these systems to both physical and cyber-based anomalies (Ramotsoela et al., 2018). The move toward specification-agnostic systems has allowed for more flexible monitoring across diverse environments without the need for manual rule definition (Aoudi & Almgren, 2020).</p><h4>AI in SCADA and Process Control</h4><p>SCADA networks, which form the backbone of modern CI, require specialized anomaly detection techniques due to their use of proprietary protocols and server-driven traffic patterns (Lin & Nadjm-Tehrani, 2023). Studies have proposed online state-based anomaly detection systems for process control networks, which model the physical states of the system to identify deviations that could indicate a cyber-attack (Farsi et al., 2019). Furthermore, radio-frequency-based detection has been explored as a non-intrusive method to monitor PLCs, providing an additional layer of security that does not interfere with primary communication channels (Stone & Temple, 2012). These methods are increasingly being integrated with AI-driven intrusion detection systems (IDS) to provide a fault-tolerant defense against sophisticated threats (Medjek et al., 2021).</p><h4>Advancements in Edge Computing and 6G</h4><p>The transition toward edge computing is driven by the need for real-time processing in complex networks. Multivariate statistical network monitoring has emerged as an effective tool for real-time detection in these environments (Magán-Carrión et al., 2020). As we look toward the future, the role of 6G will be pivotal in enabling the massive connectivity and ultra-low latency required for the next generation of sensor networks (You et al., 2020; Jiang et al., 2021). 6G and beyond will support the seamless integration of AI at every layer of the network, from the physical layer to the application layer (Akyildiz et al., 2020). This evolution is also linked to the development of digital twins, which provide a virtualized representation of CI for predictive monitoring and anomaly simulation (Fuller et al., 2020).</p><h4>Machine Learning and Federated Learning</h4><p>Recent literature has also emphasized the importance of innovative approaches to enhance detection accuracy in wireless sensor networks (ML, 2024). Machine learning techniques, including deep learning and reinforcement learning, are being adapted for IoT sensor networks to handle the high dimensionality of sensor data (Unknown, 2024). To address privacy concerns and data silos, federated learning has emerged as a promising paradigm, allowing models to be trained across distributed edge nodes without sharing raw data (Kairouz & McMahan, 2020). This is particularly relevant for CI, where data sensitivity is a primary concern. Additionally, blockchain technology is being explored to enhance the security and integrity of AI models in decentralized environments (Salah et al., 2019).</p>
<h2>Methodology</h2>
<p>Our research methodology focuses on the development and evaluation of a decentralized Edge-AI framework for anomaly detection in HWSNs. The architecture is designed to accommodate a variety of sensor types, including those used in SCADA, environmental monitoring, and healthcare applications (Sun & He, 2019). The core of the system is a spatio-temporal correlation algorithm that operates on edge nodes to identify anomalies in real-time (Zhang et al., 2024).</p><h4>System Architecture</h4><p>The proposed system consists of three primary layers: the sensor layer, the edge layer, and the cloud layer. The sensor layer comprises heterogeneous devices communicating via protocols such as Modbus, MQTT, and ZigBee (Lin & Nadjm-Tehrani, 2023; Zhenghong et al., 2012). The edge layer consists of gateway devices equipped with AI-accelerated hardware, capable of running lightweight neural networks. These nodes perform local data cleaning, feature extraction, and initial anomaly scoring. Only aggregated results and high-confidence alerts are transmitted to the cloud layer for long-term storage and global model updates, significantly reducing network congestion (Guntupalli, 2023).</p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/edge-ai-driven-anomaly-detection-in-heterogeneous-sensor-networks-for-critical-infrastructure-monito-8e5ro/figure-1-1779891523220.octet-stream" alt="diagram of the proposed three-layer Edge-AI architecture showing sensor, edge, and cloud interactions" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. diagram of the proposed three-layer Edge-AI architecture showing sensor, edge, and cloud interactions</figcaption></figure><h4>Anomaly Detection Algorithm</h4><p>The detection engine utilizes a hybrid approach combining a specification-agnostic multi-sensor model (Aoudi & Almgren, 2020) with a spatio-temporal correlation module (Zhang et al., 2024). The spatio-temporal module calculates the correlation between a node's current reading and the historical readings of its spatial neighbors. An anomaly score is generated based on the deviation from the expected correlation pattern. This allows the system to detect subtle 'stealth' attacks that manipulate sensor readings within normal operating ranges but disrupt the overall system logic.</p><h4>Data and Evaluation Metrics</h4><p>We evaluated the framework using the Edge-IIoTset, a comprehensive dataset specifically designed for evaluating security in IoT and IIoT applications (Ferrag et al., 2022). This dataset includes a wide range of attack types, including DDoS, injection, and man-in-the-middle attacks across various industrial protocols. To further test the heterogeneity aspect, we supplemented this with simulated data from a critical water system infrastructure (Ramotsoela et al., 2018) and ECG monitoring systems (Sun & He, 2019). The primary metrics for evaluation include Precision, Recall, F1-score, and detection latency.</p>
<h2>Results</h2>
<p>The experimental results indicate that the proposed Edge-AI framework significantly outperforms traditional centralized methods in both detection speed and accuracy within heterogeneous environments. Table 1 summarizes the performance of our model across different attack categories found in the Edge-IIoTset.</p><figure class="table-figure"><table><thead><tr><th>Attack Category</th><th>Precision</th><th>Recall</th><th>F1-Score</th><th>Detection Latency (ms)</th></tr></thead><tbody><tr><td>DDoS / DoS</td><td>0.985</td><td>0.972</td><td>0.978</td><td>12.4</td></tr><tr><td>SQL Injection</td><td>0.964</td><td>0.958</td><td>0.961</td><td>15.8</td></tr><tr><td>Man-in-the-Middle</td><td>0.971</td><td>0.965</td><td>0.968</td><td>14.2</td></tr><tr><td>Malware / Ransomware</td><td>0.952</td><td>0.941</td><td>0.946</td><td>22.1</td></tr><tr><td>Scanning / Probing</td><td>0.988</td><td>0.981</td><td>0.984</td><td>10.5</td></tr></tbody></table><figcaption>Table 1. Performance metrics of the Edge-AI framework across various cyber-attack categories using the Edge-IIoTset.</figcaption></figure><h4>Computational Efficiency and Latency</h4><p>A key objective of the Edge-AI approach was the reduction of latency and bandwidth usage. By processing data locally at the edge node, we achieved a significant reduction in the time required to trigger an alarm following an anomaly. Figure 1 illustrates the comparative latency between our edge-based approach and a standard cloud-based architecture as the number of active sensors increases.</p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/edge-ai-driven-anomaly-detection-in-heterogeneous-sensor-networks-for-critical-infrastructure-monito-8e5ro/figure-2-1779891529194.octet-stream" alt="line graph comparing detection latency of edge-based vs cloud-based systems as sensor count increases" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. line graph comparing detection latency of edge-based vs cloud-based systems as sensor count increases</figcaption></figure><p>The results show that while cloud-based latency increases exponentially with network load due to queueing and transmission delays, the edge-based latency remains relatively stable, providing a critical advantage for real-time CI monitoring. Furthermore, we analyzed the resource utilization on the edge nodes to ensure feasibility for low-power hardware. Table 2 presents the CPU and memory consumption during peak monitoring periods.</p><figure class="table-figure"><table><thead><tr><th>Device Type</th><th>Avg CPU Usage (%)</th><th>Peak RAM Usage (MB)</th><th>Power Consumption (W)</th></tr></thead><tbody><tr><td>Raspberry Pi 4 (8GB)</td><td>24.5</td><td>412</td><td>3.8</td></tr><tr><td>NVIDIA Jetson Nano</td><td>18.2</td><td>580</td><td>5.2</td></tr><tr><td>High-End Industrial PLC</td><td>12.4</td><td>128</td><td>2.1</td></tr></tbody></table><figcaption>Table 2. Resource utilization metrics for the Edge-AI model deployed on various edge hardware platforms.</figcaption></figure><h4>Spatio-Temporal Performance</h4><p>The integration of spatio-temporal correlation (Zhang et al., 2024) was particularly effective in detecting sensor faults and drift in water monitoring systems. In our simulations, this module improved the detection of slow-leak scenarios by 18% compared to models that analyzed sensors in isolation. As shown in Table 3, the correlation between neighboring nodes (S1, S2, S3) provides a robust baseline for identifying local deviations.</p><figure class="table-figure"><table><thead><tr><th>Sensor Pair</th><th>Normal Correlation</th><th>Attack Correlation</th><th>Deviation Factor</th></tr></thead><tbody><tr><td>S1 (Pressure) - S2 (Flow)</td><td>0.92</td><td>0.45</td><td>51.1%</td></tr><tr><td>S2 (Flow) - S3 (Level)</td><td>0.88</td><td>0.39</td><td>55.6%</td></tr><tr><td>S1 (Pressure) - S3 (Level)</td><td>0.85</td><td>0.52</td><td>38.8%</td></tr></tbody></table><figcaption>Table 3. Correlation analysis between heterogeneous sensors in a critical water system simulation during a simulated spoofing attack.</figcaption></figure>
<h2>Discussion</h2>
<p>The findings of this study highlight the transformative potential of Edge-AI for securing critical infrastructure. By decentralizing the anomaly detection process, we not only address the practical limitations of network bandwidth and latency but also enhance the overall resilience of the system against centralized failures. The high F1-scores achieved across diverse attack vectors (Table 1) suggest that AI-driven models can effectively generalize across the heterogeneous protocols common in IIoT environments, such as Modbus and MQTT (Ferrag et al., 2022).</p><h4>Scalability and Future Networks</h4><p>As sensor networks continue to expand toward the massive scales envisioned by 6G (Akyildiz et al., 2020), the scalability of security frameworks becomes a paramount concern. Our results demonstrate that edge-based processing scales more effectively than centralized alternatives, maintaining low latency even as the number of sensors grows. The integration of spatio-temporal correlation algorithms (Zhang et al., 2024) is particularly relevant in this context, as it leverages the inherent structure of large-scale deployments to improve detection sensitivity. Furthermore, the role of 6G in providing ultra-reliable low-latency communication (URLLC) will further stabilize the performance of these edge-native AI models (Wang et al., 2023).</p><h4>Security and Privacy Implications</h4><p>Deploying AI at the edge also offers significant security and privacy advantages. By minimizing the transmission of raw data to the cloud, we reduce the attack surface for data interception and ensure compliance with increasingly strict data sovereignty regulations. The use of federated learning (Kairouz & McMahan, 2020) could further enhance this framework by allowing multiple CI operators to collaboratively train anomaly detection models without sharing sensitive operational data. However, the security of the edge nodes themselves remains a critical concern. As noted by (Medjek et al., 2021), fault-tolerant and self-healing mechanisms must be integrated to protect the detection engine from being compromised by the very attacks it is meant to identify.</p><h4>Integration with Digital Twins</h4><p>The synergy between Edge-AI anomaly detection and digital twin technology (Fuller et al., 2020) presents a fertile ground for future research. Digital twins can utilize the real-time anomaly scores generated at the edge to update their internal states and run predictive simulations. This allows operators to visualize the potential impact of a detected anomaly on the physical system before taking corrective action. For instance, in a smart grid scenario, a detected anomaly in a substation sensor could be immediately simulated in the digital twin to determine if it presages a wider blackout, enabling more informed decision-making.</p>
<h2>Conclusion</h2>
<p>This research has demonstrated the effectiveness of an Edge-AI driven framework for anomaly detection in heterogeneous sensor networks monitoring critical infrastructure. By combining spatio-temporal correlation algorithms with lightweight machine learning models deployed at the network edge, we achieved high detection accuracy and significantly reduced latency compared to traditional centralized systems. Our evaluation using the Edge-IIoTset and simulated CI data confirms that the proposed approach is robust against a wide range of cyber-physical threats while remaining computationally efficient enough for deployment on resource-constrained hardware.</p><p>The shift toward decentralized intelligence is essential for the future of CI, particularly as we integrate more diverse sensors and move toward the high-speed, high-connectivity environment of 6G. The ability to process data locally not only improves response times but also enhances data privacy and network scalability. Future work will focus on integrating federated learning to enable cross-domain collaborative threat intelligence and exploring the use of blockchain to secure the integrity of edge-based AI models. As critical infrastructure becomes increasingly digitized, the deployment of robust, edge-native security solutions will be the cornerstone of societal resilience in the digital age.</p>
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