The widespread use of sensor devices in IoT networks imposes a significant burden on energy consumption at the network’s edge. To address energy concerns, a prompt anomaly detection strategy is required on demand for troubleshooting resource-constrained IoT devices. It enables devices to adapt their configuration according to the dynamic signal quality and transmission settings. However, obtaining accurate energy data from IoT nodes without external devices is unfeasible. This paper proposes a framework for energy anomaly detection of IoT nodes using data transmission analysis. We use a public dataset that contains peer-to-peer IoT communication energy and link quality data. Our framework first utilizes linear regression to analyze and identify the dominant features of data communication for IoT transceivers. Later, a deep neural network modifies the gradient flow to focus on the dominant features. This modification improves the detection accuracy of anomalies by minimizing the associated reconstruction error. Finally, the energy stabilization feedback provides nodes with insight to change their transmission configuration for future communication. The experimental results show that the proposed approach outperforms other unsupervised models in anomalous energy detection. It also proves that redesigning the conventional loss function by enhancing the impact of our dominant features can dramatically improve the reliability of the anomaly detection method.

The supply chain plays an essential role in the cost reduction of offshore wind energy. Supply chain complexity is a major driver of end-to-end supply chain costs and at the same time a source of competitive advantage. In this study, a strategic complexity management approach is suggested for analysing and controlling the complexity of the supply chain in offshore wind energy. The adoption of blockchain via the development of software architecture and a discussion of its impact on complexity are provided. A comparative study focused on two UK offshore wind farms based on real industrial data illustrates the complexity analysis and the contribution of blockchain technology to the strategic management of this complexity.

As the number of connected Internet of Things (IoT) devices continues to grow, securing these systems against cyber threats remains a pressing challenge, especially within environments constrained by limited computational and energy resources. This article presents an edge-centric intrusion detection system (IDS) framework that seamlessly integrates lightweight machine learning (ML)-based IDS models with pretrained large language models (LLMs) to enhance detection accuracy, semantic interpretability, and operational efficiency at the network edge. The system evaluates six ML-based IDS models: decision tree (DT), $K$ -nearest neighbor (KNN), random forest (RF), convolutional neural network (CNN), long short-term memory (LSTM), and a hybrid model of CNN and LSTM, on low-power edge gateways, achieving accuracy up to 98% under real-world cyberattacks. Furthermore, for anomaly detection, the system transmits a compact, secure telemetry snapshot (e.g., CPU usage, memory usage, latency, and energy consumption) via low-bandwidth application programming interface (API) calls to LLMs, including GPT-4-turbo, DeepSeek V2, and LLaMA 3.5. These models employ zero-shot, few-shot, and chain-of-thought (CoT) reasoning to deliver human-readable threat analyses and actionable mitigation recommendations. Extensive evaluations across diverse attacks such as denial of service (DoS), distributed denial of service (DDoS), brute force, and port scanning, demonstrate the system’s ability to enhance interpretability while maintaining low latency (<1.5 s), minimal bandwidth usage (<1.2 kB per prompt), and energy efficiency (<75 J), establishing it as a practical and scalable IDS solution for the edge gateway.