An IoE-Powered Framework for Adaptive Energy-Security Trade-Off in IoT
Bibliographic record
Abstract
The Internet of Energy (IoE) is emerging as a key enabler for integrated energy and security management in resource-constrained IoT environments. This convergence offers new opportunities for intelligent and sustainable energy and security operations. However, the inherent computational and energy limitations of IoT devices, combined with the complexity of security configurations, often lead to the neglect of protection mechanisms. The lack of lightweight, adaptable solutions makes standardized security protocols – such as Transport Layer Security (TLS) and Datagram TLS (DTLS) – unsuitable for large, heterogeneous deployments. Their static and rigid nature causes excessive overhead and limits adaptability under dynamic network conditions. To address these challenges, this article introduces an IoE-enabled architectural vision that tightly integrates energy and security management. In this architecture the IoE Controller (IoE-C) is introduced. It operates as an orchestration layer, leveraging data from energy and security monitoring platforms to dynamically adapt tailored security measures on IoT constrained devices. The Energy and Security aware Index (ESaI) is proposed as a composite metric to evaluate and guide the security-energy trade-off. The proposed paradigm generalizes the concept of dynamic and lightweight security across widely adopted IoT protocols, such as MQTT and CoAP. Evaluation results demonstrate improvements in device lifetime and energy efficiency compared to traditional TLS/DTLS-based solutions, while preserving adequate security guarantees. The framework outlines a scalable and sustainable path for secure IoT deployments in 6G and future energy-intelligent infrastructures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".