Understanding the Impact of IoT Security Patterns on CPU Usage and Energy Consumption on IoT Devices
Bibliographic record
Abstract
The Internet of Things (IoT) has given rise to numerous security issues that require effective solutions. IoT security patterns have been suggested as an effective approach to address recurrent security design issues. Although several IoT security patterns are proposed in the literature, it remains unclear how they impact the energy consumption and CPU usage of IoT-edge-based applications. We conducted an empirical study using three testbed IoT applications (i.e., smart home, smart city, and healthcare) to shed light on this issue. We evaluated the impact of six IoT security patterns, including Personal Zone Hub, Trusted Communication Partner, Outbound-Only Connection, Blacklist, Whitelist, and Secure Sensor Node, both in pairs and in combination (i.e., all patterns). Specifically, we conducted multiple penetration tests to first assess the pattern’s effectiveness against attacks. Then, we conducted a comprehensive analysis of the energy consumption and CPU usage of the applications with/without the implemented security patterns, aiming to evaluate the potential impact of these patterns on energy efficiency and CPU usage. Our findings demonstrate a statistically significant increase in energy consumption and CPU usage. Based on these findings, we provide guidelines for IoT developers to follow when implementing IoT-edge-based applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".