Evaluating the Trade-offs between Wireless Security and Performance in IoT Networks: A Case Study of Web Applications in AI-Driven Home Appliances
Bibliographic record
Abstract
The integration of the Internet of Things (IoT) with artificial intelligence (AI) is transforming home appliances into smarter, more responsive tools that enhance daily living. However, this technological fusion introduces significant security challenges, necessitating a careful balance between security and performance within IoT networks. First, the study answers the question of the trade-offs between security measures and performance metrics in web applications for AI-driven home appliances, and second, how can these trade-offs be optimized to ensure both robust security and high system performance? Using qualitative content analysis, the study identified key security flaws in web application architectures, while quantitative analysis assessed the impact of security protocols on system performance metrics such as latency, throughput, and CPU usage. Atlas.ti and Cisco’s Packet Tracer were utilized for thematic coding and network simulation, respectively, and multivariate regression analysis quantified the influences of security protocols. The results revealed that enhanced security protocols, such as encryption and authentication, significantly impact performance, with encryption increasing latency by an average of 50 milliseconds and reducing throughput by 10% under peak loads. Additionally, CPU usage increased by up to 75% in high-threat scenarios. The proposed security-performance optimization framework dynamically adjusts security measures based on current threat assessments and operational demands, aiming to sustain high performance while ensuring robust security. These findings have real-world applications in the design and implementation of AI-driven home appliances, offering a roadmap for manufacturers to enhance device security without compromising performance. By adopting adaptive security measures and leveraging edge computing, the framework can improve user satisfaction and trust in smart home technologies.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".