Securing the Future: A Survey on Smart Home Security in IoT-Integrated Smart Cities
Bibliographic record
Abstract
The rapid growth of urbanization and technological advancements have led to the rise of smart cities and smart homes, where the Internet of Things (IoT) plays a pivotal role. Smart homes enhance energy efficiency, security, and convenience through automated systems and interconnected devices. This survey provides a comprehensive review of smart home architectures, communication technologies, and applications, emphasizing their integration within smart city infrastructures. It explores key components such as sensors, controllers, and cloud-based platforms that enable seamless automation. Additionally, this paper discusses major challenges in smart home security, including privacy risks, cyber threats, and interoperability issues among IoT devices. Security concerns such as unauthorized access, data breaches, and denial-of-service attacks are analyzed, alongside strategies to mitigate these risks. The study also highlights the importance of secure communication protocols, authentication mechanisms, and encryption techniques to ensure the resilience of smart home systems. Furthermore, this survey examines emerging research directions in smart home technology, including AI-driven automation, energy-efficient systems, and blockchain-based security solutions. As smart homes continue to evolve, addressing these challenges will be crucial for their widespread adoption. This paper aims to serve as a valuable resource for researchers, developers, and policymakers seeking to enhance the security and functionality of smart homes within the broader framework of smart cities.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".