Dataset for Empirical Evaluation of a Layered Edge–Cloud IoT Security Architecture.
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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Zenodo
2026
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| _version_ | 1866902326333145088 |
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| author | Jama, Yahye Abdalle Jama Waberi, Khadar Waberi Ahmed Ali, Roble Mohamed Ali |
| author_facet | Jama, Yahye Abdalle Jama Waberi, Khadar Waberi Ahmed Ali, Roble Mohamed Ali |
| contents | <p>This dataset contains empirical measures obtained from a 14-day longitudinal assessment of an ESP32-based biometric and RFID access control system. The study evaluates a stratified edge–cloud framework designed to maintain security and operational continuity during network disruptions and power fluctuations in emerging urban settings.</p> <p>Data Structure for Replication: The primary raw data is provided in the file "14-Day Longitudinal IoT Smart Lock Performance Log Dataset 50", which encompasses the variables necessary to reproduce all study findings:</p> <ul> <li> <p><strong>Cycle_ID:</strong> Sequential identifier for each of the 50 authentication test cycles.</p> </li> <li> <p><strong>Date & Time:</strong> Timestamps for each access event.</p> </li> <li> <p><strong>Auth_Method:</strong> Indicates whether Fingerprint (Biometric) or RFID was used.</p> </li> <li> <p><strong>Attempt_Type:</strong> Categorized as "Authorized" (registered user) or "Unauthorized" (intruder) to calculate FAR and FRR.</p> </li> <li> <p><strong>Result:</strong> A Binary outcome (Success/Fail) used to derive the 98.0% overall accuracy.</p> </li> <li> <p><strong>Latency_ms:</strong> The raw verification time in milliseconds for each attempt (used to calculate the mean latency of 0.85s and 1.21s reported in the study).</p> </li> <li> <p><strong>Network_Status:</strong> Logs indicating "Online" vs "Offline" states to validate the 100% edge-autonomy resilience.</p> </li> <li> <p><strong>Cloud_Sync:</strong> Verification of successful asynchronous log synchronization via MQTT protocols upon reconnection.</p> </li> </ul> <p>Replication Transparency: This repository offers the data underlying the means, standard deviations, and metrics presented in the publication. It encompasses the precise numerical frequency counts utilized to create Figure 4 (Authentication Success Distribution) and the temporal intervals employed for Figure 5 (Network Resilience Timeline).</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19009558 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Dataset for Empirical Evaluation of a Layered Edge–Cloud IoT Security Architecture. Jama, Yahye Abdalle Jama Waberi, Khadar Waberi Ahmed Ali, Roble Mohamed Ali Keywords: Internet of Things (IoT); Edge Computing; Biometric Security; ESP32; MQTT Protocol; Residential Access Control; Network Resilience; Digital Governance; Smart Cities; Somalia. Subjects (Fields of Study): Information Technology > Internet of Things. Computer Science > Computer Security and Reliability. Engineering > Electronic and Electrical Engineering <p>This dataset contains empirical measures obtained from a 14-day longitudinal assessment of an ESP32-based biometric and RFID access control system. The study evaluates a stratified edge–cloud framework designed to maintain security and operational continuity during network disruptions and power fluctuations in emerging urban settings.</p> <p>Data Structure for Replication: The primary raw data is provided in the file "14-Day Longitudinal IoT Smart Lock Performance Log Dataset 50", which encompasses the variables necessary to reproduce all study findings:</p> <ul> <li> <p><strong>Cycle_ID:</strong> Sequential identifier for each of the 50 authentication test cycles.</p> </li> <li> <p><strong>Date & Time:</strong> Timestamps for each access event.</p> </li> <li> <p><strong>Auth_Method:</strong> Indicates whether Fingerprint (Biometric) or RFID was used.</p> </li> <li> <p><strong>Attempt_Type:</strong> Categorized as "Authorized" (registered user) or "Unauthorized" (intruder) to calculate FAR and FRR.</p> </li> <li> <p><strong>Result:</strong> A Binary outcome (Success/Fail) used to derive the 98.0% overall accuracy.</p> </li> <li> <p><strong>Latency_ms:</strong> The raw verification time in milliseconds for each attempt (used to calculate the mean latency of 0.85s and 1.21s reported in the study).</p> </li> <li> <p><strong>Network_Status:</strong> Logs indicating "Online" vs "Offline" states to validate the 100% edge-autonomy resilience.</p> </li> <li> <p><strong>Cloud_Sync:</strong> Verification of successful asynchronous log synchronization via MQTT protocols upon reconnection.</p> </li> </ul> <p>Replication Transparency: This repository offers the data underlying the means, standard deviations, and metrics presented in the publication. It encompasses the precise numerical frequency counts utilized to create Figure 4 (Authentication Success Distribution) and the temporal intervals employed for Figure 5 (Network Resilience Timeline).</p> |
| title | Dataset for Empirical Evaluation of a Layered Edge–Cloud IoT Security Architecture. |
| topic | Keywords: Internet of Things (IoT); Edge Computing; Biometric Security; ESP32; MQTT Protocol; Residential Access Control; Network Resilience; Digital Governance; Smart Cities; Somalia. Subjects (Fields of Study): Information Technology > Internet of Things. Computer Science > Computer Security and Reliability. Engineering > Electronic and Electrical Engineering |
| url | https://doi.org/10.5281/zenodo.19009558 |