An Integrated IoT and Forensic Framework for Institutional Arson Mitigation in Boarding Facilities

Institutional arson within Kenyan secondary schools represents a recurring, destructive crisis that claims student lives, decimates infrastructure, and undermines educational continuity. Despite historical tragedies such as the Kyanguli, Moi Girls, and Utumishi Girls Academy disasters, systemic vulnerabilities persist due to delayed detection, obstructed egress routes, and evidentiary deficits. This paper proposes an IoT-based architecture to address these gaps, outlining anticipated benefits in detection and forensic integrity. Applying Robert K. Merton’s Strain Theory, we analyze how structural pressures—including authoritarian administrative practices, overcrowded dormitories, and acute examination anxiety—drive students toward deviant coping mechanisms. To dismantle these vulnerabilities and resolve persistent evidentiary gaps, we present a novel 4-tier IoT ecosystem. This infrastructure integrates pre-ignition Photoionization Detectors (PIDs) for volatile hydrocarbon vapor tracking, multi-spectral edge-AI computer vision for zero-visibility ignition profiling, and fail-safe electromagnetic maglocks for automated, congestion-aware egress. Crucially, the architecture is anchored by hardware-enforced cryptographic logging (TPM 2.0) to secure an unassailable digital chain of custody for courtroom prosecution. This technical telemetry feeds into a newly defined Governance and Policy Layer, transforming raw sensor data into auditable compliance metrics for national child protection frameworks. Finally, a holistic stakeholder operational framework translates these socio-technical mechanisms into actionable governance, positioning Kenyan educational facilities as global test cases for systemic policy reform, institutional resilience, and proactive life-safety engineering.

Institutional Arson, Strain Theory, IoT Life Safety Networks, Forensic Chain of Custody

1. Agnew, R. (1992). Foundation for a general strain theory of crime and delinquency. Criminology, 30(1), 47-88. [Google Scholar] [Crossref]

2. Al-Kashoash, H. A., Alreshoodi, M., & Al-Bayatti, A. H. (2019). Congestion-aware routing protocols for emergency smart building evacuations. IEEE Access, 7(1), 56782-56793. [Google Scholar] [Crossref]

3. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall. [Google Scholar] [Crossref]

4. Burton, D. L., McNiel, D. E., & Binder, R. L. (2012). Firesetting, arson, and youth: Structural inequalities and psychological stress. Journal of the American Academy of Psychiatry and the Law, 40(3), 355-364. [Google Scholar] [Crossref]

5. Camacho-Magriñán, P. (2025). Leveraging Low-Cost Sensor Data and Predictive Modelling for IoT-Driven Indoor Air Quality Monitoring. Sensors, 25(6), 200. [Google Scholar] [Crossref]

6. Cooper, D. (2014). Smart safety systems and institutional accountability. Journal of Safety Technology, 12(2), 115-132. [Google Scholar] [Crossref]

7. Fang, Z., Wang, J., & Li, H. (2021). Advances in smoke detection technologies: Ionization and photoelectric sensors. Fire Safety Journal, 120, 103092. [Google Scholar] [Crossref]

8. Li, J., Chen, Y., & Zhang, H. (2023). Trusted Platform Modules and blockchain integration for forensic accountability. Computers & Security, 126, 103056. [Google Scholar] [Crossref]

9. Malki, H., Al-Mousa, A., & Al-Khalifa, H. (2022). Deep neural networks for fire detection using uncooled LWIR microbolometers. Sensors, 22(14), 5124. [Google Scholar] [Crossref]

10. Merton, R. K. (1939). Social structure and anomie. American Sociological Review, 3(5), 672-682. [Google Scholar] [Crossref]

11. Mugamu, T. (2018). Fire safety risk perception and disaster preparedness in township schools: A case study of Kayamandi, South Africa. Journal of African Safety Science, 14(2), 89-104. [Google Scholar] [Crossref]

12. Mwangi, P. (2022). Smart systems for fire prevention in Kenyan schools: IoT and AI-driven approaches. African Journal of Technology and Education, 8(2), 45-62. [Google Scholar] [Crossref]

13. Naderpour, M., Lu, J., & Zhang, G. (2020). API integration between building control networks and emergency dispatch systems. Safety Science, 127, 104701. [Google Scholar] [Crossref]

14. Ngesu, L. (2021). Student voice and violent protest in Kenyan secondary schools. International Journal of Education and Research, 9(4), 123-135. [Google Scholar] [Crossref]

15. Nyabuto, M. (2018). Media framing and the diffusion of student protest behaviors in Kenya. Journal of African Media Studies, 10(3), 321-338. [Google Scholar] [Crossref]

16. Saeed, M., Khan, A., & Ali, S. (2021). Multi-gas MOS sensor clusters for pre-ignition fire detection. Sensors and Actuators B: Chemical, 329, 129-138. [Google Scholar] [Crossref]

17. Waruhiu, J., Massite, P., Gathura, K., Kiriungi, J., & Kwa, M. (2016). Student unrest and arson in Kenyan secondary schools: Policy and discipline challenges. Kenya Journal of Education Studies, 12(2), 45-59. [Google Scholar] [Crossref]

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