Toward Secure, IoT-Enabled Adaptive Illumination in Smart Buildings: A Systematic Framework for Demand-Side Lighting Energy Management and Embedded Security Hardening in Developing Economies

Artificial lighting represents one of the largest addressable components of building energy consumption in both developed and developing economies. In Sub-Saharan Africa, where chronic electricity supply deficits impose severe socioeconomic constraints, the optimization of demand-side lighting energy management constitutes a priority intervention. This paper presents a systematic conceptual and technological framework for the progressive evolution of sensor-based occupancy-driven illumination systems, from standalone Passive Infrared (PIR) microcontroller implementations toward fully IoT-enabled, adaptive, and predictive smart building lighting architectures, with embedded security hardening treated as a first-class design dimension rather than a downstream afterthought. Drawing on a structured review of fifteen empirical and theoretical studies published between 2010 and 2025, and grounded in empirical findings from a prototype PIR-Arduino implementation achieving 85% detection accuracy and an estimated 94% reduction in effective energy consumption, the paper characterizes the current state of the field, identifies six critical research gaps: stationary occupant insensitivity, offline operational resilience, multi-zone scalability, adaptive threshold learning, user acceptance in low-income settings, and security hardening across the connectivity and edge-intelligence tiers; and proposes a five-tier technology enhancement roadmap spanning from low-cost standalone deployment to edge-intelligence-enabled predictive control, with tier-specific security requirements specified at each connectivity transition. Quantitative demand-side management analyses across five building typologies demonstrate that occupancy-driven lighting automation could yield energy savings of 30–95% depending on space type and operational schedule. Because the proposed roadmap explicitly introduces wireless connectivity (Tier 4) and edge machine learning (Tier 5), the paper undertakes a structured security analysis of the architecture, identifying authentication, transport encryption, firmware integrity, and physical actuator access control as the principal hardening requirements that must accompany each connectivity transition; without these safeguards, the same IoT and MQTT-based mechanisms that enable remote monitoring and predictive control also expand the system's exposure to unauthorized access, sensor spoofing, and denial-of-service against building lighting infrastructure. The framework is specifically calibrated for developing-economy deployment contexts characterized by infrastructure constraints, cost sensitivity, and irregular electricity supply, offering a pragmatic, context-appropriate, and security-conscious pathway toward sustainable, intelligent building energy management.