A Theoretical Framework for Context-Aware Energy Harvesting in Heterogeneous IoT Networks for Rural Nigerian Environments

Energy supply is the primary constraint on the deployment of internet of things sensor networks in rural Nigerian communities where grid electricity and solar panel maintenance are both unreliable, and existing energy harvesting theoretical frameworks do not account for the multi-source, context-varying energy environment characteristic of tropical rural settings. This study developed an original theoretical framework for context-aware multi-source energy harvesting in heterogeneous IoT networks specifically designed for rural Nigerian deployment conditions. The framework development employed a four-stage methodology: systematic review of 78 energy harvesting literature sources from 2019 to 2024, empirical characterisation of ambient energy availability (solar irradiance variability, radio frequency ambient energy density, thermal gradient availability, and vibration from agricultural machinery) at 12 sites across Benue, Kebbi, Sokoto, and Taraba States over 90 days; theoretical modelling of energy harvesting state transitions using a Markov decision process formulation; and expert validation with 15 IoT hardware and energy systems specialists. The empirical dataset revealed strong daily and seasonal periodicity in solar availability but poor diurnal correlation with network traffic demand patterns, invalidating uniform duty-cycling assumptions in standard frameworks. The original Context-Aware Harvesting Optimisation Framework proposes three theoretical constructs: adaptive source selection based on a multi-armed bandit context model, cooperative energy sharing between heterogeneous source nodes using a Nash bargaining fairness criterion, and predictive duty-cycle adjustment using a lightweight LSTM demand forecasting module. Simulation using NS-3 and a custom energy module validated 34.7 percent improvement in network lifetime over static duty-cycle alternatives.