ENERGY–AWARE MODELLING OF IOT NETWORK LIFE–CYCLE UNDER INDUCED FALSE–EVENT FLOWS

Abstract

An energy-aware analytical model of the IoT network life-cycle under induced false-event traffic is proposed. The study considers event-driven clustered IoT networks operating under external influences that generate false event messages and thereby cause unnecessary sensing, transmission, reception, and forwarding operations. Unlike conventional approaches that usually treat traffic behaviour, communication energy consumption, and network geometry separately, the proposed model integrates these components within a unified formal framework. Within this framework, the energy cost of a single false event is formalised and linked to the network-level energy balance, false-event arrival parameters, and node mobility. On this basis, closed-form analytical expressions are derived for the temporal evolution of residual energy and for the duration of the network life-cycle under fixed spatial topology. The model thus establishes an explicit relationship between induced false-event intensity and the depletion of network resources. The analysis shows that the intensity and regularity of false-event arrivals significantly affect the degradation trajectory. In particular, more regular induced traffic changes the early-stage depletion pattern, whereas at high intensities different traffic regimes converge. The model is validated by simulation for a LEACH-based clustered IoT network. The simulation results confirm the analytical dependencies over the investigated range 1 ≤ λ_f ≤ 10 and show that the proposed formulation remains in close agreement with the simulation reference. In the comparative analysis, the prediction error of the proposed model remains within about 0%-2%, whereas the traffic-only baseline reaches about 10.2% and the classical LEACH baseline about 20.4%. These results demonstrate that the proposed model provides a more adequate estimate of network life-cycle because it explicitly incorporates induced-loss effects ignored or oversimplified in conventional approaches.