The adoption of IoT is increasingly challenged by
device energy constraints and growing environmental concerns, especially as device densities surge in future wireless networks. Energy Harvesting (EH) and Simultaneous Wireless Information and Power Transfer (SWIPT) offer promising solutions by enabling devices to recharge from ambient and wireless sources. However, efficiently managing network energy consumption
without compromising connectivity remains unresolved due to SWIPT’s nonlinear and non-monotonic energy dynamics. Existing load-shifting and traffic steering strategies assume linear load-energy relations, failing in the SWIPT context where both user and network energy dynamics fundamentally change. In this work, we bridge this gap by proposing a novel analytical framework for dynamic load shifting in SWIPT-enabled Radio Access Networks, leveraging stochastic geometry and realistic IoT
energy models. We formulate an operator-centric optimization that accounts for delay-tolerant traffic and device duty cycling, targeting optimal load shifting strategies that ensure both communication and energy harvesting QoS. Numerical results suggest that our approach enables adaptive load distribution, strategically
exploiting periods when network energy consumption is
least sensitive to load and allows energy savings up to 15%. These findings highlight how integrating EH, SWIPT, and load management unlocks new avenues for scalable, energy-aware IoT networks, paving the way for sustainable 6G architectures.
