For real-time data acquisition and analytics, the vast array of internet-of-things (IoT) sensors, edge devices, software systems, and communication networks necessitate a large computing power. The energy consumption and carbon footprint are severely impacted by this. Energy-conscious sustainable computing solutions are essential for achieving carbon neutrality. This paper presents a simultaneous transmission and reflection reconfigurable intelligent surfaces (STAR RIS) assisted wireless powered IoT network. By utilising the concept of energy harvesting, efficient power control is introduced by the STAR RIS with optimal phase shifts. The numerical formulations for phase shift optimization, harvested energy, achievable rate, system spectral efficiency are obtained. Further, a hardware aware model to compute the total power of the system is proposed to carry out the energy efficiency analysis. It includes transmit power, RIS control/ drive power and circuit power consumption. To overcome the energy overhead, an access point (AP) scheduling algorithm is proposed which allocates specific user groups to specific APs based on maximum energy harvested with optimal phase shifts. A computational complexity analysis of the proposed algorithm is also presented. It is observed that STAR RIS assisted system improves the energy efficiency by 13.3% with the proposed scheduling algorithm. In the end, the performance comparison of system models with passive IRS and no IRS is presented along with other alternative RIS designs. The scalability study for larger networks is also discussed.
Alqahtani et al. (Thu,) studied this question.