Energy Efficient Digital Twin Enabled Massive IoT Network With Use Case in Consumer Health

Abstract

Consumer electronics such as smart home devices, smartphones and smart wearables rely on accurate collection and sharing of data for massive user interaction. These form essential components of next generation internet-of-things (IoT) which require connected support. But owing to the limited battery life, these consumer nodes suffer from power limitations requiring frequent battery charging or replacements. This paper presents digital twin enabled wireless powering scheme for a massive IoT network. Digital twin creates a virtual replica of communication network involving large number of sensor nodes and access points (APs). This network is tested for energy deficient consumer IoT nodes and energy harvesting is enabled for the sensor nodes to harness the RF energy transmitted by the APs. Minimum Interference AP Clustering (MIAC), an energy conservation scheme for wireless powering of cell-free massive IoT is proposed which forms subsets of APs or AP clusters for each active node. In this paper, the performance of a digital twin IoT network is evaluated with energy harvesting and AP clustering for different transmission schemes. The amount of energy harvested and the spectral efficiency achieved is obtained along with the mathematical formulations. It is shown that the coherent transmission with linear energy harvesting achieves maximum average spectral efficiency of 2.659 bits/s/Hz. Further, the impact of incorporating AP clustering in the network transmission model suggests that the spectral efficiency achieved with AP clustering is 12% higher than the transmission scheme with no AP clustering. In the end, the proposed network is compared with two other network models for performance evaluation. A use case scenario of digital twin in consumer health for enabling smart healthcare ecosystem is also provided.