Energy-as-a-Service for RF-Powered IoE Networks: A Percolation Theory Approach

2025-02-11

AI Summary

For 6G Internet-of-Everything (IoE) networks, conventional shared energy stations (ESs) face deployment constraints due to high capital expenditure (CAPEX) and limited wireless energy transfer (WET) coverage, leading to spatial energy gaps and undermining large-scale device-to-device (D2D) connectivity. Method: This paper pioneers the application of continuum percolation theory to RF energy harvesting IoE networks. Leveraging a Poisson point process and the Gilbert disk model, we establish an Energy-as-a-Service (EaaS)-driven joint power-supply-and-communication connectivity framework. Contribution/Results: We derive an analytical expression for the critical ES density required to ensure asymptotically unit-scale D2D connectivity probability, jointly optimizing CAPEX minimization and connectivity guarantees. Furthermore, we obtain a practical, closed-form approximate solution, providing rigorous theoretical foundations and design guidelines for cost-effective EaaS infrastructure deployment.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Due to the involved massive number of devices, radio frequency (RF) energy harvesting is indispensable to realize the foreseen Internet-of-Everything (IoE) within 6G networks. Analogous to the cellular networks concept, shared energy stations (ESs) are foreseen to supply energy-as-a-service (EaaS) in order to recharge devices that belong to different IoE operators who are offering diverse use cases. Considering the capital expenditure (CAPEX) for ES deployment along with their finite wireless energy transfer (WET) zones, spatial energy gaps are plausible. Furthermore, the ESs deployment cannot cover 100% of the energy-harvesting devices of all coexisting IoE use cases. In this context, we utilize percolation theory to characterize the feasibility of large-scale device-to-device (D2D) connectivity of IoE networks operating under EaaS platforms. Assuming that ESs and IoE devices follow independent Poisson point processes (PPPs), we construct a connectivity graph for the IoE devices that are within the WET zones of ESs. Continuum percolation on the construct graph is utilized to derive necessary and sufficient conditions for large-scale RF-powered D2D connectivity in terms of the required IoE device density and communication range along with the required ESs density and WET zone size. Fixing the IoE network parameters along with the size of WET zones, we obtain the approximate critical value of the ES density that ensures large-scale connectivity using the inner-city and Gilbert disk models. By imitating the bounds and combining the approximations, we construct an approximate expression for the critical ES density function, which is necessary to minimize the EaaS CAPEX under the IoE connectivity constraint.

Problem

• RF energy harvesting for IoE networks
• Spatial energy gaps in ES deployment
• Percolation theory for D2D connectivity

Innovation

• RF energy harvesting
• Percolation theory application
• Device-to-device connectivity optimization