# An Energy-Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks
## 1 Introduction
This strategy enables more equitable energy distribution among sensor nodes and reduces long-range transmissions that often cause early energy depletion in sparsely connected networks [22]. Moreover, LEAD-RP introduces a dynamic clustering radius adjustment mechanism that modifies cluster boundaries based on current node density and residual energy trends. Inspired by the access control model in role-based access control for Internet of Things (RBAC-IoT) protocol-based microgrids [1], LEAD-RP assigns roles and communication privileges dynamically to ensure not only energy efficiency but also routing robustness. The protocol operates in a fully distributed manner and relies only on local node information, thus minimizing communication overhead and ensuring scalability. Simulations conducted under realistic microgrid deployment scenarios show that LEAD-RP significantly improves network lifetime, the packet delivery ratio, and cluster stability when compared to conventional LEACH and Modified Low-Energy Adaptive Clustering Hierarchy (M-LEACH) protocols. Electromagnetic interference from power converters causes packet loss rates up to 25% in 600 V substations [14]. Metal-enclosed environments create non-uniform path loss (\(n=2.7\)-\(4.1\)) [13]. Battery aging leads to \(>\)30% capacity variance in 5-year deployments [21]. LEAD-RP's dynamic radius (Equation (10)) and energy-weighted selection (Equation (14)) inherently compensate for these variances through localized adaptations. In summary, while prior studies have contributed extensively to energy-efficient, secure, and intelligent routing protocols in IoT-based systems, the unique characteristics of microgrid sensing layers--namely, static deployment, high reliability demands, and severe energy constraints--necessitate specialized solutions. This work fills a critical gap by offering a practical, lightweight, and energy-aware clustering framework designed specifically for the microgrid context, contributing both theoretically and practically to the advancement of IoT-based energy infrastructure. Unlike EDsHEED which lacks localizedenergy adaptation, or DD-TL-LEACH designed for mobile nodes, LEAD-RP explicitly incorporates static deployment constraints through dynamic cluster radius (Equation (10)) and distance-energy joint optimization (Equation (14)). To quantitatively demonstrate LEAD-RP's advantages, we present two comparative analyses: Table 1 provides a high-level protocol feature comparison, while Table 2 offers an in-depth evaluation against contemporary solutions using multi-dimensional metrics derived from our simulation results and literature analysis. Table 3 further highlights the limitations of conventional LEACH-based protocols, where LEAD-RP achieves optimal microgrid suitability through its unique combination of topology adaptation, energy awareness, and distance optimization while maintaining low control overhead. This comprehensive improvement addresses the key challenges identified in Section 2. In summary, while prior studies have contributed extensively to energy-efficient, secure, and intelligent routing protocols in IoT-based systems, the unique characteristics of microgrid sensing layers--namely, static deployment, high reliability demands, and severe energy constraints--necessitate specialized solutions. This work fills a critical gap by offering a practical, lightweight, and energy-aware clustering framework designed specifically for the microgrid context, contributing both theoretically and practically to the advancement of IoT-based energy infrastructure. Unlike EDsHEED which lacks localized energy adaptation, or DD-TL-LEACH designed for mobile nodes, LEAD-RP explicitly incorporates static deployment constraints through dynamic cluster radius (Equation (10)) and distance-energy joint optimization (Equation (14)), as applied to the Micro-grid and MDMS architecture shown in Figure 1.