# An Energy-Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks
## 1 Introduction
Also, [11] discusses the transformation of the Internet of Things (IoT) driving the hardware and software, service, and industrialization of the future factory under the background of Industry 5.0. [PERSON]. [12] proposes a three-layer IoT architecture combined with machine learning, which is applied to the medical health field to realize the early detection of heart disease. In addition to energy efficiency and security, the architectural robustness of microgrid communication networks has garnered increasing attention. Stack-vector routing [13] and cross-layer protocol designs such as Cooperative Link-aware Deterministic Data Routing (COLiDeR) [14] provide advanced routing capabilities by considering encapsulation, tunneling, and protocol stack awareness in relay selection. These designs are particularly valuable in complex or large-scale deployments where multi-hop communication and heterogeneous transmission technologies coexist. Moreover, information-centric network models such as Cross-layer Cluster-based IoT for Constrained Wireless Sensor Networks(CCIC-WSN) offer architectural improvements by decoupling data from device addresses, allowing for more flexible and content-oriented communication--beneficial in scenarios where sensor nodes frequently join or leave the network. However, CCIC-WSN's publish-subscribe model increases control packets by 40% in static deployments (Figure 9 in [15]), making it unsuitable for energy-constrained microgrids. LEAD-RP avoids this overhead through location-aware clustering without metadata synchronization. Recent work has also explored the integration of artificial intelligence and optimization theory into routing and control algorithms for IoT-based microgrids. [PERSON] et al. [3] proposed a mean field Stackelberg game model for optimal resource allocation in wireless-powered IoT systems, allowing hybrid access points and nodes to dynamically negotiate power and transmission strategies. In a similar vein, [PERSON] et al. [16] applied deep reinforcement learning combined with non-orthogonal multiple access (NOMA) technology for task scheduling and power control in vehicular networks, demonstrating that such methods can significantly reduce system latency and energy consumption under complex constraints. Machine learning-based clustering and routing approaches have been explored as well to enhance scalability and responsiveness. [PERSON] et al. [4] developed a multi-hop routing protocol based on a grey-wolf-optimized coalition game model; it was tailored for precision agriculture wireless sensor networks (WSNs). Their method demonstrated notable improvements in the packet delivery ratio, energy efficiency, and delay reduction. [PERSON] and [PERSON] [17] applied a hybrid two-stage routing and prediction model to urban water networks using cloud-assisted WSNs, showcasing how intelligent routing can optimize both energy and application-layer performance. Despite these advances, traditional LEACH-based clustering mechanisms still dominate Internet of Things wireless sensor network (IoT WSN) protocol designs, as evidenced by recent comprehensive surveys [18]. Yet, they fall short when deployed in microgrid scenarios with stationary sensor nodes, heterogeneous energy profiles, and high demands for communication stability [19; 20]. Many variants fail to dynamically adjust cluster sizes or selection metrics based on environmental or energy feedback. Energy-efficient distributed self-organized hybrid energy-efficient distributed clustering (EDsHEED) [21], for example, introduced a simplified hybrid energy-efficient distributed clustering framework, but it lacked adaptive mechanisms to cope with localized energy depletion or congestion. To address these deficiencies, this paper proposes a novel routing protocol named LEAD-RP. Unlike conventional LEACH implementations that select cluster heads randomly or purely based on probability, LEAD-RP incorporates residual energy levels and the inter-node distance into a joint cost function for cluster head election.