# An Energy-Distance-Balanced Cluster Routing Protocol for Smart Microgrid IoT Sensing Networks
## 1 Introduction  
The integration of Internet of Things (IoT) technologies into microgrids has introduced a paradigm shift in the way modern energy systems are structured and managed. IoT-based microgrids leverage embedded sensing, real-time communication, and distributed control mechanisms to realize decentralized energy autonomy and improved operational flexibility. These systems consist of interconnected sensing and control devices, distributed generation units, storage systems, and smart loads, which work collaboratively to enhance energy resilience and efficiency [1, 2]. The evolution from traditional monolithic grids to such intelligent microgrids allows for fine-grained monitoring, localized decision-making, and seamless grid-to-island transitions [3, 4]. Notably, the decentralized nature of microgrids provides an effective means of supporting renewable energy integration and demand-side management, especially in regions with limited infrastructure or unstable supply conditions. At the core of IoT-enabled microgrid functionality lies the perception layer, which is responsible for data acquisition and event detection from the physical environment [5]. This layer comprises heterogeneous devices such as sensors, actuators, embedded controllers, and communication modules. These devices interact to form low-power wireless sensor networks (WSNs), which serve as the foundation for data-driven microgrid operations [6]. However, deploying reliable and scalable perception-layer infrastructures faces numerous challenges. Energy limitations are among the most pressing issues, as sensor nodes typically operate on constrained battery resources, making long-term unattendeddeployment difficult [7]. Furthermore, conventional sensing frameworks often exhibit poor adaptability to dynamic environments, suffer from unbalanced data transmission loads, and lack efficient data aggregation strategies--factors that severely impact both communication efficiency and system longevity. To tackle these limitations, considerable research has been devoted to designing energy-efficient routing and clustering protocols tailored for WSNs and IoT environments. One of the earliest and most cited clustering algorithms is the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, which selects cluster heads probabilistically to rotate communication responsibility and balance energy usage [8]. While LEACH improves energy distribution compared to direct transmission and multi-hop protocols, it remains suboptimal for microgrid scenarios with static sensor deployment and varying energy profiles. Various LEACH extensions, such as Modified Low-Energy Adaptive Clustering Hierarchy (M-LEACH) and Distributed Dynamic Threshold-based Low-Energy Adaptive Clustering Hierarchy (DD-TL-LEACH), attempt to improve scalability and energy fairness by introducing multi-level hierarchy and distance-awareness into cluster formation [8]. However, these protocols generally assume uniform node deployment, fail to incorporate residual energy into cluster head selection, and rely on static cluster configurations, resulting in energy imbalance and limited network lifetime. Beyond routing efficiency, security is another critical concern for microgrid IoT infrastructures. The open nature of wireless communication and constrained computing capabilities of embedded devices expose the system to a variety of threats, including memory heap attacks, unauthorized access, and data tampering [9; 10]. [PERSON] et al. [9] proposed a lightweight cryptography (LWC) model based on elliptic curve cryptography and one-way hash algorithms to mitigate memory-based attacks while ensuring energy-efficient security mechanisms suitable for low-resource devices. Similarly, [PERSON] et al. [10] introduced a cloud-based fine-grained access control framework that supports dynamic auditing and attribute revocation, enabling secure health information exchange over lightweight IoT devices--an approach that can be extrapolated to smart grid environments.