# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 1 Introduction  
Calibration ensures the accuracy and reliability of electricity meters [1, 2, 3], which is critical for ensuring operational efficiency, minimising energy losses, and adhering to regulatory standards. The increasing adoption of electricity meters across various industries has significantly increased the demand for calibration. Traditional methods of calibration often involve moving meters to central facilities or sending technicians to remote sites. These methods require a lot of manual labour and have high operational costs, which can lead to mistakes in process control and high transportation costs. They can no longer satisfy the growing calibration demands, necessitating scalable and efficient solutions. Advanced technology called remote calibration combines electricity meters, smart detection systems, and computer technologies to make calibration processes much more accurate and efficient [4, 5]. As illustrated in Figure 1, a remote calibration system is builton three key components: the central laboratory, the detection point, and reliable internet connectivity. Standard equipment and metering devices at the detection point collect data and perform calibration tasks. Concurrently, experts at the central laboratory remotely monitor the calibration process through internet-based systems, offer real-time guidance, and issue calibration certificates. Compared to traditional on-site calibration, which required metrologists to perform operations in person, remote calibration enhances personnel efficiency through improved task management and record-keeping while also streamlining the transportation process. This area requires further research and technological development to enable remote calibration, meeting the increasing demand for calibrating various electricity measuring devices. Many metrology laboratories, including the National Institute of Standards and Technology (NIST) [6] and the National Physical Laboratory (NPL) [7], have begun developing remote calibration systems customized to their requirements. The goal is to make open remote calibration systems that use Internet of Things (IoT) technologies and smart algorithms to let equipment talk to each other, get data, and be calibrated from afar. We anticipate these advancements to enhance the calibration efficiency of devices like frequency meters, optical frequency standards, and coordinate measuring machines. However, existing research [6; 7; 8; 9] has predominantly focused on isolated remote calibration architectures designed for single-laboratory use. In contrast, distributed remote calibration systems--capable of integrating multiple nodes and advanced computational resources--are still in their nascent stages and require additional research and development [10]. For distributed remote calibration systems, it is essential to develop advanced task scheduling methodologies specifically designed to leverage the strengths of distributed systems. However, current research on task scheduling primarily focuses on asynchronous tasks [11; 12; 13]. Scheduling paradigms employed by platforms such as orderly power management [14], core-level Dynamic Voltage and Frequency Scaling (DVFS) systems [15], and similar frameworks utilize distinctive models. Similarly, methodologies in control system scheduling [16] and cloud resource task scheduling [17; 18] exhibit unique characteristics but differ significantly in their approaches. These models, however, fail to adequately represent tasks requiring synchronous processing across multiple devices and do not sufficiently address the demands of distributed remote systems, which necessitate real-time coordination across geographically dispersed nodes. Although attempts such as [PERSON]. [PERSON] et al. 's Dynamic Synchronous Scheduling (DSS) algorithm [19], which employs a multi-layered greedy approach, have been proposed to address task synchronization in remote sensor calibration systems, their effectiveness remains limited. To optimize resource  
Figure 1: Basic framework for the remote calibration technique.