# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 2 Related Work
### Task Scheduling Systems  
With advancements in technology and growing application demands, task scheduling research remains an active field, continually contributing to enhanced system performance and efficiency. To address the demands of higher collection frequency in a high-frequency power load orderly consumption management platform, [PERSON] et al. [14] proposed a priority-based intelligent task scheduling strategy that considers channel capacity, concurrent tasks, collection frequency, and task execution immediacy. Similarly, [PERSON] et al. [15]introduced a power allocation algorithm that assigns real-time periodic tasks to energy-efficient platforms, optimizing multi-core processor power consumption while adhering to deadline constraints. Building on their research into communication network scheduling mechanisms and methods, as well as computation tasks in power distribution grid control systems, [PERSON] et al. [16] proposed a distributed control system scheduling strategy. This strategy aims to reduce information conflicts and congestion in power distribution control networks, thereby enhancing the operational performance of networked control systems. [PERSON] et al. [28] investigated the challenge of scheduling task packet applications on heterogeneous platforms under deadlines and energy consumption budget constraints. They introduced an energy-aware stochastic task scheduling algorithm named ESTS, a heuristic approach that achieves high scheduling efficiency for Bag of Tasks (BoT) applications with low time complexity. Addressing the challenge of non-periodic independent real-time task scheduling for multiple airships under emergency conditions, [PERSON] et al. [29] proposed an ABDS strategy incorporating a novel two-way announcement mechanism based on Conflict and Network-awareness (CNP). This strategy effectively improves the global optimization of airship resources and load balancing. In the context of cloud resource task scheduling, [PERSON] and [PERSON] et al. [17] proposed a two-phase strategy that pre-creates virtual machines based on historical scheduling data by incorporating task requirements such as deadlines and costs into the scheduling considerations. They came up with a way to schedule tasks that can cut down on the time it takes to finish time-sensitive tasks while keeping the workload balanced across all cloud resources in a changing cloud environment. [PERSON] et al. [18] put forth a multi-queue scheduling approach to address temporal discrepancies in Hybrid Green Infrastructure-as-a-Service Clouds (HGICs). This study also suggested a meta-heuristic optimization method that combines particle swarm optimization, simulated annealing, and genetic algorithms to solve the problem of how to make the most money in HGICs. [PERSON] et al. [30] presented an energy-efficient dynamic scheduling scheme for real-time tasks in virtualized cloud data centers, aimed at conserving energy and optimizing the task guarantee rate, average response time, and resource utilization of cloud data centers. Addressing the task offloading decision problem in edge computing environments, [PERSON] et al. [31] established network and task scheduling models and defined a task scheduling methodology that reduces task latency. They created an optimization algorithm to find the best way to complete tasks while taking into account delays. This improved edge task scheduling and cut down on task execution time and latency. [PERSON] et al. [32] suggest and test a two-approach algorithm that can be used to improve both the deployment of microservices and the routing of requests in order to reduce the time it takes for services to respond in a Mobile Edge Computing (MEC) setting. [PERSON] et al. [33] propose and validate a novel routing and scheduling algorithm for PTS-assisted multidrone parcel delivery, optimizing distance and time costs over large areas. [PERSON] et al.