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Transient simulation in power engineering is crucial as it models the dynamic behavior of power systems during sudden events like faults or short circuits. Electromagnetic transient simulations involve multiple coordinated tasks. Traditional simulations are centralized and struggle to meet scalability requirements. To achieve these goals, distributed electromagnetic transient simulation has emerged as a new trend. Nevertheless, the distributed electromagnetic transient simulation introduces network communication. Achieving real-time simulation across distributed nodes poses the challenge of minimizing communication costs. In this paper, our proposal focuses on optimizing the task orchestration to reduce communication costs. Specifically, in the electromagnetic transient simulation, these tasks has certain communication pattern where the communicated objects of each task are pre-defined. We represent the pattern as a graph, with tasks represented as nodes and communications as edges. Furthermore, we propose to use graph partition with the objective of minimal communication costs and fine tune the partitions with the resource requirements of each distributed node. The experimental results demonstrate that our proposal has strength in achieving high-performance electromagnetic transient simulation.

In the context of the burgeoning cloud-edge collaboration paradigm, powered by advancements in the Internet of Things (IoT), cloud computing, and 5G technology, this paper proposes a task orchestrating strategy for cloud-edge collaborative environments based on intuitionistic fuzzy sets. The proposed strategy prioritizes efficient resource utilization, minimizes task failures, and reduces service time. First, WAN bandwidth, edge server virtual machine utilization, delay sensitivity of the task, and task length are used to determine whether the task should be executed on the cloud or edge device. Then, the cloud-edge collaborative decision-making algorithm is used to select the task's target edge servers (either the local edge servers or the neighboring edge servers). Finally, simulation experiments are conducted to demonstrate the effectiveness and efficacy of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency. [ABSTRACT FROM AUTHOR]

In the context of the burgeoning cloud-edge collaboration paradigm, powered by advancements in the Internet of Things (IoT), cloud computing, and 5G technology, this paper proposes a task orchestrating strategy for cloud-edge collaborative environments based on intuitionistic fuzzy sets. The proposed strategy prioritizes efficient resource utilization, minimizes task failures, and reduces service time. First, WAN bandwidth, edge server virtual machine utilization, delay sensitivity of the task, and task length are used to determine whether the task should be executed on the cloud or edge device. Then, the cloud-edge collaborative decision-making algorithm is used to select the task’s target edge servers (either the local edge servers or the neighboring edge servers). Finally, simulation experiments are conducted to demonstrate the effectiveness and efficacy of the proposed algorithm.

Many real-life problems have different contradicting goals and no simple solution. Therefore, an analysis is made to select the appropriate solution based on the scenario, which is considered the best compromise toward the achievement of a goal. In literature, it is known as complex problem-solving and is a kind of paradigm that has been around since the last century, but the cognition involved in complex problem-solving has purely relied on experts in the field. However, with the evolution of the current stack of technologies such as artificial intelligence and the Internet of Things, it is quite possible to perform the cognition process with the help of machines based on the previously-trained historical data. Our previous work proposed the complex problem-solving as a service for smart cities. In this article, we extend this work and propose a generic architecture for complex problem-solving using task orchestration and predictive optimization in Internet of Things–enabled generic smart space. The proposed framework makes use of historical data for artificial cognition of the complexity of the given problem. For this, predictive optimization is used, which identifies the problem and intelligently predict the solution based on the given constraints. The task orchestration architecture is used to decompose the complex problem into small tasks for real-world deployment into sensors and actuators. The architecture is evaluated against different load conditions and different categories of problems, and the results suggest that the proposed architecture can be used a commonplace for different smart space solutions. [ABSTRACT FROM AUTHOR]

The highly efficient network resources are provided by multi-access edge computing at the edge of the network,but high security capability is required also due to its distributed position and organization.Based on mimic defense theory,mimic defense structure for distributed multi-access edge computing was proposed.By segmenting data,padding check data and processing data at multiple edge node,dynamic scheduling and decision-making functions according to checksum were implemented.The simulation results show that with the increase of delay cost,the data manipulation and leak rates can be reduced effectively by the proposed structure.The edge node scheduling strategy based on trust and cost is proposed to improve the efficiency and security of the system.

• The study concentrates on designing an optimal energy trading service architecture within a peer-to-peer nano-grid network, with a primary focus on achieving two distinct objectives. • One key contribution lies in minimizing energy trading costs in the peer-to-peer nano-grid architecture. This is achieved by employing a Particle Swarm Optimization (PSO)-based objective function, driven by predicted and actual load values. Predictive load values are generated using the tabNET architecture, and these predictions are benchmarked against BD-LSTM, a state-of-the-art approach. • The study's second significant contribution is the optimization of Energy Storage System (ESS) power. This optimization is realized through the application of a PSO-based objective function, enhancing the efficiency of energy trading operations among connected peers in the nano-grid. • Another noteworthy aspect of this research is the implementation of a digital twin-based concept. This approach virtualizes the entire energy trading process by replicating physical resources such as photovoltaic and ESS components involved in the trading process. • To gauge the system's potential, the study conducts a case study using data from 24 nano-grid households equipped with photovoltaic and ESS installations. This assessment encompasses the optimization modules. • Performance Evaluation: The virtualization module's performance is assessed through standard evaluation measures, including round trip time (RTT), latency, throughput analysis, and response time analysis. • Comparison with State-of-the-Art: Additionally, a comparative analysis is conducted against existing studies to establish the significance and contributions of this research in relation to the current state-of-the-art in nano-grid energy trading optimization. Over the past decade, IoT-orchestrated frameworks have demonstrated significant advantages in enhancing the performance of mission-critical applications. However, distributed generation systems like microgrids and nanogrids have not fully harnessed the capabilities of IoT technology to address the limitations of conventional methods. The effectiveness of a nanogrid's energy trading system depends on several factors, including the efficient management of core components like energy storage systems (ESS) and renewable energy devices. To address these issues, we introduce an optimal power management system for nanogrid energy trading. This system incorporates an RNN prediction module to provide valuable insights to energy distributors. It comprises three core optimization modules: (1) Minimizing Grid Power Consumption, (2) Optimizing Energy Trading Costs, and (3) Managing ESS Power. Our proposed model operates within an IoT-orchestrated framework, utilizing IoT sensors and Raspberry Pi-based Edge technology for virtual execution of operations. We evaluate the model's performance through a case study involving data from 12 nanogrid-equipped houses, resulting in a robust energy trading optimization system. Furthermore, the outcomes of IoT orchestration highlight the potential for virtual operations to enhance system performance significantly. [ABSTRACT FROM AUTHOR]