Abstract ：

With the evolution of wireless communication services, the requirement for reliability and latency is more and more strict, especially in the field of personal consumption area and the Industrial Internet of Things (IIoT), such as autonomous vehicles, remote healthcare, smart factory, augmented reality (AR)/virtual reality (VR), and so on. Moreover, the requirement for reliability and latency is also diverse for different types of communication services. Under the requirement of the enhancement of such hyper reliability and low latency communications (HRLLC) and the fact of diver user equipment capabilities, this paper proposes a collaborative ranging aware UE aggregation transmission mechanism for future IoT networks. Under the proposed mechanism, a ranging aware scheduling (RAS) based communication procedure is designed for reliability enhancement and latency reduction, wherein some UEs or particular higher-end devices play as cooperation nodes (CNs) for packet duplication through multiple paths simultaneously. Considering each CN's system signaling overhead and signal processing capability with a restriction of latency requirement, the scheduling algorithm RAS is performed at each CN. According to the presented simulation results, the research work in this paper can provide insight into the distributed cooperation system for future IoT networks.

Keyword ：

RAS Ranging Aware Multiple Paths IIoT Collaborative Transmission HRLLC UE Aggregation

Cite：

Copy from the list or Export to your reference management。

Abstract ：

With the rapid development of communication technology, the scale of user data grows exponentially. In the diversified service scenarios, the data processing speed is required more strictly in order to guarantee the service quality. Mobile edge computing technology makes the server closer to the terminal and assists the terminal to process data nearby, bringing better user experience. In mobile edge computing system, most terminal devices have strong mobility, and it is easy to cause service interruption because terminal devices are out of the service scope of the server after a few moments. In addition, the importance of the offloading task varies from one task to another, so the importance index should also be considered in the offloading request scheduling. This paper mainly studies the request scheduling problem in the field of computational offloading, and divides the problem into two stages: two-dimensional decision making and offloading. During the research, the mobility of terminal devices and the characteristics of tasks are considered, and a reward evaluation algorithm based on DDQN (Double Deep Q Network) is designed to generate scheduling strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Keyword ：

Mobile edge computing Computation offloading Decision making Data handling

Cite：

Copy from the list or Export to your reference management。

Abstract ：

Planning management is the most important aspect of management activities in large manufacturing enterprises. In addition to their large scale, large manufacturing enterprises are also reflected in the complexity of their manufacturing environment, such as multiple workshops with different specialties, multiple production projects, complex market environments, and so on. How to use a planning management model to effectively undertake project plans upwards, penetrate into end plans downwards, and carry out efficient management has become an important research topic for modern enterprises. This article proposes a new multi-level planning management structure and deploys a new priority algorithm at the workshop planning layer. This algorithm focuses on factors such as device loading and task preemption in the earliest deadline priority scheduling algorithm EDF, and has improvements in reducing task loss and aligning with actual production. In addition, innovation focuses on the study of planning, assessment, and management models in the field of scheduling research. The article proposes a plan performance model that can accurately evaluate project teams of different scales, resulting in a 14.74% increase in the plan execution rate of a certain enterprise. © 2024 SPIE.

Keyword ：

Response time (computer systems) Scheduling algorithms

Cite：

Copy from the list or Export to your reference management。

Abstract ：

Using Unmanned Aerial Vehicles as mobile base stations is a promising way to collect data from sensor nodes, especially for large-scale wireless sensor networks. Previous works mainly focus on improving the freshness of the collected data or the energy efficiency by scheduling UAVs. Considering the fact that the sensing data in some applications is time-sensitive, that is, the value of the sensing data is based on its Timeliness of Information (ToI), which decays over time. Therefore, in this paper, we investigate the UAV Trajectory optimization problem for Maximizing the ToI-based data utility (TMT). We propose an improved deep reinforcement learning-based algorithm to address the problem, and the experience results demonstrate the effectiveness of our designs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Keyword ：

Unmanned aerial vehicles (UAV) Sensor nodes Deep reinforcement learning

Cite：

Copy from the list or Export to your reference management。

Abstract ：

The development of industrial production and product updates boost the demand for waste disassembly. Rule-based or heuristic scheduling methods have been applied to the job-shop scheduling problem in disassembly factories. However, dynamic factors and unbalanced waste type distributions might influence the machine utilization rate. We proposed a reinforcement learning-based disassembly job-shop scheduling method to optimize the dynamic scheduling process, which takes into account the knowledge-specific characteristics of disassembly factories. We embedded the dynamic features and requirements of the disassembly process into the design of the environment. We incorporated the waste waiting time and machine utilization rate into the reward function to improve job-shop scheduling collaboratively. We conducted experiments in a disassembly factory layout compared to rule-based scheduling methods. The experiments showed our method had superior performance in the disassembly machine utilization rate.

Keyword ：

Job-shop scheduling problem Reinforcement learning Disassembly factory Dynamic factors

Cite：

Copy from the list or Export to your reference management。

Abstract ：

With the escalating severity of environmental pollution caused by effluent, the wastewater treatment process (WWTP) has gained significant attention. The wastewater treatment efficiency and effluent quality are significantly impacted by effluent scheduling that adjusts the hydraulic retention time. However, the sequential batch and continuous nature of the effluent pose challenges, resulting in complex scheduling models with strong constraints that are difficult to tackle using existing scheduling methods. To optimize maximum completion time and effluent quality simultaneously, this article proposes a restructured set-based discrete particle swarm optimization (RS-DPSO) algorithm to address the WWTP effluent scheduling problem (WWTP-ESP). First, an effective encoding and decoding method is designed to effectively map solutions to feasible schedules using temporal and spatial information. Second, a restructured set-based discrete particle swarm algorithm is introduced to enhance the searching ability in discrete solution space via restructuring the solution set. Third, a constraint handling strategy based on violation degree ranking is designed to reduce the waste of computational resources. Fourth, a Sobel filter based local search is proposed to guide particle search direction to enhance search efficiency ability. The RS-DPSO provides a novel method for solving WWTP-ESP problems with complex discrete solution space. The comparative experiments indicate that the novel designs are effective and the proposed algorithm has superior performance over existing algorithms in solving the WWTP-ESP.

Keyword ：

wastewater treatment process (WWTP) Sobel filters-based local search effluent scheduling Constraint handling strategy set-based discrete particle swarm algorithm

Cite：

Copy from the list or Export to your reference management。

Abstract ：

Smart mobile devices (SMDs) are integral for running advanced applications that demand significant computing resources and quick response time, e.g., immersive gaming and advanced image editing. However, SMDs often face constraints in computational capacity and battery duration, restricting their ability to process these tasks instantaneously. Cloud computing can circumvent these limitations by computation offloading, but cloud data centers (CDCs) are often deployed at long distances from users, which results in longer computational latency. To address the latency issue, the incorporation of small base stations (SBSs) in the vicinity of the user provides services with high bandwidth and low latency. The primary challenge lies in balancing the economics of the system consisting of different SMDs, SBSs, and a CDC, i.e., minimizing cost while still meeting the latency requirements of applications. In this work, a cost-minimized computation offloading framework is formulated and solved by a two-stage optimization algorithm named L & eacute;vy flight and simulated annealing-based grey wolf optimizer (LSAG). The optimal edge selection strategy is defined in the first stage for dealing with the case of several available SBSs. The second stage coordinates task scheduling and optimizes the allocation of resources among SMDs, SBSs, and CDC. LSAG integrates the extended search property of L & eacute;vy flight and the individual selection strategy of simulated annealing in the grey wolf optimizer, which reduces the risk of falling into local optima and finds the global optimum. Experimental results of executing real-life tasks show that LSAG outperforms its state-of-the-art peers in terms of cost and speed of convergence.

Keyword ：

Task analysis Computer architecture Energy consumption Servers Cloud computing computation offloading Costs edge computing Optimization Resource management swarm intelligence algorithms grey wolf optimizer (GWO)

Cite：

Copy from the list or Export to your reference management。

Abstract ：

Electricity plays a crucial role in the development of countries, serving as the foundation for the functioning of modern societies and as a key indicator of a nation’s level of modernization and overall strength. Predicting a country’s electricity demand provides valuable insights into the evolving patterns of electrical energy provision and usage and helps to improve the flexibility of the energy distribution network. In addition, it provides early warnings and decision support for governments, businesses, and individuals about future electricity consumption. This plays a pivotal function in optimizing power distribution, adjusting the proportion of power generation sources, and ensuring a continuous and secure electricity supply. This paper uses the example of electricity demand in Panama and uses the LSTM model and Prophet model to compare their predictions for electricity demand in lower-income countries, with the aim of improving accuracy. Furthermore, it introduces a data set representing the electricity demand of higher-income countries, using the United Kingdom as a representative, to compare the forecast results between the two countries. The results indicate that within the Panama data set, the Prophet model exhibits better accuracy than the LSTM model. On the contrary, in the data set representing higher-income countries with the United Kingdom as a representative, the LSTM model outperforms the Prophet model. This leads to the conclusion that the selection of a predictive model is specific to each country, influenced by factors such as development status, geographic location, policies, energy markets, and more. A specific model may not be universally applicable due to these unique conditions, requiring tailored-modeling approaches for precise and efficient allocation and scheduling of electricity resources. This approach ensures the improvement of residents’ living standards and quality of life while meeting the typical demands of a country’s economic and social development. © 2024 Institute of Physics Publishing. All rights reserved.

Keyword ：

Demand response Prediction models

Cite：

Copy from the list or Export to your reference management。

Abstract ：

Digital twin technology has evolved from a theoretical concept to practical application, facilitating seamless data exchange between virtual and physical domains. Although there has been progress, the infrastructure industry, which is recognized for its intricate nature and the need for timely action, is still in the first phases of digital twin advancement. A significant obstacle in this field is the absence of established definitions and modeling standards, which impede the precise depiction of infrastructure systems. To address these challenges, this paper proposes a high-precision digital twin modeling method tailored for pumping stations. The method focuses on two key scenarios: first, we construct an overall digital twin model that contains both physical entities and operational processes of pumping stations; second, we design a modeling process applicable to pumping stations by analyzing the deficiencies of the existing standard system. Additionally, we selected the East-West Water Transfer Project in China as a case study to demonstrate the high-precision digital twin model of a pumping station. This model will include essential components, such as the modeling of pumping stations, the operational processes of pumping stations, and the modeling of system operation analysis. Serving as the database for the digital twin, it can complete the automatic inspection of the pumping station, optimization of scheduling, prediction and regulation of energy and carbon emissions, and visualization of results for display and other applications. The model realized the benefits of 100% automatic inspection rate, reduction of eight corresponding operating personnel, and comprehensive cost saving of RMB 2.25 million. The objective of this research is to narrow the divide between theoretical concepts and real-world implementations by pushing the boundaries of digital twin modeling and offering valuable insights for its utilization in the infrastructure industry. It establishes the foundation for progress in the field of digital twin technology in the specific context of intricate infrastructure projects. This project aims to improve the practicality of digital twin technology in real-world situations, namely in the infrastructure industry.

Keyword ：

method research digital twin effective modeling pumping station

Cite：

Copy from the list or Export to your reference management。

Abstract ：

The COVID-19 pandemic has caused fluctuations in electricity demand, altering people's lifestyles and electricity usage patterns, thereby affecting the accuracy of demand predictions. However, existing studies on electricity forecasting have not adequately considered the incorporation of COVID-19-related features and the analysis of electricity usage characteristics across different regions of the UK. Therefore, this paper, based on data of the UK's national electricity demand, conducts an analysis around the scenario of a large-scale health emergency in society. We explore the changing patterns and regional characteristics of electricity consumption during the COVID-19 pandemic, comparing the forecast results before and during the pandemic to illustrate its impact on the UK's electricity demand. By introducing COVID-19-related features into the models, we compare the forecast results before and after their inclusion. The results indicate that the COVID-19 pandemic has had a certain impact on the electricity prediction in the UK, leading to a 22.8% decrease in prediction accuracy. However, the models' correlation improved with the inclusion of COVID-19-related features, resulting in a 13.2% enhancement in prediction accuracy compared to the previous models. Additionally, the study summarizes other factors influencing electricity demand, such as power imports/exports and clean energy usage, as considerations for electricity distribution planning. This contributes to improving the accuracy of predicting the UK's electricity demand during COVID-19 pandemic, enabling the government to adjust power dispatching plans reasonably based on relevant factors, achieving rational distribution and efficient scheduling of power resources.

Keyword ：

Short-term UK Electricity Combined model Power distribution Load forecasting

Cite：

Copy from the list or Export to your reference management。