Terminal Energy Consumption Research Articles

Investigating the impacts of the Dual Carbon Targets on energy and carbon flows in China

As the world’s largest carbon emitter, China has committed to ambitious “Dual Carbon Targets” to address climate change. To investigate the impact of the Dual Carbon Targets on energy consumption and carbon dioxide (CO2) emissions, CO2 emissions were calculated, and Sankey diagrams of energy and CO2 flows for 2018-2022 were drawn based on the latest energy statistics. This study finds that China’s primary energy supply was 5.429 Gtce, with terminal energy consumption at 3.801 Gtce in 2022. CO2 emissions reached 12.01 Gt, marking a 12.24% increase since 2018. Emission intensity varies regionally, being higher in the north and lower in the south, with a national average of 0.1 kg/CNY. Coal continues to dominate energy consumption at 64%, though its share of emissions is declining, particularly in transportation and residential sectors. By 2060, electricity is expected to become the primary energy source, significantly lowering carbon emissions, with Carbon Capture, Utilization, and Storage technologies playing a crucial role in achieving the targets. This analysis provides critical insights into China’s transition to a low-carbon economy, serving as a valuable resource for policymakers to optimize the energy structure and meet environmental goals.

Decomposition analysis of regional differences in China's carbon emissions based on socio-economic factors

Developing policies and setting carbon reduction targets for various regions in China are important actions to narrow the gap of regional carbon emissions. This study reports on the differences in carbon emissions in the eastern, central, and western regions of China from 2005 to 2021 using the Theil index, and decomposes carbon emissions into emission intensity, industrial structure, economic scale, and population size effects using the Logarithmic Mean Divisia Index. In order to analyze the impact of socio-economic factors on carbon emissions, the industrial structure and emission intensity effects are divided into six socio-economic sectors. The major outcomes reported that: (1) the regional differences account for 70 % of the total carbon emissions difference, and the eastern region reported the most significant carbon emission difference; (2) the emission intensity and the economy scale are the top two effects on carbon emissions. The economic scale effect always drives an increase in carbon emissions, while emission intensity promotes a decrease in carbon emissions. The industrial structure effect shows a gradually decreasing trend in all regions, especially in the eastern region, the effect has turned negative since 2013. This is due to the proposed industrial transformation and upgrading in China since 2013, which has promoted the reconstruction of competitive advantages for high polluting heavy industry enterprises and accelerating the green transformation of industrial technology; (3) the industry sector contributes to the highest carbon emissions, followed by the transportation sector. Both sectors are the top two emitters and should be targeted for carbon reduction. The carbon emissions were generated based on the terminal energy consumption. The contribution effects of multiple industries and the differences in carbon emissions as affected by the driving factors and economic mechanisms among different regions in China were analyzed.

Multi-agent reinforcement learning based computation offloading and resource allocation for LEO Satellite edge computing networks

Due to the limitations caused by geographical conditions and economic requirements, it is difficult to provide computing services by terrestrial networks for mobile terminals in remote areas. To address this issue, mobile edge computing (MEC) servers can be deployed in the low earth orbit (LEO) satellites to act as a complement and accommodate the unserved terminals. However, offloading computing tasks to servers in satellites may increase the energy consumption of ground terminals. Considering the limited battery capacity of ground terminals, how to perform the computation offloading and resource allocation are key challenges in the LEO satellite edge computing networks. Therefore, in this paper, we investigate the energy minimization problem for LEO satellite edge computing networks, where a multi-agent deep reinforcement learning algorithm with global rewards is proposed to optimize the transmit power, CPU frequency, bit allocation, offloading decision and bandwidth allocation via a decentralized method. Simulation results show that our proposed algorithm can converge faster. Most importantly, compared with the random algorithm, the proximal policy optimization (PPO) algorithm, and the deep deterministic policy gradient (DDPG) algorithm, the ground terminals’ energy consumption can be effectively reduced by our proposed algorithm.

Heterogeneous Internet of Things Big Data Analysis System Based on Mobile Edge Computing

The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

Pathway for China's provincial carbon emission peak: A case study of the Jiangsu Province

This study establishes the LEAP-Jiangsu model and uses the improved multilevel logarithmic mean Divisia index, Tapio decoupling, and synergistic effect models to explore the carbon reduction paths. The improved multilevel logarithmic mean Divisia index model analyzed the influencing factors of the historical and future carbon emissions. A provincial LEAP model was established to predict the time and value of carbon emission peaks. The Tapio decoupling and synergistic effect models were used to clarify the relationship between carbon emissions and economic development, the synergistic effect of carbon and pollutant reduction, and the terminal sector's carbon reduction potential. The results show that Jiangsu Province will most likely achieve carbon peaking in 2025∼2030, and the peak carbon emission is about 792∼853.9 Mt. Terminal energy intensity, power production structure, and terminal energy consumption structure are the main influencing factors of carbon reduction, with a cumulative contribution rate of about 74.4 %–83.5 %. The power production structure is critical in promoting the decoupling of economic development and carbon emissions. The contribution rates of energy intensity reduction, industrial structure optimization, improving terminal electrification level, and energy structure adjustment are 30.3 %, 26.2 %, 20.9 %, and 17 %, respectively. Coordinating carbon reduction measures will have better synergistic carbon and pollutant reduction effects.

Study on the cognitive system and key issues of green and lowcarbon production mode and lifestyle

To promote the construction of ecological civilization and fundamentally solve ecological and environmental problems, it is necessary to form green and low-carbon production and lifestyle. This article focuses on the cognitive system of green and low-carbon production methods and lifestyles, analyzes the key elements of green and low-carbon production methods and lifestyle systems, and proposes typical scenarios and models of energy and electricity promoting terminal green energy consumption from an energy perspective. Based on economic and social perspectives, it constructs green cost guidance mechanisms and green behavior training mechanisms to comprehensively support the development of green and low-carbon production methods and lifestyles, and accelerate the construction of a green background for ecological civilization.

Monte Carlo‐based service migration under multiple constraints in mobile edge computing

AbstractMobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience for the user. However, it is difficult to obtain the optimal migration policy in real time due to the huge state space. Considering delay‐sensitive data‐intensive applications run by mobile terminals with limited battery power, an efficient service migration policy should be able to make a good tradeoff among service cost, service delay and terminal energy consumption. Here, an online Monte Carlo‐based service migration (MCSM) policy is proposed to minimize service cost under constraints of deadline and terminal energy consumption. A penalty mechanism is designed to update reward when partial or all constraints are not meet. State‐action value estimation and policy improvement are triggered only on the completion of each episode. Each episode is traversed reversely to calculate the average cumulative reward so as to improve policy. Experimental results show that the proposed approach can improve service success ratio and reduce average service cost compared to the existing service migration policies.

Mathematical modeling method based on heterogeneous cellular network algorithm and computer multi-dimensional space

Due to the rapid development of services such as computer multi-dimensional space, traditional cellular networks are quantified and diversified, and it is difficult to meet the future connection needs of green network users. The heterogeneous cellular network algorithm places multiple microcells and relay nodes under the macrocell. This multi-layer network architecture can not only shorten the data transmission link, but also reduce the energy consumption of base stations and terminals. In addition, through spectrum space reuse Capability and spectrum efficiency improve the efficiency of the system, but this also increases the number of base stations and network construction costs, and significantly increases energy consumption and operating costs of operators' electricity expenditures. In addition, in order to achieve parallel transmission of data, placing multiple millimeter wave input and output antenna systems in heterogeneous network microcells can meet the high-speed data needs of indoor users, but the antenna array requires many RF links to drive, which increases the hardware implementation. Complexity, cost and energy consumption. According to the above-mentioned problems, a reconfigurable modular robot is established based on the multi-dimensional computer space. It is composed of modules with the same interface and can be assembled into different configurations according to different tasks. Compared with the original robot, the reconfigurable modular robot is more adaptable to the work and environment, and flexible. Then the basic and core technologies such as kinematics, type synthesis, scale synthesis, error and control of the reconfigurable modular robot are mathematically modeled, so as to effectively apply and promote the reconfigurable modular robot in practice, and make it has theoretical and practical value.

Deep Reinforcement Learning-Based Energy Minimization Task Offloading and Resource Allocation for Air Ground Integrated Heterogeneous Networks

With the popularization of Internet of Things, a number of emerging applications all require both efficient communication and computing service, which poses enormous challenges to the computing ability and battery capacity of terminal equipment. Moreover, the ground-based 5G system cannot provide seamless service especially for hotspot and remote area. In order to deal with the mentioned issues, we first propose an air ground integrated heterogeneous networks model, which consists of multiple UAVs and GBSs equipped with edge servers. Then, by jointly taking into account terminal power control, computing resource allocation, and task offloading decision, an energy-minimization issue is formulated, which, however, is a mixed integer nonlinear programming problem due to the tight coupling between optimization variables. Therefore, we decompose it into two subproblems and design a deep actor-critic-based online offloading algorithm to solve the first offloading decision-making issue facing dimensionality curse. For the second power control and computing resource allocation subproblem, a difference-of-convex-based solution is presented. The proposed approach can achieve superior performance in terms of terminal energy consumption and convergence speed with lower complexity compared with other benchmark methods. Specifically, it outperforms DDQN, AC&Greedy, and UCB algorithm by 7.62%, 17.32%, and 23.14%, respectively.

China’s urban energy system transition towards carbon neutrality: Challenges and experience of Beijing and Suzhou

Urban areas account for more than 70% of total carbon emissions. This proportion for China is higher, approximately 80%, closely related to human activities in urban energy consumption. In this context, the low-carbon transition of urban energy systems has become an important strategic goal for China to deal with climate change and seek sustainable development. Based on the comparative analysis of the low-carbon transition of urban energy systems in Beijing and Suzhou, this study examines the effectiveness of decarbonization policies on the evolution of urban energy consumption and carbon emissions; provides methodologies for the low-carbon transition of urban energy systems with a macro framework and a comprehensive evaluation system; and reveals the main challenges and analyzes potential solutions from the aspects of technological innovation, policy guidance, and systematic governance. The findings suggest that Beijing should promote terminal electrification and demand-side response, and Suzhou should promote the cleanliness of local generation and external energy. Under the goal of carbon neutrality, urban energy systems can promote green and low-carbon transitions by accelerating the substitution of clean energy in supply systems, promoting electrification of terminal energy consumption, developing digital and refined carbon emission accounting and management, and innovating collaborative governance mechanisms for achieving carbon neutrality.

UAV-based edge computing system used in energy-saving offloading method

This paper proposes an offloading strategy based on unmanned aerial vehicle (UAV) managed computer resources to provide dependable communication and intensive processing power in complicated terrain. In order to reduce compute and offload user terminal energy consumption, a UAV-assisted edge computing model is built, taking into account the computing requirements of user terminals, the delay restrictions of computing jobs, and the energy limits of UAVs. The trajectory can be used to reduce the user terminal’s energy usage in the allotted amount of time. The simulation results demonstrate that the suggested approach is appropriate for various channel circumstances, allowing the user terminal to accomplish the work while outperforming existing benchmark systems in terms of energy usage. Optimizing the flight trajectory of the UAV will have a substantial impact when the signal-to-noise ratio SNR=20dB. The results demonstrate that, at T=5s, the UAV’s flying trajectory optimization strategy reduces energy consumption by 56.26%.

Forecast of natural gas supply and demand in China under the background of “Dual Carbon Targets”

As a kind of clean energy which creates little carbon dioxide, natural gas will play a key role in the process of achieving “Peak Carbon Dioxide Emission” and “Carbon Neutrality”. The Long-range Energy Alternatives Planning System (LEAP) model was improved by using new parameters including comprehensive energy efficiency and terminal effective energy consumption. The Back Propagation (BP) Neural Network—LEAP model was proposed to predict key data such as total primary energy consumption, energy mix, carbon emissions from energy consumption, and natural gas consumption in China. Moreover, natural gas production in China was forecasted by the production composition method. Finally, based on the forecast results of natural gas supply and demand, suggestions were put forward on the development of China's natural gas industry under the background of “Dual Carbon Targets”. The research results indicate that under the background of carbon peak and carbon neutrality, China's primary energy consumption will peak (59.4×108 tce) around 2035, carbon emissions from energy consumption will peak (103.4×108 t) by 2025, and natural gas consumption will peak (6100×108 m3) around 2040, of which the largest increase will be contributed by the power sector and industrial sector. China's peak natural gas production is about (2800–3400)×108 m3, including (2100–2300)×108 m3 conventional gas (including tight gas), (600–1050)×108 m3 shale gas, and (150–220)×108 m3 coalbed methane. Under the background of carbon peak and carbon neutrality, the natural gas consumption and production of China will further increase, showing a great potential of the natural gas industry.

QoS-aware offloading based on communication-computation resource coordination for 6G edge intelligence

Driven by the demands of diverse artificial intelligence (AI)-enabled application, Mobile Edge Computing (MEC) is considered one of the key technologies for 6G edge intelligence. In this paper, we consider a serial task model and design a quality of service (QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems, which can mitigate the I/O interference brought by resource reuse among virtual machines. Then we construct the system utility measuring QoS based on application latency and user devices' energy consumption. We also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O interference. Simulation results demonstrate the proposed algorithm's significant advantages in terms of task completion time, terminal energy consumption and system resource utilization.

Joint Allocations of Radio and Computational Resource for User Energy Consumption Minimization Under Latency Constraints in Multi-Cell MEC Systems

This paper investigates the joint allocation of radio resources (i.e., base stations (BSs), sub-channels, and uplink transmission powers) and computational resources for a multicell mobile edge computing (MEC) system, aimed at minimizing the sum energy consumption of all mobile terminals under strict delay and signal-to-interference-plus-noise ratio (SINR) constraints. The problem is challenging to solve due to the highly coupled mixed-integer variables and the complicated expressions of the constraints. To tackle the difficulty, we decompose the problem into two sub-problems: the power and computational resource allocation sub-problem and the user-BS-subchannel association sub-problem. For the first sub-problem, we derive the optimal computational resource as a function of the optimal transmission power. Then the power optimization problem is distributed to each cell, and the local-optimal power solution within each cell is derived in closed-form expressions. For the second sub-problem, we design an optimal modified-cutting-plane (MCP) algorithm with exponential time-complexity. A polynomial-time near-optimal pivoting-and-subgradient (PS) algorithm is further proposed to reduce the complexity. Simulation results demonstrate that the PS algorithm achieves comparable performance to the optimal MCP algorithm, while they both outperform the benchmark schemes in most cases.

Analysis of influencing factors and prediction of carbon emissions of typical urban agglomerations in China: a case study of Beijing-Tianjin-Hebei region.

Achieving the "double carbon" goal is a major task and challenge facing China. The emission reduction actions in typical urban agglomerations are of great significance. Based on the consideration of the impact of regional coordinated development, this study analyzed influencing factors and conducted prediction of carbon emissions from terminal energy consumption in the Beijing-Tianjin-Hebei (BTH) region. Firstly, the factors affecting carbon emissions were screened through the STIRPAT model. Then, the paper designs different scenarios and finally uses the genetic algorithm extreme learning machine (GA-ELM) algorithm to predict the carbon emissions of the BTH region, with and without considering the impact of the coordinated development strategy. The research shows that the increase in energy intensity and the improvement of energy consumption structure have the largest promotion effect on carbon emission reduction. At the same time, the significant role of the coordinated development strategy in promoting regional carbon emission reduction was verified. Therefore, the BTH region should adhere to the path of coordinated development, innovate low-carbon technology, and deepen the concept of green consumption to promote the realization of regional carbon emission reduction goals.

A computation offloading algorithm based on multi-objective evolutionary optimization in mobile edge computing

For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, terminal equipment is dynamic, and their number, mobility, and continuous changes in wireless channels will affect the balance between the mentioned energy consumption and delay. Different from available works, we model the COP in MEC as a dynamic multi-objective problem (DMOP) in this paper, and propose an improved dynamic multi-objective evolutionary optimization based on decomposition (DMOEA/D) to solve it, namely DMOEA/D-COPMEC. In the proposed algorithm, the environmental change is detected by a fixed detector, and whether the current change is similar to the historical change is determined. If so, the difference prediction is used to re-locate the population individual in the new MEC environment, otherwise, the memory-based strategy is used to response environmental change. In MOEA/D, an adaptive weight adjustment strategy based on chain segmentation (CS) is adopted to generate a set of uniformly distributed weight vectors. The simulation results show that the proposed algorithm can better balance the application delay and the terminal energy consumption if there is environment change. The solution set is closer to reality and better than the related algorithms.

Task-Offloading and Resource Allocation Strategy in Multidomain Cooperation for IIoT

This study proposes a task-offloading and resource allocation strategy in multidomain cooperation (TARMC) for the industrial Internet of Things (IIoT) to resolve the problem of the non-uniform distribution of task computation among various cluster domain networks in the IIoT and the solidification of traditional industrial wireless network architecture, which produces low efficiency of static resource allocation and high delay in closed-loop data processing. Based on the closed-loop process of task interaction of intelligent terminals in wireless networks, the proposed strategy constructs a network model of multidomain collaborative task-offloading and resource allocation in IIoT for flexible and dynamic resource allocation among intelligent terminals, edge servers, and cluster networks. Considering the partial offloading mechanism, various tasks were segmented into multiple subtasks marked at bit-level per demand, which enabled local and edge servers to process all subtasks in parallel. Moreover, this study established a utility function for the closed-loop delay and terminal energy consumption of task processing, which transformed the process of multidomain collaborative task-offloading and resource allocation into the problem of task computing revenue. Furthermore, an improved Cuckoo Search algorithm was developed to derive the optimal offloading position and resource allocation decision through an alternating iterative method. The simulation results revealed that TARMC performed better than strategies.

Research on Technology Foresight of Terminal Energy Consumption Based on Delphi Survey

In view of China's "carbon peak, carbon neutral" strategy, especially the important point of reaching the carbon peak by 2035 and achieving socialist modernisation, it is important to select a number of priority end-use energy consumption technology topics to promote the company's technological innovation. Based on the results of the two rounds of the Delphi survey, eight end-use energy consumption technologies with a "dual carbon" focus should be prioritised, namely: virtual power plant technology, electric vehicle technology, impact assessment and costing of carbon emissions and reduction, and carbon dioxide capture and storage technology. The results of the Delphi survey show that R&D investment, policy mechanisms and market viability are the main constraints to the completion of these technologies. In response to these constraints, relevant recommendations are made in terms of funding input, institutional development and market development.

DRL-Based Dependent Task Offloading Strategies with Multi-Server Collaboration in Multi-Access Edge Computing

Many applications in Multi-access Edge Computing (MEC) consist of interdependent tasks where the output of some tasks is the input of others. Most of the existing research on computational offloading does not consider the dependency of the task and uses convex relaxation or heuristic algorithms to solve the offloading problem, which lacks adaptability and is not suitable for computational offloading in the dynamic environment of fast fading channels. Therefore, in this paper, the optimization problem is modeled as a Markov Decision Process (MDP) in multi-user and multi-server MEC environments, and the dependent tasks are represented by Directed Acyclic Graph (DAG). Combined with the Soft Actor–Critic (SAC) algorithm in Deep Reinforcement Learning (DRL) theory, an intelligent task offloading scheme is proposed. Under the condition of resource constraint, each task can be offloaded to the corresponding MEC server through centralized control, which greatly reduces the service delay and terminal energy consumption. The experimental results show that the algorithm converges quickly and stably, and its optimization effect is better than existing methods, which verifies the effectiveness of the algorithm.

Airport terminal passenger forecast under the impact of COVID-19 outbreaks: A case study from China

Passengers significantly affect airport terminal energy consumption and indoor environmental quality. Accurate passenger forecasting provides important insights for airport terminals to optimize their operation and management. However, the COVID-19 pandemic has greatly increased the uncertainty in airport passenger since 2020. There are insufficient studies to investigate which pandemic-related variables should be considered in forecasting airport passenger trends under the impact of COVID-19 outbreaks. In this study, the interrelationship between COVID-19 pandemic trends and passenger traffic at a major airport terminal in China was analyzed on a day-by-day basis. During COVID-19 outbreaks, three stages of passenger change were identified and characterized, i.e., the decline stage, the stabilization stage, and the recovery stage. A typical “sudden drop and slow recovery” pattern of passenger traffic was identified. A LightGBM model including pandemic variables was developed to forecast short-term daily passenger traffic at the airport terminal. The SHapley Additive exPlanations (SHAP) values was used to quantify the contribution of input pandemic variables. Results indicated the inclusion of pandemic variables reduced the model error by 27.7% compared to a baseline model. The cumulative numbers of COVID-19 cases in previous weeks were found to be stronger predictors of future passenger traffic than daily COVID-19 cases in the most recent week. In addition, the impact of pandemic control policies and passengers' travel behavior was discussed. Our empirical findings provide important implications for airport terminal operations in response to the on-going COVID-19 pandemic.