In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and ot...In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.展开更多
The ubiquitous power Internet of Things(UPIoT)is an intelligent service system with comprehensive state perception,efficient processing,and flexible application of information.It focuses on each link of the power syst...The ubiquitous power Internet of Things(UPIoT)is an intelligent service system with comprehensive state perception,efficient processing,and flexible application of information.It focuses on each link of the power system and makes full use of the mobile internet,artificial intelligence,and other advanced information and communication technologies in order to realize the inter-human interaction of all things in all links of the power system.This article systematically presents to the national and international organizations and agencies in charge of UPIoT layer standardization the status quo of the research on the Internet of Things(IoT)-related industry standards system.It briefly describes the generic standard classification methods,layered architecture,conceptual model,and system tables in the UPIoT application layer.Based on the principles of inheritance,innovation,and practicability,this study divides the application layer into customer service,power grid operation,integrated energy,and enterprise operation,emerging business and analyzes the standard requirements of these five fields.This study also proposes a standard plan.Finally,it summarizes the research report and provides suggestions for a follow-up work.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
The integration of electricity technology and information technology,such as the Internet of Things(IoT),enables the construction of new power systems,along with the inno-vation of application scenarios and business s...The integration of electricity technology and information technology,such as the Internet of Things(IoT),enables the construction of new power systems,along with the inno-vation of application scenarios and business scope.The key technologies of power IoT and the data flow process are summarised first.The IoT technology and application scenario requirements of power generation,transmission,loading,and storage of new power systems are studied.Thus,the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems.The key problems and so-lutions faced by the power IoT under the digital transformation are described,and the cross-integration of key technologies and promotion of application scenario innovation are prospected.Finally,the key issues of future technological development were dis-cussed,providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.展开更多
The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power g...The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing linkage is proposed in this paper. The primary objective of this strategy is to maximize the confidentiality capacity of the system. To tackle this, an optimization problem is formulated, taking into consideration interruption probability and interception probability as constraints. To efficiently solve this optimization problem, a low-complexity algorithm rooted in deep reinforcement learning is designed, which aims to derive a suboptimal solution for the problem at hand. Ultimately, through simulation results, the validity of the proposed strategy in guaranteed communication security, stability, and timeliness is substantiated. The results confirm that the proposed intelligent security transmission strategy significantly contributes to the safeguarding of communication integrity, system stability, and timely data delivery.展开更多
Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reli...Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.展开更多
The use of Internet of Things(IoT)applications become dominant in many systems.Its on-chip data processing and computations are also increasing consistently.The battery enabled and low leakage memory system at subthre...The use of Internet of Things(IoT)applications become dominant in many systems.Its on-chip data processing and computations are also increasing consistently.The battery enabled and low leakage memory system at subthreshold regime is a critical requirement for these IoT applications.The cache memory designed on Static Random-Access Memory(SRAM)cell with features such as low power,high speed,and process tolerance are highly important for the IoT memory system.Therefore,a process tolerant SRAM cell with low power,improved delay and better stability is presented in this research paper.The proposed cell comprises 11 transistors designed with symmetric approach for write operations and single ended circuit for read operations that exhibits an average dynamic power saving of 43.55%and 47.75%for write and 35.59%and 36.56%for read operations compared to 6 T and 8 T SRAM cells.The cell shows an improved write delay of 26.46%and 37.16%over 6 T and 8T and read delay is lowered by 50.64%and 72.90%against 6 T and 10 T cells.The symmetric design used in core latch to improve the write noise margin(WNM)by 17.78%and 6.67%whereas the single ended separate read circuit improves the Read Static Noise Margin(RSNM)by 1.88x and 0.33x compared to 6 T and 8T cells.The read power delay product and write power delay product are lower by 1.94x,1.39x and 0.17x,2.02x than 6 T and 8 T cells respectively.The lower variability from 5000 samples validates the robustness of the proposed cell.The simulations are carried out in Cadence virtuoso simulator tool with Generic Process Design Kit(GPDK)45 nm technology file in this work.展开更多
Far-field wireless power transfer(WPT)is a major breakthrough technology that will enable the many anticipated ubiquitous Internet of Things(IoT)applications associated with fifth generation(5G),sixth generation(6G),a...Far-field wireless power transfer(WPT)is a major breakthrough technology that will enable the many anticipated ubiquitous Internet of Things(IoT)applications associated with fifth generation(5G),sixth generation(6G),and beyond wireless ecosystems.Rectennas,which are the combination of rectifying circuits and antennas,are the most critical components in far-field WPT systems.However,compact application devices require even smaller integrated rectennas that simultaneously have large electromagnetic wave capture capabilities,high alternating current(AC)-to-direct current(DC)(AC-to-DC)conversion efficiencies,and facilitate a multifunctional wireless performance.This paper reviews various rectenna miniaturization techniques such as meandered planar inverted-F antenna(PIFA)rectennas;miniaturized monopole-and dipole-based rectennas;fractal loop and patch rectennas;dielectric-loaded rectennas;and electrically small near-field resonant parasitic rectennas.Their performance characteristics are summarized and then compared with our previously developed electrically small Huygens rectennas that are proven to be more suitable for IoT applications.They have been tailored,for example,to achieve batteryfree IoT sensors as is demonstrated in this paper.Battery-free,wirelessly powered devices are smaller and lighter in weight in comparison to battery-powered devices.Moreover,they are environmentally friendly and,hence,have a significant societal benefit.A series of high-performance electrically small Huygens rectennas are presented including Huygens linearly-polarized(HLP)and circularly-polarized(HCP)rectennas;wirelessly powered IoT sensors based on these designs;and a dual-functional HLP rectenna and antenna system.Finally,two linear uniform HLP rectenna array systems are considered for significantly larger wireless power capture.Example arrays illustrate how they can be integrated advantageously with DC or radio frequency(RF)power-combining schemes for practical IoT applications.展开更多
Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT gen...Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.展开更多
As the core industry of energy supply,the efficiency of storage and logistics management of thermal power plant has a direct impact on the safety,economy and sustainability of power generation.At present,the tradition...As the core industry of energy supply,the efficiency of storage and logistics management of thermal power plant has a direct impact on the safety,economy and sustainability of power generation.At present,the traditional thermal power plant warehousing and logistics system is faced with decentralized management,information lag,high dependence on manual and insufficient cost control,which cannot meet the needs of modern management.The in-depth application of Internet of Things(IoT)technology provides technical support for the digital upgrade of warehousing and logistics system,but there are still challenges in data integration depth,prediction model accuracy and adaptability to complex environment.The intelligent warehouse and logistics management system developed in this study for thermal power plants integrates IoT,AI,and automation technologies to create a smart management platform that covers full lifecycle tracking of material information,automated warehouse scheduling,intelligent logistics path optimization,and multidimensional data analysis.By deploying RFID tags,smart sensor terminals,and AGV logistics equipment,combined with recursive neural networks and reinforcement learning algorithms,the system achieves real-time material status monitoring,precise inventory demand forecasting,and dynamic optimization of transportation routes.展开更多
The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(...The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.展开更多
With the accelerated growth of the Internet of Things(IoT),real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local.Since these d...With the accelerated growth of the Internet of Things(IoT),real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local.Since these devices often handle personal information under limited resources,cryptographic algorithms must be executed efficiently.Their computational characteristics strongly affect system performance,making it necessary to analyze resource impact and predict usage under diverse configurations.In this paper,we analyze the phase-level resource usage of AES variants,ChaCha20,ECC,and RSA on an edge device and develop a prediction model.We apply these algorithms under varying parallelism levels and execution strategies across key generation,encryption,and decryption phases.Based on the analysis,we train a unified Random Forest model using execution context and temporal features,achieving R2 values up to 0.994 for power and 0.988 for temperature.Furthermore,the model maintains practical predictive performance even for cryptographic algorithms not included during training,demonstrating its ability to generalize across distinct computational characteristics.Our proposed approach reveals how execution characteristics and resource usage interacts,supporting proactive resource planning and efficient deployment of cryptographic workloads on edge devices.As our approach is grounded in phase-level computational characteristics rather than in any single algorithm,it provides generalizable insights that can be extended to a broader range of cryptographic algorithms that exhibit comparable phase-level execution patterns and to heterogeneous edge architectures.展开更多
As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power pla...As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.展开更多
为快速阻断未披露漏洞对于电力物联网(Power Internet of Things,PIoT)终端的持续性威胁,提出一种群智协同缓解未披露漏洞的黑板积分激励方案,在抑制自私性和抵御共谋团体的条件下加快漏洞加固流程。采用黑板积分,引入群智协同缓解方案...为快速阻断未披露漏洞对于电力物联网(Power Internet of Things,PIoT)终端的持续性威胁,提出一种群智协同缓解未披露漏洞的黑板积分激励方案,在抑制自私性和抵御共谋团体的条件下加快漏洞加固流程。采用黑板积分,引入群智协同缓解方案质量评估方法辅助量化协同贡献度,构建电力物联网漏洞缓解的全流程激励方案。联合信任机制和协同组划分规则动态选取每轮参与缓解的群智协同者,抑制共谋团体形成。为进一步提升协同缓解效率,设置多智能体可信协调器,分散协同角色责任避免单点失效。通过与无激励方案、基于声誉的区块链矿工激励方案、基于历史行为的群智感知激励方案相比,仿真结果表明,所提方案可在抑制恶意提交的条件下,有效激励协同者积极参与未披露漏洞协同缓解。展开更多
A self-charging hybrid power unit has been developed by integrating a water-evaporation-induced nano- generator with a flexible nano-patterned supercapacitor. The nanogenerator can harvest environmental thermal energy...A self-charging hybrid power unit has been developed by integrating a water-evaporation-induced nano- generator with a flexible nano-patterned supercapacitor. The nanogenerator can harvest environmental thermal energy and mechanical energy through the water evaporation process, and the supercapacitor can be charged simultaneously. The former offers stable electrical power as output, whereas the Ppy- based supercapacitor shows a capacitance of 12.497 m F/cm^2 with 96.42% retention after 4,000 cycles. After filling the power unit with water as the fuel, it can be fully charged in about 20 min. The power unit can be flexibly integrated with electronic devices such as sensor nodes and wireless transmitters employ- ing the Internet of Things. This new approach can offer new possibilities in continuous future operation of randomly distributed electronic devices incorporated in the Internet of Things.展开更多
The Internet of Things(IoT)is playing an important role in providing access to affordable,clean and green energy worldwide through the use of smart devices.The current electric power networks will be more reliable,sec...The Internet of Things(IoT)is playing an important role in providing access to affordable,clean and green energy worldwide through the use of smart devices.The current electric power networks will be more reliable,secure,flexible and durable by implementing IoT in power systems.This paper presents a brief discussion about IoT contributions in the development of power systems from a generation,transmission,distribution and consumption point of view.5G cellular networks have a great potential for the development of IoT technology.Additionally,5G cellular networks can be instrumental in supporting the greater communication needs of IoT.This review provides a comprehensive analysis about the role of 5G cellular networks in the growth of IoT technology and power systems.Large amounts of data will be generated due to the incorporation of renewable energy,deployment of the smart grid and the improvements to the electricity market.In this way,in order to realize the connection between things and people,things and things and people and people in power systems,it is essential to apply IoT in power systems.In this case,5G is providing numerous advantages to Power IoT(PIoT)by offering greater opportunities in progress and improvements;however,there are also numerous challenges with the deployment of 5G in PIoT.Finally,this review article provides an overview of the role,implications and challenges of 5G in PIoT.展开更多
The rapid development of Internet Plus Smart Energy requires further strengthening of three kinds of interconnections based on traditional power systems: physical interconnection, information interconnection, and comm...The rapid development of Internet Plus Smart Energy requires further strengthening of three kinds of interconnections based on traditional power systems: physical interconnection, information interconnection, and commercial interconnection. Due to the integration of renewable energy, the reform of the electricity market, and the deployment of the Smart Grid, a large amount of data will be generated. Thus, it is necessary to establish a Ubiquitous Power Internet of Things (UPIoT) to realize connections among people and things, things and things, and people and people in power systems. This paper studies the concept and architecture of the UPIoT and indicates the deployment of the perception layer and network layer as the key to building UPIoT in the initial stage. As UPIoT tends to cover a wide area and produce massive and distributed data, signal processing and data analytics theories and techniques are needed to handle the data and observe the state of the large-scale system. Further studies on distributed sensing and cooperative estimation theories and techniques of UPIoT are also required. Finally, the application prospects of UPIoT and the directions for future research are discussed.展开更多
Ubiquitous power Internet of Things(IoT)is a smart service system oriented to all aspects of the power system,and has the characteristics of universal interconnection,human-computer interaction,comprehensive state per...Ubiquitous power Internet of Things(IoT)is a smart service system oriented to all aspects of the power system,and has the characteristics of universal interconnection,human-computer interaction,comprehensive state perception,efficient information processing,and other convenient and flexible applications.It has become a hot topic in the field of IoT.We summarize some existing research work on the IoT and edge computing framework.Because it is difficult to meet the requirements of ubiquitous power IoT for edge computing in terms of real time,security,reliability,and business function adaptation using the general edge computing framework software,we propose a trusted edge computing framework,named“EdgeKeeper,”adapting to the ubiquitous power IoT.Several key technologies such as security and trust,quality of service guarantee,application management,and cloud-edge collaboration are desired to meet the needs of the edge computing framework.Experiments comprehensively evaluate EdgeKeeper from the aspects of function,performance,and security.Comparison results show that EdgeKeeper is the most suitable edge computing framework for the electricity IoT.Finally,future directions for research are proposed.展开更多
The ubiquitous power Internet of Things(UPIoT)uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system.UPI...The ubiquitous power Internet of Things(UPIoT)uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system.UPIoT has the characteristics of comprehensive state perception and efficient information processing,and has broad application prospects for transformation of the energy industry.The fundamental facility of the UPIoT is the sensor-based information network.By using advanced sensors,Wireless Sensor Networks(WSNs),and advanced data processing technologies,Internet of Things can be realized in the power system.In this paper,a framework of WSNs based on advanced sensors towards UPIoT is proposed.In addition,the most advanced sensors for UPIoT purposes are reviewed,along with an explanation of how the sensor data obtained in UPIoT is utilized in various scenarios.展开更多
Non-orthogonal multiple access is a promising technique to meet the harsh requirements for the internet of things devices in cognitive radio networks.To improve the energy efficiency(EE)of the unlicensed secondary use...Non-orthogonal multiple access is a promising technique to meet the harsh requirements for the internet of things devices in cognitive radio networks.To improve the energy efficiency(EE)of the unlicensed secondary users(SU),a power allocation(PA)algorithm with polynomial complexity is investigated.We first establish the feasible range of power consumption ratio using Karush-Kuhn-Tucker optimality conditions to support each SU’s minimum quality of service and the effectiveness of successive interference cancellation.Then,we formulate the EE optimization problem considering the total transmit power requirements which leads to a non-convex fractional programming problem.To efficiently solve the problem,we divide it into an inner-layer and outer-layer optimization sub-problems.The inner-layer optimization which is formulated to maximize the sub-carrier PA coefficients can be transformed into the difference of convex programming by using the first-order Taylor expansion.Based on the solution of the inner-layer optimization sub-problem,the concave-convex fractional programming problem of the outer-layer optimization sub-problem may be converted into the Lagrangian relaxation model employing the Dinkelbach algorithm.Simulation results demonstrate that the proposed algorithm has a faster convergence speed than the simulated annealing algorithm,while the average system EE loss is only less than 2%.展开更多
基金supported by National Natural Science Foundation of China(12174350)Science and Technology Project of State Grid Henan Electric Power Company(5217Q0240008).
文摘In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.
基金supported by Science and Technology Foundation of State Grid Corporation of China(Ubiquitous Power Internet of Things Technical Standard System)5442HL 190008National Key Research and Development Program of China(2020YFB0905900)。
文摘The ubiquitous power Internet of Things(UPIoT)is an intelligent service system with comprehensive state perception,efficient processing,and flexible application of information.It focuses on each link of the power system and makes full use of the mobile internet,artificial intelligence,and other advanced information and communication technologies in order to realize the inter-human interaction of all things in all links of the power system.This article systematically presents to the national and international organizations and agencies in charge of UPIoT layer standardization the status quo of the research on the Internet of Things(IoT)-related industry standards system.It briefly describes the generic standard classification methods,layered architecture,conceptual model,and system tables in the UPIoT application layer.Based on the principles of inheritance,innovation,and practicability,this study divides the application layer into customer service,power grid operation,integrated energy,and enterprise operation,emerging business and analyzes the standard requirements of these five fields.This study also proposes a standard plan.Finally,it summarizes the research report and provides suggestions for a follow-up work.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
基金National Natural Science Foundation of China,Grant/Award Number:52177085DGRI‐CSG Innovative Project,Grant/Award Number:210000KK52220036。
文摘The integration of electricity technology and information technology,such as the Internet of Things(IoT),enables the construction of new power systems,along with the inno-vation of application scenarios and business scope.The key technologies of power IoT and the data flow process are summarised first.The IoT technology and application scenario requirements of power generation,transmission,loading,and storage of new power systems are studied.Thus,the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems.The key problems and so-lutions faced by the power IoT under the digital transformation are described,and the cross-integration of key technologies and promotion of application scenario innovation are prospected.Finally,the key issues of future technological development were dis-cussed,providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.
文摘The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing linkage is proposed in this paper. The primary objective of this strategy is to maximize the confidentiality capacity of the system. To tackle this, an optimization problem is formulated, taking into consideration interruption probability and interception probability as constraints. To efficiently solve this optimization problem, a low-complexity algorithm rooted in deep reinforcement learning is designed, which aims to derive a suboptimal solution for the problem at hand. Ultimately, through simulation results, the validity of the proposed strategy in guaranteed communication security, stability, and timeliness is substantiated. The results confirm that the proposed intelligent security transmission strategy significantly contributes to the safeguarding of communication integrity, system stability, and timely data delivery.
基金supported by Science and Technology Project of China Southern Power Grid Company Limited under Grant Number 036000KK52200058(GDKJXM20202001).
文摘Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.
文摘The use of Internet of Things(IoT)applications become dominant in many systems.Its on-chip data processing and computations are also increasing consistently.The battery enabled and low leakage memory system at subthreshold regime is a critical requirement for these IoT applications.The cache memory designed on Static Random-Access Memory(SRAM)cell with features such as low power,high speed,and process tolerance are highly important for the IoT memory system.Therefore,a process tolerant SRAM cell with low power,improved delay and better stability is presented in this research paper.The proposed cell comprises 11 transistors designed with symmetric approach for write operations and single ended circuit for read operations that exhibits an average dynamic power saving of 43.55%and 47.75%for write and 35.59%and 36.56%for read operations compared to 6 T and 8 T SRAM cells.The cell shows an improved write delay of 26.46%and 37.16%over 6 T and 8T and read delay is lowered by 50.64%and 72.90%against 6 T and 10 T cells.The symmetric design used in core latch to improve the write noise margin(WNM)by 17.78%and 6.67%whereas the single ended separate read circuit improves the Read Static Noise Margin(RSNM)by 1.88x and 0.33x compared to 6 T and 8T cells.The read power delay product and write power delay product are lower by 1.94x,1.39x and 0.17x,2.02x than 6 T and 8 T cells respectively.The lower variability from 5000 samples validates the robustness of the proposed cell.The simulations are carried out in Cadence virtuoso simulator tool with Generic Process Design Kit(GPDK)45 nm technology file in this work.
基金supported by the University of Technology Sydney (UTS) Chancellor’s Postdoctoral Fellowship (PRO18-6147)Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) (PRO20-9959)
文摘Far-field wireless power transfer(WPT)is a major breakthrough technology that will enable the many anticipated ubiquitous Internet of Things(IoT)applications associated with fifth generation(5G),sixth generation(6G),and beyond wireless ecosystems.Rectennas,which are the combination of rectifying circuits and antennas,are the most critical components in far-field WPT systems.However,compact application devices require even smaller integrated rectennas that simultaneously have large electromagnetic wave capture capabilities,high alternating current(AC)-to-direct current(DC)(AC-to-DC)conversion efficiencies,and facilitate a multifunctional wireless performance.This paper reviews various rectenna miniaturization techniques such as meandered planar inverted-F antenna(PIFA)rectennas;miniaturized monopole-and dipole-based rectennas;fractal loop and patch rectennas;dielectric-loaded rectennas;and electrically small near-field resonant parasitic rectennas.Their performance characteristics are summarized and then compared with our previously developed electrically small Huygens rectennas that are proven to be more suitable for IoT applications.They have been tailored,for example,to achieve batteryfree IoT sensors as is demonstrated in this paper.Battery-free,wirelessly powered devices are smaller and lighter in weight in comparison to battery-powered devices.Moreover,they are environmentally friendly and,hence,have a significant societal benefit.A series of high-performance electrically small Huygens rectennas are presented including Huygens linearly-polarized(HLP)and circularly-polarized(HCP)rectennas;wirelessly powered IoT sensors based on these designs;and a dual-functional HLP rectenna and antenna system.Finally,two linear uniform HLP rectenna array systems are considered for significantly larger wireless power capture.Example arrays illustrate how they can be integrated advantageously with DC or radio frequency(RF)power-combining schemes for practical IoT applications.
基金supported in part by National Key R&D Program of China under Grant 2016YFE0200900part by Scientific Research Program of Beijing Municipal Commission of Education under Grant KM201910853003part by Major projects of Beijing Municipal Science and Technology Commission under Grant Z181100003218010
文摘Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.
文摘As the core industry of energy supply,the efficiency of storage and logistics management of thermal power plant has a direct impact on the safety,economy and sustainability of power generation.At present,the traditional thermal power plant warehousing and logistics system is faced with decentralized management,information lag,high dependence on manual and insufficient cost control,which cannot meet the needs of modern management.The in-depth application of Internet of Things(IoT)technology provides technical support for the digital upgrade of warehousing and logistics system,but there are still challenges in data integration depth,prediction model accuracy and adaptability to complex environment.The intelligent warehouse and logistics management system developed in this study for thermal power plants integrates IoT,AI,and automation technologies to create a smart management platform that covers full lifecycle tracking of material information,automated warehouse scheduling,intelligent logistics path optimization,and multidimensional data analysis.By deploying RFID tags,smart sensor terminals,and AGV logistics equipment,combined with recursive neural networks and reinforcement learning algorithms,the system achieves real-time material status monitoring,precise inventory demand forecasting,and dynamic optimization of transportation routes.
基金funded by the Deanship of Scientific Research at Najran University for this research through a Grant(NU/RG/SERC/12/50)under the Research Groups at Najran University,Saudi Arabia.
文摘The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.
基金supported in part by the National Research Foundation of Korea(NRF)(No.RS-2025-00554650)supported by the Chung-Ang University research grant in 2024。
文摘With the accelerated growth of the Internet of Things(IoT),real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local.Since these devices often handle personal information under limited resources,cryptographic algorithms must be executed efficiently.Their computational characteristics strongly affect system performance,making it necessary to analyze resource impact and predict usage under diverse configurations.In this paper,we analyze the phase-level resource usage of AES variants,ChaCha20,ECC,and RSA on an edge device and develop a prediction model.We apply these algorithms under varying parallelism levels and execution strategies across key generation,encryption,and decryption phases.Based on the analysis,we train a unified Random Forest model using execution context and temporal features,achieving R2 values up to 0.994 for power and 0.988 for temperature.Furthermore,the model maintains practical predictive performance even for cryptographic algorithms not included during training,demonstrating its ability to generalize across distinct computational characteristics.Our proposed approach reveals how execution characteristics and resource usage interacts,supporting proactive resource planning and efficient deployment of cryptographic workloads on edge devices.As our approach is grounded in phase-level computational characteristics rather than in any single algorithm,it provides generalizable insights that can be extended to a broader range of cryptographic algorithms that exhibit comparable phase-level execution patterns and to heterogeneous edge architectures.
文摘As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.
文摘为快速阻断未披露漏洞对于电力物联网(Power Internet of Things,PIoT)终端的持续性威胁,提出一种群智协同缓解未披露漏洞的黑板积分激励方案,在抑制自私性和抵御共谋团体的条件下加快漏洞加固流程。采用黑板积分,引入群智协同缓解方案质量评估方法辅助量化协同贡献度,构建电力物联网漏洞缓解的全流程激励方案。联合信任机制和协同组划分规则动态选取每轮参与缓解的群智协同者,抑制共谋团体形成。为进一步提升协同缓解效率,设置多智能体可信协调器,分散协同角色责任避免单点失效。通过与无激励方案、基于声誉的区块链矿工激励方案、基于历史行为的群智感知激励方案相比,仿真结果表明,所提方案可在抑制恶意提交的条件下,有效激励协同者积极参与未披露漏洞协同缓解。
基金supported by the National Natural Science Foundation of China(11674048)the Fundamental Research Funds for the Central Universities(N170505001,and N160502002)Program for Shenyang Youth Science and Technology Innovation Talents(RC170269)
文摘A self-charging hybrid power unit has been developed by integrating a water-evaporation-induced nano- generator with a flexible nano-patterned supercapacitor. The nanogenerator can harvest environmental thermal energy and mechanical energy through the water evaporation process, and the supercapacitor can be charged simultaneously. The former offers stable electrical power as output, whereas the Ppy- based supercapacitor shows a capacitance of 12.497 m F/cm^2 with 96.42% retention after 4,000 cycles. After filling the power unit with water as the fuel, it can be fully charged in about 20 min. The power unit can be flexibly integrated with electronic devices such as sensor nodes and wireless transmitters employ- ing the Internet of Things. This new approach can offer new possibilities in continuous future operation of randomly distributed electronic devices incorporated in the Internet of Things.
文摘The Internet of Things(IoT)is playing an important role in providing access to affordable,clean and green energy worldwide through the use of smart devices.The current electric power networks will be more reliable,secure,flexible and durable by implementing IoT in power systems.This paper presents a brief discussion about IoT contributions in the development of power systems from a generation,transmission,distribution and consumption point of view.5G cellular networks have a great potential for the development of IoT technology.Additionally,5G cellular networks can be instrumental in supporting the greater communication needs of IoT.This review provides a comprehensive analysis about the role of 5G cellular networks in the growth of IoT technology and power systems.Large amounts of data will be generated due to the incorporation of renewable energy,deployment of the smart grid and the improvements to the electricity market.In this way,in order to realize the connection between things and people,things and things and people and people in power systems,it is essential to apply IoT in power systems.In this case,5G is providing numerous advantages to Power IoT(PIoT)by offering greater opportunities in progress and improvements;however,there are also numerous challenges with the deployment of 5G in PIoT.Finally,this review article provides an overview of the role,implications and challenges of 5G in PIoT.
基金Supported by National Key Research and DevelopmentProgram of China(2016YFB0900100).
文摘The rapid development of Internet Plus Smart Energy requires further strengthening of three kinds of interconnections based on traditional power systems: physical interconnection, information interconnection, and commercial interconnection. Due to the integration of renewable energy, the reform of the electricity market, and the deployment of the Smart Grid, a large amount of data will be generated. Thus, it is necessary to establish a Ubiquitous Power Internet of Things (UPIoT) to realize connections among people and things, things and things, and people and people in power systems. This paper studies the concept and architecture of the UPIoT and indicates the deployment of the perception layer and network layer as the key to building UPIoT in the initial stage. As UPIoT tends to cover a wide area and produce massive and distributed data, signal processing and data analytics theories and techniques are needed to handle the data and observe the state of the large-scale system. Further studies on distributed sensing and cooperative estimation theories and techniques of UPIoT are also required. Finally, the application prospects of UPIoT and the directions for future research are discussed.
基金Project supported by the State Grid Corporation Science and Technology Project,China。
文摘Ubiquitous power Internet of Things(IoT)is a smart service system oriented to all aspects of the power system,and has the characteristics of universal interconnection,human-computer interaction,comprehensive state perception,efficient information processing,and other convenient and flexible applications.It has become a hot topic in the field of IoT.We summarize some existing research work on the IoT and edge computing framework.Because it is difficult to meet the requirements of ubiquitous power IoT for edge computing in terms of real time,security,reliability,and business function adaptation using the general edge computing framework software,we propose a trusted edge computing framework,named“EdgeKeeper,”adapting to the ubiquitous power IoT.Several key technologies such as security and trust,quality of service guarantee,application management,and cloud-edge collaboration are desired to meet the needs of the edge computing framework.Experiments comprehensively evaluate EdgeKeeper from the aspects of function,performance,and security.Comparison results show that EdgeKeeper is the most suitable edge computing framework for the electricity IoT.Finally,future directions for research are proposed.
基金the National Natural Science Foundation of China(No.51921005).
文摘The ubiquitous power Internet of Things(UPIoT)uses modern information technology and advanced communication technologies to realize interconnection and human-computer interaction in all aspects of the power system.UPIoT has the characteristics of comprehensive state perception and efficient information processing,and has broad application prospects for transformation of the energy industry.The fundamental facility of the UPIoT is the sensor-based information network.By using advanced sensors,Wireless Sensor Networks(WSNs),and advanced data processing technologies,Internet of Things can be realized in the power system.In this paper,a framework of WSNs based on advanced sensors towards UPIoT is proposed.In addition,the most advanced sensors for UPIoT purposes are reviewed,along with an explanation of how the sensor data obtained in UPIoT is utilized in various scenarios.
基金supported in part by the Science and Technology Research Program of the National Science Foundation of China(No.61671096)Chongqing Research Program of Basic Science and Frontier Technology(No.cstc2017jcyj BX0005)+1 种基金Chongqing Municipal Education Commission(No.KJQN201800642)Doctoral Student Training Program(No.BYJS2016009)。
文摘Non-orthogonal multiple access is a promising technique to meet the harsh requirements for the internet of things devices in cognitive radio networks.To improve the energy efficiency(EE)of the unlicensed secondary users(SU),a power allocation(PA)algorithm with polynomial complexity is investigated.We first establish the feasible range of power consumption ratio using Karush-Kuhn-Tucker optimality conditions to support each SU’s minimum quality of service and the effectiveness of successive interference cancellation.Then,we formulate the EE optimization problem considering the total transmit power requirements which leads to a non-convex fractional programming problem.To efficiently solve the problem,we divide it into an inner-layer and outer-layer optimization sub-problems.The inner-layer optimization which is formulated to maximize the sub-carrier PA coefficients can be transformed into the difference of convex programming by using the first-order Taylor expansion.Based on the solution of the inner-layer optimization sub-problem,the concave-convex fractional programming problem of the outer-layer optimization sub-problem may be converted into the Lagrangian relaxation model employing the Dinkelbach algorithm.Simulation results demonstrate that the proposed algorithm has a faster convergence speed than the simulated annealing algorithm,while the average system EE loss is only less than 2%.
