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MEACC: an energy-efficient framework for smart devices using cloud computing systems Research Articles

Khalid Alsubhi, Zuhaib Imtiaz, Ayesha Raana, M. Usman Ashraf, Babur Hayat,usman.ashraf@skt.umt.edu.pk

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.1900198

Abstract: Rapidly increasing capacities, decreasing costs, and improvements in computational power, storage, and communication technologies have led to the development of many applications that carry increasingly large amounts of traffic on the global networking infrastructure. lead to emerging technologies and play a vital role in rapid evolution. have become a primary 24/7 need in today’s information technology world and include a wide range of supporting processing-intensive applications. Extensive use of many applications on results in increasing complexity of mobile software applications and consumption of resources at a massive level, including smart device battery power, processor, and RAM, and hinders their normal operation. Appropriate resource utilization and energy efficiency are fundamental considerations for because limited resources are sporadic and make it more difficult for users to complete their tasks. In this study we propose the model of mobile energy augmentation using (MEACC), a new framework to address the challenges of massive and inefficient resource utilization in . MEACC efficiently filters the applications to be executed on a smart device or offloaded to the cloud. Moreover, MEACC efficiently calculates the total execution cost on both the mobile and cloud sides including communication costs for any application to be offloaded. In addition, resources are monitored before making the decision to offload the application. MEACC is a promising model for load balancing and reduction in emerging environments.

Keywords： 卸载；智能设备；云计算；移动计算；能耗    

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Edge Computing Technology: Development and Countermeasures

Hong Xuehai and Wang Yang

Strategic Study of CAE 2018, Volume 20, Issue 2,   Pages 20-26 doi: 10.15302/J-SSCAE-2018.02.004

Abstract:

Edge computing is an emerging technology that reduces transmission delays and bandwidth consumption by placing computing, storage, bandwidth, applications, and other resources on the edge of the network. Moreover, application developers and content providers can provide perceptible services based on real-time network information. Mobile terminals, Internet of things, and other devices provide the necessary front-end support for computing sensitive applications, such as image recognition and network games, to share the cloud work load with the processing capability of edge computing. This paper discusses the concept of edge computing, key problems that require solutions, main advances in edge computing, influence of edge computing developments, and opportunities and development countermeasures of edge calculation.

Keywords： cloud computing     edge computing     fog computing     mobile edge computing     internet of things     front-end intelligence    

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Software Architecture of Fogcloud Computing for Big Data in Ubiquitous Cyberspace

Jia Yan, Fang Binxing, Wang Xiang, Wang Yongheng, An Jingbin,Li Aiping, Zhou Bin

Strategic Study of CAE 2019, Volume 21, Issue 6,   Pages 114-119 doi: 10.15302/J-SSCAE-2019.10.001

Abstract:

The cyberspace has expanded from traditional internet to ubiquitous cyberspace which interconnects human, machines,things, services, and applications. The computing paradigm is also shifting from centralized computing in the cloud to combined computing in the front end, middle layer, and cloud. Therefore, traditional computing paradigms such as cloud computing and edge computing can no longer satisfy the evolving computing needs of big data in ubiquitous cyberspace. This paper presents a computing architecture named Fogcloud Computing for big data in ubiquitous cyberspace. Collaborative computing by multiple knowledge actors in the fog, middle layer, and cloud is realized based on the collaborative computing language and models, thereby providing a solution for big data computing in ubiquitous cyberspace.

Keywords： fogcloud computing     ubiquitous cyberspace     big data     Internet of Things     cloud computing    

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Analog Optical Computing for Artificial Intelligence Review

Jiamin Wu,Xing Lin,Yuchen Guo,Junwei Liu,Lu Fang,Shuming Jiao,Qionghai Dai,

Engineering 2022, Volume 10, Issue 3,   Pages 133-145 doi: 10.1016/j.eng.2021.06.021

Abstract:

The rapid development of artificial intelligence (AI) facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data. Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth, low latency, and high energy efficiency. In this review, we introduce the latest developments of optical computing for different AI models, including feedforward neural networks, reservoir computing, and spiking neural networks (SNNs). Recent progress in integrated photonic devices, combined with the rise of AI, provides a great opportunity for the renaissance of optical computing in practical applications. This effort requires multidisciplinary efforts from a broad community. This review provides an overview of the state-of-the-art accomplishments in recent years, discusses the availability of current technologies, and points out various remaining challenges in different aspects to push the frontier. We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.

Keywords： Artificial intelligence     Optical computing     Opto-electronic framework     Neural network     Neuromorphic computing     Reservoir computing     Photonics processor    

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Integrated and Intelligent Manufacturing: Perspectives and Enablers

Yubao Chen

Engineering 2017, Volume 3, Issue 5,   Pages 588-595 doi: 10.1016/J.ENG.2017.04.009

Abstract:

With ever-increasing market competition and advances in technology, more and more countries are prioritizing advanced manufacturing technology as their top priority for economic growth. Germany announced the Industry 4.0 strategy in 2013. The US government launched the Advanced Manufacturing Partnership (AMP) in 2011 and the National Network for Manufacturing Innovation (NNMI) in 2014. Most recently, the Manufacturing USA initiative was officially rolled out to further “leverage existing resources… to nurture manufacturing innovation and accelerate commercialization” by fostering close collaboration between industry, academia, and government partners. In 2015, the Chinese government officially published a 10-year plan and roadmap toward manufacturing: Made in China 2025. In all these national initiatives, the core technology development and implementation is in the area of advanced manufacturing systems. A new manufacturing paradigm is emerging, which can be characterized by two unique features: integrated manufacturing and intelligent manufacturing. This trend is in line with the progress of industrial revolutions, in which higher efficiency in production systems is being continuously pursued. To this end, 10 major technologies can be identified for the new manufacturing paradigm. This paper describes the rationales and needs for integrated and intelligent manufacturing (i2M) systems. Related technologies from different fields are also described. In particular, key technological enablers, such as the Internet of Things and Services (IoTS), cyber-physical systems (CPSs), and cloud computing are discussed. Challenges are addressed with applications that are based on commercially available platforms such as General Electric (GE)’s Predix and PTC’s ThingWorx.

Keywords： Integrated manufacturing     Intelligent manufacturing     Cloud computing     Cyber-physical system     Internet of Things     Industrial Internet     Predictive analytics     Manufacturing platform    

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Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories Article

Aftab Ahmed CHANDIO,Nikos TZIRITAS,Fan ZHANG,Ling YIN,Cheng-Zhong XU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 12,   Pages 1305-1319 doi: 10.1631/FITEE.1600027

Abstract: Smart cities have given a significant impetus to manage traffic and use transport networks in an intelligent way. For the above reason, intelligent transportation systems (ITSs) and location-based services (LBSs) have become an interesting research area over the last years. Due to the rapid increase of data volume within the transportation domain, cloud environment is of paramount importance for storing, accessing, handling, and processing such huge amounts of data. A large part of data within the transportation domain is produced in the form of Global Positioning System (GPS) data. Such a kind of data is usually infrequent and noisy and achieving the quality of real-time transport applications based on GPS is a difficult task. The map-matching process, which is responsible for the accurate alignment of observed GPS positions onto a road network, plays a pivotal role in many ITS applications. Regarding accuracy, the performance of a map-matching strategy is based on the shortest path between two con-secutive observed GPS positions. On the other extreme, processing shortest path queries (SPQs) incurs high computational cost. Current map-matching techniques are approached with a fixed number of parameters, i.e., the number of candidate points (NCP) and error circle radius (ECR), which may lead to uncertainty when identifying road segments and either low-accurate results or a large number of SPQs. Moreover, due to the sampling error, GPS data with a high-sampling period (i.e., less than 10 s) typically contains extraneous datum, which also incurs an extra number of SPQs. Due to the high computation cost incurred by SPQs, current map-matching strategies are not suitable for real-time processing. In this paper, we propose real-time map-matching (called RT-MM), which is a fully adaptive map-matching strategy based on cloud to address the key challenge of SPQs in a map-matching process for real-time GPS trajectories. The evaluation of our approach against state-of-the-art approaches is per-formed through simulations based on both synthetic and real-world datasets.

Keywords： Map-matching     GPS trajectories     Tuning-based     Cloud computing     Bulk synchronous parallel    

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Cost-effective resource segmentation in hierarchical mobile edge clouds Special Feature on Future Network-Research Article

Ming-shuang JIN, Shuai GAO, Hong-bin LUO, Hong-ke ZHANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 9,   Pages 1209-1220 doi: 10.1631/FITEE.1800203

Abstract: The fifth-generation (5G) network cloudification enables third parties to deploy their applications (e.g., edge caching and edge computing) at the network edge. Many previous works have focused on specific service strategies (e.g., cache placement strategy and vCPU provision strategy) for edge applications from the perspective of a certain third party by maximizing its benefit. However, there is no literature that focuses on how to efficiently allocate resources from the perspective of a mobile network operator, taking the different deployment requirements of all third parties into consideration. In this paper, we address the problem by formulating an optimization problem, which minimizes the total deployment cost of all third parties. To capture the deployment requirements of the third parties, the applications that they want to deploy are classified into two types, namely, computation-intensive ones and storage-intensive ones, whose requirements are considered as input parameters or constraints in the optimization. Due to the NP-hardness and non-convexity of the formulated problem, we have designed an elitist genetic algorithm that converges to the global optimum to solve it. Extensive simulations have been conducted to illustrate the feasibility and effectiveness of the proposed algorithm.

Keywords： Edge clouds     Edge computing     Edge caching     Resource segmentation     Virtual machine (VM) allocation    

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MEC-Empowered Non-Terrestrial Network for 6G Wide-Area Time-Sensitive Internet of Things Article

Chengxiao Liu, Wei Feng, Xiaoming Tao, Ning Ge

Engineering 2022, Volume 8, Issue 1,   Pages 96-107 doi: 10.1016/j.eng.2021.11.002

Abstract:

In the upcoming sixth-generation (6G) era, the demand for constructing a wide-area time-sensitive Internet of Things (IoT) continues to increase. As conventional cellular technologies are difficult to directly use for wide-area time-sensitive IoT, it is beneficial to use non-terrestrial infrastructures, including satellites and unmanned aerial vehicles (UAVs). Thus, we can build a non-terrestrial network (NTN) using a cell-free architecture. Driven by the time-sensitive requirements and uneven distribution of IoT devices, the NTN must be empowered using mobile edge computing (MEC) while providing oasisoriented on-demand coverage for devices. Nevertheless, communication and MEC systems are coupled with each other under the influence of a complex propagation environment in the MEC-empowered NTN, which makes it difficult to coordinate the resources. In this study, we propose a process-oriented framework to design communication and MEC systems in a time-division manner. In this framework, large-scale channel state information (CSI) is used to characterize the complex propagation environment at an affordable cost, where a nonconvex latency minimization problem is formulated. Subsequently, the approximated problem is provided, and it can be decomposed into sub-problems. These sub-problems are then solved iteratively. The simulation results demonstrated the superiority of the proposed process-oriented scheme over other algorithms, implied that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of using resources, and confirmed that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT.

Keywords： Cell-free Mobile edge computing     Non-terrestrial networks     Sixth-generation     Wide-area time-sensitive IoT    

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Calculating Method for Area Source Model of Particulates

Gu Qing,Yang Xinxing,Li Yunsheng

Strategic Study of CAE 2005, Volume 7, Issue 1,   Pages 41-44

Abstract:

The area source model of particulates was deeply researched, and the places of the initial precipitation of the area sources of particulates were determined. The precipitation of the particulates into the inside of the area source would not be considered. The concentration of the particulates in the inside of the area source block equalled the source intensity of the pollutants of the unit area that was multiplied by the reflection coefficient of the ground surface and divided by the wind speed of the ground surface.

The particulate precipitation of the outside of the area source would be considered. Using the back set method of the virtual point source, and referring to the tilt smoke and cloud model of the part reflex of the point source, the area source model of the particulates was fully given. The edge concentration of the area source was processed by the part of the linear insertion, lest the discontinuity of the calculated results. The central point place of the area sources should be moved horizontally a micro-distance at X and Y directions so that the possibility of the coincidence of both the calculated point and the central point of the area sources would be eliminated.

Keywords： atmospheric environment     particulate     area source model     calculating method    

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Development and Prospect of Edge Intelligence for Industrial Internet

Ren Yaodanjun, Qi Zhengwei, Guan Haibing, Chen Lei

Strategic Study of CAE 2021, Volume 23, Issue 2,   Pages 104-111 doi: 10.15302/J-SSCAE-2021.02.014

Abstract:

As the industrial Internet deeply integrated with manufacturing, the drive capability of industrial intelligence becomes prominent regarding the digitization and informatization of the manufacturing industry. Meanwhile, new applications propose higher requirements for service quality. Edge intelligence, a product of edge computing and artificial intelligence, completes intelligent tasks using computing resources near the data origin. It can alleviate bandwidth transmission pressure, shorten service response delay, and protect the security of private data. Hence, edge intelligence provides a possible approach to satisfy the performance requirements in industrial intelligence applications. This study reviews the research status of cooperative computing, resource isolation, privacy protection, and other key technologies in edge intelligence. Then the typical applications of edge intelligence in equipment management services, production process automation, and manufacturing assistance in the industrial Internet are analyzed in detail. Moreover, the development trend of edge intelligence for the industrial Internet is analyzed in terms of business driving mode, industrial ecology composition, alliance role, and business model. Furthermore, relevant policy suggestions are proposed. We suggest that superior resources should be integrated to establish industry standards; investment increased in basic common resources to deepen the application of the industrial Internet; a good industrial ecology created in the subdivided fields; and university–enterprise cooperation promoted to cultivate interdisciplinary personnel.

Keywords： industrial Internet，edge computing，edge intelligence，cooperative computing，resource isolation    

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Flexible Resource Scheduling for Software-Defined Cloud Manufacturing with Edge Computing Article

Chen Yang,Fangyin Liao,Shulin Lan,Lihui Wang,Weiming Shen,George Q. Huang

Engineering 2023, Volume 22, Issue 3,   Pages 60-70 doi: 10.1016/j.eng.2021.08.022

Abstract:

This research focuses on the realization of rapid reconfiguration in a cloud manufacturing environment to enable flexible resource scheduling, fulfill the resource potential and respond to various changes. Therefore, this paper first proposes a new cloud and software-defined networking (SDN)-based manufacturing model named software-defined cloud manufacturing (SDCM), which transfers the control logic from automation hard resources to the software. This shift is of significance because the software can function as the “brain” of the manufacturing system and can be easily changed or updated to support fast system reconfiguration, operation, and evolution. Subsequently, edge computing is introduced to complement the cloud with computation and storage capabilities near the end things. Another key issue is to manage the critical network congestion caused by the transmission of a large amount of Internet of Things (IoT) data with different quality of service (QoS) values such as latency. Based on the virtualization and flexible networking ability of the SDCM, we formalize the time-sensitive data traffic control problem of a set of complex manufacturing tasks, considering subtask allocation and data routing path selection. To solve this optimization problem, an approach integrating the genetic algorithm (GA), Dijkstra’s shortest path algorithm, and a queuing algorithm is proposed. Results of experiments show that the proposed method can efficiently prevent network congestion and reduce the total communication latency in the SDCM.

Keywords： Cloud manufacturing     Edge computing     Software-defined networks     Industrial internet of things     Industry 4.0    

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An Intelligent Control Method for the Low-Carbon Operation of Energy-Intensive Equipment Article

Tianyou Chai, Mingyu Li, Zheng Zhou, Siyu Cheng, Yao Jia, Zhiwei Wu

Engineering 2023, Volume 27, Issue 8,   Pages 84-95 doi: 10.1016/j.eng.2023.05.018

Abstract:

Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment, this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning, linking control and optimization with prediction, and integrating decision-making with control. This method, which consists of setpoint control, self-optimized tuning, and tracking control, ensures that the energy consumption per tonne is as low as possible, while remaining within the target range. An intelligent control system for low-carbon operation is developed by adopting the end–edge–cloud collaboration technology of the Industrial Internet. The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions. 

Keywords： Energy-intensive equipment     Low-carbon operation     Intelligent control     End&ndash     edge&ndash     cloud collaboration technology    

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Generation of noise-like pulses and soliton rains in a graphene mode-locked erbium-doped fiber ring laser Research

Weiyong Yang, Wei Liu, Xingshen Wei, Zixin Guo, Kangle Yang, Hao Huang, Longyun Qi,yangkangle@sgepri.sgcc.com.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 3,   Pages 287-436 doi: 10.1631/FITEE.1900636

Abstract: Ubiquitous power (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 framework. Because it is difficult to meet the requirements of ubiquitous power IoT for in terms of real time, security, reliability, and business function adaptation using the general framework software, we propose a trusted 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 framework. Experiments comprehensively evaluate EdgeKeeper from the aspects of function, performance, and security. Comparison results show that EdgeKeeper is the most suitable framework for the electricity IoT. Finally, future directions for research are proposed.

Keywords： 物联网；泛在电力物联网；边缘计算；可信计算；网络安全    

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Application of Mobile Agent in Intelligent Transportation System (ITS)

Zhang Yunyong,Liu Jinde

Strategic Study of CAE 2002, Volume 4, Issue 7,   Pages 46-50

Abstract:

The history and architecture of intelligent transportation system is reviewed. After the excellence of mobile agent is introduced，the application of mobile agent in intelligent transportation system is focused from the following fields： network management, wireless communication, traffic control system, simulation system and graphics information system.

Keywords： intelligent transportation system ( ITS)     mobile agent     traffic control system     graphics information system (GIS)     agent communication language (ACL)     agent transfer protocol (ATP)    

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High End Computing in China and the Exploration of “Sunway” Computers

Chen Zuoning

Strategic Study of CAE 2004, Volume 6, Issue 9,   Pages 23-28

Abstract:

In the beginning of the 21st century, driven by application requirements, the research and development of HEC are getting a new upsurge both in China and abroad. This paper analyses the development status and trends of international HEC, describes the overall development and application status of HEC in China, introduces the major technical features and application achievements of Sunway series high-performance computers; discusses the problems faced by today's HEC technologies, and finally makes some suggestions on how to extend progress in the HEC industry in China.

Keywords： high-end computing     grid computing     high performance service     high productivity computing     total cost of ownership     Sunway series computers    

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Title Author Date Type Operation

MEACC: an energy-efficient framework for smart devices using cloud computing systems

Khalid Alsubhi, Zuhaib Imtiaz, Ayesha Raana, M. Usman Ashraf, Babur Hayat,usman.ashraf@skt.umt.edu.pk

Journal Article

Edge Computing Technology: Development and Countermeasures

Hong Xuehai and Wang Yang

Journal Article

Software Architecture of Fogcloud Computing for Big Data in Ubiquitous Cyberspace

Jia Yan, Fang Binxing, Wang Xiang, Wang Yongheng, An Jingbin,Li Aiping, Zhou Bin

Journal Article

Analog Optical Computing for Artificial Intelligence

Jiamin Wu,Xing Lin,Yuchen Guo,Junwei Liu,Lu Fang,Shuming Jiao,Qionghai Dai,

Journal Article

Integrated and Intelligent Manufacturing: Perspectives and Enablers

Yubao Chen

Journal Article

Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories

Aftab Ahmed CHANDIO,Nikos TZIRITAS,Fan ZHANG,Ling YIN,Cheng-Zhong XU

Journal Article

Cost-effective resource segmentation in hierarchical mobile edge clouds

Ming-shuang JIN, Shuai GAO, Hong-bin LUO, Hong-ke ZHANG

Journal Article

MEC-Empowered Non-Terrestrial Network for 6G Wide-Area Time-Sensitive Internet of Things

Chengxiao Liu, Wei Feng, Xiaoming Tao, Ning Ge

Journal Article

Calculating Method for Area Source Model of Particulates

Gu Qing,Yang Xinxing,Li Yunsheng

Journal Article

Development and Prospect of Edge Intelligence for Industrial Internet

Ren Yaodanjun, Qi Zhengwei, Guan Haibing, Chen Lei

Journal Article

Flexible Resource Scheduling for Software-Defined Cloud Manufacturing with Edge Computing

Chen Yang,Fangyin Liao,Shulin Lan,Lihui Wang,Weiming Shen,George Q. Huang

Journal Article

An Intelligent Control Method for the Low-Carbon Operation of Energy-Intensive Equipment

Tianyou Chai, Mingyu Li, Zheng Zhou, Siyu Cheng, Yao Jia, Zhiwei Wu

Journal Article

Generation of noise-like pulses and soliton rains in a graphene mode-locked erbium-doped fiber ring laser

Weiyong Yang, Wei Liu, Xingshen Wei, Zixin Guo, Kangle Yang, Hao Huang, Longyun Qi,yangkangle@sgepri.sgcc.com.cn

Journal Article

Application of Mobile Agent in Intelligent Transportation System (ITS)

Zhang Yunyong,Liu Jinde

Journal Article

High End Computing in China and the Exploration of “Sunway” Computers

Chen Zuoning

Journal Article