基于区块链的边缘计算IIoT架构研究

在智能制造系统中,工业物联网通过先进的管理技术将制造设备互连,实现了信息的实时传输、设备的范在化感知和数据的快速分析处理。但是由于制造设备的异构性、物联网网关（IoT网关）数据分析能力的有限性、制造设备的存储力低下,设备和数据的低安全性等缺陷严重阻碍了智能制造的发展。BEIIoT架构从制造企业的实际生产过程与应用角度出发,将区块链技术与边缘计算相结合,通过对服务器进行P2P组网以实现对设备去中心化管理;通过对边缘设备进行服务化封装,增强设备的安全性与实时分析能力,降低设备的异构性;使用DAG双链式数据存储结构,提高数据的冗余度与安全性,实现生产线数据的异步并发备份存储。BEIIoT架构为智能制造的实施提供了体系支持。     

Scalable and flexible wireless distributed architecture for intelligent video surveillance systems

Multimedia Tools and Applications - This paper presents a novel distributed intelligent video surveillance architecture based on Wireless Multimedia Sensor Networks (WMSNs). This architecture is...     

Reefer container monitoring system using PLC-based communication technology for maritime edge computing

The Journal of Supercomputing - Most of the container transports by sea and land from the Republic of Korea to other countries or continents take more than a week. Under such environment, cargoes...     

Fog computing: from architecture to edge computing and big data processing

The Journal of Supercomputing - Cloud computing plays a vital role in processing a large amount of data. However, with the arrival of the Internet of Things, huge data are generated from these...     

Robust architecture for distributed intelligence in an IP-based mobile wide-area surveillance system

Modern IP-based wide-area surveillance systems often build on networks of multi-modal, intelligent and mobile sensor units. Detection of complex events is performed on intelligent sensors and fusing input in the sensor units or centralized control room components. The domain of surveillance and public safety creates requirement for robustness and fault-tolerance. This article will present an automated intelligence architecture for mobile surveillance, which provides capabilities for combining on-board event detection in sensor units, centralized decision making on the server side, and automated exploitation of mobile surveillance unit positioning data. This architecture must be very reliable to provide services in the face of challenges such as natural disasters and fire, potentially damaging the infrastructure of the surveillance system. To increase its reliability and robustness, we study the introduction of a self-healing system into the architecture and examine the combined system’s operation in three case studies.     

移动边缘计算中资源受限的串行任务卸载策略

边缘计算通过在靠近用户的网络边缘侧部署计算和存储资源,使用户可将高延迟、高耗能应用程序卸载到网络边缘侧执行,从而降低应用延迟和本地能耗. 已有的卸载研究通常假设卸载的任务之间相互独立,且边缘服务器缓存有执行任务所需的所有服务. 然而,在真实场景中,任务之间往往存在依赖关系,且边缘服务器因其有限的存储资源只能缓存有限的服务. 为此,提出一种在边缘服务器计算资源和服务缓存有限的约束下,权衡时延和能耗（即成本）的依赖性任务卸载方法. 首先,松弛研究问题中的约束将其转换为凸优化问题;采用凸优化工具求最优解,并用解计算卸载任务的优先级. 然后,按照优先级将任务卸载到成本最小的边缘服务器,若多个依赖任务卸载到不同的边缘服务器,为了使总成本最小,则采用改进粒子群算法求解边缘服务器的最佳传输功率. 最后,为了验证所提方法的有效性,基于真实数据集进行了充分的实验. 实验结果表明,所提方法与其他方法相比能够降低总成本8%~23%.

     

Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms

Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber‐physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city‐wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off‐load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off‐loading pattern, where processing tasks are pushed “vertically” to the cloud.     

Scalable replica selection based on node service capability for improving data access performance in edge computing environment

The replica strategies in traditional cloud computing often result in excessive resource consumption and long response time. In the edge cloud environment, if the replica node cannot be managed efficiently, it will cause problems such as low user’s access speed and low system fault tolerance. Therefore, this paper proposed replica creation and selection strategy based on the edge cloud architecture. The dynamic replica creation algorithm based on access heat (DRC-AH) and replica selection algorithms based on node service capability (DRS-NSC) were proposed. The DRC-AH uses data block as replication granularity and Grey Markov chain to dynamically adjust the number of replicas. After the replica is created, when client receives the user’s request, the DRS-NSC selects the best replica node to respond to the user. The experiments show that the proposed algorithms have significant advantages in prediction accuracy, user’s request response time, resource utilization, etc., and improve the performance of the system to a certain extent.

     

Non-uniform image deblurring using an optical computing system

Removing non-uniform blur caused by camera shaking is troublesome because of its high computational cost. We analyze the efficiency bottlenecks of a non-uniform deblurring algorithm and propose an efficient optical computation deblurring framework that implements the time-consuming and repeatedly required modules, i.e., non-uniform convolution and perspective warping, by light transportation. Specifically, the non-uniform convolution and perspective warping are optically computed by a hybrid system that is composed of an off-the-shelf projector and a camera mounted on a programmable motion platform. Benefitting from the high speed and parallelism of optical computation, our system has the potential to accelerate existing non-uniform motion deblurring algorithms significantly. To validate the effectiveness of the proposed approach, we also develop a prototype system that is incorporated into an iterative deblurring framework to effectively address the image blur of planar scenes that is caused by 3D camera rotation around the x-, y- and z-axes. The results show that the proposed approach has a high efficiency while obtaining a promising accuracy and has a high generalizability to more complex camera motions.     

Scalable risk assessment method for cloud computing using game theory (CCRAM)

Cloud computing is one of the most popular information processing concepts of today's IT world. The security of the cloud computing is complicated because each service model uses different infrastructure elements. Current security risk assessment models generally cannot be applied to cloud computing systems that change their states very rapidly. In this work, a scalable security risk assessment model has been proposed for cloud computing as a solution of this problem using game theory. Using this method, we can evaluate whether the risk in the system should be fixed by cloud provider or tenant of the system.     

Conceptual design of an architecture for hard real time computing

The following paper describes efforts to develop a processor architecture that meets the requirements of hard real time computing. The architecture is of the RISC-type with a single, modular CPU. The modules are a Kernel Processor, a Task Processor, a Memory Module and a Controller for internal and external communication. By integrating multiple register files directly accessible by the ALU, the number of main memory accesses decreases and the time for context-switches is reduced considerably. While OS functions, scheduling, time management and interrupt handling are performed by the Kernel Processor, the Task Processor focuses on its primary function, viz., to execute application program code. Assigning the traditionally sequentially performed program-, operating system- and memory-operations to different modules working in parallel results in a significant increase of performance. The reduced instruction set interfacing this architecture allows for a complete and convenient implementation of real time algorithms, especially in distributed systems, without loosing the operational determinism, which was one of the major design guidelines.     

Scalable network-on-chip architecture for configurable neural networks

Providing highly flexible connectivity is a major architectural challenge for hardware implementation of reconfigurable neural networks. We perform an analytical evaluation and comparison of different configurable interconnect architectures (mesh NoC, tree, shared bus and point-to-point) emulating variants of two neural network topologies (having full and random configurable connectivity). We derive analytical expressions and asymptotic limits for performance (in terms of bandwidth) and cost (in terms of area and power) of the interconnect architectures considering three communication methods (unicast, multicast and broadcast). It is shown that multicast mesh NoC provides the highest performance/cost ratio and consequently it is the most suitable interconnect architecture for configurable neural network implementation. Routing table size requirements and their impact on scalability were analyzed. Modular hierarchical architecture based on multicast mesh NoC is proposed to allow large scale neural networks emulation. Simulation results successfully validate the analytical models and the asymptotic behavior of the network as a function of its size.     

Generative design in architecture using an expert system

The mathematician-architect Christopher Alexander has devised a theory of objective architectural design. He believes that all architectural forms can be described as interacting patterns, all possible relationships of which are governed by generative rules. These form a ‘pattern language’ capable of generating forms appropriate for a given environmental context. The complexity of interaction among these rules leads to difficulties in their representation by conventional methods. This paper presents a Prolog-based expert system which implements Alexander's design methodology to produce perspective views of partially and fully differentiated 3-dimensional architectural forms.     

A3M: an agent architecture for automated manufacturing

This paper proposes a software architecture based on mobile agents for distributed process control applications. A set of agents is employed to handle, in a single manufacturing cell, automatic assignment of control tasks to controllers, monitoring of cell functionalities and dynamic cell reconfiguration. The agents operate in a two‐layered structure: at the highest level, the planning agents analyse the inputs of the system designer and automatically create the field agents, which operate at the lowest level and embed the control tasks to be executed. Field agents, which are mobile, are able to reach autonomously the controllers of the cell, in order to perform the control activity there. Exploiting the mobility enables a field agent to change its running device when the variation of the design parameters or a system fault requires a new task distribution. A load‐balancing algorithm is introduced, with the objective of assigning each field agent to a controller of the manufacturing cell in order to fairly distribute the computation load. The algorithm uses a branch‐and‐bound technique to explore all possible solutions and applies two heuristics to throw away non‐feasible solutions and select the best branch to analyse. The algorithm is designed to run on‐line in order to allow a fast task redistribution when a fault condition occurs in the process control environment. Copyright © 2008 John Wiley & Sons, Ltd.     

A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19

The Journal of Supercomputing - This paper designs and develops a computational intelligence-based framework using convolutional neural network (CNN) and genetic algorithm (GA) to detect COVID-19...     

Scalable offloading using machine learning methods for distributed multi-controller architecture of SDN networks

The Journal of Supercomputing - Machine learning advanced tactics provide flexible assistance to organize, maintain, and optimize SDN flat topology, despite that intelligence is hard to apply and...     

A concurrent network architecture for cost-efficient parallel computing using workstation clusters

Within the past few years, workstation clusters have gained an increasing importance as platforms for parallel high-performance simulation problems. In contrast to the specialized and cost-intensive interconnection network of distributed memory multiprocessor systems, workstation clusters utilize local area networks (LANs) and common communication protocols. Therefore, the cost-efficiency of workstation clusters for parallel tasks is high while the communication performance is limited compared to parallel computer systems. To improve the communication performance of clusters, new protocols can be applied as well as specialized interconnection networks. On one hand, these solutions decrease the cost-efficiency of clusters, while on the other hand the performance of local area networks is increasing because of new technologies such as FastEthernet, GigaEthernet or ATM. In this contribution, we propose increasing the communication performance of clusters through the concurrent network architecture (CNA) with multi-channel communication systems. Through the use of parallel and independent LANs, the communication performance of a cluster can be improved while maintaining the cost-efficiency of the wide-spread LAN technology and protocols. This paper gives an overview of the CNA, the requirements of an implementation and a performance evaluation of a CNA workstation cluster.     

移动边缘计算中利用BPSO的任务卸载策略

为提高移动边缘计算任务卸载方案的性能,提出一种移动边缘计算中利用BPSO的任务卸载策略.构建三层移动边缘计算(M EC)网络架构,移动设备根据任务情况进行本地计算,或者将其卸载至边缘计算节点与云服务器;根据M EC网络中的计算模型、通信模型设计计算卸载目标,即任务最优分配、节点负载均衡,使计算任务得到及时、有序、高效的分配;利用二进制粒子群(BPSO)算法对优化目标进行求解,得到最优卸载策略,实现能量消耗最小且时延最短,系统整体负载最为均衡.实验结果表明,所提策略能量损耗最小且系统整体负载性能明显提升.     

Rule-based soft computing for edge detection

In this paper, we present a robust rule-based edge detection method. Although generalized edge detection approaches are effective for most images they often fail in others. Thus the goal of our method is to provide more reliable edge detection results that are effective in most images. We implement the proposed method as follows: (1) transform RGB images to YCbCr format, (2) apply Sobel mask in four edge directions (horizontal, vertical, diagonal, anti-diagonal), (3) apply a bi-directional mask in four edge directions (horizontal–diagonal, vertical–diagonal, horizontal–anti-diagonal, vertical–anti-diagonal), and (4) detect rule-based edges by calculating membership degrees. Simulation results demonstrate that the proposed method is effective in most given images. We used three benchmarks approaches (Canny edge mask, high-pass filter, and Sobel mask) to compare the subjective performance quality.