Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy

Sun, Zeyu; Li, Chuan‐Feng; Li-li, Wei; Li, Zhixian; Min, Zhiyu; Zhao, Guozeng

doi:10.3390/s19235083

Cited by 19 publications

(7 citation statements)

References 49 publications

(49 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The traditional Cramer-Rao lower bound on mean square error provides a performance limit for any unbiased estimation of fixed parameters. This article assumesx an estimate of x, and the posterior Cramer-Rao inequality gives the mean square error bound of the estimator as shown in Equation (15).…”

Section: B Methods and Analysismentioning

confidence: 99%

CSR-IM: Compressed Sensing Routing-Control- Method With Intelligent Migration-Mechanism Based on Sensing Cloud-Computing

Sun

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Considering the event-based WSNs routing, the frequent changes of topology may result to the large energy costs in the whole network. Therefore, this paper proposes a Compressed Sensing Routingcontrol-method with Intelligent Migration-mechanism based on Sensing Cloud-computing (CSR-IM). First, the method gives a determining the moving speed and position of the target node through compressed sensing theory, and at the same time, it gives the lower bound calculation process of the target node state estimation value at k + 1 time by the probability knowledge. Second, with the purpose of reducing the network load, the routing tree with the center of fog nodes is established to obtain the data in the route effectively and optimize the data aggregation routing process, and then energy cost of the whole network is balanced. Finally, the simulation experiments show that method of this paper (CSR-IM) and other algorithms have improved the average data aggregation rate by 8.19%, and the average network coverage has increased by 12.65%, which proves that the proposed algorithm is effective and practical. INDEX TERMS Intelligent migration-mechanism, sensing cloud-computing, routing-control-method, compressed sensing.

show abstract

Section: B Methods and Analysismentioning

confidence: 99%

CSR-IM: Compressed Sensing Routing-Control- Method With Intelligent Migration-Mechanism Based on Sensing Cloud-Computing

Sun

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…A hierarchical data job scheduling strategy (HDJS) was introduced by Sun et al 101 on the basis of Intelligent Sensor‐Cloud in Fog Computer. It was capable of adjusting the job priority in a dynamic way to evade job starvation and take full advantage of the available resources.…”

Section: Organization Of the Task Schedulingmentioning

confidence: 99%

Task scheduling approaches in fog computing: A systematic review

Alizadeh

Khajehvand

Rahmani

et al. 2020

Int J Communication

View full text Add to dashboard Cite

SummaryThe Internet of Things (IoT) interconnects billions of physical objects to collect and exchange information and makes available various applications. Despite all the advantages of the IoT, some of its applications are not feasible because of the existing restrictions in the IoT sensors. To overcome these restrictions, cloud computing has been developed in recent years. Although cloud computing has removed some of these restrictions, the cloud itself has faced some other challenges. One of these challenges is the distance between the cloud and the end‐devices, which is not ideal for delay‐sensitive applications and also leads to high communication costs and security problems. To handle these challenges, the fog computing has been introduced in literature, which places the resources and services at the edge of the network, close to the end‐devices. On the other hand, fog nodes suffer from heterogeneity, uncertainty, and dispersion of resources, and also processing, storage, and memory limitations. Thus, there is need for a proper task scheduling approach to use these resources optimally. In this study, the task scheduling algorithms proposed by different researchers for the cloud‐fog environment, their advantages and disadvantages, and also various tools and issues regarding the scheduling methods and their restrictions were discussed. The results indicated that about 58% of the scheduling algorithms use static scheduling and that the other 42% use dynamic scheduling, and the delay metric is the most noteworthy parameter considered in most studies with a share of nearly 17%. Finally, we identified the open issues related to the task scheduling based on cloud‐fog computing and provided some directions for future works.

show abstract

“…Dwivedi et al [14] constructed a Gaussian distribution model to analyze abnormal data events. Sun et al [15] proposed a distributed intelligent processing scheme. Dash et al [16] analyzed the data in cloud, edge and fog computing to realize data management.…”

Section: Introductionmentioning

confidence: 99%

An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

Ren¹,

Qiu²,

Cheng³

2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Star sensors are an important means of autonomous navigation and access to space information for satellites. They have been widely deployed in the aerospace field. To satisfy the requirements for high resolution, timeliness, and confidentiality of star images, we propose an edge computing algorithm based on the star sensor cloud. Multiple sensors cooperate with each other to form a sensor cloud, which in turn extends the performance of a single sensor. The research on the data obtained by the star sensor has very important research and application values. First, a star point extraction model is proposed based on the fuzzy set model by analyzing the star image composition, which can reduce the amount of data computation. Then, a mapping model between content and space is constructed to achieve low-rank image representation and efficient computation. Finally, the data collected by the wireless sensor is delivered to the edge server, and a different method is used to achieve privacy protection. Only a small amount of core data is stored in edge servers and local servers, and other data is transmitted to the cloud. Experiments show that the proposed algorithm can effectively reduce the cost of communication and storage, and has strong privacy.

show abstract

Intelligent Sensor-Cloud in Fog Computer: A Novel Hierarchical Data Job Scheduling Strategy

Cited by 19 publications

References 49 publications

CSR-IM: Compressed Sensing Routing-Control- Method With Intelligent Migration-Mechanism Based on Sensing Cloud-Computing

CSR-IM: Compressed Sensing Routing-Control- Method With Intelligent Migration-Mechanism Based on Sensing Cloud-Computing

Task scheduling approaches in fog computing: A systematic review

An Edge Computing Algorithm Based on Multi-Level Star Sensor Cloud

Contact Info

Product

Resources

About