A Distributed Intelligent Hungarian Algorithm for Workload Balance in Sensor-Cloud Systems Based on Urban Fog Computing

Liang, Junbin; Long, Yuxuan; Mei, Yaxin; Wang, Tian; Jin, Qun

doi:10.1109/access.2019.2922322

“…A comparative study with existing solutions shows that the proposed method outperforms existing solutions. In another study by Liang et al, proposed a distributed intelligent algorithm to address the issue of transmission delay and high energy consumption of fog nodes [2]. The result reveals that the proposed algorithm can attain minimal delay and decrease energy consumption in comparison to existing works.…”

Section: B Data Collection In Sensor Cloudmentioning

confidence: 98%

“…As defined by IntelliSys, sensor cloud is: "An infrastructure that allows truly pervasive computation using sensors as an interface between physical and cyber worlds, the datacompute clusters as the cyber backbone and the internet as the communication medium" [4]. Moreover, according to Micro Strains, sensor cloud is a different sensor data storage, visualization, and remote management platform that makes use of strong cloud computing technologies to deliver exceptional data scalability, rapid visualization, and user programmable analysis [2], [12]. Sensor cloud is getting more attention from academic and industrial communities, which is essentially a new paradigm driven by incorporating the pervasive data collection abilities of WSNs and its strong data storage and processing capabilities.…”

Section: B Sensor Cloudmentioning

confidence: 99%

“…The interest in this research area has high momentum from the year 2016 with 12 publications, respectively. This interest is partially due to the need to address data collection issues in WSNs (high energy consumption and data loss) [14] [15] [16]and also the introduction of sensor-cloud [2], [12], [13].…”

Section: A Publication Yearmentioning

confidence: 99%

“…Therefore, in most of the cases the resources are deficient for examining and storing the data for future use. With the developments in Wireless Sensor Networks (WSNs), it will be beneficial if sensor data can be uploaded to online communities, forums, and social network applications [2], [3]. This revolution would help to be useful for the techniques developed for emerging cloud computing, data taken from sensor networks from information-needed applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

Ali¹

2020

Preprint

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<div>In recent years, Wireless Sensor Networks (WSNs) technologies are used in many important</div><div>areas, including weather forecasting, security, environmental monitoring, health care, and industry.</div><div>Nonetheless, due to the constraints of WSNs in terms of energy, processing, connectivity, computation and</div><div>data integrity. An important issue in this research field is the efficient management of the large number</div><div>of WSN’s data. Therefore, there is a need for strong and scalable high-performance computing and large</div><div>storage infrastructure for real-time processing and storage of WSN’s data. This article attempts to discuss</div><div>data collection methods in WSNs, sensor cloud, and IoT. Also, in this context, a general overview of</div><div>WSNs and sensor cloud platforms, including their definitions, architectures, and applications are given.</div><div>We investigate, highlight and report recent advances in data collection technology by categorizing and</div><div>classifying data collection methods in WSNs, sensor cloud, IoT and developing a taxonomy based on</div><div>these classifications. Furthermore, the analysis and synthesis of existing works in this research domain</div><div>is highlighted. The identification of key research challenges for future work and use case in this research</div><div>domain were also highlighted and discussed.</div>

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“…This revolution would help to be useful for the techniques developed for emerging cloud computing, data taken from sensor networks from information-needed applications. Highperformance computing methods may usually be replaced or find a room in data processing before the data is transmitted to the cloud [2]. In this paper, a review on data collection in WSNs, sensor cloud, and IoT is presented.…”

Section: Introductionmentioning

confidence: 99%

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

Ali¹

2020

Preprint

0

View full text Add to dashboard Cite

<div>In recent years, Wireless Sensor Networks (WSNs) technologies are used in many important</div><div>areas, including weather forecasting, security, environmental monitoring, health care, and industry.</div><div>Nonetheless, due to the constraints of WSNs in terms of energy, processing, connectivity, computation and</div><div>data integrity. An important issue in this research field is the efficient management of the large number</div><div>of WSN’s data. Therefore, there is a need for strong and scalable high-performance computing and large</div><div>storage infrastructure for real-time processing and storage of WSN’s data. This article attempts to discuss</div><div>data collection methods in WSNs, sensor cloud, and IoT. Also, in this context, a general overview of</div><div>WSNs and sensor cloud platforms, including their definitions, architectures, and applications are given.</div><div>We investigate, highlight and report recent advances in data collection technology by categorizing and</div><div>classifying data collection methods in WSNs, sensor cloud, IoT and developing a taxonomy based on</div><div>these classifications. Furthermore, the analysis and synthesis of existing works in this research domain</div><div>is highlighted. The identification of key research challenges for future work and use case in this research</div><div>domain were also highlighted and discussed.</div>

show abstract

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Hashemi,

Sahafi,

Rahmani

et al. 2023

Softw Pract Exp

2

0

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The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

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A Distributed Intelligent Hungarian Algorithm for Workload Balance in Sensor-Cloud Systems Based on Urban Fog Computing

Cited by 26 publications

References 36 publications

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

Data Collection in Sensor Cloud: Recent Advances, Taxonomy, Use Cases and Open Challenges

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

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