Data Delivery from WSNs to Cloud Based on a Fog Structure

Zeng, Jiandian; Wang, Tian; Lai, Yongxuan; Liang, Junbin; Chen, Hongyu

doi:10.1109/cbd.2016.028

Cited by 13 publications

(4 citation statements)

References 17 publications

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“…Furthermore, fog computing has a better performance on processing data and controlling underlying sensors as it is more efficient and easier to access compared with cloud computing. The fog layer is deployed between WSNs and cloud layer in architecture [28]. Besides, a data delivery mechanism is designed.…”

Section: Related Workmentioning

confidence: 99%

Software‐defined dynamic QoS provisioning for smart metering in energy Internet using fog computing and network calculus

Huang

Liu

et al. 2018

IET cyber-phys. syst.

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Section: Related Workmentioning

confidence: 99%

Software‐defined dynamic QoS provisioning for smart metering in energy Internet using fog computing and network calculus

Huang

Liu

et al. 2018

IET cyber-phys. syst.

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“…Stojmenovic et al [ 2 ] elaborated previous scenarios and further expanded this concept on a series of real scenarios, such as the smart grid, smart traffic lights in vehicular networks and software-defined networks. More recently, Kai et al [ 31 ] gave a survey about some opportunities and challenges related to the context of fog computing in VANETs; and Zeng et al [ 12 ] proposed a three-layer framework based on a fog structure for uploading data from sensor readings to the cloud. Static sensor nodes can forward their data to their nearby mobile fog nodes, so that the data transmission latency to the cloud center can be greatly reduced.…”

Section: Related Workmentioning

confidence: 99%

“…With the rapid development of embedded hardware and communication technologies, there emerges a new kind of node called fog nodes [ 2 , 11 , 12 ], which are deployed at the edge of networks and with the support of location, distribution, scalability, density of devices and mobility. The concept of fog computing extends the traditional cloud computing paradigm to the edge of VANETs.…”

Section: Introductionmentioning

confidence: 99%

Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks

Lai

Yang

et al. 2017

Sensors

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Vehicular nodes are equipped with more and more sensing units, and a large amount of sensing data is generated. Recently, more and more research considers cooperative urban sensing as the heart of intelligent and green city traffic management. The key components of the platform will be a combination of a pervasive vehicular sensing system, as well as a central control and analysis system, where data-gathering is a fundamental component. However, the data-gathering and monitoring are also challenging issues in vehicular sensor networks because of the large amount of data and the dynamic nature of the network. In this paper, we propose an efficient continuous event-monitoring and data-gathering framework based on fog nodes in vehicular sensor networks. A fog-based two-level threshold strategy is adopted to suppress unnecessary data upload and transmissions. In the monitoring phase, nodes sense the environment in low cost sensing mode and generate sensed data. When the probability of the event is high and exceeds some threshold, nodes transfer to the event-checking phase, and some nodes would be selected to transfer to the deep sensing mode to generate more accurate data of the environment. Furthermore, it adaptively adjusts the threshold to upload a suitable amount of data for decision making, while at the same time suppressing unnecessary message transmissions. Simulation results showed that the proposed scheme could reduce more than 84 percent of the data transmissions compared with other existing algorithms, while it detects the events and gathers the event data.

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“…Users need to upload the photos to the server in order to finish the photo crowdsensing task. They prefer uploading the photos through PoIs freely rather than uploading through a costly cellular network [21]. Therefore, they copy the photos when they enter the communication area of each other, in order to upload the photos as soon as possible.…”

Section: Introductionmentioning

confidence: 99%

Storage Management Strategy in Mobile Phones for Photo Crowdsensing

Wang

Liang

et al. 2020

Sensors

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In mobile crowdsensing, some users jointly finish a sensing task through the sensors equipped in their intelligent terminals. In particular, the photo crowdsensing based on Mobile Edge Computing (MEC) collects pictures for some specific targets or events and uploads them to nearby edge servers, which leads to richer data content and more efficient data storage compared with the common mobile crowdsensing; hence, it has attracted an important amount of attention recently. However, the mobile users prefer uploading the photos through Wifi APs (PoIs) rather than cellular networks. Therefore, photos stored in mobile phones are exchanged among users, in order to quickly upload them to the PoIs, which are actually the edge services. In this paper, we propose a utility-based Storage Management strategy in mobile phones for Photo Crowdsensing (SMPC), which makes a sending/deleting decision on a user's device for either maximizing photo delivery ratio (SMPC-R) or minimizing average delay (SMPC-D). The decision is made according to the photo's utility, which is calculated by measuring the impact of reproducing or deleting a photo on the above performance goals. We have done simulations based on the random-waypoint model and three real traces: roma/taxi, epfl, and geolife. The results show that, compared with other storage management strategies, SMPC-R gets the highest delivery ratio and SMPC-D achieves the lowest average delay.

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Data Delivery from WSNs to Cloud Based on a Fog Structure

Cited by 13 publications

References 17 publications

Software‐defined dynamic QoS provisioning for smart metering in energy Internet using fog computing and network calculus

Software‐defined dynamic QoS provisioning for smart metering in energy Internet using fog computing and network calculus

Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks

Storage Management Strategy in Mobile Phones for Photo Crowdsensing

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