Mobility-aware task delegation model in mobile cloud computing

Mukherjee, Anwesha; Roy, Deepsubhra Guha; De, Debashis

doi:10.1007/s11227-018-02729-x

Cited by 10 publications

(13 citation statements)

References 23 publications

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“…In this work, the authors have focused on privacy-aware task allocation and data aggregation. In [3], task offloading to cloud and delivery of result based on serialization of session information has been discussed. However, the user if gets disconnected has to retrieve the result by sending a request to the cloud.…”

Section: Related Workmentioning

confidence: 99%

“…However, the user if gets disconnected has to retrieve the result by sending a request to the cloud. For the cloudlet based scenario, virtual machine migration has been discussed in [3]. Nevertheless, the VM migration has to take place from one cloudlet to another until the processing gets finished and the result is delivered to the user.…”

Section: Related Workmentioning

confidence: 99%

“…The mobility dataset 3 is collected from 100 mobile users from their GPS-enabled smart phones and Google Map timeline for 6 months in the Kharagpur and Kolkata region of India. The dataset consists of timeseries data of GPS traces with the total time-duration of 26,8041 hours.…”

Section: Mobility Datasetmentioning

confidence: 99%

“…The round-trip delay and power consumption of the mobile device (user-device) in the proposed approach, are presented in Fig.8 and Fig.9. The delay and power are compared with the existing mobility-aware task delegation method [3]. In our approach the RSU works as a fog device and performs the data processing.…”

Section: Delay and Power Consumptionmentioning

confidence: 99%

“…If the device gets disconnected due to mobility, the delivery of the result becomes a challenge. In [3], the task delegation using push notification or serializing session information and application offloading using virtual machine migration have been discussed based on user mobility. Nevertheless, the device has to receive the result from the cloud by serializing session information when gets connected [3].…”

Section: Introductionmentioning

confidence: 99%

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Mobi-IoST: Mobility-Aware Cloud-Fog-Edge-IoT Collaborative Framework for Time-Critical Applications

Ghosh

Mukherjee

Ghosh

et al. 2020

IEEE Trans. Netw. Sci. Eng.

Self Cite

100

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The design of mobility-aware framework for edge/fog computing for IoT systems with back-end cloud is gaining research interest. In this paper, a mobility-driven cloud-fog-edge collaborative real-time framework, Mobi-IoST, has been proposed, which has IoT, Edge, Fog and Cloud layers and exploits the mobility dynamics of the moving agent. The IoT and edge devices are considered to be the moving agents in a 2-D space, typically over the road-network. The framework analyses the spatio-temporal mobility data (GPS logs) along with the other contextual information and deploys machine learning algorithm to predict the location of the moving agents (IoT and Edge devices) in real-time. The accumulated spatio-temporal traces from the moving agents are modelled using probabilistic graphical model. The major features of the proposed framework are: (i) hierarchical processing of the information using IoT-Edge-Fog-Cloud architecture to provide better QoS in real-time applications, (ii) uses mobility information for predicting next location of the agents to deliver processed information, and (iii) efficiently handles delay and energy consumption. The performance evaluations yield that the proposed mobility prediction algorithm has approximately 93% accuracy and reduced the delay and power by approximately 23-26% and 37-41% respectively than compared to the existing mobility-aware task delegation system.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Mobility Datasetmentioning

confidence: 99%

Section: Delay and Power Consumptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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