• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.89-92
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• 2020

According to the various IoT(Internet of Things) services, there have been lots of task offloading researches for IoT devices. Since there are service response delay and core network load issues in conventional cloud computing based offloadings, fog computing based offloading has been focused whose location is close to the IoT devices. However, even in the fog computing architecture, the load can be concentrated on the for computing node when the number of requests increase. To solve this problem, the opportunistic fog computing concept which offloads task to available computing resources such as cars and drones is introduced. In previous fog and opportunistic fog node researches, the offloading is performed immediately whenever the service request occurs. This means that the service requests can be offloaded to the opportunistic fog nodes only while they are available. However, if the service response delay requirement is satisfied, there is no need to offload the request immediately. In addition, the load can be distributed by making the best use of the opportunistic fog nodes. Therefore, this paper proposes a delayed offloading scheme to satisfy the response delay requirements and offload the request to the opportunistic fog nodes as efficiently as possible.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.51-57
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• 2021

In this paper, we propose a task distribution scheme in fog computing environment considering opportunistic fog computing nodes. As latency is one of the important performance metric for IoT(Internet of Things) applications, there have been lots of researches on the fog computing system. However, since the load can be concentrated to the specific fog computing nodes due to the spatial and temporal IoT characteristics, the load distribution should be considered to prevent the performance degradation. Therefore, this paper proposes a task distribution scheme which considers the static as well as opportunistic fog computing nodes according to their mobility feature. Especially, based on the task requirements, the proposed scheme supports the delay sensitive task processing at the static fog node and delay in-sensitive tasks by means of the opportunistic fog nodes for the task distribution. Based on the performance evaluation, the proposed scheme shows low service response time compared to the conventional schemes.