Abstract
The rapid growth of mobile devices in recent years has led to the rapid progress of mobile computing. However, this has exposed certain limitations that have, first, been addressed by mobile cloud computing. Once the Internet-of-Things devices have started being put online, a new step in the evolution of mobile networks was taken through the addition of edge and fog computing, where small nodes at the edge of the network take up some of the load on the cloud backend. Nevertheless, even this model has shown some limitations, which is why in this paper, we address the problem of off-loading data and computations from a mobile device to the cloud to fog nodes, or to other mobile devices in the vicinity. The novelty of our proposal is the addition of a layer composed exclusively of mobile devices that collaborate in an opportunistic fashion, as a first resort when needing some computations to be off-loaded. Through a thorough analysis using the MobEmu mobile network simulator, we show that our solution is able to reduce total computation time by as much as 19%, decrease the cloud usage with up to 40%, and reduce battery consumption with more than 6%.
Highlights
Mobile devices are becoming our everyday companions, and, whether they are smartphones or wearables, they represent an essential part of our life
PROPOSED SOLUTION On top of the Drop Computing paradigm presented in the previous section, we propose offloading mechanisms that aim to improve the quality of experience (QoE) for users, the costs for developers, as well as battery consumption
In [7], we introduced the Drop Computing paradigm and proposed a simple offloading model that only takes into account the cloud and other mobile devices located in close proximity
Summary
Mobile devices are becoming our everyday companions, and, whether they are smartphones or wearables, they represent an essential part of our life. A formal definition of MCS specifies that it is a new sensing paradigm that empowers ordinary citizens to contribute data sensed or generated from their mobile devices, which is aggregated and fused in the cloud for crowd intelligence extraction and people-centric service delivery All these components combine together into the paradigm of Drop Computing [7], which assumes that mobile nodes can offload data and computations to the cloud, to fog devices, and to other neighboring nodes through close-range protocols. Such a network requires a set of new and improved offloading solutions, that are able to adapt to conditions and select one or more of the available offloading methods.
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