Recently, new computing paradigms such as fog and edge computing started to emerge in an attempt to cope with the low latency requirements of new classes of IoT applications. In order for these paradigms to fully realize their potential, an important challenge to address is the discovery of fog nodes (FNs) with spare computational resources that can be used to host time- sensitive and computationally intensive application components. We particularly focus on this problem in this paper, considering the case of a WiFi-based FN discovery process. More specifically, we evaluate how practical it is to trigger the discovery of fog nodes based on a mobile phone's historical cellular footprints in order to obtain a high discovery rate and a low energy overhead. To this end, we conducted a small-scale cellular data collection to be used to test different learning approaches, including the K-Nearest Neighbors and the decision tree algorithms as well as a Hidden Markov Model (HMM). According to our evaluation results, HMM was found to achieve the maximum discovery and energy saving ratios. The impact of initial FN misdetections on the user-FN contact ratio has also been studied.

Peer Reviewed