Emerging intelligent algorithms: challenges and applications

Excerpt

The emerging edge computing technologies, IoT and rich cloud services are used to create a novel technology called Edge of Things (EoT). In EoT, data processing occurs in part at the network edge or between the cloud-to-end that can best meet customer necessities, rather than entirely processing in a comparatively less number of massive clouds. The main challenge in EoT is how to manage with emerging IoT environments, where a large number of connected devices participate for restricted wireless resources and where heterogeneity is ever-increasing. In order to overcome this issue, there is an urgent need for more intelligent algorithms and architectures that lead to more interoperable solutions and that can make effective decisions in emerging EoT. This special issue focuses on emerging intelligent algorithms’ challenges and applications for IoT and EoT platforms. The selected papers are summarized as follows: Hassan et al. [1] have proposed HAAL-NBFA framework with a five-phase classification technique to handle big imbalanced datasets, resulting from long-term monitoring of elderly patients. In this paper, the firefly algorithm (FA) has been used to optimize naïve Bayes classifier (NB) which selects the minimum features that give the highest accuracy. …