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%.
As medical technologies are continuously evolving, consumer involvement in health is also increasing significantly. The integration of the Internet of Things (IoT) concept in the health domain may improve the quality of healthcare through the use of wearable sensors and the acquisition of vital and environmental parameters. Currently, there is significant progress in developing new approaches to provide medical care and maintain the safety of the life of the population remotely and around the clock. Despite the standards for emissions of harmful substances into the atmosphere established by the legislation of different countries, the level of pollutants in the air often exceeds the permissible limits, which is a danger not only for the population but also for the environment as a whole. To control the situation an Air Quality Index (AQI) was introduced. For today, many works discuss AQI, however, most of them are aimed rather at studying the methodologies for calculating the index and comparing air quality in certain regions of different countries, rather than creating a system that will not only calculate the index in real-time but also make it publicly available and understandable to the population. Therefore we would like to present a decision support algorithm for a solution called “Environmental Sensing to Act for a Better Quality of Life: Smart Health” with the primary goal of ensuring the transformation of raw environmental data collected by special sensors (data which typically require scientific interpretation) into a form that can be easily understood by the average user; this is achieved through the proposed algorithm. The obtained result is a system that increases the self-awareness and self-adaptability of people in environmental monitoring by offering easy to read and understand suggestions. The algorithm considers three types of parameters (concentration of PM10 (particulate matter), PM2.5, and NO2) and four risk levels for each of them. The technical implementation is presented in a step-like procedure and includes all the details (such as calculating the Air Quality Index—AQI, for each parameter). The results are presented in a front-end where the average user can observe the results of the measurements and the suggestions for decision support. This paper presents a supporting decision algorithm, highlights the basic concept that was used in the development process, and discusses the result of the implementation of the proposed solution.
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