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Nowadays, every kind of devices with different sizes and shapes, from lamp to kitchen appliances and industrial machines, are connected and shares information digitally in large scale. Despite this tendency to use Internet in such gadgets, vast amounts of data are generated creating new challenges for researchers to analyze and control them. On the other side, Deep Learning (DL) is an appropriate tool for dealing with Internet of Things (IoT) needs, such as analyzing data, making predictions, classifying data. Acquiring the most accurate neural network inside a sensible run-time is a challenge. However, metaheuristics are the key to the success of the application of DL on IoT big data due to non-deterministic polynomial time (NP hard) problems in these areas. Many papers were published about metaheuristic in optimizing deep leaning models, but the literature lacks a study that precisely investigate the relationship between IoT, deep learning and metaheuristic. In this paper, a review of the metaheuristic's usages in the realm of IoT are presented. [ABSTRACT FROM AUTHOR]

• Atmosphere is a three-tier software architecture for IoT edge-fog-cloud computing. • Atmosphere benefits from Complex Event Processing and Agent-Oriented Software. • Two-way communications are allowed among the three tiers of the architecture. The Internet of Things (IoT) has grown significantly in popularity, accompanied by increased capacity and lower cost of communications, and overwhelming development of technologies. At the same time, big data and real-time data analysis have taken on great importance and have been accompanied by unprecedented interest in sharing data among citizens, public administrations and other organisms, giving rise to what is known as the Collaborative Internet of Things. This growth in data and infrastructure must be accompanied by a software architecture that allows its exploitation. Although there are various proposals focused on the exploitation of the IoT at edge, fog and/or cloud levels, it is not easy to find a software solution that exploits the three tiers together, taking maximum advantage not only of the analysis of contextual and situational data at each tier, but also of two-way communications between adjacent ones. In this paper, we propose an architecture that solves these deficiencies by proposing novel technologies which are appropriate for managing the resources of each tier: edge, fog and cloud. In addition, the fact that two-way communications along the three tiers of the architecture is allowed considerably enriches the contextual and situational information in each layer, and substantially assists decision making in real time. The paper illustrates the proposed software architecture through a case study of respiratory disease surveillance in hospitals. As a result, the proposed architecture permits efficient communications between the different tiers responding to the needs of these types of IoT scenarios. [ABSTRACT FROM AUTHOR]