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Mobile Cloud Computing is one of today's more disruptive paradigms of computation due to its effects on the performance of mobile computing and the development of Internet of Things. It is able to enhance the capabilities of devices by outsourcing the workload to external computing platforms deployed along the network, such as cloud servers, cloudlets, or other edge platforms. The research described in this work presents a computational model of a multilayer architecture for increasing the performance of devices using the Mobile Cloud Computing paradigm. The main novelty of this work lies in defining a comprehensive model where all the available computing platforms along the network layers are involved to perform the outsourcing of the application workload. This proposal provides a generalization of the Mobile Cloud Computing paradigm which allows handling the complexity of scheduling tasks in such complex scenarios. The behaviour of the model and its ability of generalization of the paradigm are exemplified through simulations. The results show higher flexibility for making offloading decisions. This work was supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF), under Project CloudDriver4Industry TIN2017-89266-R, and by the Conselleria de Educación, Investigación, Cultura y Deporte, of the Community of Valencia, Spain, within the program of support for research under Project AICO/2017/134.

The amount of data available every day is not only enormous but growing at an exponential rate. Over the last ten years there has been an increasing interest in using complex methods to analyse and visualise massive datasets, gathered from very different sources and including many different features: social networks, surveillance systems, smart cities, medical diagnosis systems, business information, cyberphysical systems, and digital media data. Nowadays, there are a large number of researchers working in complex methods to process, analyse, and visualise all this information, which can be applied to a wide variety of open problems in different domains. This special issue presents a collection of research papers addressing theoretical, methodological, and practical aspects of data processing, focusing on algorithms that use complex methods (e.g., chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory) in a variety of domains (e.g., software engineering, digital media data, bioinformatics, health care, imaging and video, social networks, and natural language processing). A total of 27 papers were received from different research fields, but sharing a common feature: they presented complex systems that process, analyse, and visualise large amounts of data. After the review process, 8 papers were accepted for publication (around 30% of acceptance ratio).

Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems