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In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.

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The introduction of the Internet of Things (IoT) and Big Data applications have garnered a massive amount of digital data. Processing and analysing these data demand vast computing resources, proportionately. The major downside of producing and using computing resources in such volumes is the deterioration of the Earth's environment. The production process of the electronic devices involves hazardous and toxic substances which not only harm human and other living being’s health but also contaminate the water and soil. The production and operations of these computers in largescale also results in massive energy consumption and greenhouse gas generation. Moreover, the low use cycle of these devices produces a huge amount of not-easy-to-decompose e-waste. In this outlook, instead of buying new devices, it is advisable to use the existing resources to their fullest, which will minimize the environmental penalties of production and e-waste. This paper advocates for using smartphones and smartphone crowd computing (SCC) to ease off the use of PCs/laptops and centralized high-performance computers (HPCs) such as data centres and supercomputers. The paper aims to establish SCC as the most feasible computing system solution for sustainable computing. Detailed comparisons, in terms of environmental effects (e.g., energy consumption, greenhouse gas generation, etc.), between SCC and supercomputers and other green computing initiatives such as Grid and Cloud Computing, are presented. The key enablers of SCC are identified and discussed. One of them is today's computationally powerful smartphones. A comprehensive statistical survey of the various commercial CPUs, GPUs, SoCs for smartphones is presented confirming the capability of the SCC as an alternative to HPC. The challenges involved in realizing SCC are also considered. One of the major challenges is handling the issue of limited battery in smartphones. The reasons for battery drain are recognized with probable measures. An exhaustive survey is presented on the present and optimistic future of the continuous improvement and research on different aspects of smartphone battery and other alternative power sources which will allow users to use their smartphones for SCC without worrying about the battery running out.

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Big data has unlocked a new opening in healthcare. Thanks to the considerable benefits and opportunities, it has attracted the momentous attention of all the stakeholders in the healthcare industry. This chapter aims to provide an overall but thorough understanding of healthcare big data. The chapter covers the 10 ‘V's of healthcare big data as well as different healthcare data analytics including predictive and prescriptive analytics. The obvious advantages of implementing big data technologies in healthcare are meticulously described. The application areas and a good number of practical use cases are also discussed. Handling big data always remains a big challenge. The chapter identifies all the possible challenges in realizing the benefits of healthcare big data. The chapter also presents a brief survey of the tools and platforms, architectures, and commercial infrastructures for healthcare big data.

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