Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing

The use of application media, gamming, entertainment, and healthcare engineering has expanded as a result of the rapid growth of mobile technologies. This technology overcomes the traditional computing methods in terms of communication delay and energy consumption, thereby providing high reliability and bandwidth for devices. In today’s world, mobile edge computing is improving in various forms so as to provide better output and there is no room for simple computing architecture for MEC. So, this paper proposed a secure and energy-efficient computational offloading scheme using LSTM. The prediction of the computational tasks is done using the LSTM algorithm, the strategy for computation offloading of mobile devices is based on the prediction of tasks, and the migration of tasks for the scheme of edge cloud scheduling helps to optimize the edge computing offloading model. Experiments show that our proposed architecture, which consists of an LSTM-based offloading technique and routing (LSTMOTR) algorithm, can efficiently decrease total task delay with growing data and subtasks, reduce energy consumption, and bring much security to the devices due to the firewall nature of LSTM.

Development and Application of Medicine-Engineering Integration in the Rehabilitation of Traumatic Brain Injury

The rapid progress of the combination of medicine and engineering provides better chances for the clinical treatment and healthcare engineering. Traumatic brain injury (TBI) and its related symptoms have become a major global health problem. At present, these techniques has been widely used in the rehabilitation of TBI. In this review article, we summarizes the progress of the combination of medicine and industry in the rehabilitation of traumatic brain injury in recent years, mainly from the following aspects: artificial intelligence (AI), brain-computer interfaces (BCI), noninvasive brain stimulation (NIBS), and wearable-assisted devices. We believe the summary of this article can improve insight into the combination of medicine and industry in the rehabilitation of traumatic brain injury.

Fifth-generation (5G) communication networks and sustainability: A research agenda

Communication networks hugely improved many fields during the past decades (i.e. education, healthcare, engineering sector, management, etc.). However, the new fifth-generation (5G) communication networks is expected to be able to bring new developments in this sector even faster than the past technologies. This will happen, not only, as the technology will be presented in the field as a new way of approaching data and services, but also, as there is more pressure exerted on 5G, due to the rising need of data that companies and individuals feel in everyday life. The paper suggests that emerging trends in this filed will also be able to create improvements for society, economy, management and environment. Better use of energy, information sharing and resource efficiency, in fact, are some of the main goals that the 5G and the sustainability approach are aiming to achieve. This shows that 5G and sustainability have several features in common that are yet not fully explored nor in theory nor in practice. Thus, proposing a research agenda, the paper aims at analysing this common characteristics, but also to highlight that the 5G can be considered a strategic tool that enables companies to be involved in boosting sustainable development.

A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems

PurposeThe objective of this paper is to assess and synthesize the published literature related to the application of data analytics, big data, data mining and machine learning to healthcare engineering systems.Design/methodology/approachA systematic literature review (SLR) was conducted to obtain the most relevant papers related to the research study from three different platforms: EBSCOhost, ProQuest and Scopus. The literature was assessed and synthesized, conducting analysis associated with the publications, authors and content.FindingsFrom the SLR, 576 publications were identified and analyzed. The research area seems to show the characteristics of a growing field with new research areas evolving and applications being explored. In addition, the main authors and collaboration groups publishing in this research area were identified throughout a social network analysis. This could lead new and current authors to identify researchers with common interests on the field.Research limitations/implicationsThe use of the SLR methodology does not guarantee that all relevant publications related to the research are covered and analyzed. However, the authors' previous knowledge and the nature of the publications were used to select different platforms.Originality/valueTo the best of the authors' knowledge, this paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining and machine learning applied to healthcare engineering systems.

Future Location Prediction for Emergency Vehicles Using Big Data: A Case Study of Healthcare Engineering

The number of devices equipped with GPS sensors has increased enormously, which generates a massive amount of data. To analyse this huge data for various applications is still challenging. One such application is to predict the future location of an ambulance in the healthcare system based on its previous locations. For example, many smart city applications rely on user movement and location prediction like SnapTrends and Geofeedia. There are many models and algorithms which help predict the future location with high probabilities. However, in terms of efficiency and accuracy, the existing algorithms are still improving. In this study, a novel algorithm, NextSTMove, is proposed according to the available dataset which results in lower latency and higher probability. Apache Spark, a big data platform, was used for reducing the processing time and efficiently managing computing resources. The algorithm achieved 75% to 85% accuracy and in some cases 100% accuracy, where the users do not change their daily routine frequently. After comparing the prediction results of our algorithm, it was experimentally found that it predicts processes up to 300% faster than traditional algorithms. NextSTMove is therefore compared with and without Apache Spark and can help in finding useful knowledge for healthcare medical information systems and other data analytics related solutions especially healthcare engineering.

Ecopsychosocial environments for health and mental health

Theories of architectural morphology and society contributed to understanding the interconnections and synergies between society and space. Lack of knowledge of how space influences communities can foster social issues (Hillier & Hanson, 1984). This complements links between health and society identified by epidemiolohists (Marmot, 2015). Thus, theories connecting health and the environment unite all three disciplines by connecting health to the built environment. We characterise this holistic interconectedness of space, health and society as ecopsychosocial (Chrysikou, 2019) and we will use it as the underlying theory of the workshop. Spatial interventions could support prevention or disease-fighting mechanisms. We briefly mention anthropocentric examples related to space and vulnerability from the field of therapeutic architecture. The aim is to utilise those spatial features that could affect vulnerable people's physiology and perception. This approach does not replace medical intervention or treatment. Contrary, it aims to support healthcare professionals, carers and patients optimizing the healthcare delivery and recovery processes and subsequently reduce efforts required to overcome stressful situations through restaurative environment. This ecopsychosocial system would is more helpful for of low diagnostic and interventional accuracy such as mental illness (Christensen et al., 2009). References Christensen, CM., Grossman, JH. and Hwang, J. (2009) The innovator's prescription. New York USA: McGraw-Hill Chrysikou, E. (2019) Psychiatric institutions and the physical environment: combining medical architecture methodologies and architectural morphology to increase our understanding. Journal of Healthcare Engineering, vol. 2019, Article ID 4076259, 16 pages Hillier, B. & Hanson, J. (1984) The Social Logic of Space. Cambridge: Cambridge University Press Marmot, M. (2015) The health Gap: the challenge of an unequal world. Bloomsbury Publishing, London