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The impact of Internet of Things has been revolutionized in all fields of life, but its impact on the healthcare system has been significant due to its cutting edge transition. The role of Internet of Things becomes more dominant when it is supported by the features of mobile computing. The mobile computing extends the functionality of IoT in healthcare environment by bringing a massive support in the form of mobile health (m-health). In this research, a systematic literature review protocol is proposed to study how mobile computing assists IoT applications in healthcare, contributes to the current and future research work of IoT in the healthcare system, brings privacy and security in health IoT devices, and affects the IoT in the healthcare system. Furthermore, the intentions of the paper are to study the impacts of mobile computing on IoT in healthcare environment or smart hospitals in light of our systematic literature review protocol. The proposed study reports the papers that were included based on filtering process by title, abstract, and contents, and a total of 116 primary studies were included to support the proposed research. These papers were then analysed for research questions defined for the proposed study.

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Internet of Things for Healthcare Using Effects of Mobile Computing: A Systematic Literature Review

The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone’s energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device’s energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone’s power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones’ batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed.

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Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage

Building management systems are costly for small- to medium-sized buildings. A massive volume of data is collected on different building contexts by the Internet of Things (IoT), which is then further monitored. This intelligence is integrated into building management systems (BMSs) for energy consumption management in a cost-effective manner. Electric fire safety is paramount in buildings, especially in hospitals. Facility managers focus on fire protection strategies and identify where system upgrades are needed to maintain existing technologies. Furthermore, BMSs in hospitals should minimize patient disruption and be immune to nuisance alarms. This paper proposes an intelligent detection technology for electric fires based on multi-information fusion for green buildings. The system model was established by using fuzzy logic reasoning. The extracted multi-information fusion was used to detect the arc fault, which often causes electrical fires in the low-voltage distribution system of green buildings. The reliability of the established multi-information fusion model was verified by simulation. Using fuzzy logic reasoning and the membership function in fuzzy set theory to solve the uncertain relationship between faults and symptoms is a widely applied method. In order to realize the early prediction and precise diagnosis of faults, a fuzzy reasoning system was applied to analyze the arcs causing electrical fires in the lines. In order to accurately identify the fault arcs that easily cause electrical fires in low-voltage distribution systems for building management, this paper introduces in detail a fault identification method based on multi-information fusion, which can consolidate the complementary advantages of different types of judgment. The results demonstrate that the multi-information fusion method reduces the deficiency of a single criterion in fault arc detection and prevents electrical fires in green buildings more comprehensively and accurately. For the real-time dataset, the data results are presented, showing disagreements among the testing methods.

https://www.mdpi.com/2071-1050/13/6/3405/pdf

Design of Multi-Information Fusion Based Intelligent Electrical Fire Detection System for Green Buildings

Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

/pdf/industry-4-0-disruption-and-its-neologisms-in-major-4316cu9gbs.pdf

Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art

Advances in smart medical devices and pervasive systems are turning connected healthcare into a prospering platform for pervasive healthcare. The Internet of Things (IoT), smart sensors, and wearables have augmented the healthcare system, enabling remote monitoring and supporting the medical condition of the patient in and out of clinics. This chapter aims to serve as a brief primer on the development of smart and pervasive healthcare systems. The chapter starts with an overview of IoT, smart sensors, and pervasive systems and also their interrelationships. The differences between IoT and pervasive systems are meticulously laid out to clear any confusion between these two computing paradigms. The chapter highlights the challenges faced by today's healthcare systems and analyzes how to overcome these with the help of pervasive healthcare. It discusses the basics of mobile and pervasive healthcare, including context-aware and connected healthcare, while distinguishing them from telemedicine. It elaborates on the role of IoT in healthcare while discussing a number of sensors and smart devices used in healthcare, fitness, and medical care units. The benefits of and challenges involved in pervasive healthcare are reviewed comprehensively. Several real-life applications and use cases of IoT and smart sensors are presented, showing the effectiveness of pervasive healthcare. The chapter also presents an assessment of the current and future IoT healthcare market along with a listing of the key players.

Internet of things, smart sensors, and pervasive systems: Enabling connected and pervasive healthcare

The Internet of Things (IoT) has already been infiltrated to our everyday life in the forms of smartphones, smart TVs, fitness trackers, health monitoring systems, smart watches, vending machines, smart meters, city traffic, building security systems and much more like this. It has steered the automation to a new high. But IoT alone has limited capability. To reap the actual benefit of IoT, it has to be intelligent. In this chapter, we have reasoned why IoT needs Artificial Intelligence (AI). The intelligent IoT that we discuss here, is termed as the Cognitive IoT (CIoT) . Cognitive IoT uses a new computing paradigm called Cognitive Computing, often popularly dubbed as the third era of computing. Cognitive Computing will make IoT more sophisticated, more intelligent, and more interactive. This chapter essentially focuses on the convergence of IoT with Cognitive AI. In addition to introducing a preliminary idea of Cognitive Computing, the chapter discusses several aspects of CIoT. It explains how Cognitive Computing has been used in IoT through different forms such as Cognitive Network, Cognitive Devices, and Cognitive Analytics. The desirable properties of the CIoT are mentioned. A special discussion is presented on how CIoT has taken automation to a new level. Realizing CIoT is not straightforward for the reason that it is a complex system and is quite different from other computing systems we have been familiar with. The implementation challenges along with the societal and ethical apprehensions of CIoT have been identified and discussed in detail. The business world is showing great interest in IoT and subsequently in CIoT. Considering that, we have highlighted the business values of CIoT and some prospective application areas along with a few prominent use cases. But, will excessive adoption of AI and tendering human-like intellect to inorganic devices be a threat to the human race? Do we need to be warned? A rational discussion on that has been presented before winding up.

Beyond Automation: The Cognitive IoT. Artificial Intelligence Brings Sense to the Internet of Things

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.

Healthcare Big Data: A Comprehensive Overview

Despite the success and the popularity of the online learning system, it still lacks in dynamically adapting suitable pedagogical methods according to the changing emotions and behaviour of the learner, as can be done in the face-to-face mode of learning. This makes the learning process mechanized, which significantly affects the learning outcome. To resolve this, the first and necessary step is to assess the emotion of a learner and identify the change of emotions during a learning session. Usually, images of facial expressions are analysed to assess one's state of mind. However, human emotions are far more complex, and these psychological states may not be reflected only through the basic emotion of a learner (i.e. analysing a single image), but a combination of two or more emotions which may be reflected on the face over a period of time. From a real survey, we derived four complex emotions that are a combination of basic human emotions often experienced by a learner, in concert, during a learning session. To capture these combined emotions correctly, we considered a fixed set of continuous image frames, instead of discrete images. We built a CNN model to classify the basic emotions and then identify the states of mind of the learners. The outcome is verified mathematically as well as surveying the learners. The results show a 65% and 62% accuracy respectively, for emotion classification and state of mind identification.

Facial Emotion Detection to Assess Learner's State of Mind in an Online Learning System

This chapter describes how traditionally, Cloud Computing has been used for processing Internet of Things (IoT) data. This works fine for the analytical and batch processing jobs. But most of the IoT applications demand real-time response which cannot be achieved through Cloud Computing mainly because of inherent latency. Fog Computing solves this problem by offering cloud-like services at the edge of the network. The computationally powerful edge devices have enabled realising this idea. Witnessing the exponential rise of IoT applications, Fog Computing deserves an in-depth exploration. This chapter establishes the need for Fog Computing for processing IoT data. Readers will be able to gain a fair comprehension of the various aspects of Fog Computing. The benefits, challenges and applications of Fog Computing with respect to IoT have been mentioned elaboratively. An architecture for IoT data processing is presented. A thorough comparison between Cloud and Fog has been portrayed. Also, a detailed discussion has been depicted on how the IoT, Fog, and Cloud interact among them. Processing IoT Data: From Cloud to Fog—It’s Time to Be Down to Earth

Saurabh Pal

Papers

Internet of things, smart sensors, and pervasive systems: Enabling connected and pervasive healthcare

Beyond Automation: The Cognitive IoT. Artificial Intelligence Brings Sense to the Internet of Things

Healthcare Big Data: A Comprehensive Overview

Facial Emotion Detection to Assess Learner's State of Mind in an Online Learning System

Processing IoT Data: From Cloud to Fog. It’s Time to be Down-to-Earth