Internet of Things Architectures, Technologies, Applications, Challenges, and Future Directions for Enhanced Living Environments and Healthcare Systems: A Review

Does Texting While Walking Affect Gait's Plantar Pressure Parameters?

BACKGROUND

This study aims to examine the possible effects of mobile phone use on plantar pressure and spatiotemporal parameters during walking.

MATERIALS AND METHODS

Thirty volunteers (18 males and 12 females) participated in the study. A 10-m walking path was prepared, and a messaging connection was established. They were asked to write three posts without word or character mistakes and participants walked on the path walk as much as they wanted on the trail to make sure they were walking at their own pace. The gait's spatiotemporal parameters and plantar pressure parameters were recorded while walking. A paired samples t-test was used to determine whether there was a difference between normal walking and walking while texting.

RESULTS

While walking and writing a message, cadence, speed, and step length decreased significantly (P < 0.05). In the plantar pressure parameters, the fore- and midfoot load and pressure were significantly increased (P < 0.05).

CONCLUSIONS

Compared to normal walking, the forces on the forefoot and midfoot and the pressure per unit area increased in walking while texting. It is thought that the pace of walking slows down, and focus and attention shift to the front of the body.

A systematic and comprehensive review and investigation of intelligent IoT-based healthcare systems in rural societies and governments

Healthcare needs in rural areas differ significantly from those in urban areas. Addressing the healthcare challenges in rural communities is of paramount importance, as these regions often lack access to adequate healthcare facilities. Moreover, technological advancements, particularly in the realm of the Internet of Things (IoT), have brought about significant changes in the healthcare industry. IoT involves connecting real-world objects to digital devices, opening up various possibilities for improving healthcare delivery. One promising application of IoT is its use in monitoring the spread of diseases in remote villages through interconnected sensors and devices. Surprisingly, there has been a noticeable absence of comprehensive research on this topic. Therefore, the primary objective of this study is to conduct a thorough and systematic review of intelligent IoT-based healthcare systems in rural communities and their governance. The analysis covers research papers published until December 2022 to provide valuable insights for future researchers. The selected articles have been categorized into three main groups: monitoring, intelligent services, and body sensor networks. The findings indicate that IoT research has garnered significant attention within the healthcare community. Furthermore, the results illustrate the potential benefits of IoT for governments, especially in rural areas, in improving public health and strengthening economic ties. It is worth noting that establishing a robust security infrastructure is essential for implementing IoT effectively, given its innovative operational principles. In summary, this review enhances scholars' understanding of the current state of IoT research in rural healthcare settings while highlighting areas that warrant further investigation. Additionally, it keeps healthcare professionals informed about the latest advancements and applications of IoT in rural healthcare.

Technology adoption review for ageing well: analysis of technical solutions

While several technological solutions are available for older adults to improve their wellbeing and quality of life, little is known about the gaps between the needs, provided solutions, and their adoption from a more pragmatic perspective. This paper reports on reviewing existing technological solutions for older adults, which span the work life, life in the community, and wellbeing at home. We analyzed 50 different solutions to uncover both negative and positive features of these solutions from the perspective of the impact of technology adoption on the quality of life of older adults. Our approach harnesses holistic reasoning to determine the most suitable technologies available today and provides suggestions for improvement toward designing and implementing better solutions.

One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case

Smart cities and digital public health are closely related. Managing digital transformation in urbanization and living spaces is challenging. It is critical to prioritize the emotional and physical health and well-being of humans and their animals in the dynamic and ever-changing environment they share. Human-animal bonds are continuous as they live together or share urban spaces and have a mutual impact on each other's health as well as the surrounding environment. In addition, sensors embedded in the Internet of Things are everywhere in smart cities. They monitor events and provide appropriate responses. In this regard, accident and emergency informatics (A&EI) offers tools to identify and manage overtime hazards and disruptive events. Such manifold focuses fit with One Digital Health (ODH), which aims to transform health ecosystems with digital technology by proposing a comprehensive framework to manage data and support health-oriented policies. We showed and discussed how, by developing the concept of ODH intervention, the ODH framework can support the comprehensive monitoring and analysis of daily life events of humans and animals in technologically integrated environments such as smart homes and smart cities. We developed an ODH intervention use case in which A&EI mechanisms run in the background. The ODH framework structures the related data collection and analysis to enhance the understanding of human, animal, and environment interactions and associated outcomes. The use case looks at the daily journey of Tracy, a healthy woman aged 27 years, and her dog Mego. Using medical Internet of Things, their activities are continuously monitored and analyzed to prevent or manage any kind of health-related abnormality. We reported and commented on an ODH intervention as an example of a real-life ODH implementation. We gave the reader examples of a "how-to" analysis of Tracy and Mego's daily life activities as part of a timely implementation of the ODH framework. For each activity, relationships to the ODH dimensions were scored, and relevant technical fields were evaluated in light of the Findable, Accessible, Interoperable, and Reusable principles. This "how-to" can be used as a template for further analyses. An ODH intervention is based on Findable, Accessible, Interoperable, and Reusable data and real-time processing for global health monitoring, emergency management, and research. The data should be collected and analyzed continuously in a spatial-temporal domain to detect changes in behavior, trends, and emergencies. The information periodically gathered should serve human, animal, and environmental health interventions by providing professionals and caregivers with inputs and "how-to's" to improve health, welfare, and risk prevention at the individual and population levels. Thus, ODH complementarily combined with A&EI is meant to enhance policies and systems and modernize emergency management.

Managing Security of Healthcare Data for a Modern Healthcare System

The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data. Therefore, this research proposes a novel Lionized remora optimization-based serpent (LRO-S) encryption method to encrypt sensitive data and reduce privacy breaches and cyber-attacks from unauthorized users and hackers. The LRO-S method is the combination of hybrid metaheuristic optimization and improved security algorithm. The fitness functions of lion and remora are combined to create a new algorithm for security key generation, which is provided to the serpent encryption algorithm. The LRO-S technique encrypts sensitive patient data before storing it in the cloud. The primary goal of this study is to improve the safety and adaptability of medical professionals' access to cloud-based patient-sensitive data more securely. The experiment's findings suggest that the secret keys generated are sufficiently random and one of a kind to provide adequate protection for the data stored in modern healthcare management systems. The proposed method minimizes the time needed to encrypt and decrypt data and improves privacy standards. This study found that the suggested technique outperformed previous techniques in terms of reducing execution time and is cost-effective.

Monitoring indoor air quality using smart integrated gas sensor module (IGSM) for improving health in COPD patients

Technology advancement, industrialisation, and globalisation have been significant reasons for air pollution outdoors and indoors. It may surprise us that we spend about 80% of our time indoors breathing toxic, stale, polluted air, making us sluggish and fretful. In contrast to outdoor air, indoor air does not recycle consistently. It traps and builds pollutants from wood and coal stoves, furniture and building materials, paints and solvents, cigarette smoke, and cleaning supplies. The prolonged exposure to these hidden pollutants can prompt respiratory disorders such as lung disease, pneumonitis, asthma, pulmonary hypertension, and chronic obstructive pulmonary disease (COPD). It is enduring and is not curable, which has been a threat to humanity for ages. COPD's major cause is airborne particulate matter and other toxic compounds emitted from indoor and outdoor sources. Outdoor air pollution can be controlled only by acquiring changes in the vast population, wherein for indoor, every individual may create a major impact on improving air purity, thereby promoting health. The proposed design model for monitoring indoor air quality was tested in a normal and stimulating environment where we live. Parameters tested included temperature, humidity, amount of PM2.5, and the concentration of CO, CO₂, and NH₃. These parameters were monitored for five to 6 h per day for 8 days. Results indicate that the total air quality lies in the moderate range. Further study will be helpful to utilise this module as an effective Indoor air quality (IAQ) monitoring system. HIGHLIGHTS: • A simple, effective, inexpensive integrated gas sensor module (IGSM) has been proposed in this study to monitor the indoor air quality index (IAQI). • Indoor air quality was tested in a normal and stimulating environment for 8 days. • The integrated gas sensor module (IGSM) was composed of sensors in series, and the outputs for the respective parameter were measured easily. • Among the parameters tested, CO and PM2.5 lie in the moderate range, while other pollutants within the normal range reveal that the tested air quality is moderate.

QoS Review: Smart Sensing in Wake of COVID-19, Current Trends and Specifications With Future Research Directions

Smart Sensing has shown notable contributions in the healthcare industry and revamps immense advancement. With this, the present smart sensing applications such as the Internet of Medical Things (IoMT) applications are elongated in the COVID-19 outbreak to facilitate the victims and alleviate the extensive contamination frequency of this pathogenic virus. Although, the existing IoMT applications are utilized productively in this pandemic, but somehow, the Quality of Service (QoS) metrics are overlooked, which is the basic need of these applications followed by patients, physicians, nursing staff, etc. In this review article, we will give a comprehensive assessment of the QoS of IoMT applications used in this pandemic from 2019 to 2021 to identify their requirements and current challenges by taking into account various network components and communication metrics. To claim the contribution of this work, we explored layer-wise QoS challenges in the existing literature to identify particular requirements, and set the footprint for future research. Finally, we compared each section with the existing review articles to acknowledge the uniqueness of this work followed by the answer of a question why this survey paper is needed in the presence of current state-of-the-art review papers.

Qualitative analysis on the driving force behind upcycling practices associated with mobile applications: Circular economy perspective

Upcycling is a type of practice included within the circular economy. Although interest in this type of activity has grown in recent years, academic analysis of the drivers that impel these activities is poorly developed. This work focuses on upcycling activities developed in the context of rural tourism. Through field work based on structured interviews applied in the region of Andalusia (Spain), evidence was sought about the drivers that promote this type of practice. In particular, attention was paid to the role of mobile applications as facilitators of upcycling. The main finding was that technology helps in using upcycling practices when considering rural tourism, as demonstrated by the use of mobile applications and websites such as Wallapop. There was also evidence of the different factors that drive the development of upcycling practices: the existence of a traditional social network as a means of obtaining second-hand items, technology as an element that facilitates access to items owned by strangers, the need to offer the image sought by tourists, and a desire to maintain the cultural essence of the region.

Towards Dynamic Model-Based Agile Architecting of Cyber-Physical Systems

A model-based approach to large-scale distributed system architecting is suggested, which is based on the use of dynamic digital twins. This approach can be considered as an integration of known paradigms, such as digital twins, evolutionary architecture and agile architecture. It can also be considered as one of the possible realizations of the digital thread paradigm. As part of this approach, a three-level digital thread reference architecture is suggested, which includes the following levels: (i) digital thread support level; (ii) agile architecture support level; (iii) digital shadow support level. This approach has been used in the development of a number of real systems, and has shown its effectiveness in supporting system agility at the exploitation and modernization stages. The proposed approach is focused on building digital twin-based systems. This article may be interesting for specialists engaged in research and development in the domain of IoT- and IIoT-based information systems, primarily architects.

Industry 4.0 and Digitalisation in Healthcare

Industry 4.0 in healthcare involves use of a wide range of modern technologies including digitisation, artificial intelligence, user response data (ergonomics), human psychology, the Internet of Things, machine learning, big data mining, and augmented reality to name a few. The healthcare industry is undergoing a paradigm shift thanks to Industry 4.0, which provides better user comfort through proactive intervention in early detection and treatment of various diseases. The sector is now ready to make its next move towards Industry 5.0, but certain aspects that motivated this review paper need further consideration. As a fruitful outcome of this review, we surveyed modern trends in this arena of research and summarised the intricacies of new features to guide and prepare the sector for an Industry 5.0-ready healthcare system.

Health Monitoring Using Smart Home Technologies: A Scoping Review (Preprint)

BACKGROUND

The Internet of Things (IoT) has become integrated into everyday life, with devices becoming permanent fixtures in many homes. As countries face increasing pressure on their health care systems, smart home technologies have the potential to support population health through continuous behavioral monitoring.

OBJECTIVE

This scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring.

METHODS

Peer-reviewed papers from 2008 to 2021 related to smart home technologies for health care were extracted from 4 databases (PubMed, Scopus, ScienceDirect, and CINAHL); 49 papers met the inclusion criteria and were analyzed.

RESULTS

Most of the studies were from Europe and North America. The largest proportion of the studies were proof of concept or pilot studies. Approximately 78% (38/49) of the studies used real human participants, most of whom were older females. Demographic data were often missing. Nearly 60% (29/49) of the studies reported on the health status of the participants. Results were primarily reported in engineering and technology journals. Almost 62% (30/49) of the studies used passive infrared sensors to report on motion detection where data were primarily binary. There were numerous data analysis, management, and machine learning techniques employed. The primary challenges reported by authors were differentiating between multiple participants in a single space, technology interoperability, and data security and privacy.

CONCLUSIONS

This scoping review synthesizes the current state of research on smart home technologies for health care. We were able to identify multiple trends and knowledge gaps-in particular, the lack of collaboration across disciplines. Technological development dominates over the human-centric part of the equation. During the preparation of this scoping review, we noted that the health care research papers lacked a concrete definition of a smart home, and based on the available evidence and the identified gaps, we propose a new definition for a smart home for health care. Smart home technology is growing rapidly, and interdisciplinary approaches will be needed to ensure integration into the health sector.

Emerging IoT Applications in Sustainable Smart Cities for COVID-19: Network Security and Data Preservation Challenges with Future Directions

COVID-19 is a global infectious disease that can be easily spread by the contiguity of infected people. To prevent from COVID-19 and reduce its impact in sustainable smart cities, the global research communities are working relentlessly by harnessing the emerging technologies to develop the safest diagnosis, evaluation, and treatment procedures, and Internet of Things (IoT) is one of the pioneers among them. IoT can perform a pivotal role to diminish its immense contagious rate by suitable utilization in emerging healthcare IoT applications in sustainable smart cities. Therefore, the focus of this paper is to outline a survey of the emerging healthcare IoT applications practiced in the perspective of COVID-19 pandemic in terms of network architecture security, trustworthiness, authentication, and data preservation followed by identifying existing challenges to set the future research directions. The salient contributions of this work deal with the accomplishment of a detailed and comprehensive literature review of COVID-19 starting from 2019 through 2021 in the context of emerging healthcare IoT technology. In addition, we extend the correlated contributions of this work by highlighting the weak aspects of the existing emerging healthcare IoT applications, security of different network layers and secure communication environment followed by some associated requirements to address these challenges. Moreover, we also identify future research directions in sustainable smart cities for emerging healthcare IoT utilization in the context of COVID-19 with the most productive results and least network implementation costs.

Resource Allocation Schemes for 5G Network: A Systematic Review

Fifth-generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, lower power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising device-to-device networks, macro-cells, and different categories of small cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need for cellular service and the limited resources to provide it, capably handling network traffic and operation has become a problem of resource distribution. One of the utmost serious problems is to alleviate the jamming in the network in support of having a better QoS. However, although a limited number of review papers have been written on resource distribution, no review papers have been written specifically on 5G resource allocation. Hence, this article analyzes the issue of resource allocation by classifying the various resource allocation schemes in 5G that have been reported in the literature and assessing their ability to enhance service quality. This survey bases its discussion on the metrics that are used to evaluate network performance. After consideration of the current evidence on resource allocation methods in 5G, the review hopes to empower scholars by suggesting future research areas on which to focus.

A threat intelligence framework for protecting smart satellite-based healthcare networks

Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightweight protocols, such as constrained application protocol (CoAP), have been widely utilised in transferring sensing data of medical devices to end-users in smart satellite-based healthcare IIoT networks (SmartSat-IIoHT). However, such protocols are extensively deployed without appropriate security configurations, making attackers' mission easier for abusing these protocols to launch advanced cyber threats. This paper, therefore, presents a new threat intelligence framework to examine and model CoAP protocol's attacks in these systems. We present a ransom denial of service (RDoS) as a new threat that would exploit this protocol's vulnerabilities. We propose many RDoS attack's techniques to understand the attack indicators and analyse their behaviour on systems. Moreover, we present a real-time discovery of attacks' network behaviours using deep learning. The experiment results demonstrate that this proposed discovery model obtains a better performance in revealing RDoS than other conventional machine learning algorithms and accomplishing high fidelity of protecting SmartSat-IIoHT networks.

Using the Context-Sensitive Policy Mechanism for Building Data Acquisition Systems in Large Scale Distributed Cyber-Physical Systems Built on Fog Computing Platforms

The article deals with the use of context-sensitive policies in the building of data acquisition systems in large scale distributed cyber-physical systems built on fog computing platforms. It is pointed out that the distinctive features of modern cyber-physical systems are their high complexity and constantly changing structure and behavior, which complicates the data acquisition procedure. To solve this problem, it is proposed to use an approach according to which the data acquisition procedure is divided into two phases: model construction and data acquisition, which allows parallel realization of these procedures. A distinctive feature of the developed approach is that the models are built in runtime automatically. As a top-level model, a multi-level relative finite state operational automaton is used. The automaton state is described using a multi-level structural-behavioral model, which is a superposition of four graphs: the workflow graph, the data flow graph, the request flow graph and the resource graph. To implement the data acquisition procedure using the model, the context-sensitive policy mechanism is used. The article discusses possible approaches to implementation of suggested mechanisms and describes an example of application.

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China's success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

Optimal Consensus with Dual Abnormality Mode of Cellular IoT Based on Edge Computing

The continuous development of fifth-generation (5G) networks is the main driving force for the growth of Internet of Things (IoT) applications. It is expected that the 5G network will greatly expand the applications of the IoT, thereby promoting the operation of cellular networks, the security and network challenges of the IoT, and pushing the future of the Internet to the edge. Because the IoT can make anything in anyplace be connected together at any time, it can provide ubiquitous services. With the establishment and use of 5G wireless networks, the cellular IoT (CIoT) will be developed and applied. In order to provide more reliable CIoT applications, a reliable network topology is very important. Reaching a consensus is one of the most important issues in providing a highly reliable CIoT design. Therefore, it is necessary to reach a consensus so that even if some components in the system is abnormal, the application in the system can still execute correctly in CIoT. In this study, a protocol of consensus is discussed in CIoT with dual abnormality mode that combines dormant abnormality and malicious abnormality. The protocol proposed in this research not only allows all normal components in CIoT to reach a consensus with the minimum times of data exchange, but also allows the maximum number of dormant and malicious abnormal components in CIoT. In the meantime, the protocol can make all normal components in CIoT satisfy the constraints of reaching consensus: Termination, Agreement, and Integrity.

Sensors for indoor air quality monitoring and assessment through Internet of Things: a systematic review

The growing populations around the world are closely associated with rising levels of air pollution. The impact is not restricted to outdoor areas. Moreover, the health of building occupants is also deteriorating due to poor indoor air quality. As per the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Therefore, numerous companies have started the development of low-cost sensors to monitor indoor air pollution with the Internet of Things-based applications. However, due to the close association of air pollution levels to the mortality and morbidity rates, communities face several limitations while selecting sensors to address this public health challenge. The main contribution of this systematic review is to present a qualitative and quantitative evaluation of low-cost sensors while providing deep insights into the selection criteria for adequate monitoring. The authors in this paper discussed studies published after the year 2015, and it includes an analysis of papers published in the English language only. Moreover, this study highlights crucial research questions, states answers, and provides recommendations for future research studies. The outcomes of this paper will be useful for students, researchers, and industry members concerning the upcoming research and manufacturing activities. The results show that 28 studies (70%) include indoor thermal comfort assessment, 26 (65%) and 12 (30%) studies include CO₂ and CO sensors, respectively. In total, 32 (45.7%) out of 71 sensors (whose prices are available) discussed in this study are available in a price below the US $20 over online marketplaces. Furthermore, the authors conclude that 77.5% of the analyzed literature does not include calibration details, and the accuracy specification is missing for 39.4% sensors.

Internet of Things (IoT)-Based Wireless Health: Enabling Technologies and Applications

Wireless health is transforming health care by integrating wireless technologies into conventional medicine, including the diagnosis, monitoring, and treatment of illness [...]

Modelling the Influence of Electromagnetic Field on the User of a Wearable IoT Device Used in a WSN for Monitoring and Reducing Hazards in the Work Environment

The aim of this study was to evaluate the absorption in a user's head of an electromagnetic field (EMF) emitted by the Wi-Fi and/or Bluetooth module of a wearable small Internet of Things (IoT) electronic device (emitting EMF of up to 100 mW), in order to test the hypothesis that EMF has an insignificant influence on humans, and to compare the levels of such EMF absorption in various scenarios when using this device. The modelled EMF source was a meandered inverted-F antenna (MIFA)-type antenna of the ESP32-WROOM-32 radio module used in wearable devices developed within the reported study. To quantify the EMF absorption, the specific energy absorption rate (SAR) values were calculated in a multi-layer ellipsoidal model of the human head (involving skin, fat, skull bones and brain layers). The obtained results show up to 10 times higher values of SAR from the MIFA located in the headband, in comparison to its location on the helmet. Only wearable IoT devices (similar in construction and way of use to the investigated device) emitting at below 3 mW equivalent isotropically radiated power (EIRP) from Wi-Fi/Bluetooth communications modules may be considered environmentally insignificant EMF sources.

A comprehensive review of water quality monitoring and assessment in Nigeria

Nigeria is the most populated black nation in the world with about 199 million people. About 66.3 million Nigerians do not have access to safe drinking water. In this study, research findings on water quality monitoring and assessment in Nigeria over the past two decades were systematically reviewed. There are still grave enforcement issues in Nigeria as quality guidelines are still being contravened at no cost to the infringer due to the corrupt socio-political circumstances of the country. The quality of surface water, groundwater, rainwater and commercially available water was discussed in line with their pollution sources. The quality of surface water was generally poor. Groundwater pollution has come due to landfill leachate, oil and gas exploration and production, sewage and hydrogeological interactions of the groundwater with the base rock. The hydrogeological effect has led to the observation of lead and barium in groundwater in many locations across the country. The main issue with rainwater in Nigeria is the low pH but it was observed to be fairly clean. Commercially available water (bottled or sachet) is currently the best source of drinking water for the Nigerian populace. Bottled water quality is higher than for sachet water and the latter largely influenced by microbe contamination. Future perspectives in water quality monitoring and assessment are suggested in the evaluation of emerging contaminants and micro-pollutants and the utilisation of internet-enabled technologies.

Indoor air quality prediction using optimizers: A comparative study

Indoor air pollution (IAP) has become a serious concern for developing countries around the world. As human beings spend most of their time indoors, pollution exposure causes a significant impact on their health and well-being. Long term exposure to particulate matter (PM) leads to the risk of chronic health issues such as respiratory disease, lung cancer, cardiovascular disease. In India, around 200 million people use fuel for cooking and heating needs; out of which 0.4% use biogas; 0.1% electricity; 1.5% lignite, coal or charcoal; 2.9% kerosene; 8.9% cow dung cake; 28.6% liquified petroleum gas and 49% use firewood. Almost 70% of the Indian population lives in rural areas, and 80% of those households rely on biomass fuels for routine needs. With 1.3 million deaths per year, poor air quality is the second largest killer in India. Forecasting of indoor air quality (IAQ) can guide building occupants to take prompt actions for ventilation and management on useful time. This paper proposes prediction of IAQ using Keras optimizers and compares their prediction performance. The model is trained using real-time data collected from a cafeteria in the Chandigarh city using IoT sensor network. The main contribution of this paper is to provide a comparative study on the implementation of seven Keras Optimizers for IAQ prediction. The results show that SGD optimizer outperforms other optimizers to ensure adequate and reliable predictions with mean square error = 0.19, mean absolute error = 0.34, root mean square error = 0.43, R2 score = 0.999555, mean absolute percentage error = 1.21665%, and accuracy = 98.87%.

Data Collection Technology for Ambient Intelligence Systems in Internet of Things

Ambient Intelligence System (AmIS) can be constructed using data collected from Internet of Things (IoT). In this paper, the IoT data collection problem is studied for AmIS with dynamic structure and dynamic behavior of participants (devices), where constraints on resources consumption and performance are essential. A novel technology is proposed, which includes the following steps: (1) definition of the data collection (DC) problem (considering the model of the observed system, DC conditions, etc.); (2) DC policy assignment; (3) construction of DC models; (4) evaluation and presentation of the data processing results. The proposed DC technology supports the development of data collecting subsystems in AmIS. Such subsystems provide data that reflect the changes in structure, state, situation, and behavior of participants in their IoT environment in time. Therefore, we show how this “cognitive” function of the DC process increases the intelligence level of IoT environment.

Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT

Insect detection and control at an early stage are essential to the built environment (human-made physical spaces such as homes, hotels, camps, hospitals, parks, pavement, food industries, etc.) and agriculture fields. Currently, such insect control measures are manual, tedious, unsafe, and time-consuming labor dependent tasks. With the recent advancements in Artificial Intelligence (AI) and the Internet of things (IoT), several maintenance tasks can be automated, which significantly improves productivity and safety. This work proposes a real-time remote insect trap monitoring system and insect detection method using IoT and Deep Learning (DL) frameworks. The remote trap monitoring system framework is constructed using IoT and the Faster RCNN (Region-based Convolutional Neural Networks) Residual neural Networks 50 (ResNet50) unified object detection framework. The Faster RCNN ResNet 50 object detection framework was trained with built environment insects and farm field insect images and deployed in IoT. The proposed system was tested in real-time using four-layer IoT with built environment insects image captured through sticky trap sheets. Further, farm field insects were tested through a separate insect image database. The experimental results proved that the proposed system could automatically identify the built environment insects and farm field insects with an average of 94% accuracy.

Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test

The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.

Enabling Emergent Configurations in the Industrial Internet of Things for Oil and Gas Explorations: A Survey

Several heterogeneous, intelligent, and distributed devices can be connected to interact with one another over the Internet in what is termed internet of things (IoT). Also, the concept of IoT can be exploited in the industrial environment for enhancing the production of goods and services and for mitigating the risk of disaster occurrences. This application of IoT for enhancing industrial production is known as industrial IoT (IIoT). Emergent configuration (EC) is a technology that can be adopted to enhance the operation and collaboration of IoT connected devices in order to improve the efficiency of the connected IoT systems for maximum user satisfaction. To meet user goals, the connected devices are required to cooperate with one another in an adaptive, interoperable, and homogeneous manner. In this paper, a survey of the concept of IoT is presented in addition to a review of IIoT systems. The application of ubiquitous computing-aided software define networking (SDN)-based EC architecture is propounded for enhancing the throughput of oil and gas production in the maritime ecosystems by managing the exploration process especially in emergency situations that involve anthropogenic oil and gas spillages.

Mobile Computing Technologies for Health and Mobility Assessment: Research Design and Results of the Timed Up and Go Test in Older Adults

Due to the increasing age of the European population, there is a growing interest in performing research that will aid in the timely and unobtrusive detection of emerging diseases. For such tasks, mobile devices have several sensors, facilitating the acquisition of diverse data. This study focuses on the analysis of the data collected from the mobile devices sensors and a pressure sensor connected to a Bitalino device for the measurement of the Timed-Up and Go test. The data acquisition was performed within different environments from multiple individuals with distinct types of diseases. Then this data was analyzed to estimate the various parameters of the Timed-Up and Go test. Firstly, the pressure sensor is used to extract the reaction and total test time. Secondly, the magnetometer sensors are used to identify the total test time and different parameters related to turning around. Finally, the accelerometer sensor is used to extract the reaction time, total test time, duration of turning around, going time, return time, and many other derived metrics. Our experiments showed that these parameters could be automatically and reliably detected with a mobile device. Moreover, we identified that the time to perform the Timed-Up and Go test increases with age and the presence of diseases related to locomotion.

Indoor Air Quality Monitoring Systems for Enhanced Living Environments: A Review toward Sustainable Smart Cities

Smart cities follow different strategies to face public health challenges associated with socio-economic objectives. Buildings play a crucial role in smart cities and are closely related to people’s health. Moreover, they are equally essential to meet sustainable objectives. People spend most of their time indoors. Therefore, indoor air quality has a critical impact on health and well-being. With the increasing population of elders, ambient-assisted living systems are required to promote occupational health and well-being. Furthermore, living environments must incorporate monitoring systems to detect unfavorable indoor quality scenarios in useful time. This paper reviews the current state of the art on indoor air quality monitoring systems based on Internet of Things and wireless sensor networks in the last five years (2014–2019). This document focuses on the architecture, microcontrollers, connectivity, and sensors used by these systems. The main contribution is to synthesize the existing body of knowledge and identify common threads and gaps that open up new significant and challenging future research directions. The results show that 57% of the indoor air quality monitoring systems are based on Arduino, 53% of the systems use Internet of Things, and WSN architectures represent 33%. The CO2 and PM monitoring sensors are the most monitored parameters in the analyzed literature, corresponding to 67% and 29%, respectively.

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.

Indoor Positioning for Monitoring Older Adults at Home: Wi-Fi and BLE Technologies in Real Scenarios

This paper presents our experience on a real case of applying an indoor localization system for monitoring older adults in their own homes. Since the system is designed to be used by real users, there are many situations that cannot be controlled by system developers and can be a source of errors. This paper presents some of the problems that arise when real non-expert users use localization systems and discusses some strategies to deal with such situations. Two technologies were tested to provide indoor localization: Wi-Fi and Bluetooth Low Energy. The results shown in the paper suggest that the Bluetooth Low Energy based one is preferable in the proposed task.

Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution.

Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer

The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).

Circular Economy for Clothes Using Web and Mobile Technologies—A Systematic Review and a Taxonomy Proposal

Nowadays, it is possible to buy clothing using online platforms, either by accessing online brand stores, general online stores or circular economy platforms. This paper presents a study on mobile applications that support online commerce for clothing, focusing on the review of the mobile applications with features that are characteristic of the circular economy paradigm. Findings include the fact that almost all the mobile applications analysed have pictures illustrative of the clothes and accessories that are available for trading as well as their brief description. Furthermore, this paper presents a study of various scientific articles about the circular economy of clothes and how it can be beneficial to the future of the environment. It is a junction with a Web platform for its growth and its disclosure. The paper builds conclusions upon the assumption that the circular economy is a growing business that is part of a sustainable development where the main goal is to reduce the environmental impact. The paper proposes the analysis of an innovative taxonomy of mobile applications about the circular economy.

A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry

Data management and processing to enable predictive analytics in cyber physical systems holds the promise of creating insight over underlying processes, discovering anomalous behaviours and predicting imminent failures threatening a normal and smooth production process. In this context, proactive strategies can be adopted, as enabled by predictive analytics. Predictive analytics in turn can make a shift in traditional maintenance approaches to more effective optimising their cost and transforming maintenance from a necessary evil to a strategic business factor. Empowered by the aforementioned points, this paper discusses a novel methodology for remaining useful life (RUL) estimation enabling predictive maintenance of industrial equipment using partial knowledge over its degradation function and the parameters that are affecting it. Moreover, the design and prototype implementation of a plug-n-play end-to-end cloud architecture, supporting predictive maintenance of industrial equipment is presented integrating the aforementioned concept as a service. This is achieved by integrating edge gateways, data stores at both the edge and the cloud, and various applications, such as predictive analytics, visualization and scheduling, integrated as services in the cloud system. The proposed approach has been implemented into a prototype and tested in an industrial use case related to the maintenance of a robotic arm. Obtained results show the effectiveness and the efficiency of the proposed methodology in supporting predictive analytics in the era of Industry 4.0.

A Systematic Literature Review on Privacy by Design in the Healthcare Sector

In this digital age, we are observing an exponential proliferation of sophisticated hardware- and software-based solutions that are able to interact with the users at almost every sensitive aspect of our lives, collecting and analysing a range of data about us. These data, or the derived information out of it, are often too personal to fall into unwanted hands, and thus users are almost always wary of the privacy of such private data that are being continuously collected through these digital mediums. To further complicate the issue, the infringement cases of such databanks are on a sharp rise. Several frameworks have been devised in various parts of the globe to safeguard the issue of data privacy; in parallel, constant research is also being conducted on closing the loopholes within these frameworks. This study aimed to analyse the contemporary privacy by design frameworks to identify the key limitations. Seven contemporary privacy by design frameworks were examined in-depth in this research that was based on a systematic literature review. The result, targeted at the healthcare sector, is expected to produce a high degree of fortification against data breaches in the personal information domain.

A Research on the Classification and Applicability of the Mobile Health Applications

Mobile health applications are applied for different purposes. Healthcare professionals and other users can use this type of mobile applications for specific tasks, such as diagnosis, information, prevention, treatment, and communication. This paper presents an analysis of mobile health applications used by healthcare professionals and their patients. A secondary objective of this article is to evaluate the scientific validation of these mobile health applications and to verify if the results provided by these applications have an underlying sound scientific foundation. This study also analyzed literature references and the use of mobile health applications available in online application stores. In general, a large part of these mobile health applications provides information about scientific validation. However, some mobile health applications are not validated. Therefore, the main contribution of this paper is to provide a comprehensive analysis of the usability and user-perceived quality of mobile health applications and the challenges related to scientific validation of these mobile applications.

Internet of Things and Enhanced Living Environments: Measuring and Mapping Air Quality Using Cyber-physical Systems and Mobile Computing Technologies

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.

A Review of Internet of Things Technologies for Ambient Assisted Living Environments

The internet of things (IoT) aims to extend the internet to real-world objects, connecting smart and sensing devices into a global network infrastructure by connecting physical and virtual objects. The IoT has the potential to increase the quality of life of inhabitants and users of intelligent ambient assisted living (AAL) environments. The paper overviews and discusses the IoT technologies and their foreseen impacts and challenges for the AAL domain. The results of this review are summarized as the IoT based gerontechnology acceptance model for the assisted living domain. The model focuses on the acceptance of new technologies by older people and underscores the need for the adoption of the IoT for the AAL domain.

StoolNet for Color Classification of Stool Medical Images

The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare.

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

This paper presents iAirBot, an assistive robot for indoor air quality monitoring based on Internet of Things. The system can communicate with occupants and triggers alerts automatically using social networks. The information can be accessed by the caregiver to plan interventions for enhanced living environments in a timely manner. The results are promising, as the proposed architecture presents a cost-effective assistive robot for indoor quality monitoring. It connects several technological fields and knowledge areas, such as ambient assisted living, Internet of Things, wireless sensor networks, social networks, and indoor air quality. When compared to other systems, iAirBot stands out for the modularity and scalability of its sensors network, as well as the use of social networks for information sharing. Therefore, iAirBot is a significant system for enhanced living environments, occupational health, and well-being.