In recent years, the Internet of Things (IoT) has gained convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT-enabled/linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.

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Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study.

Background/objectives The Internet of Things (IoT) can create disruptive innovation in healthcare Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare For this, a brief study is required for research directions Methods Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology’s capabilities This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic Results Briefly studied the significant achievements of IoT with the help of a process chart Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them Conclusions In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases IoT’s sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic In the medical field, IoT’s focus is to help perform the treatment of different COVID-19 cases precisely It makes the surgeon job easier by minimising risks and increasing the overall performance By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital Medical students can now be better trained for disease detection and well guided for the future course of action IoT’s proper usage can help correctly resolve different medical challenges like speed, price, and complexity It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days

Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic

Before inception of Internet of Things (IoT), personal computers and laptop were used to handle daily tasks of individuals like mail surfing, access to bank portal, observing current temperature, among others. Nowadays, IoT-enabled smart devices like smart mobile phones, PDAs, and tablets are being used by them for such tasks due to rapid growth in IoT. Smart homes have been widely accepted by individuals and organizations world wide due to their many advantages. Home security systems can be defined as monitoring of complete home/some portion of home from a remotely located or centralized location. It allows the user to watch all activities inside the home from a remote location that ultimately gives satisfaction to the owner of the home. Many home security systems exist, but they have some challenging issues like: delay, non-web enabled and difficult to handle during transfer of alerts to user in situation where any unusual event occurred inside the home. If any unusual event encountered inside the home, where security systems deployed, then system must be capable enough to send alert to the user without any delay by phone, text, or email. Cameras and other latest network technologies have enabled us to remotely monitor the home more effectively and efficiently from our smart phone. Hence, considering the above mentioned facts, in this paper, we have proposed an advanced Internet of Thing based Security Alert System for Smart Home in order to detect an intruder or any unusual event at home, when nobody is available there. This low-cost home security system utilizes a small pyroelectric Infrared (PIR) module and raspberry pi for minimizing the delay during process of e-mail alert. This paper also confirms the advantage of Raspberry Pi flexibility and broad probability of its usage. Preliminary analyses have shown encouraging results.

An advanced Internet of Thing based Security Alert System for Smart Home

The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to the general way of life. Major sectors of the world industry and economy have been affected and the Internet of Things (IoT) management and framework is no exception in this regard. This article provides an up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies. It looks at the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation. The associated challenges of deployment of sensor hardware in the face of a rapidly spreading pandemic have been looked into as part of this review article. The effects of a global pandemic on the evolution of IoT architectures and management have also been addressed, leading to the likely outcomes on future IoT implementations. In general, this article provides an insight into the advancement of sensor-based E-health towards the management of global pandemics. It also answers the question of how a global virus pandemic has shaped the future of IoT networks.

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IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution

In recent years, the Internet of Things (IoT) has drawn convincing research ground as a new research topic in a wide variety of academic and industrial disciplines, especially in healthcare. The IoT revolution is reshaping modern healthcare systems by incorporating technological, economic, and social prospects. It is evolving healthcare systems from conventional to more personalized healthcare systems through which patients can be diagnosed, treated, and monitored more easily. The current global challenge of the pandemic caused by the novel severe contagious respiratory syndrome coronavirus 2 presents the greatest global public health crisis since the pandemic influenza outbreak of 1918. At the time this paper was written, the number of diagnosed COVID-19 cases around the world had reached more than 31 million. Since the pandemic started, there has been a rapid effort in different research communities to exploit a wide variety of technologies to combat this worldwide threat, and IoT technology is one of the pioneers in this area. In the context of COVID-19, IoT enabled /linked devices/applications are utilized to lower the possible spread of COVID-19 to others by early diagnosis, monitoring patients, and practicing defined protocols after patient recovery. This paper surveys the role of IoT-based technologies in COVID-19 and reviews the state-of-the-art architectures, platforms, applications, and industrial IoT-based solutions combating COVID-19 in three main phases, including early diagnosis, quarantine time, and after recovery.

Internet of Things for Current COVID-19 and Future Pandemics: An Exploratory Study

The world health organization (WHO) has declared the novel coronavirus disease (nCoV or COVID-19) outbreak a pandemic, and quarantines are playing a vital role in containing its spread. But globally, the defections of the quarantined subjects are raising serious concerns. If COVID-19 positive, the absconding quarantine subjects can infect more people, which makes the timely detection of them vital. As the literature suggests, a wearable makes a subject more compliant towards healthcare routines/restrictions; thus, in this work, we have designed an IoT based wearable quarantine band (IoT-Q-Band) to detect the absconding. While designing it, we kept in mind the cost, global supply chain disruption, and COVID-19 quarantine duration, which the WHO recommends. This wearable prototype, with the bundled mobile app, reports and tracks the absconding quarantine subjects in real-time. IoT-Q-Band is an economical solution that could benefit low income regions to prevent the spread of COVID-19.

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IoT-Q-Band: A low cost internet of things based wearable band to detect and track absconding COVID-19 quarantine subjects

Over the past few decades, with rapid growth in infrastructure, there has been tremendous growth in energy consumption. Along with this, more and more electronic appliances are added to the existing infrastructure every day. Furthermore, the existing energy bills just provide an aggregate number of units consumed but fail to provide any actionable details of appliance level usage. With the quest for long-term energy sustainability and to reduce this ever-growing energy consumption, research groups across the globe have started looking into energy disaggregation as a means of providing feedback. Some promising techniques such as non-intrusive appliance load monitoring have been adopted to provide detailed energy breakdown to the end consumer. Despite all these efforts, energy attribution to the electrical activities still seems to be a farfetched goal, especially in shared spaces. In this paper, we have analyzed the possibility of using radio frequency (RF) emissions from electronic appliances to detect electrical activity. Besides their known operation, these appliances are known to radiate high-frequency noise in the ambient environment, also called RF interference (RFI). Hence, by utilizing these RFI emissions from electronic appliances, electrical activity from the appliance can be detected in multiple frequency bands and at varying distances. An eight-fit Gaussian mixture model and $k$ -peak finder are used for feature extraction from RFI data, followed by appliance activity recognition using $k$ -nearest neighbor-based classification. Appliance detection is performed with a mean accuracy of 71.9% across seven-class classification problem. Finally, the characteristic features of RFI observed from these appliances are discussed.

Appliance Activity Recognition Using Radio Frequency Interference Emissions

In orthogonal time frequency space (OTFS) systems, channel estimation (CE) is often performed using a pilot based approach. This is usually done in two steps: first, valid channel paths are detected by comparing the magnitude of received symbols against a threshold, and then the associated channel coefficient is estimated. In an attempt to avoid channel path misdetection, existing approaches often deploy large guard bands surrounding a relatively high-power pilot symbol. Furthermore, it is generally assumed that this guard region is sufficiently large to ensure that the time/frequency spreading of the channel does not result in any interference between the pilot and data symbols, thus facilitating simple channel estimation schemes. In this paper, we propose a channel estimation scheme which works even when this assumption is no longer true, i.e., when the channel results in pilot-data interference as may occur in scenarios involving high mobility and/or large delay-spread. The ability of a receiver to operate in such scenarios allows system designers the freedom to use smaller guard bands based on typical (not worst-case) channel spread parameters, thereby yielding higher spectral efficiency. In this work, we derive a maximum likelihood channel path detection scheme followed by a minimum mean-square error channel estimator. The performance advantage of the proposed receiver is verified using extensive bit error rate simulations.

Maximum Likelihood Channel Path Detection and MMSE Channel Estimation in OTFS Systems

The presence of intrinsic inter-carrier interference (ICI) and inter-symbol interference (ISI) at the output of the matched filter receiver in filter bank multi-carrier with offset quadrature amplitude modulation (FBMC-OQAM) systems complicates the task of channel estimation (CE). The conventional approach is to use a pilot symbol preamble structure, such as in the interference approximation/cancellation methods (IAM/ICM), together with a simple virtual symbol (VS) based CE scheme. However, this virtual symbol approach ignores the residual interference from the data portion of the FMBC burst as well as the correlation of the noise across the sub-carriers; both of these factors ultimately limit the performance. In this paper, we propose a generalized least squares (GLS) based CE scheme which, when applied to the IAM/ICM pilot structures, takes into account both the correlated noise and data interference effects. Closed-form expressions are derived for the channel estimation mean-squared error (CE-MSE) of the proposed GLS method and for the conventional VS method, and it is shown that the proposed method leads to a significant reduction of the CE-MSE by up to 6 dB when compared with the VS based method. This is demonstrated to result in improved bit error rate (BER) performance, especially when higher-order modulation is employed, making it an attractive channel estimation method for many modern deployment scenarios. The proposed GLS-based channel estimation scheme is parameterized allowing a flexible trade-off between channel frequency selectivity, estimation complexity and CE-MSE gain. Finally, symmetry properties of the intrinsic interference coefficients for a general FBMC-OQAM system are also proved, which facilitate the computation of the noise and interference autocorrelation matrices required by the proposed algorithm.

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Generalized Least Squares Based Channel Estimation for FBMC-OQAM

In a country with an extensive road network, it is very tough for authorities to identify and repair the potholes on time, which emerge due to casual wear and tear of the road. These potholes are dangerous for unsuspecting high-speed vehicles and results in multiple life-threatening accidents year-round. Apart from potholes, another severe concern about the time spent on roads is air pollution. Breathing the polluted air, mainly containing the particulate matter that has a diameter of fewer than 2.5 micrometers, is toxic to humans. In this work, we have judiciously designed an Internet of Things based smart helmet, which uses crowdsourcing to report potholes and collect crucial on-road air pollution data so that a person could avoid risk to life and health. We have also introduced the novel concept of remembrance factor and severity index, which could be useful in dealing with the stale and invalid pothole data in the database.

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Vibhutesh Kumar Singh

Papers

IoT-Q-Band: A low cost internet of things based wearable band to detect and track absconding COVID-19 quarantine subjects

Appliance Activity Recognition Using Radio Frequency Interference Emissions

Maximum Likelihood Channel Path Detection and MMSE Channel Estimation in OTFS Systems

Generalized Least Squares Based Channel Estimation for FBMC-OQAM

SmartPPM: An Internet of Things Based Smart Helmet Design for Potholes and Air Pollution Monitoring