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Wearables are getting large acceptance in the continuous monitoring of health status and physiological data. Medical devices and their connectivity through Internet along with the electronics health record (EHR) and AI analytics making smart healthcare possible. Internet-of-Medical-Things (IoMT)-end devices like wearables and implantables are key for smart healthcare. Smart garment is a specific wearable which can be used for smart healthcare. This paper presents the design and development of a smart garment called MyWear that continuously monitors and collects physiological data. It can analyze muscle activity, stress levels, and heart rate variations and send all the data to the cloud. Through abnormal variations in vitals, it can also predict the risk of heart failure and with the in-built alert system, it can notify the associated medical officials if necessary. We also propose a deep neural network model that classifies heartbeat data into abnormalities with 96.9% accuracy and 97.3% precision. [ABSTRACT FROM AUTHOR]

Considering today’s lifestyle, people just sleep forgetting the benefits sleep provides to the human body. Smart-Yoga Pillow (SaYoPillow) is proposed to help in understanding the relationship between stress and sleep and to fully materialize the idea of “Smart-Sleeping” by proposing an edge device. An edge processor with a model analyzing the physiological changes that occur during sleep along with the sleeping habits is proposed. Based on these changes during sleep, stress prediction for the following day is proposed. The secure transfer of the analyzed stress data along with the average physiological changes to the IoT cloud for storage is implemented. A secure transfer of any data from the cloud to any third party applications is also proposed. A user interface is provided allowing the user to control the data accessibility and visibility. SaYoPillow is novel, with security features as well as consideration of sleeping habits for stress reduction, with an accuracy of up to 96%. [ABSTRACT FROM AUTHOR]

People around the globe rely on their blood samples for their glucose level measurement. There is a demand for non-invasive, precise and cost-effective solutions to monitor blood glucose level and control of diabetes. Serum glucose is an accurate blood glucose measurement method in comparison to capillary glucose measurement. Presently, the serum glucose is measured through laboratory setup with an invasive approach. The invasive method is painful and is not suitable for continuous glucose measurement. In this paper, we propose a novel wearable non-invasive consumer device (called iGLU 2.0) which can be used by consumers for accurate continuous blood glucose monitoring. This device uses a novel short near infrared (NIR) spectroscopy developed by us. It is incorporated with Internet-of-Medical-Things (IoMT) for smart healthcare where the healthcare data is stored on the cloud and is accessible to the users and caregivers. Analysis of the optimized regression model is performed and the system is calibrated and validated through healthy, prediabetic and diabetic patients. The robust regression models of serum glucose level is then deployed as the mechanism for precise measurement in iGLU 2.0. The performance of iGLU 2.0 is validated with the prediction of capillary blood glucose using Average Error (AvgE) and Mean Absolute Relative Difference (mARD) which are calculated as 6.09% and 6.07%, respectively, whereas for serum glucose, AvgE and mARD are estimated as 4.88% and 4.86%, respectively. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue including the complementary metal oxide semiconductors (CMOS) LC voltage oscillator design, high-throughput digital microfluidic biochip layout design, and retail beamed power using millimeter waves.

A novel approach for obfuscated JPEG compression/decompression (CODEC) IP core design methodology, suitable for use in re-usable IP core designs, is presented. This incorporates structural obfuscation for architecture or structure hiding from an adversary in order to maximize the complexity against reverse engineering. Additionally, the proposed methodology performs the entire compression and decompression through a single dedicated hardware IP core. To obtain a lightweight (low cost version) of the proposed obfuscated JPEG CODEC IP, particle swarm optimization driven design space exploration is employed. Results of the proposed low cost, obfuscated JPEG CODEC IP core design when compared to un-protected (un-obfuscated) design yielded enhancement in strength of obfuscation of 76%, as well as reduction of 5% compared to un-optimized design. [ABSTRACT FROM AUTHOR]

In medical practices, the detection of diseases highly depends on different medical tests. Electrocardiogram (ECG) technique is commonly used for heart disease diagnosis. Doctors can measure pulse and other heart boundaries with the aid of it. Fast and precise detection of forms of arrhythmia is critical while identifying heart disease. In this work, we proposed an intelligent ECG device (called iKardo) with the built-in automatic capability to classify into critical and non-critical data from an imbalanced ECG dataset for the smart IoT or Internet of Things based smart healthcare device. Particular emphasis is given to the reduction of data misclassification by converting imbalanced data into a balanced dataset using necessary techniques. This proposed iKardo helps in the accurate detection of critical ECG beats with an accuracy of 99.58% and result in a smart healthcare monitoring device that would make the disease detection fast and precise. [ABSTRACT FROM AUTHOR]

E-health systems have witnessed widespread usage in the last few years, mainly due to advancements in health monitoring hardware and the availability of remote diagnostic services. Given the numerous benefits, there are two main challenges: management of the e-health ecosystem and security and privacy of sensitive data. The former results from nonunified, redundant, and often replicated information across numerous independent e-health service providers. While the later stems from sensitive information stored in centralized systems that can be compromised. Blockchain has emerged as a promising technology, which can be used to secure access and privacy of data and provide an umbrella management solution to large-scale distributed and decentralized enterprise systems. In this article, we present a unified system for migrating independent conventional e-health systems to a single blockchain-based ecosystem. More specifically, we address the issues of difference in data structures for conventional relational databases and blockchain file databases. The solution describes the conversion process and synchronization of information in a unified system for large-scale e-health data. The implementation and analysis show that significant improvements in data storage, access control, and seamless migration can be achieved. [ABSTRACT FROM AUTHOR]

Blockchain as a distributed ledger technology can be very effective in providing access control and big data management in health-care systems. Because implementing or migrating to a pure blockchain solution is an extremely challenging task, several design and implementation dynamics should be considered. [ABSTRACT FROM AUTHOR]

A correct balance of nutrient intake is very important, particularly in infants. When the body is deprived of essential nutrients, it can lead to serious disease and organ deterioration which can cause serious health issues in adulthood. Automated monitoring of the nutritional content of food provided to infants, not only at home but also in daycare facilities, is essential for their healthy development. To address this challenge, this paper presents a new Internet of Things (IoT)-based fully automated nutrition monitoring system, called Smart-Log, to advance the state-of-art in smart healthcare. For the realization of Smart-Log, a novel 5-layer perceptron neural network and a Bayesian network-based accurate meal prediction algorithm are presented in this paper. Smart-Log is prototyped as a consumer electronics product which consists of WiFi enabled sensors for food nutrition quantification, and a smart phone application that collects nutritional facts of the food ingredients. The Smart-Log prototype uses an open IoT platform for data analytics and storage. Experimental results consisting of 8172 food items for 1000 meals show that the prediction accuracy of Smart-Log is 98.6%. [ABSTRACT FROM AUTHOR]

Fog computing is a recent research trend to bring cloud computing services to network edges. EDCs are deployed to decrease the latency and network congestion by processing data streams and user requests in near real time. EDC deployment is distributed in nature and positioned between cloud data centers and data sources. Load balancing is the process of redistributing the work load among EDCs to improve both resource utilization and job response time. Load balancing also avoids a situation where some EDCs are heavily loaded while others are in idle state or doing little data processing. In such scenarios, load balancing between the EDCs plays a vital role for user response and real-time event detection. As the EDCs are deployed in an unattended environment, secure authentication of EDCs is an important issue to address before performing load balancing. This article proposes a novel load balancing technique to authenticate the EDCs and find less loaded EDCs for task allocation. The proposed load balancing technique is more efficient than other existing approaches in finding less loaded EDCs for task allocation. The proposed approach not only improves efficiency of load balancing; it also strengthens the security by authenticating the destination EDCs. [ABSTRACT FROM PUBLISHER]

This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber-physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient's Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle's Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient's gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient's BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time.

Due to the enormous amounts of data being generated between users, Intelligent Transportation Systems (ITS) are complex Cyber-Physical Systems that necessitate a reliable and safe infrastructure. Internet of Vehicles (IoV) is the term that describes the interconnection for every single node, device, sensor, and actuator that are Internet enabled, whether attached or unattached to vehicles. A single smart vehicle will generate a huge amount of data. Concurrently, it needs an instant response to avoid accidents since vehicles are fast-moving objects. In this work, we explore Distributed Ledger Technology (DLT) and collect data about consensus algorithms and their applicability to be used in the IoV as the backbone of ITS. Multiple distributed ledger networks are currently in operation. Some are used in finance or supply chains, and others are used for general decentralized applications. Despite the secure and decentralized nature of the blockchain, each of these networks has trade-offs and compromises. Based on the analysis of consensus algorithms, a conclusion has been made to design one that fits the requirements of ITS-IOV. FlexiChain 3.0 is proposed in this work to serve as a Layer0 network for different stakeholders in the IoV. A time analysis has been conducted and shows a capacity of 2.3 transactions per second, which is an acceptable speed to be used in IoV. Moreover, a security analysis was conducted as well and shows high security and high independence of the node number in terms of security level per the number of participants.

Groundwater overuse in different domains will eventually lead to global freshwater scarcity. To meet the anticipated demands, many governments worldwide are employing innovative and traditional techniques for forecasting groundwater availability by conducting research and studies. One challenging step for this type of study is collecting groundwater data from different sites and securely sending it to the nearby edges without exposure to hacking and data tampering. In the current paper, we send raw data formats from the Internet of Things to the Distributed Data Storage (DDS) and Blockchain (BC) edges. We use a distributed and decentralized architecture to store the statistics, perform double hashing, and implement access control through smart contracts. This work demonstrates a modern and innovative approach combining DDS and BC technologies to overcome traditional data sharing, and centralized storage, while addressing blockchain limitations. We have shown performance improvements with increased data quality and integrity.

It is a known fact that large quantities of farm and meat products rot and are wasted if correct actions are not taken, which may lead to serious health issues if consumed. There is no proper system for tracking and communicating the status of the goods to their respective stakeholders in a secure way. Consumers have every right to know the quality of the products they consume. Using monitoring tools, such as the Internet of Agricultural Things (IoAT), and modern data protection techniques for storing and sharing, will help mitigate data integrity issues during the transmission of sensor records, increasing the data quality. The visibility state at the customer end is also improved, and they are aware of the agricultural product's conditions throughout the real-time distribution process. In this paper, we developed and implemented a CorDapp application to manage the data for the supply chain, called "agroString". We collected the temperature and humidity data using IoAT-Edge devices and various datasets from multiple sources. We then sent those readings to the CorDapp agroString and successfully shared them among the relevant parties. With the help of a Corda private blockchain, we attempted to increase data integrity, trust, visibility, provenance, and quality at each logistic step, while decreasing blockchain and central system limitations.

The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in the detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models are become inadequate in handling the pandemic mitigation process using real-time data. The present models are server-centric and controlled by a single party, where the management of confidentiality, integrity, and availability (CIA) of data is doubtful. Therefore, a decentralised user-centric model is necessary, where the CIA of user data is assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system (iBlock). The iBlock uses robust technologies like hybrid computing and IPFS to support system functionality. A pseudo-anonymous personal identity is introduced using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML., Competing Interests: Conflicts of InterestThe authors declare that they have no conflict of interest and there was no human or animal testing or participation involved in this research. All data were obtained from public domain sources., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.)

This paper proposes two differential detection techniques for signal detection in mobile molecular communication (MMC) for targeted drug delivery (TDD) application. In MMC, a nano-transmitter and a nano-receiver are considered to be in Brownian motion in an extracellular fluid medium. Transmitter uses calcium molecules to communicate with the receiver. Detection is performed using concentration difference based detector (CDD) at the receiver which calculates the maximum absolute concentration difference of the received signal within the same bit interval to detect the bit. This improves the bit error rate (BER) performance in MMC. The performance is further enhanced using manchester coded transmission with differential detection (MCD). In MCD, Bit-1 is coded by the symbol [1 0] and Bit-0 is coded by the symbol [0 1] and the difference between peaks of signals received in consecutive bit duration is taken to detect the bit. Simulation results prove that the MCD technique is 3 dB less sensitive to inter symbol interference (ISI) than the CDD technique. The detection threshold is selected using maximum a posteriori probability (MAP) rule. The performance of these detectors is compared with other existing detection techniques. Results reveal that BER performance of the CDD and MCD better by at least 3 dB and 6 dB, respectively. The proposed CDD and MCD techniques perform better in different bit-sequence length, various initial distance and different bit duration than other existing techniques.