Abstract

Coronavirus 2019, called COVID-19, is a transmissible disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It earlier impacted the citizens of China alone. However, it has rapidly spread all over the world. The COVID-19 supply chain system aims to facilitate access to several critical items, such as personal protective equipment (PPE), biomedical equipment, diagnostics supplies, and vaccines. In this article, we discuss a robust security framework for vaccine distribution and tracking in an Internet of Medical Things (IoMT)-based cloud-assisted COVID-19 environment by considering both intra-country and inter-country scenarios. Various transactions related to vaccine requests, orders, distribution, and tracking are put into the blockchain in the form of blocks. Since blockchain technology offers immutability, transparency, and decentralization, the security of the proposed framework has been improved significantly. The proposed framework also supports artificial intelligence(AI)-based big data analytics on the information stored into the blocks in the blockchain. Furthermore, a practical demonstration of the proposed framework has been done through a blockchain simulation study.

Abstract

—In an Internet of Things (IoT) enabled e-health system, smart health devices sense the health data continuously and share the collected data with the neighboring controller device (i.e., personal server) via some wireless communication mechanism (i.e., bluetooth and zigbee), and finally the data is stored on some health server (i.e., a server over the cloud). The health data is then accessible to healthcare service providers (for example, doctors, nursing staff, relatives of patient) for tracking and monitoring of health conditions of the patients for their better treatment at the earliest. In an IoT enabled health system, the smart healthcare devices communicate over public channel, which causes various types of threats and attacks on the ongoing communication. Therefore, we need a powerful privacypreserving security mechanism to secure the communication happens in an IoT enabled e-health system as the health data is strictly private and confidential. In this paper, we propose a new privacy-preserving access control and key management scheme for the secure communication of IoT enabled e-health system (SPCS-IoTEH). We also conduct informal security analysis of the proposed SPCS-IoTEH to show its robustness against various types of active and passive attacks. The performance of SPCSIoTEH is also shown to be better than other existing competing schemes.

Abstract

In recent years, the Internet of Things (IoT) enabled drones, also called as unmanned aerial vehicles (UAVs), are widely used in many applications ranging from military to civilian applications, such as wildlife monitoring. Since the drones provide a risk-free as well as low-cost facility in order to quickly and persistently monitor natural circumstances at high spacial temporal resolution, they help in wildlife monitoring research. Due to wireless communication nature, the communication among the deployed drones in their respective flying zones and the IoT smart devices installed in animal bodies, and also among the drones and their respective ground station server (GSS), is susceptible to various passive and active attacks. To mitigate these issues, we propose a public blockchain based access control implementation for wild-life monitoring purpose. The application of both access control and blockchain at the same time not only protects various attacks, but also maintains immutability, transparency as well as decentralization properties. Next, we simulate the proposed security framework for the blockchain part to measure the total computational time needed to add a varied number of blocks in a blockchain and also a varied number of transactions per block. Finally, a practical testbed experiment has been implemented to show the feasibility of the proposed framework. Index Terms—Wildlife monitoring, blockchain, consensus, access control, security, testbed experiments.

Abstract

— The effect of adjusting damping factor α and tolerance τ on iterations needed for PageRank computation is studied here. Relative performance of PageRank computation with L1, L2, and L∞ norms used as convergence check, are also compared with six possible mean ratios. It is observed that increasing the damping factor α linearly increases the iterations needed almost exponentially. On the other hand, decreasing the tolerance τ exponentially decreases the iterations needed almost exponentially. On average, PageRank with L∞ norm as convergence check is the fastest, quickly followed by L2 norm, and then L1 norm. For large graphs, above certain tolerance τ values, convergence can occur in a single iteration. On the contrary, below certain tolerance τ values, sensitivity issues can begin to appear, causing computation to halt at maximum iteration limit without convergence. The six mean ratios for relative performance comparison are based on arithmetic, geometric, and harmonic mean, as well as the order of ratio calculation. Among them GM-RATIO, geometric mean followed by ratio calculation, is found to be most stable, followed by AM-RATIO.

Abstract

The effect of adjusting damping factor α, from a small initial value α0 to the final desired αf value, upon then iterations needed for PageRank computation is observed. Adjustment of the damping factor is done in one or more steps. Results show no improvement in performance over a fixed damping factor based PageRank.

Abstract

In this paper, we investigate the design of a system that is both energy efficient and has low hardware complexity. A fully analog architecture is considered in the system model to reduce the hardware complexity. The low beamforming gain of a fully analog system is improved by using double phase shifters at each antenna element, and multi-user(MU) transmission is enabled by using non-orthogonal multiple access(NOMA) as the radio access technique. The problem is formulated as an appropriately constrained energy efficiency(EE) maximization problem. This formulation is non-convex because it is fractional. To simplify this into a convex problem, we propose to use a quadratic transform. We then present an iterative algorithm to obtain optimal beamformer and power allocation. Finally, we evaluate the proposed system model via simulations.

Abstract

In recent times, a few new low-cost sensors have been introduced to the global market for monitoring particulate matter (PM). In this paper, the performance of three such low-cost PM sensors, namely SDS011, Prana Air, and SPS30, for measuring PM2.5 and PM10 levels is evaluated against a standard reference Aeroqual Series-500. The test setup was exposed to PM concentrations ranging from 30 µg/cm3 to 600 µg/cm3 . The results were based on 1 min, 15 min, 30 min, and 1 hr average readings. The experiments were carried out in indoor as well as outdoor environments. The comparative evaluation was performed before and after calibration. The performance of these sensors is evaluated in terms of coefficient of determination (R 2 ), coefficient of variation (Cv) and root mean square error (RMSE). Evaluation results show that these low-cost sensors have good performance after calibration with a reference sensor.

Abstract

During the COVID-19 pandemic, India’s complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad’s lockdown, the capital of Telangana State. For this, two datasets are employed. The first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIITH in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown

Abstract

Healthy-Unhealthy Music Scale (HUMS), a compact 13-item instrument was developed for assessing musical engagement that is indicative of adolescent well-being (Saarikallio et al, 2015, Child and Adolescent Mental Health). However, there have been no studies examining the validity of HUMS in a clinically depressed cohort (DC).

Abstract

Dementia is a syndrome chronic or progressive that usually affects the cognitive functioning of the subjects. Alzheimer’s, a neurodegenerative disorder, is the leading cause of dementia. One of the many symptoms of Alzheimer’s Dementia is the inability to speak and understand language clearly. The last decade has seen a surge in the research done in Alzheimer’s Dementia detection using Linguistics and acoustic features. This paper takes up the Alzheimer’s Dementia classification task of ADReSS INTERSPEECH-2020 challenge, "Alzheimer’s Dementia Recognition through Spontaneous Speech: The ADReSS Challenge". It uses eight different acoustic features to find the attributes in the human speech production system (vocal track and excitation source) affected by Alzheimer’s Dementia. In this study, the Alzheimer’s dementia classification is performed using five different Machine Learning models using ADReSS INTERSPEECH-2020 challenge dataset. Since most of the studies in the previous literature have used linguistic features successfully for Alzheimer’s dementia classification, the current study also demonstrates the performance of the BERT model for the dementia classification task. The maximum accuracy obtained by the acoustic feature is 64.5%, and the BERT Model provides a classification accuracy of 79.1% over the test dataset. Finally, the score-level fusion of the acoustic model with the BERT Model shows an improvement of 6.1% classification accuracy over the BERT Model, which indicates the complementary nature of acoustic features to linguistic features.