arXiv subjects

Saraju P. Mohanty

Publications and source records attributed to Saraju P. Mohanty.

At least 37 records · Page 2Linked to original sources

arXiv2022

Easy-Sec: PUF-Based Rapid and Robust Authentication Framework for the Internet of Vehicles

With the rapid growth of new technological paradigms such as the Internet of Things (IoT), it opens new doors for many applications in the modern era for the betterment of human life. One of the recent applications of the IoT is the Internet of Vehicles (IoV) which helps to see unprecedented growth of connected vehicles on the roads. The IoV is gaining attention due to enhancing traffic safety and providing low route information. One of the most important and major requirements of the IoV is preserving security and privacy under strict latency. Moreover, vehicles are required to be authenticated frequently and fast considering limited bandwidth, high mobility, and density of the vehicles. To address the security vulnerabilities and data integrity, an ultralight authentication scheme has been proposed in this article. Physical Unclonable Function (PUF) and XOR function are used to authenticate both server and vehicle in two message flow which makes the proposed scheme ultralight, and less computation is required. The proposed Easy-Sec can authenticate vehicles maintaining low latency and resisting known security threats. Furthermore, the proposed Easy-Sec needs low overhead so that it does not increase the burden of the IoV network. Computational ( around 4 ms) and Communication (32 bytes) overhead shows the feasibility, efficiency, and also security features are depicted using formal analysis, Burrows, Abadi, and Needham (BAN) logic, and informal analysis to show the robustness of the proposed mechanisms against security threats.

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arXiv2022

PharmaChain: A Blockchain to Ensure Counterfeit Free Pharmaceutical Supply Chain

Access to essential medication is a primary right of every individual in all developed, developing and underdeveloped countries. This can be fulfilled by pharmaceutical supply chains (PSC) in place which will eliminate the boundaries between different organizations and will equip them to work collectively to make medicines reach even the remote corners of the globe. Due to multiple entities, which are geographically widespread, being involved and very complex goods and economic flows, PSC is very difficult to audit and resolve any issues involved. This has given rise to many issues, including increased threats of counterfeiting, inaccurate information propagation throughout the network because of data fragmentation, lack of customer confidence and delays in distribution of medication to the place in need. Hence, there is a strong need for robust PSC which is transparent to all parties involved and in which the whole journey of medicine from manufacturer to consumer can be tracked and traced easily. This will not only build safety for the consumers, but will also help manufacturers to build confidence among consumers and increase sales. In this article, a novel Distributed Ledger Technology (DLT) based transparent supply chain architecture is proposed and a proof-of-concept is implemented. Efficiency and scalability of the proposed architecture is evaluated and compared with existing solutions.

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arXiv2022

Everything You wanted to Know about Smart Agriculture

The world population is anticipated to increase by close to 2 billion by 2050 causing a rapid escalation of food demand. A recent projection shows that the world is lagging behind accomplishing the "Zero Hunger" goal, in spite of some advancements. Socio-economic and well being fallout will affect the food security. Vulnerable groups of people will suffer malnutrition. To cater to the needs of the increasing population, the agricultural industry needs to be modernized, become smart, and automated. Traditional agriculture can be remade to efficient, sustainable, eco-friendly smart agriculture by adopting existing technologies. In this survey paper the authors present the applications, technological trends, available datasets, networking options, and challenges in smart agriculture. How Agro Cyber Physical Systems are built upon the Internet-of-Agro-Things is discussed through various application fields. Agriculture 4.0 is also discussed as a whole. We focus on the technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) which support the automation, along with the Distributed Ledger Technology (DLT) which provides data integrity and security. After an in-depth study of different architectures, we also present a smart agriculture framework which relies on the location of data processing. We have divided open research problems of smart agriculture as future research work in two groups - from a technological perspective and from a networking perspective. AI, ML, the blockchain as a DLT, and Physical Unclonable Functions (PUF) based hardware security fall under the technology group, whereas any network related attacks, fake data injection and similar threats fall under the network research problem group.

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arXiv2021

iThing: Designing Next-Generation Things with Battery Health Self-Monitoring Capabilities for Sustainable IoT in Smart Cities

An accurate and reliable technique for predicting Remaining Useful Life (RUL) for battery cells proves helpful in battery-operated IoT devices, especially in remotely operated sensor nodes. Data-driven methods have proved to be the most effective methods until now. These IoT devices have low computational capabilities to save costs, but Data-Driven battery health techniques often require a comparatively large amount of computational power to predict SOH and RUL due to most methods being feature-heavy. This issue calls for ways to predict RUL with the least amount of calculations and memory. This paper proposes an effective and novel peak extraction method to reduce computation and memory needs and provide accurate prediction methods using the least number of features while performing all calculations on-board. The model can self-sustain, requires minimal external interference, and hence operate remotely much longer. Experimental results prove the accuracy and reliability of this method. The Absolute Error (AE), Relative error (RE), and Root Mean Square Error (RMSE) are calculated to compare effectiveness. The training of the GPR model takes less than 2 seconds, and the correlation between SOH from peak extraction and RUL is 0.97.

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arXiv2021

CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19

The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread all over the world causing serious health problems as well as serious impact on the global economy. Reliable and fast testing of the COVID-19 has been a challenge for researchers and healthcare practitioners. In this work we present a novel machine learning (ML) integrated X-ray device in Healthcare Cyber-Physical System (H-CPS) or smart healthcare framework (called CoviLearn) to allow healthcare practitioners to perform automatic initial screening of COVID-19 patients. We propose convolutional neural network (CNN) models of X-ray images integrated into an X-ray device for automatic COVID-19 detection. The proposed CoviLearn device will be useful in detecting if a person is COVID-19 positive or negative by considering the chest X-ray image of individuals. CoviLearn will be useful tool doctors to detect potential COVID-19 infections instantaneously without taking more intrusive healthcare data samples, such as saliva and blood. COVID-19 attacks the endothelium tissues that support respiratory tract, X-rays images can be used to analyze the health of a patient lungs. As all healthcare centers have X-ray machines, it could be possible to use proposed CoviLearn X-rays to test for COVID-19 without the especial test kits. Our proposed automated analysis system CoviLearn which has 99% accuracy will be able to save valuable time of medical professionals as the X-ray machines come with a drawback as it needed a radiology expert.

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arXiv2021

Smart Village: An IoT Based Digital Transformation

Almost 46% of the world's population resides in a rural landscape. Smart villages, alongside smart cities, are in need of time for future economic growth, improved agriculture, better health, and education. The smart village is a concept that improves the traditional rural aspects with the help of digital transformation. The smart village is built up using heterogeneous digital technologies pillared around the Internet-of-Thing (IoT). There exist many opportunities in research to design a low-cost, secure, and efficient technical ecosystem. This article identifies the key application areas, where the IoT can be applied in the smart village. The article also presents a comparative study of communication technology options.

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arXiv2021

Fortifying Vehicular Security Through Low Overhead Physically Unclonable Functions

Within vehicles, the Controller Area Network (CAN) allows efficient communication between the electronic control units (ECUs) responsible for controlling the various subsystems. The CAN protocol was not designed to include much support for secure communication. The fact that so many critical systems can be accessed through an insecure communication network presents a major security concern. Adding security features to CAN is difficult due to the limited resources available to the individual ECUs and the costs that would be associated with adding the necessary hardware to support any additional security operations without overly degrading the performance of standard communication. Replacing the protocol is another option, but it is subject to many of the same problems. The lack of security becomes even more concerning as vehicles continue to adopt smart features. Smart vehicles have a multitude of communication interfaces would an attacker could exploit to gain access to the networks. In this work we propose a security framework that is based on physically unclonable functions (PUFs) and lightweight cryptography (LWC). The framework does not require any modification to the standard CAN protocol while also minimizing the amount of additional message overhead required for its operation. The improvements in our proposed framework results in major reduction in the number of CAN frames that must be sent during operation. For a system with 20 ECUs for example, our proposed framework only requires 6.5% of the number of CAN frames that is required by the existing approach to successfully authenticate every ECU.

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arXiv2021

Personal Internet of Things (PIoT): What is it Exactly?

The use of Internet of Things (IoT) devices in homes and the immediate proximity of an individual communicates to create Personal IoT (PIoT) networks. The exploratory study of PIoT is in its infancy, which will explore the expansion of new use cases, service requirements, and the proliferation of PIoT devices. This article provides a big picture of PIoT architecture, vision, and future research scope.

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arXiv2021

Eternal-Thing 2.0: Analog-Trojan Resilient Ripple-Less Solar Energy Harvesting System for Sustainable IoT in Smart Cities and Smart Villages

Recently, harvesting natural energy is gaining more attention than other conventional approaches for sustainable Internet-of-Things (IoT). System on chip (SoC) power requirement for the IoT and generating higher voltages on-chip is a massive challenge for on-chip peripherals and systems. Many sensors are employed in smart cities and smart villages in decision-making, whose power requirement is an issue, and it must be uninterrupted. Previously, we presented Security-by-Design (SbD) principle to bring energy dissipation and cybersecurity together through our "Eternal-Thing". In this paper, an on-chip reliable energy harvesting system (EHS) is designed for IoT end node devices which is called "Eternal-Thing 2.0". The management section monitors the process load and also the recharging of the battery or super-capacitor. An efficient maximum power point tracking (MPPT) algorithm is used to avoid quiescent power consumption. The reliability of the proposed EHS is improved by using an aging tolerant ring oscillator. The proposed EHS is intended and simulated in CMOS 90nm technology. The output voltage is within the vary of 3-3.55V with an input of 1-1.5V. The EHS consumes 22 micro Watt of power, that satisfies the ultra-low-power necessities of IoT sensible nodes.

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arXiv2020

MyWear: A Smart Wear for Continuous Body Vital Monitoring and Emergency Alert

Smart healthcare which is built as healthcare Cyber-Physical System (H-CPS) from Internet-of-Medical-Things (IoMT) is becoming more important than before. Medical devices and their connectivity through Internet with alongwith the electronics health record (EHR) and AI analytics making H-CPS possible. IoMT-end devices like wearables and implantables are key for H-CPS based smart healthcare. Smart garment is a specific wearable which can be used for smart healthcare. There are various smart garments that help users to monitor their body vitals in real-time. Many commercially available garments collect the vital data and transmit it to the mobile application for visualization. However, these don't perform real-time analysis for the user to comprehend their health conditions. Also, such garments are not included with an alert system to alert users and contacts in case of emergency. In MyWear, we propose a wearable body vital monitoring garment that captures physiological data and automatically analyses such heart rate, stress level, muscle activity to detect abnormalities. A copy of the physiological data is transmitted to the cloud for detecting any abnormalities in heart beats and predict any potential heart failure in future. We also propose a deep neural network (DNN) model that automatically classifies abnormal heart beat and potential heart failure. For immediate assistance in such a situation, we propose an alert system that sends an alert message to nearby medical officials. The proposed MyWear has an average accuracy of 96.9% and precision of 97.3% for detection of the abnormalities.

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arXiv2020

Smart Healthcare for Diabetes: A COVID-19 Perspective

Diabetes is considered as an critical comorbidity linked with the latest coronavirus disease 2019 (COVID-19) which spreads through Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2). The diabetic patients have higher threat of infection from novel corona virus. Depending on the region in the globe, 20% to 50% of patients infected with COVID-19 pandemic had diabetes. The current article discussed the risk associated with diabetic patients and also recommendation for controlling diabetes during this pandemic situation. The article also discusses the case study of COVID-19 at various regions around the globe and the preventive actions taken by various countries to control the effect from the virus. The article presents several smart healthcare solutions for the diabetes patients to have glucose insulin control for the protection against COVID-19.

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arXiv2020

SaYoPillow: A Blockchain-Enabled, Privacy-Assured Framework for Stress Detection, Prediction and Control Considering Sleeping Habits in the IoMT

Considering today's lifestyle, people just sleep forgetting the benefits it provides to the human body. The reasons for not having a productive sleep could be many. Smart-Yoga Pillow (SaYoPillow) is envisioned as a device that may help in recognizing the importance of a good quality sleep to alleviate stress while establishing a measurable relationship between stress and sleeping habits. A system that analyzes the sleeping habits by continuously monitoring the physiological changes that occur during rapid eye movement (REM) and non-rapid eye movement (NREM) stages of sleep is proposed in the current work. In addition to the physiological parameter changes, factors such as sleep duration, snoring range, eye movement, and limb movements are also monitored. The SaYoPillow system is processed at the edge level with the storage being at the cloud. Not having to compromise the user's privacy, SaYoPillow proposes secure data transmission for both uploading and retrieving, and secure storage and communications as an attempt to reduce malicious attacks on healthcare. A user interface is provided for the user to control data accessibility and visibility. SaYoPillow has 96% accuracy which is close to other existing research works. However, SaYoPillow is the only work with security features as well as only work that considers sleeping habits for stress.

cs.CY↗

arXiv2020

sCrop: A Internet-of-Agro-Things (IoAT) Enabled Solar Powered Smart Device for Automatic Plant Disease Prediction

Internet-of-Things (IoT) is omnipresent, ranging from home solutions to turning wheels for the fourth industrial revolution. This article presents the novel concept of Internet-of-Agro-Things (IoAT) with an example of automated plant disease prediction. It consists of solar enabled sensor nodes which help in continuous sensing and automating agriculture. The existing solutions have implemented a battery powered sensor node. On the contrary, the proposed system has adopted the use of an energy efficient way of powering using solar energy. It is observed that around 80% of the crops are attacked with microbial diseases in traditional agriculture. To prevent this, a health maintenance system is integrated with the sensor node, which captures the image of the crop and performs an analysis with the trained Convolutional Neural Network (CNN) model. The deployment of the proposed system is demonstrated in a real-time environment using a microcontroller, solar sensor nodes with a camera module, and an mobile application for the farmers visualization of the farms. The deployed prototype was deployed for two months and has achieved a robust performance by sustaining in varied weather conditions and continued to remain rust-free. The proposed deep learning framework for plant disease prediction has achieved an accuracy of 99.2% testing accuracy.

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arXiv2020

PoAh: A Novel Consensus Algorithm for Fast Scalable Private Blockchain for Large-scale IoT Frameworks

In today's connected world, resource constrained devices are deployed for sensing and decision making applications, ranging from smart cities to environmental monitoring. Those recourse constrained devices are connected to create real-time distributed networks popularly known as the Internet of Things (IoT), fog computing and edge computing. The blockchain is gaining a lot of interest in these domains to secure the system by ignoring centralized dependencies, where proof-of-work (PoW) plays a vital role to make the whole security solution decentralized. Due to the resource limitations of the devices, PoW is not suitable for blockchain-based security solutions. This paper presents a novel consensus algorithm called Proof-of-Authentication (PoAh), which introduces a cryptographic authentication mechanism to replace PoW for resource constrained devices, and to make the blockchain application-specific. PoAh is thus suitable for private as well as permissioned blockchains. Further, PoAh not only secures the systems, but also maintains system sustainability and scalability. The proposed consensus algorithm is evaluated theoretically in simulation scenarios, and in real-time hardware testbeds to validate its performance. Finally, PoAh and its integration with the blockchain in the IoT and edge computing scenarios is discussed. The proposed PoAh, while running in limited computer resources (e.g. single-board computing devices like the Raspberry Pi) has a latency in the order of 3 secs.

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arXiv2019

iGLU 1.0: An Accurate Non-Invasive Near-Infrared Dual Short Wavelengths Spectroscopy based Glucometer for Smart Healthcare

In the case of diabetes, fingertip pricking for a blood sample is inconvenient for glucose measurement. Invasive approaches like laboratory test and one-touch glucometer enhance the risk of blood-related infections. To mitigate this important issue, in the current paper, we propose a novel Internet-of-Medical-Things (IoMT) enabled edge-device for precise, non-invasive blood glucose measurement. In this work, a near-infrared (NIR) spectroscopic technique using two wavelengths (940 nm, 1300 nm) is taken to detect the glucose molecule from human blood. The novel device called iGLU is based on NIR spectroscopy and machine learning (ML) models of high accuracy. An optimal multiple polynomial regression model and deep neural network (DNN) model have been presented for precise measurement. The proposed device is validated and blood glucose values are stored on the cloud using open IoT platform for remote monitoring by an endocrinologist. For device validation, the estimated blood glucose values have been compared with blood glucose values obtained from the invasive device. It has been observed that mean absolute relative difference (MARD) and average error (AvgE) are found 4.66 % and 4.61 % respectively from predicted blood glucose concentration values. The regression coefficient is found 0.81. The proposed spectroscopic non-invasive device provides accurate and cost-effective solution for smart healthcare.

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arXiv2019

PUFchain: Hardware-Assisted Blockchain for Sustainable Simultaneous Device and Data Security in the Internet of Everything (IoE)

This article presents the first-ever blockchain which can simultaneously handle device and data security, which is important for the emerging Internet-of-Everything (IoE). This article presents a unique concept of blockchain that integrates hardware security primitives called Physical Unclonable Functions (PUFs) to solve scalability, latency, and energy requirement challenges and is called PUFchain. Data management and security (and privacy) of data, devices, and individuals, are some of the issues in the IoE architectures that need to be resolved. Integrating the blockchain into the IoE environment can help solve these issues and helps in the aspects of data storage and security. This article introduces a new blockchain architecture called PUFchain and introduces a new consensus algorithm called "Proof of PUF-Enabled Authentication" (PoP) for deployment in PUFchain. The proposed PoP is the PUF integration into our previously proposed Proof-of-Authentication (PoAh) consensus algorithm and can be called "Hardware-Assisted Proof-of-Authentication (HA-PoAh)". However, PUF integration is possible in the existing and new consensus algorithms. PoP utilizes PUFs which are responsible for generating a unique key that cannot be cloned and hence provide the highest level of security. A PUF uses the nanoelectronic manufacturing variations that are introduced during the fabrication of an integrated circuit to generate the keys. Hence, once generated from a PUF module, the keys cannot be cloned or generated from any other module. PUFchain uses a PUF and Hashing module which performs the necessary cryptographic functions. Hence the mining process is offloaded to the hardware module which reduces the processing times. PoP is approximately 1,000X faster than the well-established Proof-of-Work (PoW) and 5X faster than Proof-of-Authentication (PoAh).

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arXiv2019

cSeiz: An Edge-Device for Accurate Seizure Detection and Control for Smart Healthcare

Epilepsy is one of the most common neurological disorders affecting up to 1% of the world's population and approximately 2.5 million people in the United States. Seizures in more than 30% of epilepsy patients are refractory to anti-epileptic drugs. An important biomedical research effort is focused on the development of an energy efficient implantable device for the real-time control of seizures. In this paper we propose an Internet of Medical Things (IoMT) based automated seizure detection and drug delivery system (DDS) for the control of seizures. The proposed system will detect seizures and inject a fast acting anti-convulsant drug at the onset to suppress seizure progression. The drug injection is performed in two stages. Initially, the seizure detector detects the seizure from the electroencephalography (EEG) signal using a hyper-synchronous signal detection circuit and a signal rejection algorithm (SRA). In the second stage, the drug is released in the seizure onset area upon seizure detection. The design was validated using a system-level simulation and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry leads to a considerable reduction of power consumption compared to previous approaches. The proposed approach can be generalized to other sensor modalities and the use of both wearable and implantable solutions, or a combination of the two.

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arXiv2019

iVAMS 2.0: Machine-Learning-Metamodel-Integrated Intelligent Verilog-AMS for Fast and Accurate Mixed-Signal Design Optimization

The gap between abstraction levels in analog design is a major obstacle for advancing analog and mixed-signal (AMS) design automation and computer-aided design (CAD). Intelligent models for low-level analog building blocks are needed to bridge the accuracy gap between behavioral and transistor-level simulations. The models should be able to accurately estimate the characteristics of the analog block over a large design space. Machine learning (ML) models based on actual silicon have the capabilities of capturing detailed characteristics of complex designs. In this paper, a ML model called Artificial Neural Network Metamodels (ANNM) have been explored to capture the highly nonlinear nature of analog blocks. The application of these intelligent models to multi-objective AMS block optimization is demonstrated. Parameterized behavioral models in Verilog-AMS based on the ANN metamodels are constructed for efficient AMS design exploration. To the best of the authors' knowledge this is the first paper to integrate ANN models in Verilog-AMS, which is called iVAMS 2.0. To demonstrate the application of iVAMS 2.0, this paper presents two case studies: an operational amplifier (OP-AMP) and a phase-locked loop (PLL). A biologically-inspired "firefly optimization algorithm" is applied to an OP-AMP design in the iVAMS 2.0 framework. The optimization process is sped up by 5580X due to the use of iVAMS with negligible loss in accuracy. Similarly, for a PLL design, the physical design aware ANNs are trained and used as metamodels to predict its frequency, locking time, and power. Thorough experimental results demonstrate that only 100 sample points are sufficient for ANNs to predict the output of circuits with 21 design parameters within 3% accuracy. A proposed artificial bee colony (ABC) based algorithm performs optimization over the ANN metamodels of the PLL.

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