The automotive industry’s advancements in road safety through sensors, actuators, and customized networks are challenged by increasing accidents, particularly in vehicles operating in autopilot mode. A significant concern is the ability to tamper with sensor data through low-power electromagnetic interference (EMI) without physical contact. This article focuses on the physical layer of vehicular networks, investigating the risk of misleading sensor data caused by deliberate EMI targeting critical subsystems. Through experiments on ultrasonic and crankshaft sensors, we developed an EMI injection unit and detection unit to evaluate the potential for sensor hacking and its impact on vehicle safety. Our findings reveal that current onboard diagnostics (OBDs) cannot detect these EMI-based attacks. To address this gap, we propose SecureTrack, an EMI detection and alert system that effectively identifies interference attempts in real time. Using a microantenna system and an embedded advanced virtual reduced instruction set computer (AVR) microcontroller, our system measures EMI strength and resonant voltage. Furthermore, the Internet of Things (IoT)-based integration enhances vehicle safety by enabling OBD and central control centers to tackle such threats. To the best of authors’ knowledge, this is the first system developed to detect and alert against EMI attacks on automotive sensors, marking a significant advancement in the field of vehicular security.

In modern warfare and law enforcement, the Internet of Battlefield Things (IoBT) has emerged as a crucial technology, offering a paradigm shift in soldier and security personnel safety and threat mitigation. This letter proposes and investigates the development of a groundbreaking system: the electromagnetic (EM) defender glove, aimed at real-time protection against various threats encountered by soldiers, police, and security professionals. Integrating sensors, microcontrollers, and wireless technology, this battery-operated wearable device presents a comprehensive solution to safeguard soldiers, police officers, and security personnel from arms and ammunition misuse and hostile encounters. This novel apparatus utilizes the ability to wirelessly lock/unlock ammunition triggers, incapacitate militants, terrorists, or violent offenders with a potent stun function during one-to-one combat, and disrupt hazardous electronics through electromagnetic pulses. Leveraging long-range Bluetooth low energy, compact yet powerful microcontrollers, and integrated onboard recharging units within the wearable hand glove optimally suits it for practical applications in both battlefield and law enforcement scenarios. Through rigorous design and testing, this study demonstrates the feasibility and effectiveness of the proposed system across various operational environments. The results underscore the significant potential of the EM defender glove in enhancing safety and operational effectiveness in both warfare and policing scenarios.

This study presents a multidisciplinary framework for advancing stress detection by integrating Internet of Things (IoT) capabilities with Galvanic Skin Response (GSR) technology. Leveraging IoT for real-time data acquisition and analysis, we enhance GSR sensor functionality. Contributions from clinical psychology focus on elderly populations, providing insights into age-specific stress indicators and mental health correlations. Physics principles optimise sensor accuracy and data fidelity, while computer science provides the framework for data processing, machine learning models, and IoT infrastructure. The linguistic analysis supports psychologists' recommendations, confirming that GSR readings are best when communication stabilises with subjects. This interdisciplinary approach aims to develop a comprehensive system for effective stress monitoring and management. Our results demonstrate that advanced machine learning models, notably the Random Forest model, achieve high predictive performance in analysing stress levels from GSR data. The confusion matrix and classification report validate its efficiency in accurately distinguishing different stress levels. Feature importance analysis reveals that the GSR Stress Value is the dominant predictor, contributing approximately 90% to stress classification, with age having a minor influence of around ∼ 10%. Gender and state contribute negligibly to stress classification. However, a strong positive relationship between GSR and stress levels is confirmed by a correlation analysis with a coefficient of 0.93. Gender-based differences were minimal, though females exhibited slightly higher stress levels in extreme cases, while younger individuals showed greater fluctuations in stress variability. The findings confirm that GSR data is highly reliable for identifying stress levels and analyzing workplace stressors, offering a datadriven approach to stress analysis and management with significant implications for mental health research and wellbeing strategies.

Computer vision is a rapidly advancing technology that relies heavily on camera sensors to provide input for Machine Learning (ML) models to make decisions. It is confirmed to play a critical role in various futuristic applications, such as advancements in self-driving vehicles, autonomous & target-tracking drones, parking assistance, and collision avoidance systems. However, with the increasing prevalence of hardware-level sensor hacking, even camera sensors are susceptible to being compromised. This experimental paper proposes the idea of sensor hacking against Machine Learning capabilities of vehic-ular Computer Vision (CV) using Electromagnetic Interference (EMI). A mid-range EMI intrusion device is developed to disrupt computer vision systems' accuracy and supervisory capabilities. The evaluation examines the impact of sensor hacking on camera sensors crucial to obstacle identification models reliant on live feeds, comparing decision-making capabilities with and without sensor tampering to assess the overall effect. Our results show that EMI significantly affects camera sensor performance, reducing accuracy and frame rates in machine learning-based object detection systems. These findings underscore the vulnerability of camera sensors to sensor hacking and highlight the need for improved security measures to safeguard against such attacks in computer vision systems.

With the increasing prevalence of cyber threats, the demand for efficient and effective malware detection systems has reached unprecedented levels. This research paper presents a novel approach to detecting malware packets based on URL analysis, utilizing natural language processing (NLP) techniques. Traditional malware detection methods rely heavily on statistical approaches and anomaly detection techniques, which have inherent limitations in detecting complex and rapidly evolving malware. In contrast, our proposed approach harnesses the power of NLP to examine the payload of network traffic and identify malicious packets by analyzing specific text patterns found in the URLs in the payload. In this research, we achieved sparsity problems with TF-IDF vectorization and also demonstrated that our proposed approach, deploying the ROBERTa model in a real-world network, achieves exceptional detection rates while maintaining low false-positive rates, i.e., 2%, where as random forest 7.1 % and SVM 13.8%. It surpasses statistical methods and other NLP-based models in terms of malware packet detection. Compared to random forest (90.2% accuracy) and SVM (79.0% accuracy), which are powerful in classification, our ROBERTa-based approach achieves an impressive accuracy of 99.6 %. Moreover, our approach exhibits greater resilience against adversarial attacks as it does not rely on fixed signatures or patterns.

Sanitization is not a new term, but with the evolution of deadly COVID-19, the process came into the limelight quickly. The process was already utilized widely in hospitals, vaccination centers, food processing units, and medicine industries and suddenly became crucial in every domain related to our lives. Even though sanitization is considered the first line of defense against pandemic viruses like COVID-19, it is highly difficult to sanitize every nook and corner of bigger buildings and external structures like airports, railway stations, theaters, institutions, and hospitals. Slight carelessness to eliminate the virus from the sanitization process can reciprocate in the pandemic spread. Our proposed work deals with utilizing the accuracy and precision of robots to effectively sanitize bigger structures. The multi-faceted methodology of the work manages the comprehensive investigation of the robotic unit for the social setting. The concentrate additionally stretches out to refine the standard human behavioral reaction for modern robotic consideration in our lives. This will ease up the process and, at the same time, will reduce the chance of human error. The robotic structure is powered by a 12 V rechargeable battery, which has manual and automation cleaning modes. During manual mode, we control the robot with an Android application installed on the phone and connected with the robot through Bluetooth wireless connectivity. During automation, the mode robot moves in different directions and cleans and sanitizes the area independently. There is an ESP8266-based IoT connection unit to update the overall process for the cloud.

Stress analysis is an important parameter to understand the current status of one’s mental standing. So far, questionnaires and likers are the popular methods for collecting user data for subsequent analysis and conclusion extraction. The entire resulting of the usual process is based on the respondents’ answers. The subject’s mood, mental condition, and trustworthiness have a significant impact on the final result. The stress analysis in our work is based on biological concerns rather than mental behavioural considerations using the galvanic skin response (GSR). The GSR sensor is a one-of-a-kind skin resistance measurement sensor that procedures the electrical conductivity of human skin. Sympathetic activity is hugely variable in those who are stressed and increases sweat gland secretion, which leads to a further increase in skin conductance. The GSR sensor can be used to detect stress and other undesirable mental traits. Our proposed work blends an android application and microcontroller to decode skin conductance into a human-readable and visual form. The coordination of the Atmega 328 P microcontroller and BT-04 bluetooth provides the wireless display of readings in different ranges. Embedded C is used to programme the microcontroller, allowing easy modification and versatility. There have been several experiments to analyze stress using sensors and questionnaires but our system is based on practicality. It is portably usable even by a person who is not necessarily well familiar with the technology.

People's degrading lifestyle reflects their health graphs and occurrence of different disease in their bodies. With different food habits and exercise patterns the endurance capacity of the body changes. Foot ulcer is a disease quite common today among Indians around. The foot ulcer creates a threat to the movement for human beings. It is a deep infected sore or damage in the foot sole commonly caused by nerve/skin damage. Several infections in the foot may also result in leg amputations. Foot ulcer is very common among diabetic patients. India is the country with the highest number of diabetics worldwide. Over 3 million people are infected with diabetes which is a big number to worry about. The CPR (Crude Prevalence Rate) for cities is around 9% of the population whereas in village areas the occurrence is around 3 percent [1]. Though the foot ulcer prevalence of diabetic patients in India is 3% which looks small but it is a big number. Our proposal is an embedded system innovation to develop a foot ulcer treatment system. It deals with the combined approach to embedded electronics and biomedical engineering to overcome the situation. We create a footrest equipped with Peltier, vibrator, and UV rays to overcome the issue. It is the first of its kind foot ulcer treatment equipment that can solve the issue. We also used the android technology to provide the vocal user interface to help the user. Clear instructions are displayed and spoken upon usage to the patients. The result obtained on the patients are highly acceptable and recommended for both urban and rural populations. The Atmega 328 P PU microcontroller is interfaced with the HC-05 Bluetooth and Vibrators through Relays to perform the task. The microcontroller acts as the overall think-tank to control and coordinate the overall system.

Biometric systems are the most advanced access technology developed so far in the 21st century. It does not even require to carry key cards or passwords in mind. Today most of the commercial and private entries are protected by biometric recognition systems like fingerprint scans facial recognition, retina scans, voice matching, etc. Even our phones, laptops, and daily access devices are equipped with biometric systems. In banks, the PCs are secured by the combination of passwords and fingerprint scans. Biometric scans are considered the most secure access technology so far. Our paper is to examine whether they are secure? Should we rely on them with our hard-earned money and social identity? Is there any way we can use these services without actually compromising our data and security? Our observation is on our digital identity. Promoting digitization in every department brings our topic in the picture. All our information is saved in our phones, our daily routine, whom we talk, what we purchase, whom we chat, where we travel, etc. Almost every smartphone has biometric fingerprint locks which means our phones have our fingerprint scans in database and with internet blend it's tethered worldwide. Our fingerprints are connected to our bank accounts, PAN Cards, Passport, and SIM Cards using Aadhar Cards. If someone has our fingerprint they can easily reach our Aadhar Card and through that to all our personal information. Most of the phone companies are Chinese, Korean, German, and American. As per their country policies, they must share their data with the governing authorities. We aim to create a security system without actually using the biometric scans. The system is an advancement of the biometric system but with better accuracy and intelligence. We interface image acquisition tools to live track the red color things. The web camera or inbuilt system lens can be used as the acquisition system. When the red color object is moved in front of the lens it shows the corresponding coordinate of the object shown. We use these x and y coordinate of the objects as the authentication points. If the correct value grant access is 120 x 122 means the system grants permission only if the value of x=120,121 or 122 is obtained. Now, this is tricky. Even if you know the correct value also, it is very difficult to bring the correct point. Think about if you don't know the point and it is also possible to make it much difficult by adding y coordinate so if the desired point is x=10, y=12 (10, 12) it is way more difficult. Each point is a possible password candidate and the screen of any device have megapixels where 1 Megapixel=106 pixels. Each pixel is a possible key or password entry. It can keep all our information safe and secure. We use a microcontroller and motor driver connected gate to demonstrate the result.

Eyes considered as the most precious organ of the human body, being blind is considered one of the biggest physical disaster for the human. As per the survey, 39 Million people are blind throughout the world. These blind people go through several challenges throughout their life 24 × 7. Some of the major critical issues they face are their movement through the lanes especially in dense traffic areas, they are highly vulnerable against robbery, kidnapping, and molestation due to their impairment, and they can’t find they are moving through dark or lightened lanes. These are some of the major issues they face which can be mitigated with the efficient use of technology and electronic components. We can enable the walking sticks of the blind person to act as the Stun Gun which produces 20 KV in emergency condition for their protection using Electric Arc Generator and power source. Using Ultrasonic Sensor provides the estimation of the obstacle and vibrates the stick if comes nearby any obstacle below the threshold limit. The major electronic components we use are Ultrasonic Sensor (HC-SR04), Arduino Nano, Electric Arc Generator, LDR, Dry cell, and Vibration Motor. We can design an efficient blend of technology and logic to short out the issues of the blind community which will be our gift to humanity.