Dnyaneshwar S. Mantri

Epilepsy is one of the most common chronic neurological disorders, affecting approximately 10–12 million people in India and nearly 65 million people worldwide. It is considered the fourth most common neurological condition after migraine, Alzheimer’s disease, and stroke. Epileptic seizures can cause serious health risks such as falls, head injuries, fractures, burns, breathing difficulties, and convulsive status epilepticus, creating anxiety among patients, parents, and caregivers. To address this problem, this paper proposes a wearable IoT-based seizure alert system for real-time seizure detection and emergency notification. The proposed system uses an ESP32-C3 microcontroller, MAX30102 heart rate and SpO₂ sensor, MPU6050 accelerometer and gyroscope, and an EMG sensor to monitor physiological parameters and abnormal muscle movements associated with seizures. When a seizure is detected, the device automatically sends an SMS alert with the user’s location to caregivers or family members and activates a speaker alert to notify nearby people. Prototype-level testing under controlled laboratory conditions demonstrated an estimated seizure detection accuracy of 92.5% of 5.2%, and a response time of 3–5 seconds, demonstrating that it is a reliable, real-time, and efficient solution for epilepsy monitoring.

Menstrual pain and hygiene management remain significant concerns for many women, while existing solutions such as pain medication and heating pads often provide only temporary relief without personalized monitoring. To address this gap, this paper presents HerEase, an IoT-enabled smart wearable belt that combines controlled heat therapy, vibration-based pain relief, and real-time moisture monitoring using a capacitive moisture sensor. The system is powered by an ESP32 microcontroller, which regulates temperature through the Steinhart–Hart algorithm and provides wireless access to a browser-based dashboard without requiring a dedicated mobile application. Experimental evaluation demonstrated reliable performance, achieving ±0.72°C temperature accuracy, 96.7% moisture detection reliability, and stable operation through a soft-start mechanism that eliminated ESP32 reset issues. User testing with 20 participants resulted in an average satisfaction rating of 4.6/5.0. By integrating heat therapy, vibration therapy, moisture sensing, pad replacement reminders, menstrual cycle tracking, and a web-accessible interface into a single affordable wearable device, HerEase offers a practical solution for improving menstrual comfort and hygiene management.

The heterogeneous Internet of Things (HetIoT) infrastructure is a prime target for attackers, due to a lack of cybersecurity measures. The HetIoT devices contain various security holes because of its heterogeneous nature, and attack tactics are advancing dramatically as the world transforms from 5G to 6G technology. The attackers can easily infiltrate the HetIoT infrastructure and form botnets to launch distributed denial of service (DDoS) attacks. Heading towards 6G technology, its speed, and connectivity provide a faster communication platform for various IoT devices; however, the number of vulnerabilities and overall records vulnerable to exploits will also increase. The explosive growth of 6G connectivity and HetIoT will make spotting and blocking of DDoS attacks challenging in the upcoming era. Hence, it is necessary to spot and block the attacks intelligently using different artificial intelligence-based learning techniques. The design of intelligent security (IS) techniques for addressing security and privacy issues in Het- IoT is gaining popularity, due to its efficiency in detection and prediction of attacks. Incorporating security with the 6G technology in HetIoT using the IS approach will result in a promising solution. Therefore, the chapter concentrates on protecting HetIoT infrastructure against DDoS threats using IS approaches. Additionally, the article sheds light on the current devel- opments in IS approaches and their open challenges, issues, and solutions regarding the 6G perspective.

Technological improvements and evolutions are required beyond fifth-generation (5G) networks for wireless communications as well as in the industry. The involvement of collaborative robots (COBOT) will satisfy the personal needs of human beings as and when required leading to Industry 5.0. Industrial revolutions relate to the human–machine interactions to satisfy needs in an easy and quicker way. It also helps to convert your ideas into implementations with the help of future-generation networks such as 6G. The trillions of connected devices would demand exceptionally high-performance, interconnectivity, especially in dynamic circumstances such as varied mobility, extremely high density, and vibrant surroundings. The mass personalization in Industry 5.0 is supported by the new advent of technology support as artificial intelligence (AI) technique, big data, cloud computing (CC), and robotics, i.e., smart cyber physical cognitive systems (CPCS). The enabling technologies such as 6G may be a scalable, adaptable, and long-lasting wireless access mechanism to handle a wide range of requirements, value chains, supply chains, research, and innovations in the new design of 22business models for attaining the highest efficiency. This chapter examines the essential requirements, and enabling technologies used in Industry 5.0 with 6G evolutions. In order to accomplish the aim, it put forwards a proposal for enhancement of the 4.0 to 5.0 vision of industrial revolutions using next-generation networks and technologies such as CC, Robotics, AI, IoT, etc.

The world of IoT services offers extensive opportunities to connect devices and upload data to the cloud, enabling data-driven decisions, AImodel processing, and dynamic visualizations through powerful dashboards. Big data encompasses descriptive, diagnostic, predictive, and prescriptive analytics, managing vast volumes of high-velocity and diverse data. In today's business landscape, the ability to analyse data swiftly is not only essential but mandatory for any organization. Simultaneously, the ease of use of data visualization tools has been a driving force behind the wide spread adoption of tools like Tableau, which leads the market in this domain. This paper presents a comprehensive account of creating interactive visualizations for real-time data from IoT applications, utilizing Tableau as a business intelligence tool. It highlights the significance of real-time data analytics in the IoT context and showcases how interactive visualizations can provide valuable insights and facilitate prompt decision-making. By leveraging the capabilities of IoT services, cloud connectivity, bigdata analytics, and Table au, organizations can unlock the potential of their data, leading to better performance and a competitive edge into day's fast-paced business environment.

Blockchain technology offers a data format that has built-in security. It is built on cryptography, decentralization, and consensus concepts to ensure transaction confidence. Decentralization is enabled by blockchain technology, which allows members to participate in a dispersed network. No single user can alter the transaction, as all transactions are transparent and visible to all other users on the network. However, there are some significant security differences between blockchain and other systems. Nowadays, there are so many potential attacks found on the blockchain. The contribution of this chapter would be to match the effective defence taken by blockchain technology or a patch for related vulnerabilities offered by researchers to reduce the impact of these attacks. To keep the network secure and resilient, an overview of these security solutions for various vulnerabilities and attacks helps to reduce the attack surface in the blockchain

Distributed denial-of-service (DDoS) is the most formidable attack, and many researchers are focusing their attention on safeguarding the heterogeneous IoT (HetIoT) networks from it. The paper proposes a smart intrusion detection system (IDS) using long-short-term memory (LSTM), i.e., smart LSTM-based IDS for the HetIoT. The paper highlighted data preprocessing steps after thorough exploratory data analysis (EDA) and feature extraction using the principal component analysis (PCA) technique. The proposed smart LSTM-based IDS successfully identifies and mitigates the various DDoS attacks. The paper considers binary and multi-class classification (7-class and 13-class) of DDoS attacks for efficient detection of attacks. The performance of the proposed smart LSTM-based IDS is compared with two state-of-the-art deep learning approaches, and the results reveal that the proposed smart LSTM-based IDS outperforms it. The proposed LSTM-based IDS successfully identifies various DDoS attacks with an accuracy rate of 99.98% for binary classification, 98% for 7-classes, and 99.97% for 13-classes. The work also compares the individual accuracy of 7-classes and 13-classes with state-of-the-art work. Also, the proposed smart LSTM-based IDS is lightweight, simple, and less complex than the existing state-of-the-art work models.

In today's technological era information is the key to success. It is essential that information be secured so that it is only available to those authorized to access it in correct and squashed way. The data collection and security are prime concern in the big data handling networks. The compression of data from multiple, heterogeneous sensor along with security can no longer be a set-and-forget solution, but instead needs to be effectively developed, thoughtfully implemented, and continuously managed process. It provides not only the privacy, authentication in handling the data communicated from lower layer to upper layer but also increase the efficiency. Also, the energy required for communication and navigation is more as compared to sensing and computation. In this context the chapter focuses on the effective use of data aggregation techniques along with layered network security from Communication, Navigation, Sensing and Services (CONASENSE) perspectives. The chapter identifies potential issues in design of secure network and application of aggregation methods with remedy to overcome. The layered protocol stack used for sensing, communication and navigation of data uses hybrid model (Security + Aggregation) for resource utilization and to improve the quality of service parameters.

The crime rate has been rising at an unprecedented rate, and security has become a big concern in ATM machines. Face detection is the most common biometric technique due to its non-invasive nature. It's been used in a variety of fields, including camera auto focus, attendance, crowd monitoring, object tracking, security, system, etc. Face detection systems uses image processing techniques that are being learned to operate reliably in a variety of conditions, including changes in posture, lighting, skin color, occlusion, and face spoofing. The face detection system has become increasingly vulnerable to occlusion. Occlusion refers to the deliberate shielding of one's face with a helmet, sunglasses, scarves, or other items in order to avoid being caught. These issues have a significant impact on the development of image processing techniques and system's performance. In this paper the Haar Cascade Classifier (HCC) scheme is projected for face detection where precision as well as minimal processing time are important factors for ATM. The proposed scheme uses deep learning models such as Convolutional Neural Networks to enhance the reliability in feature extraction plus classification of images. Face biometric access control devices are becoming more common in everyday lives, but they remain vulnerable to spoofing attacks. This paper also proposes face spoofing identification using Local Binary Pattern (LBP) that has useful features for face detection. The proposed spoofing attack detection technique has yielded encouraging results.