Poonam Arora

The evolution in the Edge AI data processing capabilities directly over edge devices by using intelligent computation on the device itself paves the way for finding new measures to incorporate security and privacy considerations. Edge AI provides numerous advantages, such as reduced latency, increased bandwidth, and a straightforward user interface. Therefore, new challenges related to security and privacy become highly crucial. As in Edge AI systems, all the sensitive data is processed locally, so they are susceptible to physical tampering, adversarial attacks, malware exploitation, and eavesdropping-kind of vulnerability. The inherent distributed nature of edge devices presents significant challenges when an architecture designer tries to form a cohesive security framework for edge devices forming the critical application framework. This complexity of the local modelling at edge devices itself necessitates the need for developing robust strategies where the prime focus was laid on fundamental security measures themselves. Some of the pivot measures are using strong multi-factor authentication (MFA) processes to verify the identity of users and devices along with using strong data encryption algorithms to safeguard sensitive information so that it can be protected from unauthorised access by threat actors. In addition to the above, we have to identify and use secure communication protocols for data transmission between devices so that it remains confidential and tamper-proof, and hence provide stealth to the overall security posture in edge computing environments. At the same time, the privacy concerns have increased because edge devices tend to collect and process personal and sensitive information like patient health records in smart health monitoring systems and location data in smart transportation and smart city applications. This raises the need for compliance with regulations such as General Data Protection Regulation (GDPR), which is a European Union law governing the processing and sharing of personal data, and Health Insurance Portability and Accountability Act (HIPAA), a set of rules designed to protect the privacy and security of patients’ health information, and DPDP Act 2023, which is an Indian law applicable to the processing of digital personal data within India. These rules govern all the data collected online or offline, which further goes under digitisation at the edge devices themselves by local AI models. These regulations need to be enforced on healthcare providers, smart city developers, smart transportation entities, etc. Furthermore, due to the unique challenge posed by machine learning attacks, capabilities checks and balances need to be incorporated by continuous monitoring and regular enhancements in local model capabilities. There are many emerging technologies available like hardware-based approaches, federated learning, and trusted execution environments which can enhance privacy and reduce exposure to breaches by providing decentralised model training without sharing raw data along with a multi-layered approach to protect against diverse threats.

Edge computing represents a significant paradigm shift in distributed computing by moving computational processing away from centralized cloud infrastructures to the edge of the network, closer to data source in a decentralized fashion. This decentralized solution is designed primarily to cater to the needs of an increasing number of Internet of Things (IoT) and smart devices and thus reinforcing the capabilities of cloud computing by reducing its limitations in terms of intrinsic latency, bandwidth, and real-time responsiveness for a dedicated application. This chapter methodically examines the fundamentals of edge computing by exploring how cloud-based architectures have evolved into edge-enabled systems, highlighting the interactions between the cloud, edge nodes, and fog layers. The architectural analysis highlights the crucial communication channels and encryption protocols that allow for real-time processing and dissemination capabilities. The network flow between the fog layer, intermediate processing nodes, and edge devices to demonstrate the technologies of this paradigm along with other key technical elements are discussed to create intelligent and responsive systems. This chapter also presents numerous application areas like smart grids, autonomous cars, healthcare monitoring, and industrial IoT where edge computing plays a key role. It has been discussed that integrating edge computing with 5G networks and AI improves quality of service, guarantees a reliable low-latency communication, and maximises distributed intelligence at scale. Additionally, a critical assessment of the difficulties involved in this advanced paradigm is given, covering data governance, security and privacy issues, interoperability gaps, infrastructure heterogeneity, and regulatory compliance. In conclusion, edge computing complements cloud computing rather than replacing it, facilitating distributed ecosystems that are secure, scalable, and real-time. It will play a vital role in shaping the future digital infrastructures due to its high capacity to support data processing and decision-making at the millisecond level. It provides an advanced and promising architecture for uninterrupted research and innovative industrial applications.

Accurate timekeeping is foundational to modern society, supporting critical technologies such as global navigation satellite systems (GNSS), high-frequency financial trading, synchronization of communication networks, and the realization of SI base units. Here we review the evolution of timekeeping, with a particular emphasis on atomic clocks and their applications. We describe key dissemination methods, including Network Time Protocol (NTP), Precision Time Protocol (PTP), Global Navigation Satellite System (GNSS), and Two-Way Satellite Time and Frequency Transfer (TWSTFT). We further discuss emerging applications with stringent time-synchronization requirements, such as quantum communication and quantum computing. Finally, we discuss evolving trends in global timekeeping, including the redefinition of the SI second, lunar timekeeping, and the use of artificial intelligence and machine learning (AI/ML) for predictive control and resilience in timekeeping systems.

Sub-Doppler laser-cooled cesium-133 atoms are utilized as quantum sensors to achieve precise mapping of magnetic fields across a region in ultra-high vacuum (UHV), with a spatial resolution of 1 cm and a sensitivity of approximately 550 pT/√Hz, enabling accurate measurements within the nanotesla [nT] range. The cold cesium-133 atoms used for magnetic field measurements in this paper are a key component of the cesium fountain frequency standard at CSIR-NPL, which contributes to both timekeeping and magnetic sensing. The results show magnetic field fluctuations within 1 nT with a spatial resolution of 1 cm. The uncertainty in these measurements is of the order of 1.24 × 10⁻¹⁶, ensuring reliable and precise spatially resolved magnetic field mapping.

Continuous monitoring of drinking water quality is essential for safeguarding public health, particularly in densely populated urban areas like the National Capital Region (NCR) of Delhi. In this study, 47 water samples were randomly collected from various public sources across Delhi and its surrounding areas using a standardized sampling technique. An Arduino-based sensor system was employed to measure critical water quality parameters, including pH, total dissolved solids (TDS), and turbidity. While pH levels were consistently within the acceptable limits across all samples, approximately 14% of the samples exhibited elevated TDS and turbidity levels, raising concerns about potential contamination. In contrast, commercially packaged drinking water samples were found to have significantly lower TDS and turbidity levels, suggesting a higher level of quality control in packaged water compared to public sources. These findings emphasize the importance of continuous water quality monitoring to identify and mitigate potential health risks in real time. This study demonstrates the feasibility and effectiveness of Arduino-based systems as a cost-effective, scalable solution for real-time water quality assessment in urban environments. By leveraging sensor technology, this approach offers a practical means of ensuring safe drinking water in Delhi and similar densely populated regions.