Dr. Amitabh Mishra

Creating shared spaces is a key part of making extended reality (XR) and Internet of Things (IoT) technology more interactive and collaborative. Currently, one system which stands out in achieving this end commercially involves spatial anchors. Due to the cloud-based nature of these anchors, they can introduce connectivity and privacy issues for projects which need to be isolated from the internet. This research attempts to explore and create a different approach that does not require internet connectivity. This work involves the creation of an AprilTags-based calibration system as a local solution for creating shared XR spaces and investigates its performance. AprilTags are simple, scannable markers that, through computer vision algorithms, can help XR devices figure out position and rotation in a three-dimensional space. This implies that multiple users can be in the same virtual space and in the real-world space at the same time, easily. Our tests in XR showed that this method is accurate and works well for synchronizing multiple users. This approach could make shared XR experiences faster, more private, and easier to use without depending on cloud-based calibration systems.

Background: Rapid advancements in large language models (LLMs) have significantly enhanced Retrieval-Augmented Generation (RAG) techniques, leading to more accurate and context-aware information retrieval systems.
Methods: This article presents the creation of a RAG-based chatbot tailored for university course catalogs, aimed at answering queries related to course details and other essential academic information, and investigates its performance by testing it on several locally deployed large language models. By leveraging multiple LLM architectures, we evaluate performance of the models under test in terms of context length, embedding size, computational efficiency, and relevance of responses.
Results: The experimental analysis obtained by this research, which builds on recent comparative studies, reveals that while larger models achieve higher relevance scores, they incur greater response times than smaller, more efficient models.
Conclusions: The findings underscore the importance of balancing accuracy and efficiency for real-time educational applications. Overall, this work contributes to the field by offering insights into optimal RAG configurations and practical guidelines for deploying AI-powered educational assistants.

Industrial Internet of Things (IIoT) is focused on optimization of industrial working in several fields including assembly lines, manufacturing processes and production of any kind, including agriculture. As the equipment and system are data intensive and largely dependent on wireless transmission of data from the interface point of the primary sensing element to where it is going to be utilised, adding cognitive capabilities to the radio system involved can add benefits of optimal utilisation of the frequency spectrum and faster transmission of critical process data. IIoT also offers advanced functionalities such as preventive maintenance, asset tracking and better optimized resources in the operation and control of industrial processes for manufacturing, production, or processing. This chapter focuses on the evolution of the sensors and process control equipment from traditional versions to those being used today for IIoT.

The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.

Wireless Internet of Things (WIoT), based on the concept of ubiquitous computing, attempts to create communication networks by embedding microchips in everyday consumer electronic devices and to set those devices up to share data with each other to provide innovative connected solutions. With the explosion in the number and variety of wireless applications in the last two decades, all of them face a major challenge of availability of frequency spectrum. This challenge can be addressed by resorting to cognitive radio. The cognitive radio scheme uses a radio system that can make an informed decision to dynamically fine-tune its radio operation metrics as it can sense and is mindful of its operational setting. Combining the cognitive radio technology with WIoT can make the application design more robust and universal. In this paper, we discuss the possibilities and challenges involving such cognitive sensors, used for environmental sensing and healthcare applications.

Healthcare, lifestyle, and medical applications of Internet of Things (IoT) involve the use of wearable technology that employs sensors of various kinds to sense human physiological parameters such as steps walked, body temperature, blood pressure, heart rate and other cardiac parameters. Such sensors and associated actuators can be worn as gadgets, embedded in clothing, worn as patches in contact with the body and could even be implanted inside the body. These sensors are electronic, and any electronic activity during their sensing, processing and wireless transmission is associated with the generation of heat. This dissipated heat can cause discomfort to the subject and has the potential of damaging healthy living tissue and cells. In the proposed work, the author does a performance check on the intrinsic safety aspects of an IoT healthcare network with respect to the functioning of the wireless sensors involved and routing of sensor data samples. The author also suggests an optimized thermal and energy aware framework to address the issue of temperature rise due to processing and data transmission from sensors through signal processing approaches that help in reducing thermal hazards and simultaneously enhancing the network lifetime through energy conservation.

Validation of input data is essential in any computer system, but perhaps particularly important in pervasive IoT systems such as smart homes, smart cars, wearable health monitors, etc. In such systems, actions taken based on invalid inputs could have severe consequences. In this paper, we present statistical techniques for identifying data anomalies at the gateway that connects an edge network to its associated cloud services. We address two kinds of anomalies in environmental sensor data: data bias anomalies and sensor cut-off anomalies. In simulation experiments, we evaluate the effectiveness of applying control charts, a statistical process monitoring technique, to both kinds of anomalies. Our results show that using control charts as statistical methods for anomaly detection in IoT systems not only provides high performance in terms of accuracy and power (probability of detecting the anomaly), but also offers a graphical tool to monitor the IoT sensor data.

Traffic control systems were developed with operational performance, reliability, and safety in mind. Traffic control systems were designed well before the heavy integration of advanced communications including radio frequency (RF), the Internet and cellular transmissions. These technologies were integrated to provide more control and enable the traffic systems to become adaptive to real-time traffic flow and environmental conditions. These advances increase the opportunity for attackers to affect traffic system operations, sometimes creating a congestion which essentially halts traffic. The Secure SCADA Framework presents eight objectives which would increase the cyber resilience of an existing vulnerable cyber physical system, such as a traffic control system [1]. This approach retains the current operational performance, reliability, and safety. The concept of using a Trusted Computing Base (TCB) in a cyber-physical system is one goal of the eight presented for the Secure SCADA Framework. The SCADA TCB (STCB) project designs, develops, and verifies a core set of hardware, software, and firmware which operate in conjunction to establish a high level of security protecting a traffic control system. This research defines the requirements of a traffic control system, establishes a security policy, develops a trusted computing base, identifies and designs attacks on the system, and meets the development life-cycle requirements to proceed with implementation, verification, and testing.

This article focuses on results obtained from two cloud-based models that examine trade-offs between security, scalability, and efficiency of data collection for Internet-of-Things sensor networks. This work can provide insight for Internet-of-Things systems designers in choosing security controls and scalability features when working with cloud services. The results were obtained from a smart home Internet-of-Things prototype system in which data records from in-home sensors are transmitted wirelessly to an in-home hub, which forwards them to a cloud web service for storage and analysis. We consider different configurations and security controls on the wireless (in-home) and on the wired (home-to-web) sides. The configuration on the wireless side includes encrypted or plain-text transmission from the wireless sensors to the in-home hub for probing if software encryption of sensor data adds appreciable delay to the transmission time. The configuration on the wired side includes encryption or plain-text transmission, with or without authentication, with or without scalable cloud services. For each configuration, we measure end-to-end latency, transmission latency, and processing latency at the web service. Results of the experiments on the wired side showed much greater latencies and variability of latencies when using scalable cloud services.

Supervisory Control and Data Acquisition (SCADA) systems and Industrial Control Systems (ICS) are critical for infrastructure operations, production processes, automation systems, and other automated control systems. SCADA systems are vulnerable to cyber physical attacks from many vectors. The attack surface varies widely. The problem is complicated by the inherent focus on performance, reliability, and safety rather than cybersecurity. The Secure SCADA Framework establishes a security engineered approach evolving SCADA and ICS systems towards a more cybersecure posture. The encapsulating and integrating concepts of the Secure SCADA Framework require development and analysis of implementations achieving the eight framework goals. This work provides a solution for research, design, development, and evaluation of components of a Secure SCADA System. A Security Engineered SCADA Laboratory requires a test bed with which engineering and science designs can be developed. This paper presents a test bed which can model and collect data on simulated attacks, analyze data to evaluate performance, and provide the foundations for a Security Engineered SCADA Laboratory.