This research articulates the development and implementation of an advanced LiDAR-based framework for the detection and tracking of humans within autonomous systems, significantly enhancing situational awareness in dynamic environments. Leveraging high-resolution 3D point cloud data, the study introduces a novel algorithmic approach that integrates ground plane removal, Euclidean and DBSCAN clustering, and principal component analysis (PCA) to robustly identify and track human figures across varying conditions and occlusions. This framework is validated through a series of field tests with a Velodyne HDL-32 LiDAR system, demonstrating improved accuracy and computational efficiency in real-time human tracking. The findings underscore LiDAR's pivotal role in augmenting the safety and reliability of autonomous vehicles, robotics, and surveillance systems by effectively managing the complexities of real-world operational settings. Our pilot study provides a scalable model for future enhancements in autonomous navigational technologies.

Industry 5.0 heralds a transformative shift in the evolution of manufacturing dynamics, emphasizing a synergistic blend of human ingenuity with cutting-edge intelligent systems. This chapter focuses on the role of digital twins—sophisticated digital counterparts of physical entities—in revolutionizing data management and enhancing streamlined functionality leveraging real-time sensor data, digital twins accurately mirror and monitor complex industrial processes, providing a comprehensive analytical platform. These digital twins facilitate predictive maintenance and robust decision-making, significantly reducing downtime and operational costs. Furthermore, they enable effective risk management by simulating potential scenarios, allowing companies to proactively address possible challenges. Through seamless human-machine collaboration, digital twins enhance the potential for smarter, more sustainable operations, optimizing resource use and minimizing environmental impact. This chapter delves into how digital twins act as pivotal enablers within Industry 5.0, driving the redefinition of industrial operations towards more intelligent, efficient, and sustainable outcomes.

The rapid expansion of Internet of Things (IoT) devices and applications necessitates the need for more efficient computational and data management strategies. The fog-enabled UAV-as-a-Service (FU-Serve) platform addresses these demands by integrating fog computing to enhance the operational efficiency of UAVs in IoT environments. Despite its advantages, the FU-Serve platform faces significant challenges, including data transmission latency, resource allocation, and energy management, contributing to the underutilization of UAVs and fog nodes. To address these challenges, this article introduces a consensus-driven approach, Con-Fog, that optimizes the selection of fog nodes for UAVs within the FU-Serve platform. Con-Fog evaluates potential fog nodes within the communication range by computing utility values based on geographical distance, link quality, available computational resources, and residual energy. UAVs rank these nodes according to their utility values and select the most suitable ones through a consensus-based approach, ensuring alignment with the operational demands of IoT devices. Additionally, we apply an optimal best-fit algorithm to refine fog node allocation, maximizing resource utilization while keeping it below each node’s capacity threshold (T %) . Our simulation results show that Con-Fog significantly enhances key IoT performance metrics. Transmission time and the number of unassigned UAVs decrease by 10%–30% and 20%–40%, respectively, while residual energy increases by 30%–50% compared to existing systems. These improvements enhance the management of UAV and fog node resources, thereby advancing the effectiveness of IoT applications within the FU-Serve platform.

The limited resources available for Smart Meter (SM) devices on large-scale Smart Grid (SG) networks impose several constraints on SMs authentication. Currently, available authentication schemes are not suitable for this type of network. In particular, factors such as power and memory consumption impact the protocol efficiency and the device lifetime. Furthermore, high computational complexity leads to scalability issues in real-world scenarios, wherein large SGs need to handle a huge number of requests coming at a high rate. In this paper, we propose a lightweight authentication protocol for Smart Grids (LAP-SG), a novel scheme accounting for real resource-constrained SM providing reduced computation power, memory requirements, communication overhead, and electricity consumption. We prove the security of LAP-SG using both informal security analysis and a formal security model. We further prove the security of LAP-SG by testing it using AVISPA and ProVerif tools, showing its security against all known attacks. To assess LAP-SG performance in a real-world scenario, we measure its performance using the configuration of the Atmel family of SM devices. When compared to the state of the art, LAP-SG attains three times Smaller computation cost, reduced communication costs (up to 400 bits), and nearly four times lower storage cost.

The Fog-enabled UAV-as-a-Service (FU-Serve) platform leverages distributed fog nodes to enable real-time data processing for multiple concurrent applications. However, the computational limitations of these fog nodes significantly hamper the execution of resource-intensive deep learning (DL) algorithms, compromising both operational performance and energy sustainability. To address these challenges, we integrated Federated Learning (FL) within the FU-Serve platform, coupled with the development of three specialized FL-based models tailored for on-device image classification. First, we introduced a sustainable adaptation of MobileNetV2 that synergizes Transfer Learning (TL) with FL principles. This model achieves 97.68% accuracy with an 8.64 MB footprint by distributing pre-trained weights to optimize bandwidth efficiency. To further address resource constraints of fog nodes, we designed FUSERNet—a lightweight DL architecture employing separable convolutions and skip connections, which reduces computational overhead while preserving critical feature representations. This model achieves 97.47% accuracy with an ultra-compact footprint of 237 KB, demonstrating a 98.59% reduction in size compared to state-of-the-art models. Finally, our third model, FusionNet, combines the strengths of MobileNetV2 and FUSERNet to deliver a balanced solution, achieving 97.75% accuracy with moderate resource requirements (8.86 MB). We evaluated our models on the AIDER and NDD disaster response datasets, our models demonstrate superior performance in classifying critical natural disaster scenarios. Notably, FusionNet matches SOTA accuracy levels while reducing memory consumption by 50%, and FUSERNet's 0.23 MB size enables deployment on even the most resource-constrained UAVs. These contributions enhance the FU-Serve platform's real-time decision-making capabilities, balancing computational efficiency and mission-critical accuracy for sustainable disaster response.

Automation and digitization have always had an immense impact on healthcare. The healthcare industry stands at the precipice of a paradigm shift. Three disruptive technologies - the metaverse, explainable artificial intelligence (XAI), and blockchain - are poised to revolutionize how we deliver and experience medical care. Further, the incorporation of these technologies provides a plethora of health services seamlessly to doctors and patients with a fully immersive experience. This chapter explores the potential of the amalgamation of these approaches to enhance patient care, democratize access to healthcare by ensuring the safety, security, and privacy of data. In addition, foster a future built on transparency and trust.

In this work, we propose a Fog-enabled UAV-as-a-Service (FU-Serve) architecture to address the issues of data transmission delay for serving time-critical Internet of Things (IoT) applications. Traditionally, in a UAV-as-a-Service (UaaS) platform, different UAVs host heterogeneous sensors, which sense the physical phenomenon and transmit the sensed data to a centralized entity. Transmission of data from the sensors to the centralized entity and making any decision for an application consumes a significant amount of time. Consequently, the traditional UaaS architecture is unsuitable for serving time-critical IoT applications such as transportation, healthcare, and industries. To address these issues of service latency for time-critical IoT applications, we present the FU-Serve architecture by introducing the concept of fog computing in the UaaS platform. We discuss all the components of FU-Serve elaborately in this paper. Additionally, we architect optimal and dynamic fog node selection mechanisms for FU-Serve, which reduce the transmission delay in the networks. The simulation results show that the FU-Serve outperforms by 75% compared to the traditional UaaS platform.

This paper comprehensively reviews hand gesture datasets based on Ultraleap's leap motion controller, a popular device for capturing and tracking hand gestures in real-time. The aim is to offer researchers and practitioners a valuable resource for developing and evaluating gesture recognition algorithms. The review compares various datasets found in the literature, considering factors such as target domain, dataset size, gesture diversity, subject numbers, and data modality. The strengths and limitations of each dataset are discussed, along with the applications and research areas in which they have been utilized. An experimental evaluation of the leap motion controller 2 device is conducted to assess its capabilities in generating gesture data for various applications, specifically focusing on touchless interactive systems and virtual reality. This review serves as a roadmap for researchers, aiding them in selecting appropriate datasets for their specific gesture recognition tasks and advancing the field of hand gesture recognition using leap motion controller technology.

Sensors installed at various assembly lines are becoming basic building blocks of industry 4.0 smart manufacturing environment. The data collected by sensors can be utilised to provide recommendation engine, automate the manufacturing process and also spot anomalies. Of these, automatic anomaly detection is a very significant task which detects misleading observations, data points, and/or events that deviate from the intended behaviour. The traditional approach to anomaly detection involves the use of more efficient and convoluted techniques to achieve higher accuracy. However, these techniques typically require much larger time and hence, is not suitable for real-time applications. In this work, an adaptive N-step anomaly detection technique is proposed wherein the number of steps (or modules) in the detection technique is based on the outlier percentage in the manufacturing process. A modular approach with techniques such as Density Based Spatial Clustering for Applications with Noise (DBSCAN), Isolation Forest (IF), Local Outlier Filter (LOF), etc., are used for automatic anomaly detection. Notably, the output of one step is fed as input to the next step, thereby making use of the knowledge gained in the previous step. The proposed adaptive approach results in >99% accuracy even when the outlier population is around 25%. Such high accuracy in real-time anomaly detection would serve as an important step towards having a Digital Twin model for smart manufacturing environment.

In the current scenario, ocean observations have immense potential to monitor real-time environment changes in the Indian ocean and atmosphere. This brings new opportunities to better understand the role of the oceans in the geophysical system. Specifically, the ability to forecast the weather, study the global climate change and minimize the disastrous effects of weather events such as the cyclones, storm surges and the tsunamis is dependent on the capability to observe the oceanographic parameters such as ocean surface currents, surface wind speed, and wind direction. For the analysis of the climate and weather data, the statistical models of these oceanographic variables play a very important role. The ocean surface wind speed is one of the essential parameters influencing the ocean currents, wind direction, studies with air-sea fluxes and several coastal applications. From the literature, it is observed that the Weibull probability distribution is widely used for modeling the wind speed data in the ocean and surface applications. In this paper, an improved statistical model based on a mixture of standard models is proposed. Specifically, the Gaussian-Gamma mixture (GGM) model is proposed for pdf of the ocean surface wind speed data. The model is validated using both the quantitative and qualitative analysis for long and short range data sets. It is found that the proposed model has better fit compared to the other existing models.

Data is grouped together based on similarity this technique is called clustering which very well known in datamining. For extracting use full data from cluster most of the people are using the algorithm K-Means. In K-Means approach selecting initial centroids is the problem & these centroids are selected randomly. Because of random centroids this algorithm re-iterate a many number of times. The K-Means algorithm Correctness depends much on the chosen central values. To enhance the performance of the K-Means one should not select the original centroids randomly these must be selected carefully. A new tactic to formulate the original centroids is proposed which improves the rapidity of clustering and cuts the computational complexity by reducing the number of iterations.

In this paper, we proposed an efficient rule-based intrusion detection system for identifying sinkhole attacks in Wireless Sensor Networks (WSN). The sensor nodes in network are deployed in various hostile environments. The nature of WSNs is wireless and hence, security is the major challenging issue. Sinkhole attack is the major common internal attack on WSNs. These attacks are performed by creating a malicious node with the highest transmission range to the base station. Then this node broadcast sends fake routing message to all its neighbor nodes. We considered popular link quality-based multi-hop routing protocol named as Mint-Route protocol. To identify sinkhole attack, we have implemented an IDS system which consists of suitable rules. These rules will allow the IDS to detect the malicious node successfully. We demonstrated this method in random dissemination of sensor nodes in WSNs. We experimented to confirm the accuracy of our anticipated method.