Navigating the future of ultra-smart computing cyberspace: Beyond boundaries N. Venkateswaran, Krishnamohan Reddy Kunduru, Nanda Ashwin, C. S. Sundar Ganesh, N. Hema, et al. Applied AI and Humanoid Robotics for the Ultra Smart Cyberspace, 2024 Ultra-smart computing cyberspace is a paradigm shift that combines artificial intelligence, augmented reality, and advanced networking technologies, transforming how we interact with digital environments. This integration offers unprecedented personalization, efficiency, and connectivity, blurring traditional computing boundaries and presenting challenges and opportunities in the ever-evolving technology landscape. Ultra-smart computing cyberspace presents opportunities for creativity, collaboration, and commerce, but also presents challenges such as privacy concerns, cybersecurity threats, and ethical considerations. To address these, industry stakeholders, policymakers, and technologists must establish robust frameworks to safeguard user rights and ensure responsible innovation. However, by leveraging data-driven insights and human-centered design principles, organizations can unlock transformative value and stay ahead in the competitive digital landscape.
Smart Traffic Management for Congestion Control and Emergency Vehicle Priority Hema N, Naveen Sathyanarayanan, Vasantharaj 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems Adics 2024, 2024 In urban environments, efficient ambulance response times are critical for saving lives. This paper proposes a novel approach utilizing a multi-sensor integration system for improving ambulance control and traffic management. The system combines Radio Frequency Identification (RFID) sensors, cameras, and microphones to enhance the responsiveness of ambulance drivers and alleviate traffic congestion. The RFID sensors are strategically placed along the ambulance route to facilitate seamless communication between the ambulance and traffic signals. When an ambulance approaches, the RFID sensors trigger pre-programmed traffic signal adjustments, such as extending green lights or halting conflicting traffic flow, to expedite the ambulance's passage. Simultaneously, the camera-based detection system identifies the presence of ambulances in traffic and assesses congestion levels in real-time. Utilizing computer vision algorithms, the system analyzes live camera feeds to detect ambulance vehicles and evaluate traffic density and movement patterns. This information enables dynamic rerouting of ambulances to less congested routes, optimizing response times and minimizing delays. Furthermore, a microphone array is employed to detect the distinct audio signature of ambulance sirens. By leveraging sound analysis techniques, the system accurately identifies the approach of an ambulance and triggers additional traffic management measures, such as prioritizing ambulance lanes or temporarily rerouting vehicles to clear a path. Integration of these sensor technologies into a unified control system offers a comprehensive solution for improving ambulance navigation through urban traffic. Through proactive traffic signal adjustments, dynamic route optimization, and real-time siren detection, the proposed system enhances overall emergency response effectiveness while reducing the risk of traffic-related delays and accidents. Moreover, the system's adaptability and scalability make it suitable for deployment in diverse urban environments, contributing to safer and more efficient emergency services.
Metaheuristic-Optimized Clustering for Improving QoS in IoT-Enabled Wireless Sensor Networks Komala C R, N. Pradeep, S. Rukmani Devi, B.H. Pithadiya, Jeevanantham Arumugam, et al. Proceedings 2024 International Conference on Expert Clouds and Applications Icoeca 2024, 2024 Wireless Sensor Networks (WSNs) have grown significantly in recent years. The initial step in this approach was the deployment of smaller WSNs; later, larger WSNs based on the Internet of Things (IoT) with an increased focus on energy efficiency were deployed. WSNs can be made more energy efficient by using network clustering. In networks, clustering involves dividing nodes into smaller groups and then choosing Cluster Heads (CHs) from those groups. Normal nodes in a clustered WSN are responsible for identifying their surroundings and transmitting that data to the CH, which collects the data and sends it to the base station. Some of the benefits of node clustering in WSNs include reduced routing latency and greater energy efficiency. This study aims to improve the Quality of Service (QoS) of WSN by using metaheuristic optimization. Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Cat Swarm Optimization (CSO) are the optimization techniques used for the best CH selection. The NS-2.34 software is used for the experiments. Simulation is used to test clustering technique optimization approaches under a variety of nodes. This study compares three optimization methods based on throughput, energy efficiency, and E2E delay. Simulation data indicates that the CSO outperforms the other two techniques.
A study on an Internet of Things (IoT)-enabled smart solar grid system N. Hema, N. Krishnamoorthy, Sahil Manoj Chavan, N. M. G. Kumar, M. Sabarimuthu, et al. Handbook of Research on Deep Learning Techniques for Cloud Based Industrial Iot, 2023 Automation in the power consumption system could be applied to conserve a large amount of power. This chapter discusses the applications for the generation, transmission, distribution, and use of electricity that are IoT-enabled. It covers the physical layer implementation, used models, operating systems, standards, protocols, and architecture of the IoT-enabled SSG system. The configuration, design, solar power system, IoT device, and backend systems, workflow and procedures, implementation, test findings, and performance are discussed. The smart solar grid system's real-time implementation is described, along with experimental findings and implementation challenges.
