Browsing by Author "Sathish, A."

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    IoT-enabled smart farming: Harnessing LoRa technology for sustainable agriculture
    (IEEE, 2025-07-15) Kalaivanan, T.; Sethuraman, Priya; Abirami, B.; Rajkumar, L.; Sathish, A.; Ali, Guma
    The present-day speed of Internet of Things (IoT) technology development delivers real-time monitoring and decision-making features to precision agriculture. This research seeks to establish an IoT-smart farming system built around LoRa (Long Range) technology to create more efficient resource management while maintaining sustainable farming routines. The LoRaWAN-based sensor network distributed across different topographical areas measured environmental parameters such as soil moisture and temperature, humidity. The obtained sensor data was sent through LoRa gateways to establish real-time processing at a cloud server. The radio signals maintained peak power levels in open agricultural fields yet they faced minor reductions in receiving strength in dense farmland regions. Experimental data revealed that LoRa established reliable communication which delivered -70 dBm average RSSI in open fields coupled with -85 dBm RSSI in dense crop areas as well as achievement of over 90% data transmission success across a 5 km distance using minimal power. Results from experiments proved that LoRa technology establishes dependable long-distance transmission while using minimal energy.
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    Next-Gen smart homes: AI-enhanced Li-Fi for superior efficiency and protection
    (IEEE, 2025-09-04) Rajasubha, J.; Sathish, A.; Jackson, Beulah; Hemavathi, U.; Ajay, A. P.; Ali, Guma
    The system proposed optimizes dynamically the Li-Fi transmission parameters using a Deep Reinforcement Learning (DRL) adaptive modulation algorithm, resulting in a bit error rate of 0.00034 and a throughput of 986.45 Mbps. An intrusion detection system can enhance cybersecurity by utilizing Convolutional Neural Networks, which yield a detection rate of 99.81%. The future paradigm is more secure, reliable, and programmable than conventional Wi-Fi and fixed-modulation Li-Fi systems. The Li-Fi architecture with AI support possesses a unique edge in intelligent home applications because of its scalability and reliability compared to previous, less dependable wireless networks. This work promises a safe and effective form of data transmission by creating a platform for upcoming advancements in optical wireless communication based on AI. The Li-Fi system proposed that is supported by AI works far better on smart home wireless networking problems of low speeds, insufficient customization, and security. The optimized transmission parameters were used in real-time by the system through a DRL adaptive modulation approach, and this resulted in reducing the bit error rate to 0.00034 and boosting the throughput to 986.45 Mbps.
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    Scalable multi-connectivity approaches for AR/VR traffic management in 5G networks
    (IEEE, 2025-07-15) Dhamini, V.; Subathra, Y.; Aruna, V.; Jothi, J Salomi Backia; Sathish, A.; Ali, Guma
    The expanding use of AR/VR applications in 5G networks needs an efficient traffic management system for reaching the network requirements concerning latency and bandwidth usage. The current network architectures show limitations during changes in traffic flow patterns which results in performance deterioration. A Scalable Multi-Connectivity Approach based on Software-Defined Networking (SDN) with Artificial Intelligence (AI) traffic balancing and Multi-Access Edge Computing (MEC) serves to improve 5G network management of AR/VR traffic. The system implementation includes the combination of AI-based load balancing and QoS-aware network slicing and SDN-based adaptive routing for distributing traffic optimally across 5G, LTE, and Wi-Fi networks. Real-time immersive experiences improve significantly through simulation results which show a reduction in AR/VR jitter by 28 % together with an average latency of 15.3 ms. The study shows that artificial intelligence-based multi-connectivity methods create successful traffic management for AR/VR applications through optimized resource utilization in networks.