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    Intelligent Adaptive Lighting Control: Reinforcement Learning-Based Optimization for Smart Home Energy Efficiency
    (Institute of Electrical and Electronics Engineers Inc., 2025) Hewakapuge M.M; Gamage W.G.T; Surendra D.M.B.G.D; Thejan K.G.T; Rajapaksha, S; Rajendran, K
    This study introduces a novel research paper outlining a behavioral-based adaptive lighting system that aims to revolutionise smart home lighting by integrating user behavior tracking to enhance energy efficiency and user comfort. Unlike traditional motion-sensor-based lighting, the novelty of this approach is the ability to adapt dynamically to evolving user behaviors through reinforcement learning. The system utilises Wi-Fi-based positioning, GPS and accelerometer data to monitor user movements and classify different areas of the house. Users initially calibrate the home layout through a mobile application, marking room locations and lighting configurations. The system then collects movement data over time to predict optimal lighting schedules based on user routines and refines the predictions and updates lighting adjustments accordingly, minimising energy wastage while maximising user convenience. A serverless backend architecture ensures scalability, cost-effectiveness, and seamless data processing. The adaptive framework continuously refines lighting automation, responding to evolving behavioral patterns.
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    Compromising AODV for better performance: Improve energy efficiency in AODV
    (IEEE, 2017-01-27) Paranavithana, P; Jayakody, A
    MANETs became a principal research area as a promising routing protocol for a large scale of applications, due to its' behavior of self-configuring ability according to the infrastructure. Energy efficient in MANETs is a significant area in MANETs related researchers. Nodes in a MANET networks are basically battery operated, and thus have access to a limited amount of energy. The lack of energy can lead to a link failure during an active communication session, which affects the throughput and energy wastage due to a re-run of the algorithm. These papers presents a modified AODV algorithm, where a node calculates its residual energy and select the best path based on the existing matrices and total energy of the path. During the RREQ packet exchange, each node adds its residual energy to the packet and forwards it until it reaches the destination. At the destination, the total energy value is copied to the RREP packet from RREQ packet and sent to the source node through the reverse path. During the process of route selection, the path with the highest energy value gets the priority.