Faculty of Computing

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Has files: YesSubject: search.filters.subject.Software-Defined Networking (SDN)

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    Agri-Sense: Autonomous Tomato Farming Robotic Vehicle
    (Institute of Electrical and Electronics Engineers Inc., 2026-05-21) Bandara K.M.D.I.; Yomal M.S.; Weerasekara W.M.G.V; Nilessh P; Jayakody J.A.B.C.; Gamage N.D.U.
    The precision greenhouse cultivation of tomatoes is often hindered by the expensive labor costs, the easy spread of diseases, and the unavailability of robust digital technologies. This paper proposes Agri-Sense, a comprehensive autonomous robotic system that covers the entire scope of greenhouse management. The system includes a robotic platform that allows the crops to be traversed autonomously without the need for human input. The ripeness and leaf disease detection are done using advanced YOLO-based computer vision techniques. The robotic arm has 4 degrees of freedom, which includes a modular gripper and a precise sprayer system for disease treatment. This reduces the usage of chemicals and protects the healthy crops from disease. To solve the problem of connectivity in rural greenhouse environments, the system includes a sensor mesh using LoRa sensors and a Software-Defined Networking controller. This controller uses Quality of Service and VPN tunnels to provide robust communication services even in the presence of unreliable connectivity. Experimental results showed that the proposed system bridges the gap between passive monitoring and autonomous greenhouse management. The system is robust and efficient in greenhouse management.
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    Dynamic Resource Allocation and Management in SDN for Multi-Service Network
    (Institute of Electrical and Electronics Engineers Inc., 2025) Fernando U.S.K; Pieris M.H.N; Abhayathunge H.I; Athapattu A.R.B.L; Dharmakeerthi, U; Senarathne, A
    The increase in network traffic due to technological advancements has led to considerable network congestion, complicating manual network management. Software Defined Networking (SDN) addresses these limitations by decoupling the control plane from the data plane, thereby facilitating centralized and programmable network management. This study introduces a novel dynamic resource allocation method utilizing the Ryu controller and Mininet to optimize traffic flow by identifying congestion-free channels based on critical network attributes such as link bandwidth utilization, latency, and packet loss. The proposed method comprises four principal components: traffic categorization, a dynamic queuing system, an advanced status collection module, and a path selection module. The Ryu controller's dynamic packet direction based on priority levels ensures efficient resource utilization and improved Quality of Service (QoS). Simulated traffic scenarios demonstrate the efficacy of the proposed algorithm, hence highlighting its ability to enhance network performance beyond traditional static routing methods.