AM-RPL in a Hybrid IoT Network with Static and Dynamic Devices in Cooja Simulator

Introduction: For instance, in a static IoT network where all sensor nodes are installed in precisely determined spots, it is possible that just one of the nodes will serve as a central hub for all data flowing through the network. As all traffic is redirected to this node, congestion at this node dramatically increases, quickly consuming the node's energy, creating a hole in the network. It's possible that the network will fail as a result of the failure of this node and the subsequent breakdown in communications. In order to overcome this problem, we proposed an algorithm Articulation Node Based Mobile Node Routing protocol for LLNs AM-RPL to avoid network failure. This algorithm first locates the articulation node in a given network, and it then gives the dynamic node instructions to move in that direction. A communication link is established as soon as a new node enters the communication range, updating the new DAG to include the dynamic node. This connects two components and shares articulation nodes load, improving network connectivity and performance. The proposed algorithm is implemented in the Cooja simulator to test the protocol's performance. The experiment's findings indicate that, when compared to a typical scenario, the PDR, Radio Duty Cycle, Charge Consumption, and Parent Switches of nodes all improve as load is distributed using dynamic nodes. Objectives: This research aims to develop an efficient routing mechanism for Low-Power and Lossy Networks (LLNs) to address congestion-induced failures in static IoT networks. The Articulation Node Based Mobile Node Routing Protocol (AM-RPL) is proposed to dynamically identify articulation nodes and direct mobile nodes toward them, ensuring balanced network load distribution. By mitigating excessive energy depletion at central nodes, AM-RPL enhances network resilience, stability, and longevity. The study further evaluates the protocol’s effectiveness in optimizing key network parameters and explores its scalability across diverse IoT topologies for large-scale deployment. Methods: To achieve the research objectives, AM-RPL is designed to detect articulation nodes and guide mobile nodes to strategically reposition themselves within the network. The protocol is implemented and tested in the Cooja simulator, where various IoT network scenarios are created to assess its performance. Metrics such as Packet Delivery Ratio (PDR), Radio Duty Cycle, Charge Consumption, and Parent Switches are recorded and analyzed to determine improvements over conventional static routing protocols. A comparative analysis is conducted to evaluate AM-RPL’s effectiveness in reducing congestion, improving energy efficiency, and maintaining stable communication links. Results: The simulation results indicate that AM-RPL significantly enhances network stability and performance by reducing congestion at critical nodes. The introduction of mobile nodes enables dynamic load balancing, leading to improvements in PDR, energy consumption, and parent selection efficiency. Additionally, the protocol demonstrates better adaptability to varying network conditions, making it a viable solution for large-scale IoT deployments. While percentage improvements vary based on network scenarios, the overall trend consistently shows enhanced reliability, energy efficiency, and sustained connectivity, validating AM-RPL’s effectiveness in overcoming the limitations of traditional static routing methods. Conclusions: The deployment of the Articulation Node Based Mobile Node Routing protocol (AM-RPL) adeptly addresses the paramount challenge of network failure precipitated by congestion and energy depletion at pivotal central nodes in static IoT networks. By dynamically identifying articulation nodes and strategically directing mobile nodes to these loci, AM-RPL proficiently redistributes the load, thereby fortifying network resilience. The simulation outcomes within the Cooja environment reveal marked enhancements in Packet Delivery Ratio (PDR), Radio Duty Cycle, Charge Consumption, and Parent Switches. These results substantiate the effectiveness of the AM-RPL algorithm in preserving network stability and optimizing performance by mitigating congestion-induced failures. Prospective endeavors may encompass the validation of the protocol across varied IoT network topologies and the pursuit of further refinements to augment its scalability and robustness. While we did not measure performance improvement in percentage terms due to varying readings under the same network conditions, AM-RPL reliably showed enhancements in reliability, energy efficiency, and network stability. This consistent improvement underscores the protocol's potential for optimizing large-scale IoT networks.