DCRL‐RPL: Dual context‐based routing and load balancing in RPL for IoT networks
Ajay Kumar, Hariharan Narayanan
Abstract
Routing Protocol for Low power lossy network (RPL) is emerged to evade the energy issues in the Internet of Things (IoT) network. Although, RPL noticeably satisfies the requirement of the IoT network, still there exist certain open issues to resolve. This paper addresses the problem of routing overhead, packet losses and load imbalance in RPL based IoT network. These downsides are solved using the proposed Dual Context‐based Routing and Load Balancing in RPL based Network (DCRL‐RPL). Primarily, we implement the grid construction where the network area is split into a different level of the unequal grid. The ranking based Grid Selection process is executed to select the optimal grid head node in each grid. The grid head node in the network schedules its member node using the Reputation based Scheduling method. The elected grid head classifies the data received from its member using Adam Deep Neural Network (ADNN) to provide better routing performance. Here, the sensitive and non‐sensitive data are considered as context for objective function selection. At last, the efficacy of the DCRL‐RPL is analyzed using the Network Simulator 3.26 (NS3) tool. From comprehensive validation results, we show that DCRL‐RPL acquires better results.