@article{uninimx6402,
           title = {An enhanced opportunistic rank-based parent node selection for sustainable \& smart IoT networks},
           pages = {103079},
          author = {Premkumar Chithaluru and Aman Singh and Mahmoud Shuker Mahmoud and Sunil Kumar and Juan Luis Vidal Maz{\'o}n and Ahmed Alkhayyat and Divya Anand},
          volume = {56},
         journal = {Sustainable Energy Technologies and Assessments},
            year = {2023},
        keywords = {IoT; Sustainable; LLN; RPL; Smart cities; DODAG},
             url = {http://repositorio.unini.edu.mx/id/eprint/6402/},
        abstract = {The Internet of Things (IoT) is a network of interconnected devices that includes low-end devices (sensors) and high-end devices (servers). The routing protocol used the Low-Power and Lossy Networks (RPL) protocol, which was designed to collect data in Low-Power and Lossy Networks (LLN) efficiently and reliably. The RPL rank property specifies how sensor nodes are placed in Destination Oriented Directed Acyclic Graphs (DODAG) based on an Objective Function (OF). The OF includes information such as the Expected Transmission Count (ETX) and packet delivery rate. The rank property aids in routing path optimization, reducing control overhead, and maintaining a loop-free topology by using rank-based data path validation. However, it causes many issues, such as optimal parent selection, next-hop node selection, and network instability. We proposed an Enhanced Opportunistic Rank-based Parent Node Selection for Sustainable and Smart IoT Networks to address these issues. The optimal parent node is determined by forecasting the expected energy of each node using Received Signal Strength (RSS) and an enhanced reinforcement learning algorithm. The proposed method addresses the issue of selecting the next-hop neighbor node and improves routing stability. Furthermore, when a large number of new nodes try to join the sustainable IoT-based smart cities, the proposed technique provides optimal load balance}
}