Intelligent Energy-aware Routing Protocol in Mobile IoT Networks based on SDN. - In: IEEE transactions on green communications and networking, ISSN 2473-2400, Bd. 7 (2023), 4, S. 2093-2103
Intelligent devices and equipment have affected almost all aspects of our life and behavior. The type of connection and the manner of communication between this large volume of devices has caused the emergence of a vast field in the Internet called the Internet of Things, which significantly highlights the issue of energy management and increases the lifetime of networks. Complex communications, especially in mobile networks, have generated many challenges for network designers. To solve these challenges, the Software Defined Networking (SDN) paradigm has reduced the overhead in the devices caused by processing and computing by adding new capabilities to mobile IoT networks. This technique transfers energy-consuming tasks to the central controller, which manages continuous topological changes of the network in dynamic environments. This paper presents a new routing approach called Intelligent Energy-aware Routing Protocol in Mobile IoT Networks based on SDN (IERMIoT), which tries to manage the dynamic changes of topology due to the movement of mobile nodes to increase the network’s lifetime and prevent energy dissipation. For this purpose, it defines clusters of nodes and uses an intelligent evolutionary algorithm to determine the number of clusters required in the network and their balanced distribution in the dynamic environment. Also, this approach considers a mechanism to reduce the overhead of control packets and routing packets, which significantly affects the energy consumption of nodes. The simulation results indicate the proposed solution’s effectiveness compared to other simulated approaches with respect to packet delivery rate, average energy consumption, network lifetime, number of alive nodes, coverage, and routing overhead.
https://doi.org/10.1109/TGCN.2023.3296272
Machine learning routing protocol in mobile IoT based on software-defined networking. - In: 2022 IEEE Conference on Network Function Virtualization and Software Defined Networks, (2022), S. 108-111
The Internet's pervasive influence in all aspects of life has caused the number of heterogeneous devices connected to this network to grow exponentially. As a result, recognizing these devices and their management has led to the emergence of a new paradigm called the “Internet of Things” (IoT). Sensor networks are the essential pillar of the Internet of Things. Due to their low cost and ease of deployment, they can be implemented in a structured or unstructured way in a dynamic physical environment to manage and monitor the dynamic conditions of the desired area in various applications. Nevertheless, what is noteworthy in this regard is the limited resources of sensor networks, which cannot meet the diverse needs of the Internet of Things, so appropriate solutions must be adopted to some challenges, such as scalability and routing in dynamic topologies. Against this challenge, the SDN paradigm has attracted massive attention because it is possible to add new capabilities to networks with limited resources to reduce the overhead caused by processing and computing in sensor nodes and delegate these energy-consuming tasks to the controller. On the other hand, machine learning techniques have also shown their ability to optimize routing and increase the quality of service, reliability, and security by using statistics and information obtained from these networks. However, less research has addressed sensor nodes' mobility and challenges in resource-constrained IoT networks.
https://doi.org/10.1109/NFV-SDN56302.2022.9974791
EEC-GA: energy-efficient clustering approach using genetic algorithm for heterogeneous wireless sensor networks. - In: The 36th International Conference on Information Networking (ICOIN 2022), (2022), S. 280-286
In a wireless sensor network (WSN), sensor nodes are distributed in a predefined environment, collecting environmental data and transferring them to the base station or sink for processing and analysis. In the meantime, much research has been done on heterogeneous wireless sensor networks. Due to the fact that in a heterogeneous wireless sensor network (HWSN) factors such as primary energy, data processing ability, etc. greatly affect the life of the network, the selection of cluster heads (CHs) for coordinating a cluster of sensor nodes has created a wide range for further development of network efficiency capabilities. In this work, an event-driven energy-efficient protocol based on a genetic algorithm is proposed that uses various parameters such as the remaining energy of the node, the minimum distance of a node to the base station, and also the degree of the neighborhood of a node as fitness function values, and evaluates solutions in a heterogeneous wireless sensor network. For evaluating the proposal, network stability, energy consumption, death of the first node, and number of living nodes per round are considered as evaluation criteria.
https://doi.org/10.1109/ICOIN53446.2022.9687275