Genetic algorithm-based optimal node selection for distributed data storage in Internet of Things networks

The Internet of Things (IoT) network is a system composed of independent devices that collect, process, and exchange environmental information. In certain types of these networks, a server is responsible for storing and processing the collected data. The nodes within the network access the information from this server. Given the large number of nodes, it is possible that many nodes might simultaneously send requests to the server, which can lead to server overload and potential failure. Additionally, server malfunction for any reason, such as hardware failure, could disrupt the entire network’s functionality. To address these issues and reduce the access time to the data stored on the server, an effective approach is to distribute data storage across the nodes within the network. The key point in this approach is to identify the appropriate nodes for storing data items. This paper proposes a method based on a genetic algorithm that identifies the optimal nodes for storing data items. Initially, the genetic algorithm randomly selects nodes for the first generation, while subsequent generations are selected based on the fitness function value of each node. The main parameters in this method are: (1) The node's interest in the data; (2) The centrality of the node; (3) The remaining buffer space. The genetic algorithm uses these parameters to identify the most suitable nodes for storing data items. The simulation results show that the proposed method reduces data access time and the number of steps required to access data in wireless sensor networks.