Heuristic-aided multi-objective function for satellite controller placement and routing in integrated satellite terrestrial network

In metropolitan cities, it provides better Terrestrial communication for transmitting the data. However, places like oceans, deserts, and mountains do not have a better network coverage. The terrestrial communication network is utilized to transmit audio and videos based on the frequencies. Fifth-generation terrestrial networks have the ability to provide better coverage in the satellites. Here, the Software-Defined Networking (SDN) is designed to control the network using software programs. Without considering the underlying network technology, the managing of the network becomes a complicated issue. Still, satellite communication networks generally provide less data throughput, and also it shows high latency. The development of a successful Hybrid Satellite-Terrestrial Network (HSTN) faces several difficulties in the substantial distinctions between Terrestrial Communications (TerComs) and Satellite Communications (SatComs). Thus, it does not provide effective outcomes in terms of coverage performance, mobility, transmission delay, and channel fading. In Integrated Satellite-Terrestrial Networks (ISTNs), the satellite gateway placement becomes the challenging factor. Due to the placement of the wrong gateway, the demand arising for the network's service and coverage performance gets still affected. In order to alleviate such issues, an intelligent ISTN model is proposed for the controller placement and routing. The main intention of this approach is to determine the optimized value for placement and routing. Firstly, the gateway and controller placement are optimized using the objective function, which includes constraints such as network reliability and network latency. Secondly, the optimal routing is accomplished using the Improved Rat Swarm Optimizer (I-RSO). Moreover, the designed I-RSO algorithm provides the optimal solutions to enhance the system performance. Also, the developed model is used to derive the multi-objection function with multiple constraints. Throughout the experimental findings, the developed model shows 36.3%, 23.7%, 29.3%, and 17.9% better performance EOO, GTBO, PBO, and RSO in terms of mean. Thus, the proposed method outperforms effective performance in terms of placement and routing process in ISTN.