An intelligent approach for energy efficient trajectory design for mobile sink based IoT supported wireless sensor networks

In WSN, mobility of the sink node is worthwhile since it creates potential way to gather information from sensor nodes through direct communication. To mitigate the delay experienced during the visiting period of entire network cluster head nodes, a mobile sink only gather information from limited special nodes called as rendezvous points and rest of cluster head nodes transmit their information to the nearby RP. It is extremely problematic to discover an optimal group of rendezvous points and decide the trajectory of mobile sink. In this paper, propose an intelligent approach for energy efficient trajectory design called Neuro fuzzy Emperor Penguin Optimization (NF-EPO) approach for mobile sink based IoT supported WSNs. This paper presented an adaptive Neuro fuzzy inference system (ANFIS) for optimal cluster head selection. Here, we considered the three input parameters of residual energy, neighbour node sharing and node behaviour history to choose the best CH. Finally, the effective routing algorithm of emperor penguin optimization (EPO) is used to find the rendezvous points and travelling path for mobile sink. The simulation outcomes illustrate that proposed method provides superior performance compared to other existing routing schemes. The efficiency of the system is determined using different metrics network lifetime, energy consumption with static and dynamic sink, end to end delay, bit error rate, packet loss ratio, buffer occupancy, channel load, jitter, latency, packet delivery ratio, and throughput.