Performance evaluation of tunicate-enhanced NGO resource optimization in RF energy harvesting–assisted NOMA edge computing

Nowadays, farmers across the globe are gradually adopting intelligent farming, which is facilitated by a variety of cutting-edge technologies. The advancement of intelligent farming applications is greatly aided by the internet of farming things (IoFT). Massive IoFT devices generally possess constrained resources, making it challenging to meet the battery and computational requirements of intelligent farming applications through local computation. RF energy harvesting enabled mobile edge computing (RFE-MEC) addresses this issue by harvesting RF energy from an access point, offloading and computing tasks at the edge in a nearby access point. In the proposed scheme, multiuser nonorthogonal multiple access allows the IoFT devices to simultaneously offload computationally intensive tasks to the MEC server for processing. The delay outage probability closed-form expression is formulated for the RFE-NOMA-MEC intelligent farming system under a Rayleigh fading channel. The impact of imperfect channel state information on the RFE-NOMA-MEC is considered. Tunicate enhanced northern goshawk optimization algorithm (TNGO) has been proposed to discover the optimal parameter set to minimize delay outage probability. The results indicate that the system performance is enhanced using TNGO when the optimal time switching factor, power allocation coefficient and task allocation ratio are utilized.