An IoT and machine learning‐based routing protocol for reconfigurable engineering application

Abstract

With new telecommunications engineering applications, the cognitive radio (CR) networkbased internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR-IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross-layer routing protocol based on CR-IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine-learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR-IoT. INTRODUCTIONWireless networks reconfigurable (RWN) is mainly an adaptive network firmware developed to satisfy the demands of modern applications, changing network topologies and changing network conditions. In particular, the RWM can be reconfiguredThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

SHANN: an IoT and machine-learning-assisted edge cross-layered routing protocol using spotted hyena optimizer

“…In terms of indices, cognitive or secondary radios (SU) are specified X ∈ {1, 2,…, X} and the cognitive radios are indicated N ∈ {1, 2, …, N}. The BS of main users (BS-PU) and BS of cognitive (BS-CR) [36] are respectively related to the indexes of uses and cognitive radios. The spectral allocation architecture of CRN w.r.t.…”

“…The BScontrollers CR's are developed as a processing and capability. PU and SU are dispersed with varied heterogeneity levels and are classified into normal, advanced and superzones with an upward energy order [36].…”

“…The continuous update of the CRN helps the ML-based mechanism to accurately generate answers relating to the routing path. Distance to BS, residual energy, distance between BS-PU and BS-CR [36], the information of the era and the frequency of the CRN in licenced and non-licensed tapes are used for extracting the CRN information using a hi messages from the network layers. The BS controller in PU and SU/CR reacts in an iterative manner to the message by updating the entire CRN with current information relating to the network.…”

“…The settlement phase calculates the reconfiguration [36] in the provided CRN topology of clusters PU and SU. It also largely calculates the synchronisation of CRN stability amongst the controllers.…”

“…The estimate is performed in accordance with 10th [38] and CRN reconfiguration has the highest costeffectiveness. In determining cost-effective settings, the main consideration is the equal distribution of loads across PUs [36], with a reduced use of resources and minimum energy.…”