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

In the case of new technology application, the cognitive radio network (CRN) addresses the bandwidth shortfall and the fixed spectrum problem. The method for CRN routing, however, often encounters issues with regard to road discovery, diversity of resources and mobility. In this paper, we present a reconfigurable CRN-based cross-layer routing protocol with the purpose of increasing routing performance and optimizing data transfer in reconfigurable networks. Recently developed spotted hyena optimizer (SHO) is used for tuning the hyperparameters of machine-learning models. The system produces a distributor built with a number of tasks, such as load balance, quarter sensing and the development path of machine learning. The proposed technique is sensitive to traffic and charges, as well as a series of other network metrics and interference (2bps/Hz/W average). The tests are performed with classic models that demonstrate the residual energy and strength of the resistant scalability and resource.

Load-Optimization in Reconfigurable Networks

Emerging reconfigurable data centers introduce the unprecedented flexibility in how the physical layer can be programmed to adapt to current traffic demands. These reconfigurable topologies are commonly hybrid, consisting of static and reconfigurable links, enabled by e.g. an Optical Circuit Switch (OCS) connected to top-of-rack switches in Clos networks. Even though prior work has showcased the practical benefits of hybrid networks, several crucial performance aspects are not well understood. In this paper, we study the algorithmic problem of how to jointly optimize topology and routing in reconfigurable data centers with a known traffic matrix, in order to optimize a most fundamental metric, maximum link load. We chart the corresponding algorithmic landscape by investigating both un-/splittable flows and (non-)segregated routing policies. We moreover prove that the problem is not submodular for all these routing policies, even in multi-layer trees, where a topological complexity classification of the problem reveals that already trees of depth two are intractable. However, networks that can be abstracted by a single packet switch (e.g., nonblocking Fat-Tree topologies) can be optimized efficiently, and we present optimal polynomialtime algorithms accordingly. We complement our theoretical results with trace-driven simulation studies, where our algorithms can significantly improve the network load in comparison to the state of the art.