Software-defined networking (SDN) has predominantly become the defacto network standard for next generation networking due to its flexibility features; through a single-phase full deployment is unlikely owing to obvious tedious substitution and economic reasons. This has paved the path for hybrid SDN (h-SDN) as a practical alternative with a centralized SDN controller that connects a slow of OpenFlow switches (OFSs) and Legacy switches (LSs). However, such h-SDN imposes fresh challenges since the existing SDN solutions for key functions like topology discovery are rendered futile since LSs do not respond to the controller in the same way OFSs do. State-of-the-art SDN topology protocols, hence, leave out LSs undetected resulting in suboptimal performance. This article presents a novel indirect controller to legacy forwarding (ICLF) scheme that sets out by the controller sending a solitary PACKET_OUT (P $_{\text{out}}$ ) to an OpenFlow device, which then percolates through the slow of OFSs and LSs; by exploiting the way OFSs and LSs react to the forwarded P $_{\text{out}}$ , the controller can discover the network topology, except for terminal LSs configurations if any. Experiments performed on Mininet shows that the proposed scheme shows an improvement amounted between 34.6%–97.9% in the number of ports detected and reduction in number of messages of about 25%–58.9% under varying degree of legacy device densities, albeit at a slightly overhead of messages forwarded.

The Internet of Vehicles (IoV), equipped with sensors, generates vast amounts of data, demanding rigorous computation and network. The cloud computing platform meets these stringent computation requirements, but it has a significant latency that fog computing effectively handles. Software Defined Network (SDN) has become the de facto standard for next-generation networking due to its unique and flexible features, which handle the network pre-requisites in an agile manner. Task Offloading (TO) is a crucial issue in Software Defined-IoV (SDIoV), particularly when the vehicle’s resources are insufficient. Fog nodes schedule offloaded tasks; however, neglecting computational and network details during task scheduling can lead to longer completion times, thereby restricting the functionality of SD-IoV due to its time-sensitive nature. Therefore, this paper introduces two proactive Intelligent Task Offloading (ITO’ and ITO) schemes. These schemes schedule tasks onto fog nodes with enhanced computational capabilities, dynamically enable the network through the ovs-ofctl SDN utility, and consider vehicle mobility during task scheduling. Experiments with Mininet show that the proposed (ITO’ and ITO) schemes improve performance in terms of CPU availability, bandwidth, and throughput by 5.2 times, 50% and (30.7-13.9)% more than the existing scheme. They also reduce average packet loss, delay, Round Trip Time (RTT), and offloaded node time selection by (45.35-21.3)%, (50-33.3)%, (53.9-9.9)% and (43.2-26.06)% which demonstrates the efficacy of the proposed scheme.