Bio-Inspired ACO-based Traffic Aware QoS Routing in Software Defined Internet of Things

Abstract

The rising number of Internet of Things (IoT) devices, powered by inexpensive sensors and rapid wireless connections, places challenge on existing internet infrastructure and concerns sustainability issues. For networks to satisfy Quality-of-Service (QoS) standards in the Software-Defined IoT (SDIoT) network, efficient algorithms for routing are required. In SDIoT framework, this research proposes to develop a traffic-aware QoS routing algorithm dependent on ant behavior. In order to enhance QoS routing metrics, this work proposes an Ant Colony Optimization (ACO) based algorithm that focuses IoT device flows that are jitter, delay, and loss-sensitive. The proposed approach optimizes overall network performance with utilizing the fewest resources possible by optimizing the routing path to meet application-specific QoS standards using Yen’s k shortest path algorithm. The suggested approach outperforms current techniques in terms of fulfilling all three types of flows, resulting in sustained network performance enhancements of 5.25% in average delay, 5.15% in QoS-violated flows with Ant-inspired routing, 7% in average packet loss, and 4.65% in average jitter. This research provides an efficient practical way to deal with the growing challenges that IoT applications are posing for network sustainability.