LoRa-Based IoT Architecture Using Ant Colony Optimization for Intelligent Traffic System

Worldwide, approaches have been created to address urban transportation concerns, including the rising prevalence of traffic crashes, congestion, and poor traffic management. Intelligent traffic systems (ITSs) solutions for metropolitan subways are one solution toward such problems. Long-range wide area network (LoRa) is a technological evolution utilized with LP WAN. This paper presents an IoT-based modified LoRa architecture and machine learning technique implemented toward an intelligent traffic network. The existing LoRa protocol has been modified by adding a cross-layer smart and connected communication among interconnected nodes. It improves the monitoring of the nodes and helps take accurate traffic decisions. The technology uses an ant colony optimization-based machine learning algorithm to respond in real-time based on routes’ traffic volume. This research mainly focused on examining the use of LoRa technological advances in traffic control, vehicle tracking. It also examines the platform’s best LoRa network implementation criteria. It develops a LoRa cross-layer architecture that overcomes the shortcomings of the traditional LoRa network by making it easier to do intelligent assessment and flexible network and communication service providers. The proposed modified LoRa architecture accomplishes genuinely outstanding in terms of effectiveness, accuracy, and F-measure as compared to standard LoRa.