Intelligent Secured Traffic Optimization Model for Urban Sensing Applications With Software Defined Network

Smart communication using sensors and wireless structures is gaining rapid growth from remote sensing across the globe. Many surveillance systems use next-generation Internet of Things (IoT) technologies to get online data over the distributed network. Such a system speeds up daily routing communication and increases data observation efficiency for urban areas. In recent decades, many solutions have been developed to overcome data fusion problems in urban sensing systems. However, numerous approaches are still needed to build learning algorithms for heterogeneous networks with adequate transmission delays. Such networks are required to manage wireless technologies effectively while transporting massive data toward a cloud network. Furthermore, unauthorized devices with controlling computing capabilities should be kept away from network services. This work presents a protocol for distributed fault-tolerant data sharing (DFTDS) in smart cities with security by exploring software-defined network (SDN) technologies and offers the most reliable urban network. First, heterogeneous nodes and devices establish collaborative strategies for collecting and distributing the data on the low-constraint links with intelligent collaborative methods. Second, using the self-organizing scheme, the nodes are distributed over the paths and achieve green communication with global optimization criteria. In the end, the hashing scheme increases security levels for devices in terms of privacy and verification against network anomalies. The performance results show effective outcomes for packet delivery, network latency, link disconnection, network complexity, and alive nodes in dynamic scenarios as compared to other work.