RETRACTED ARTICLE: An adaptive traffic routing approach toward load balancing and congestion control in Cloud–MANET ad hoc networks

It would not be an overstatement to say that Cloud services have exponentially increased the computing power of mobile devices, enabling operators to undertake complex procedures including research, data mining as well as adept handling of multimedia. However, these gains are not without substantial challenges brought on by the dynamic nature of ad hoc and unpredictable network tasks, often triggered by random topological alterations in the systems. This makes network congestion and the subsequent load balancing issues very complex. In fact, network congestion and flexibility, along with channel errors, contribute most to the loss of data in wireless grid systems. While most of the relevant research work has been directed toward the issues of energy efficiency and congestion load balancing, the aim of this study was to research and build an ad hoc mobility model using Cloud computing to enable secure Internet of Things communication between smart devices. It proposed to construct a new connectivity architecture to enable secure communication between smart devices in a network. This was aided by a proficient routing protocol proposed in this work for adaptive control of network congestion and efficient load balancing in mobile ad hoc networks. The proposed technique combined the discovery of multiple alternate paths between two nodes, while calculating the cost and stability of the link and traffic load based on the available power of the participating nodes and intelligent routing of traffic based on threshold values. It took into account various aspects like packet delivery ratio versus data rate, comparison of end-to-end delay for differing traffic loads, and comparison of throughput of differing data rates in the proposed scheme vis-à-vis existing schemes. It was evident from the outcomes of the simulations that the projected approach enhanced the network lifespan by integrating load balancing and congestion control with energy-efficient algorithms. Consequently, it significantly reduced the packet dropping probability and node to node delay in the network and improved the system lifetime.