OpToGen — A genetic algorithm based framework for optimal topology generation for linear networks

Smart transportation is one of the essential components of smart cities that involves sensing traffic and pedestrians. Wireless Sensor Networks (WSN) have extensively been utilized over the years for sensing and data transfer in diverse structural deployments including mesh, ad hoc and hierarchical layouts. Several applications of WSN may involve placing the nodes in a linear topology, constituting a special class of networks called Linear Networks. Such networks are being used in smart cities to collect data from roads and highways. Additionally, in a densely deployed linear network case, issues related to optimal resource allocation and networking may persist because the standard network protocols attempt to manage the network as a mesh or an ad hoc infrastructure. In this paper, we present an optimal topology generation (OpToGen) framework that uses Genetic Algorithm (GA) to configure and deploy a heterogeneous wireless network for linear infrastructures. OpToGen framework is scalable to multiple tiers and the use of GA results in less computational overhead and fast convergence to optimal topologies that are verified by a discrete event simulator.