Smart vehicles for urban sensing based on content-centric approach

Abstract

Today internet of things (IoT) plays a major role in interconnecting physical devices, vehicles, etc. to collect, exchange data through networks. Smart Vehicles collect, store and exchange monitoring sensory content about urban streets. Uploading such monitoring data by all vehicles to the infrastructure is challenging. In-order to avoid such situations, the appropriate vehicles important for different urban sensing tasks is identified by measuring its relative importance in the network. First the different location-aware content is autonomously ranked by a vehicle. It then uses a content importance and its mobility pattern to find its importance in the network. Based on the vehicle's centrality score the best content hubs in the network are identified to provide efficient collect, storage and exchange of sensory data based on content-centric networking (CCN) where content request/response data are characterised. Due to limited bandwidth resources the response data is routed to specified vehicle based on the geo-based routing technique.