Due to the heterogeneity involved in smart interconnected devices, cellular applications, and surrounding (GPS-aware) environments there is a need to develop a realistic approach to track mobile assets. Current tracking systems are costly and inefficient over wireless data transmission systems where cost is based on the rate of data being sent. Our aim is to develop an efficient and improved geographical asset tracking solution and conserve valuable mobile resources by dynamically adapting the tracking scheme by means of context-aware personalized route learning techniques. We intend to perform this tracking by proactively monitoring the context information in a distributed, efficient, and scalable fashion. Context profiles, which indicate the characteristics of a route based on environmental conditions, are utilized to dynamically represent the values of the asset's properties. We designed and implemented an adaptive learning based scheme that makes an optimized judgment of data transmission. This manuscript is complemented with theoretical and practical evaluations that prove that significant costs can be saved and operational efficiency can be achieved.
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