Everyday around 2.5 quintillion bytes of data are created. There is also a growing trend toward integrating real world data into the Internet, which is provided by sensory devices, smart phones, GPS, and many other sources that capture and communicate real world data. The term Internet of Things (IoT) refers to billions of devices that produce and exchange data related to real world objects (i.e., Things). This paper focuses on how to optimize the data exchange between the sensory devices and applications in IoT and Cyber-Physical systems. In particular, a method to construct higher-level abstractions of data at local gateways is proposed. This will reduce the traffic load imposed on the communication networks that provide the real world data. The proposed method is based on an information processing algorithm where gateways analyze the data collected from the sensors and create higher level abstractions. We enhance the symbolic aggregate approximation (SAX) algorithm that is used as a building block of the abstraction creation framework, into an optimized version for sensor data, called sensor SAX. We extend the parsimonious covering theory that is usually used for medical purposes with a probabilistic parsimonious criterion in the temporal domain to infer abstractions based on time-dependent sensor data. The proposed method is analyzed and evaluated over a real world data set and the results are discussed in terms of the data size reduction, accuracy, and latency needed to create the abstractions.