In Internet of Things (IoT), numerous and diverse types of sensors generate a plethora of data that needs to be stored and processed with minimum loss of information. This demands efficient compression mechanisms where loss of information is minimized. Hence data generated by diverse sensors with different signal features require optimum balance between compression gain and information loss. This paper presents a unique analysis of contemporary lossy compression algorithms applied on real field sensor data with different sensor dynamics. The aim of the work is to classify the compression algorithms based on the signal characteristics of sensor data and to map them to different sensor data types to ensure efficient compression. The present work is the stepping stone for a future recommender system to choose the preferred compression techniques for the given type of sensor data.

In this paper we present a unique approach to make use of CoAP (Constrained Application Protocol) [1] from IETF (Internet Engineering Task Force) in a situation aware mode. The protocol adapts its characteristic for resource optimization depending on the indication inferred from sensed data. We consider a use-case for vehicular telemetry using a constrained in-vehicle sensor gateway which posts the vehicular information (accelerometer, GPS, device-identifier, time). In this use-case bandwidth usage is the main concern for the sensor gateway whereas usage of power which is directly impacted by overall bandwidth consumption is a key factor in case of mobile phone used as sensor gateway. We have reduced communication cost and optimized resource usage in terms of energy and bandwidth which are essential for any constrained sensor gateway by adapting characteristics of CoAP as mentioned above. The improvements are established by analyzing the data captured in real field.