In this paper we present an ongoing work towards the implementation of a framework that tackles service redundancy in IoT/WSNs as an explicit spatio-temporal phenomenon. From this perspective, redundancy is measured and explicitly stored using a spatio-temporal data model. The expected advantages of keeping an explicit history of redundancy evolution in space and time are to compare different redundancy control algorithms, to apply different knowledge extraction techniques in order to identify possible redundancy patterns, and to implement more proactive redundancy control strategies. In this paper we focus on the data model that we propose to control service redundancy at three scales: macro, meso and micro scales, respectively.

In this paper we present an ongoing work towards the implementation of a framework that tackles service redundancy in IoT/WSNs as an explicit spatio-temporal phenomenon. From this perspective, redundancy is measured and explicitly stored using a spatio-temporal data model. The expected advantages ...