Exploiting Object Group Localization in the Internet of Things: Performance Analysis

In the Internet of Things (IoT) localization of objectsis crucial for both information delivery and support of contextawareservices. Unfortunately, the huge number of mobile objectswhich will be included in the IoT can result in a significantamount of signaling traffic for the purpose of location discoveryand update. The major contributions of this paper are based ona simple evidence: in most IoT scenarios several objects movetogether as they are carried by a human or a vehicle, i.e., aphenomenon that we refer to as object group mobility (OGM)naturally emerges. OGM can be exploited to reduce signalingtraffic and to improve the accuracy of object localization. Morespecifically, in this paper: i) we introduce the OGM conceptand explain how, by means of a collective agent representinga group of objects as whole, it is possible to reduce signalingtraffic and improve accuracy in object localization; ii) we derivean analytical framework to assess the advantages of the proposedapproach; iii) we validate the analytical framework throughextensive simulations.