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

摘要

In the (IoT), localization of objects is crucial for both information delivery and support of context-aware services. Unfortunately, the huge number of mobile objects that will be included in the IoT can result in a significant amount of signaling traffic for the purpose of location discovery and update. The major contributions of this paper are based on a simple evidence: In most IoT scenarios, several objects move together as they are carried by a human or a vehicle, i.e., a phenomenon that we refer to as (OGM) naturally emerges. OGM can be exploited to reduce signaling traffic and to improve the accuracy of object localization. More specifically, in this paper, we introduce the OGM concept and explain how, by means of a collective agent representing a group of objects as whole, it is possible to reduce signaling traffic and improve accuracy in object localization; we derive an analytical framework to assess the advantages of the proposed approach, and we validate the analytical framework through extensive simulations.