Author(s)

Leandro Andrioli, Rodrigo da Rosa Righi, Cristiano André da Costa, Lucas Graebin

Applied Computing Graduate Program, Universidade do Vale do Rio dos Sinos (Unisinos), São Leopoldo, RS, Brazil.

Today, we observe that more and more, radio frequency identification (RFID) technology has been used to identify and track objects in enterprises and institutions. In addition, we also perceive the growing adoption of cloud computing, either public or private, to process and store data from the objects. In this context, the literature does not present an initiative that looks into the network on enterprise-cloud interactions, so neglecting network performance and congestion information when transmitting data to the cloud. Thus, we are presenting a model named ACMA—Automatic Control and Management of Assets. ACMA employs context awareness to control and monitor corporate assets in companies with multiple units. ACMA provides a centralized point of access in the cloud in which interested actors can get online data about each corporate asset. In particular, our scientific contribution consists in considering network congestion to control dynamically the data updating interval from sensors to the cloud. The idea is to search for reliability and integrity of operations, without losing or corrupting data when updating the information to cloud. Thus, this article describes the ACMA model, its architecture, algorithms and features. In addition, we describe the evaluation methodology and the results obtained through experiments and simulations based on the developed prototype.

Internet of Things, RFID, Cloud Computing, Network Congestion, Adaptivity, Assets

Andrioli, L. , Righi, R. , Costa, C. and Graebin, L. (2017) Observing Network Performance and Congestion on Managing Assets with RFID and Cloud Computing. Journal of Computer and Communications, 5, 43-66. doi: 10.4236/jcc.2017.59004.