Abstract-Nowadays, Internet of Things (IoT) coupled with cloud computing begins to take an important place in economic systems and in society daily life. It has got a large success in several application areas, ranging from smart city applications to smart grids. One major challenge that should be addressed is the huge amount of data generated by the sensing devices, which make the control of sending useless data very important. To face this challenge, we present a Bayesian Inference Approach (BIA), which allows avoiding the transmission of high spatio-temporal correlated data. In this paper, BIA is based on a hierarchical architecture with simple nodes, smart gateways and data centers. Belief Propagation algorithm has been chosen for performing an approximate inference on our model in order to reconstruct the missing sensing data. BIA is evaluated based on the data collected from real sensors and according to different scenarios. The results show that our proposed approach reduces drastically the number of transmitted data and the energy consumption, while maintaining an acceptable level of data prediction accuracy.
To tackle heterogeneity and to control the large data volume generated by IoT, Razafimandimby et al. REF propose a Bayesian Inference Approach (BIA).
A Bayesian and smart gateway based communication for noisy IoT scenario
2017 International Conference on Computing, Networking and Communications (ICNC)
2017 International Conference on Computing, Networking and Communications (ICNC)
Computer Science