PROJECT TITLE:

Statistical Dissemination Control in Large Machine-to-Machine Communication Networks - 2015

ABSTRACT:

Cloud primarily based machine-to-machine (M2M) Communications have emerged to achieve ubiquitous and autonomous knowledge transportation for future standard of living in the cyber-physical world. In light of the need of network characterizations, we tend to analyze the connected M2M network within the machine swarm of geometric random graph topology, as well as degree distribution, network diameter, and average distance (i.e., hops). Without the necessity of finish-to-finish info to escape catastrophic complexity, info dissemination seems an efficient approach in machine swarm. To totally understand sensible data transportation, G/G/one queuing network model is exploited to get average end-to-finish delay and most achievable system throughput. Furthermore, as real applications might need dependable NetWorking performance across the swarm, quality of service (QoS) together with large network diameter creates a replacement intellectual challenge. We tend to extend the concept of small-world network to make shortcuts among knowledge aggregators as infrastructure-swarm 2-tier heterogeneous network design, then leverage the statistical concept of network control rather than precise network optimization, to innovatively achieve QoS guarantees. Simulation results additional confirm the proposed heterogeneous network design to effectively control delay guarantees during a statistical way and to facilitate a replacement design paradigm in reliable M2M Communications.