PROJECT TITLE:

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

ABSTRACT:

Cloud based machine-to-machine (M2M) communications have emerged to realize ubiquitous and autonomous knowledge transportation for future daily life within the cyber-physical world. In light-weight of the necessity of network characterizations, we have a tendency to analyze the connected M2M network within the machine swarm of geometric random graph topology, including degree distribution, network diameter, and average distance (i.e., hops). Without the necessity of end-to-end information to escape catastrophic complexity, information dissemination seems an effective means in machine swarm. To absolutely understand sensible knowledge transportation, G/G/one queuing network model is exploited to get average finish-to-finish delay and maximum achievable system throughput. Furthermore, as real applications may require dependable networking performance across the swarm, quality of service (QoS) together with giant network diameter creates a brand new intellectual challenge. We extend the concept of little-world network to create shortcuts among knowledge aggregators as infrastructure-swarm two-tier heterogeneous network architecture, then leverage the statistical concept of network management instead of precise network optimization, to innovatively achieve QoS guarantees. Simulation results further ensure the proposed heterogeneous network architecture to effectively management delay guarantees in a very statistical manner and to facilitate a new style paradigm in reliable M2M communications.


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