Abstract: The present invention discloses a method for predicting a tag arrival rate of a mobile Radio Frequency Identification (RFID) system. The method includes: first, establishing a dynamic tag arrival model for a mobile RFID system based on modeling data and a grey model GM(1, 1); improving a weight of an initial value of a differential equation of the grey model through weighting; and predicating a tag arrival rate based on a sliding window mechanism. A weighted grey model predication algorithm based on the sliding window mechanism can be obtained, to predicate a tag arrival rate of a mobile RFID system. The method for predicting the tag arrival rate of the mobile RFID system of the present invention can reduce a prediction error rate of the system, maintain a modeling length of 4 for the system through the sliding window mechanism, and update modeling data online.

Abstract: An energy-efficient traffic scheduling algorithm based on multiple layers of virtual sub-topologies is provided. First, a mathematical optimization model for an energy-efficient traffic scheduling problem is established, to minimize network energy consumption while ensuring the capability of bearing all network data flows. Then, the mathematical optimization model is resolved using an energy-efficient traffic scheduling algorithm based on a multi-layer virtual topology, to obtain an energy-efficient scheduling scheme of the data flows. The virtual topology and switch ports in an upper layer are made dormant to save energy. The method can dynamically adjust the working state of the virtual sub-topology in the upper layer according to current link utilization. A path with a minimum number of hops and lowest maximum link utilization can be found in the booted sub-topology, to route the data flow, solving the problem that a “rich-connection” data center network has low energy resource utilization at low load.