In order to solve the problem that the existing virtual multiple-input multiple-output (Virtual MIMO) routing algorithm isn't suitable to isomorphism wireless sensor network, virtual MIMO clustering (VMC) algorithm which is applicable to small and medium scale isomorphism WSN is proposed. By combining the energy-efficient virtual MIMO communication technology with the method that cluster heads are selected randomly and cyclically, energy load of network is balanced and life of WSN is extended. We build energy test platform of wireless sensor network with microcontroller MSP430F135 and wireless radio transceiver chip CC2420. The relation between transmitting power and the RSSI is researched by the experimental platform in greenhouse, the path loss factor is solved, and the energy model of virtual MIMO clustering network is created. Then, we focus on the effect of the network size, node density and path loss factor on the virtual MIMO WSN energy-saving performance. To achieve the optimization objective that the longest life of the network, we adopt the genetic algorithm to optimize the ratio of cluster head which is a key parameter of WSN. The simulation results show that the VMC has more energy-efficient and longer lifetime than LEACH. When the parameters of network structure are appropriate, the lifetime can be extended several times

In order to fully reduce network energy consumption, an improved virtual MIMO technique applied to small and medium scale isomorphism WSN is proposed. The life of WSN is prolonged by combining the energyefficient virtual MIMO communication technology with the method that cluster heads are selected randomly and cyclically and the network energy load is balanced. Influencing factors on the virtual MIMO WSN energy-saving performance are analyzed carefully in this study, including the network size, node density and path loss factor. And the longest life of the network is used as optimization objective, the ratio of cluster head (the ratio of the number of cluster head required and the total number of nodes) which is a key parameter of WSN are optimized by genetic algorithm. Simulation results show that compared with LEACH this novel algorithm is more efficient to save energy of consumption and prolong the living time of the whole network when the parameters of network structure are properly selected.

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