Research On Energy Efficiency Optimization With Scheduling Strategy For Wireless Body Area Network

Wireless Body Area Network(WBAN) is closely with human and will be one of the most promising technology in the future. With the extensive application of wireless sensor networks in our life, more and more attention are paid to WBAN. Wireless Body Area Network is mainly used in the field of health care, these wireless sensor nodes is mainly used to capture different parts of the human like physiological signals, then transmit to the central control node data, the collected data will transmit to doctor via the Internet by device for remote deal with. Due to the special nature of wireless body area network, which volume and energy were strictly limited, the energy problem is a constraint to the development of wireless body area network, so enhance the energy efficiency of wireless body area network system is core issues in our research.This paper study network scheduling and resource allocation optimization perspective to optimization theory as the main method to establish the basic framework. of energy efficiency optimization of wireless body area network. With the Qo S of network parameters can get optimal solution of global energy consumption and network lifetime under ideal, static conditions. We proposed three different dynamic scheduling strategy to achieve energy consumption and compromise the quality of service delivery. In this paper, the innovative use of non-parametric methods of machine learning to solve the problem of optimal allocation of energy in a dynamic environment.In this paper, we mainly analysis the system model of wireless body area network, and the difference between time division multiple access(TDMA) and carrier sense multiple access(CSMA). Based on the optimization theory, we proposed the global energy minimum model and global network lifetime maximum model, energy efficiency optimization framework established wireless body area network. Both models can be converted to standard geometric programming problem, exist a global optimal solution.We proposed resource allocation in dynamic channel, based on energy efficiency optimization framework. While the usage scenarios of WBAN is complex and channel is dynamic, dynamic energy and life cycle affected by a number of parameters, we proposed three different scheduling strategies. On this basis, our innovative applications of machine learning methods are used for energy allocation.The simulation results show that the scheduling policy and energy allocation method proposed in this paper can effectively improve the energy efficiency of the network and achieve a compromise between power and transmission requirements. It has high practical value.