To overcome the shortcoming in the energy-saving algorithm of WiMAX networks that energy is unnecessarily wasted by idle mobile stations because of channel quality difference, according to the energy-saving class Ⅰ of the IEEE 802.16e standard, the formalization of Average Energy Consumption (AEC) was given for Mobile Station (MS) Quality of Service (QoS), and an algorithm of Idle-state Avoidance and Virtual Burst (IAVB) based on channel quality balancing was proposed. In this algorithm, the strategy combining a threshold of idle-state with choosing the main mobile terminal based on channel quality balancing was adopted, and the end condition of virtual-burst was perfected to avoid the energy waste between idle-state nodes when the virtual-burst did not end properly. The simulation results show that the energy saving performance of IAVB algorithm is 15% higher than that of the Longest Virtual Burst First (LVBF) algorithm. The experimental results show that the proposed algorithm can control the energy consumption of idle-state and improve the resource utilization efficiency of the WiMAX network.

In response to the issue that GPS is unable to carry out Location-Based Service (LBS) in indoor environment, a LBS indoor channel allocation model, credibility evaluation and control method was presented in this paper, which integrated GPS, Wi-Fi, ZigBee and Bluetooth technologies. It solved the problem arising from combination channel allocation, including the evaluation of the traffic load, the available Radio Frequency (RF), and non-overlapping RF channels number of each node. Each Access Point(AP)'s signal strength built the prediction model with reference point. The optimization algorithm was designed to determine and select the credibility of combination channel based on the energy evaluation. It adaptively selected neighbors with highest comprehensive effects to participate in iterative optimization. The simulation result indicates this method can effectively inhibit the proliferation of communication interference error in the network, reduce the positioning complexity, and improve the positioning accuracy in addition to improving scalability and robustness of the entire network.