This paper presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of\nWireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon\nwith transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the realtime\nlink quality accurately. So this paper firstly raises a real-time RSSI link quality evaluation method based on the exponential\nsmoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize\na complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The intermittent\nKarman algorithm is used to filter RSSI fluctuation caused by environment and other factors in data packets loss situation, and\nonline learning is adopted to set algorithm parameters considering environmental changes. The experimental measurements are\nconducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receive\nsensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area\nand confirm the validity of the detection method.
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