## 1 Introduction  
A body sensor network (BSN) enables wireless communications between several miniaturized body sensors and a single coordinator worn on the human body. Since a BSN allows continuous health monitoring with real-time updates of medical records through Internet, it plays a crucial role in the next generation healthcare technologies [1; 2; 3; 4; 5]. When a BSN that is linked to the Internet via a wireless local area network (WLAN) interface moves from one coverage zone to another, a handover is required to maintain network connectivity. There are two parameters associated with a handover decision: handover rate and outage probability. The higher the handover rate, the more network resources would be consumed to reroute the communication from one interface to another. However, when the handover rate is low, handover may not be performed promptly, which causes signal-to-noise ratio (SNR) below an acceptable threshold, called outage. In BSNs where physiological information is transmitted with limited network resources, both handover rate and outage should be strictly reduced [6; 7]. Conventional handover approaches have been mostly developed by cellular network researchers. The received signal strength indicator (RSSI) is commonly used as a metric [8; 9], where the RSSI from the serving base station (BS) is compared with that from a target BS, and decisions are made using a constant margin. However, the fluctuations of RSSI associated with shadow fading cause a call to be repeatedly handed over back and forth between neighboring BSs, referred to as unnecessary handover [8; 9]. To suppress the unnecessary handover, several location-based handover algorithms have been proposed using timers or hysteresis [10; 11; 12; 13]. Most studies assume that the location of the mobile can be determined using the global positioning system (GPS), but GPS does not fit well in indoor environments where BSNs are commonly deployed [10]. Non-GPS-based solutions for indoor localization have been developed using RSSI-based WLAN localization or sensor-based tracking. WLAN RSSI-based tracking scheme [14; 15] estimates the distance between the target and reference points using the path loss function between RSSI and distance. Unfortunately, in indoor environments, the wireless channel is very noisy and radio frequency (RF) signal may suffer from reflection, diffraction and multipath effect, which makes the RSSI a complex function of distance. To overcome this problem, the WLAN fingerprinting scheme uses a priori radio map to capture the RSSI of each access point (AP) at certain points in the area of interest and live RSSI values are then compared with the radio map to find the closest match [11]. The major disadvantages of fingerprinting tracking scheme are the needs for dense training coverage and poor extrapolation to areas not covered during training. Another alternative indoor localization is inertial sensor-based kinematic tracking, which adopts kinematic relationship to estimate pedestrian localization [16]. However, the kinematic tracking is subject to the accumulated measurement errors. In this paper, we compensate the differences of the kinematic tracking and the WLAN RSSI-based tracking to get more accurate position estimation of a BSN user, based on which handover decision is made. Note that handover performance is related not only with the current position of a BSN but also with the movement trend (movement pattern, direction, and velocity) of the BSN user [17]. In this paper, we propose an optimized handover scheme with movement trend awareness for BSNs. The proposed scheme predicts the future position of a BSN user using the movement trend extracted from historical positions, and adjusts the handover decision accordingly. Furthermore, the geometric constraints are utilized for the position prediction when the floor map of the BSN deployment area is known.