Abstract
When a body sensor network (BSN) that is linked to the backbone via a wireless network interface moves from one coverage zone to another, a handover is required to maintain network connectivity. This paper presents 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 the historical position, and adjusts the handover decision accordingly. Handover initiation time is optimized when the unnecessary handover rate is estimated to meet the requirement and the outage probability is minimized. The proposed handover scheme is simulated in a BSN deployment area in a hospital environment in UK. Simulation results show that the proposed scheme reduces the outage probability by 22% as compared with the existing hysteresis-based handover scheme under the constraint of acceptable handover rate.
Highlights
A body sensor network (BSN) enables wireless communications between several miniaturized body sensors and a single coordinator worn on the human body
The proposed scheme predicts the future position of a BSN user using the movement trend extracted from historical positions, and adjusts the handover decision
When BSNs are deployed in the hospital Emergency Department, BSNs will be operational during processes (2) and (3)
Summary
A body sensor network (BSN) enables wireless communications between several miniaturized body sensors and a single coordinator worn on the human body. 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 compared with the radio map to find the closest match [11]. 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.
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