Abstract
As wireless body area networks (WBANs) become a key element in electronic healthcare (e-healthcare) systems, the coexistence of multiple mobile WBANs is becoming an issue. The network performance is negatively affected by the unpredictable movement of the human body. In such an environment, inter-WBAN interference can be caused by the overlapping transmission range of nearby WBANs. We propose a link scheduling algorithm with interference prediction (LSIP) for multiple mobile WBANs, which allows multiple mobile WBANs to transmit at the same time without causing inter-WBAN interference. In the LSIP, a superframe includes the contention access phase using carrier sense multiple access with collision avoidance (CSMA/CA) and the scheduled phase using time division multiple access (TDMA) for non-interfering nodes and interfering nodes, respectively. For interference prediction, we define a parameter called interference duration as the duration during which disparate WBANs interfere with each other. The Bayesian model is used to estimate and classify the interference using a signal to interference plus noise ratio (SINR) and the number of neighboring WBANs. The simulation results show that the proposed LSIP algorithm improves the packet delivery ratio and throughput significantly with acceptable delay.
Highlights
In recent years, new technology allows wireless sensors to be placed in, on, or around the human body
We have proposed a link scheduling algorithm with interference prediction for multiple mobile wireless body area networks (WBANs)
We have shown that the Bayesian inference classifier, which is simple and has a low computational complexity, can be deployed to predict the interference state of WBANs
Summary
New technology allows wireless sensors to be placed in, on, or around the human body. The IEEE standard 802.15.6 includes recently developed physical and multiple access control (MAC) protocols for short-range wireless communication for sensors near, in, or on the human body. WBANs are mobile due to the unpredictable movement of humans in public places such as hospitals, bus stations, or schools. The interference prediction mechanism based on the Bayesian inference classifier is developed using SINR values and the number of neighboring WBANs. the link scheduling algorithm is proposed by exploiting superframe structure, common scheduling, and negotiation. The remainder of this paper is organized as follows: related works are reviewed focusing on interference mitigation and prediction for efficient link scheduling in coexisting WBANs. In Section 3, the interference prediction mechanism for multiple mobile WBANs is presented.
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