مقاله انگلیسی رایگان در مورد شبکه حسگر بدنه بی سیم – الزویر 2019

1. Introduction

2. Packet drop mechanism in WBSN

3. Packet dropping probability setting for different priority traffic

Conclusion

Acknowledgement

References

Considering the impact of congestion on wireless body sensor network (WBSN), an intelligent packet drop mechanism for multi-class traffic based on exponential random early detection is presented to mitigate the network congestion. A learning automaton is set in mote for “learning” intelligently from outside network environments. Obtained the learning result, an exponential random early detection algorithm is used in interactive node to control congestion by dropping packet. Meanwhile, based on the characteristics of WBSN, we also subdivide the packet dropping probability by the different priority traffic. Eventually, the packet dropping probability of traffic under different network environments, queue lengths and priorities is obtained. In consideration of a large amount of redundant data in WBSN, the proposed mechanism is able to actively dropping some low priority data, which can not only mitigate network congestion but also ensure the transmission of important vital signals.

Introduction

The WBSN is usually formed by biosensors implanted into the human body, which has different user groups from WSN. The biosensors are used to collect the physiological parameters of the wearer and transmit the data to a personal digital assistant (PDA), which is used to collect data and transmit it to the control center through interactive nodes. The health providers monitor the patient according to the received information and take actions for urgent patient in real time. Not only does WBSN provide a new solution for universal healthcare, disease monitoring and prevention, it is also an important perception and component of the Internet of things. Congestion problem in wireless sensor network is unavoidable. In traditional WSN, random early detection (RED) proposed by Floyd et al. [1] is widely adopted as an active and effective algorithm for mitigating network congestion. Congestion is detected by implicitly monitoring the average queue length of buffer, which is able to notify the sender to adjust the rate in time before congestion occurs and drop a small number of packets in case of network congestion.