Multiple parameter decision for backoff counter optimization of medical body area network

Medical Body Area Networks are emerging as remote healthcare solutions where many sensors monitor the physiological parameters of the human body and communicate the data to a central hub. In this work, the goal has been to improve the reliability of data transmission using the Carrier Sense Multiple Access with Collision Avoidance protocol used in the medium access control layer. The authors propose an algorithm for optimizing backoff counter value using a multi-parameter decision-making algorithm called Criteria Importance through Intercriteria Correlation (CRITIC). We use CRITIC for deciding the user priority and backoff counter values, to reduce collisions and improve the reliability of medical body area networks. The work has evolved into three parts, the first part being the use of CRITIC to determine the sensor node’s priority when multiple nodes with different packet sizes and data rates are ready for data transmission. The second part was to use CRITIC to compute the backoff counter value depending on node parameters. These new values have the advantage that they are dependent on the weights of various parameters associated with the sensor node and therefore minimize the waiting time. Thirdly, a Markov chain-based analytical model has been presented to obtain the expression for performance metrics such as reliability, probability of transmission, probability of channel access failure, and probability of packet drop due to maximum retransmission limit. Simulation have been done for 10 practical sensor nodes with various distinct parameters using MATLAB. The results validate that the CRITIC backoff counter value in IEEE 802.15.6 standard enhances the reliability as compared with the conventional IEEE 802.15.6 backoff counter value. The results in this work show that the probability of packet drop reduces from 0.93 to 0.49.