Abstract

A Wireless Body Area Network (WBAN) is the composition of a group of energy-efficient, miniature, invasive/non-invasive, light-weighted sensors that monitor human body health conditions for early detection and treatment for life-threatening diseases. Due to the stringent demands of WBAN, such as energy efficiency, reliability and low delay, the development of an efficient contention control algorithm is exceptionally crucial that aims to maximize throughput by reducing collisions. In this context, this paper proposes an adaptive algorithm, namely, Prioritization and Contention Control (PCC) algorithm, to minimize collisions, latency and energy consumption. The first phase of the proposed algorithm prioritizes sensors using run-time metrics to grant channel access only for the potential nodes to send their data. It leads to a lesser number of collisions among sensors, thereby reducing retransmission attempts. In the second phase, the Contention Window (CW) size is predicted using queue length and collision rate that accurately mimic the current channel status. The dynamic estimation of CW aids in minimizing channel access delay, collisions and energy consumption, thereby enhancing overall network performance. The performance of the proposed PCC algorithm is validated with the 2D Markov model and NS2 simulation in terms of throughput, packet delivery ratio, delay and remaining energy.

中文翻译：

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无线人体局域网中优先级和竞争控制算法的性能分析

摘要

无线人体局域网（WBAN）由一组高能效，微型，有创/无创，轻型传感器组成，它们监视人体健康状况，以及早发现和治疗威胁生命的疾病。由于WBAN的严格要求，例如能源效率，可靠性和低延迟，因此开发有效的竞争控制算法至关重要，该算法旨在通过减少冲突来最大化吞吐量。在这种情况下，本文提出了一种自适应算法，即优先级和竞争控制（PCC）算法，以最大程度地减少冲突，等待时间和能耗。所提出算法的第一阶段使用运行时度量对传感器进行优先级排序，以仅允许潜在节点发送其数据的通道访问权限。这样可以减少传感器之间的冲突次数，从而减少重传尝试。在第二阶段，使用准确模拟当前通道状态的队列长度和冲突率来预测竞争窗口（CW）的大小。CW的动态估计有助于最大程度地减少信道访问延迟，冲突和能耗，从而提高整体网络性能。所提出的PCC算法的性能在吞吐量，分组传输率，延迟和剩余能量方面通过2D Markov模型和NS2仿真进行了验证。CW的动态估计有助于最大程度地减少信道访问延迟，冲突和能耗，从而提高整体网络性能。所提出的PCC算法的性能在吞吐量，分组传输率，延迟和剩余能量方面通过2D Markov模型和NS2仿真进行了验证。CW的动态估计有助于最大程度地减少信道访问延迟，冲突和能耗，从而提高整体网络性能。所提出的PCC算法的性能在吞吐量，分组传输率，延迟和剩余能量方面通过2D Markov模型和NS2仿真进行了验证。