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Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95% of the theoretical optimal throughput and 0.97 of fairness index especially in dynamic and dense networks.

Super dense wireless sensor networks (WSNs) have become popular with the development of Internet of Things (IoT), Machine-to-Machine (M2M) communications and Vehicular-to-Vehicular (V2V) networks. While highly-dense wireless networks provide efficient and sustainable solutions to collect precise environmental information, a new channel access scheme is needed to solve the channel collision problem caused by the large number of competing nodes accessing the channel simultaneously. In this paper, we propose a space-time random access method based on a directional data transmission strategy, by which collisions in the wireless channel are significantly decreased and channel utility efficiency is greatly enhanced. Simulation results show that our proposed method can decrease the packet loss rate to less than 2% in large scale WSNs and in comparison with other channel access schemes for WSNs, the average network throughput can be doubled.

Channel state estimation-based backoff algorithms for channel access are being widely studied to solve wireless channel accessing and sharing problem especially in super dense wireless networks. In such algorithms, the precision of the channel state estimation determines the performance. How to make the estimation accurate in an efficient way to meet the system requirements is essential in designing the new channel access algorithms. In this paper, we first study the distribution and properties of inaccurate estimations using a novel biased estimation analysis model. We then propose an efficient backoff algorithm based on the theory of confidence interval estimation (BA-CIE), in which the minimum sample size is deduced to improve the contention window tuning efficiency, while a fault-tolerance interval structure is applied to reduce the inaccurate estimations so as to improve the contention window tuning accuracy. Our simulation results show that the throughput of our proposed BA-CIE algorithm can achieve 99% the theoretical maximum throughput of IEEE 802.11 networks, thanks to the improved contention window tuning performance.

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