An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks

Abstract

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.

A Low Collision and High Throughput Data Collection Mechanism for Large-Scale Super Dense Wireless Sensor Networks

“…In this case, collisions from neighbor nodes are so low that the possibility of collisions that can be avoided by the estimation-based backoff algorithms is very small. This conclusion can be verified according to the results in [3]. …”

“…In such algorithms, nodes adaptively schedule their time to access the channel by continuously estimating the contention levels among their neighbor nodes. It has been shown in [3] that channel estimation based backoff algorithms can efficiently decrease collisions among neighbor nodes to the theoretical low bound in cases with a wide range of contention levels. However, such algorithms are invalid for the case of collisions from hidden nodes because collisions from hidden nodes are too complex and hard to monitor on-the-fly.…”

A Fairness Oriented Neighbor-Channel-Aware MAC Protocol for Airborne Sensor Networks

“…Specially, in [ 14 ] a receiver-based time constraint model is built to help calculate precise network loadings to adaptively adjust the access threshold, but the fairness among all nodes cannot be guaranteed. In order to improve fairness among all nodes, the size of contention window of MAC layer is adjusted by collision statistics [ 17 , 18 , 19 , 20 , 21 ] or handshake mechanisms [ 22 , 23 , 24 , 25 ]. Specifically, by accurately adjusting the inter-frame space (IFS) interval in the MAC layer, a better fairness performance could be achieved [ 21 ].…”

Fairness Oriented Neighbor-Channel-Aware MAC Protocol for Airborne Sensor Networks

“…Specially, in [10] a receiver-based time constraint model is built up to help calculate precise network loading to adaptively adjust access threshold, but the fairness among all nodes cannot be guaranteed yet. In order to improve fairness among all nodes, the size of contention window of MAC layer is adjusted by collision statistics [14][15][16][17][18] or handshake mechanisms [19][20][21][22]. Specifically, by accurately adjusting inter-frame space (IFS) interval in MAC layer, a better fairness performance could be achieved [18].…”