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Traffic-aware Cooperative Binary Exponential Backoff Algorithm for Low Power and Lossy Networks

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In Low Power and Lossy Networks (LLNs), not only the transmission qualities between a sender and a receiver, but also the channel contention and resource limitations at the receiver side should be considered. Providing efficient backoff mechanism against channel access collision problem in low-power, low-cost and low data rate networks has received a lot of attention from many researchers in the field. In such networks, the IEEE 802.15.4 Medium Access Control protocol CSMA/CA uses Binary Exponential Backoff (BEB) algorithm to address the channel collision problem. Though BEB reduces collision on the multiple channel access, there is still a high packet drop probability due to the buffer limitation on the receiving node. To overcome this problem, this paper focuses on the BEB issues for LLNs and targets on RPL, which is one of the most popular cooperative routing protocols in LLNs. In RPL, it is not uncommon to have a node with relatively higher traffic than neighbor nodes because children nodes have a tendency to select a good routing metric node as a parent. If traffic concentrates on a good quality parent, it becomes inevitable to get packet loss due to the buffer overflow and channel collision. In this paper we have proposed a Traffic-aware cooperative Binary Exponential Backoff (TBEB) algorithm for LLNs with RPL routing protocol. TBEB handles the multiple channel access issue in such a way that it avoids not only the collision at the sender (child node) side but also the buffer overflow at the receiver (parent node) side without degrading the channel utilization and the throughput efficiency. MATLAB simulator is used to evaluate the performance of the proposed scheme and then compare the result with BEB and Improved BEB. Simulation results show that the TBEB algorithm improves the throughput while minimizing packet discard counts and the channel collision through maintaining good channel utilization.

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This research was supported by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the C-ITRC (Convergence Information Technology Research Center) support program (IITP-2015-H8601-15-1001) supervised by the IITP (Institute for Information & communications Technology Promotion).

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Correspondence to Ki-Hyung Kim.

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The authors declare no conflicts of interest.

Appendix: Computation for the 95 % confidence interval for the simulation

Appendix: Computation for the 95 % confidence interval for the simulation

First, we have to determine the t-value

$${\text{Desired}}\, {\text{confidence}}\, {\text{level}} = 100\left( {1 - \propto } \right)$$
$$95 = 100\left( {1 - \propto } \right)$$

A 95 % CI is equivalent to an alpha level of 0.05 and half of alpha is 0.025

$${\text{That}} \,{\text{is}}, \propto\,= 0.05\, {\text{and}}\, \propto /2 = 0.025$$

The t-value corresponding to an area of 0.025 at either end of the t-distribution for 24 degrees fo freedom is 2.064

$${\text{That}}\, {\text{is}}, t_{{\left( {0.025, 25 - 1} \right)}} = 2.064$$

Then we calculated the 95 % CI as per the following equation

$$CI_{{\left( {L, U} \right)}} = \bar{x} \pm 2.064* \frac{S}{\sqrt n }$$

where \(\bar{x}\) is the sample mean, 2.064 is the t-value, s is the sample standard deviation, n is the sample size, \(\bar{x} - 2.064 \frac{S}{\sqrt n }\) is the lower bound L value of the CI, and \(\bar{x} + 2.064 \frac{S}{\sqrt n }\) is the upper bound U value of the CI.

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Hussen, H.R., Teja, C.R., Miao, T. et al. Traffic-aware Cooperative Binary Exponential Backoff Algorithm for Low Power and Lossy Networks. Wireless Pers Commun 86, 1913–1929 (2016).

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  • LLN
  • RPL
  • Binary Exponential Backoff
  • Buffer overflow
  • Traffic-aware BEB