Javascript must be enabled to continue!
Enhancing the Selection of Backoff Interval Using Fuzzy Logic over Wireless Ad Hoc Networks
View through CrossRef
IEEE 802.11 is the de facto standard for medium access over wireless ad hoc network. The collision avoidance mechanism (i.e., random binary exponential backoff—BEB) of IEEE 802.11 DCF (distributed coordination function) is inefficient and unfair especially under heavy load. In the literature, many algorithms have been proposed to tune the contention window (CW) size. However, these algorithms make every node select its backoff interval between [0, CW] in a random and uniform manner. This randomness is incorporated to avoid collisions among the nodes. But this random backoff interval can change the optimal order and frequency of channel access among competing nodes which results in unfairness and increased delay. In this paper, we propose an algorithm that schedules the medium access in a fair and effective manner. This algorithm enhances IEEE 802.11 DCF with additional level of contention resolution that prioritizes the contending nodes according to its queue length and waiting time. Each node computes its unique backoff interval using fuzzy logic based on the input parameters collected from contending nodes through overhearing. We evaluate our algorithm against IEEE 802.11, GDCF (gentle distributed coordination function) protocols using ns‐2.35 simulator and show that our algorithm achieves good performance.
Title: Enhancing the Selection of Backoff Interval Using Fuzzy Logic over Wireless Ad Hoc Networks
Description:
IEEE 802.
11 is the de facto standard for medium access over wireless ad hoc network.
The collision avoidance mechanism (i.
e.
, random binary exponential backoff—BEB) of IEEE 802.
11 DCF (distributed coordination function) is inefficient and unfair especially under heavy load.
In the literature, many algorithms have been proposed to tune the contention window (CW) size.
However, these algorithms make every node select its backoff interval between [0, CW] in a random and uniform manner.
This randomness is incorporated to avoid collisions among the nodes.
But this random backoff interval can change the optimal order and frequency of channel access among competing nodes which results in unfairness and increased delay.
In this paper, we propose an algorithm that schedules the medium access in a fair and effective manner.
This algorithm enhances IEEE 802.
11 DCF with additional level of contention resolution that prioritizes the contending nodes according to its queue length and waiting time.
Each node computes its unique backoff interval using fuzzy logic based on the input parameters collected from contending nodes through overhearing.
We evaluate our algorithm against IEEE 802.
11, GDCF (gentle distributed coordination function) protocols using ns‐2.
35 simulator and show that our algorithm achieves good performance.
Related Results
ACM SIGCOMM computer communication review
ACM SIGCOMM computer communication review
At some point in the future, how far out we do not exactly know, wireless access to the Internet will outstrip all other forms of access bringing the freedom of mobility to the way...
PDCF-DRL: A Contention Window Backoff Scheme Based on Deep Reinforcement Learning for Differentiating Access Categories
PDCF-DRL: A Contention Window Backoff Scheme Based on Deep Reinforcement Learning for Differentiating Access Categories
Abstract
Purpose: In wireless network backoff algorithms, the application of Deep Reinforcement Learning (DRL) allows the agent to learn through interactions with the envir...
Fuzzy Chaotic Neural Networks
Fuzzy Chaotic Neural Networks
An understanding of the human brain’s local function has improved in recent years. But the cognition of human brain’s working process as a whole is still obscure. Both fuzzy logic ...
ω – SUBSEMIRING FUZZY
ω – SUBSEMIRING FUZZY
Mapping ρ is called a fuzzy subset of an empty set of S if ρ is the mapping from S to the closed interval [0,1]. A fuzzy subset ρ introduced into this paper is a fuzzy subset of se...
Konstruksi Sistem Inferensi Fuzzy Menggunakan Subtractive Fuzzy C-Means pada Data Parkinson
Konstruksi Sistem Inferensi Fuzzy Menggunakan Subtractive Fuzzy C-Means pada Data Parkinson
Abstract. Fuzzy Inference System requires several stages to get the output, 1) formation of fuzzy sets, 2) formation of rules, 3) application of implication functions, 4) compositi...
Generated Fuzzy Quasi-ideals in Ternary Semigroups
Generated Fuzzy Quasi-ideals in Ternary Semigroups
Here in this paper, we provide characterizations of fuzzy quasi-ideal in terms of level and strong level subsets. Along with it, we provide expression for the generated fuzzy quasi...
XÂY DỰNG VÀ SỬ DỤNG HỌC LIỆU SỐ TRONG DẠY HỌC “SINH HỌC VI SINH VẬT VÀ VIRUS” (SINH HỌC 10)
XÂY DỰNG VÀ SỬ DỤNG HỌC LIỆU SỐ TRONG DẠY HỌC “SINH HỌC VI SINH VẬT VÀ VIRUS” (SINH HỌC 10)
Bài viết đề cập đến những nghiên cứu về tầm quan trọng của học liệu số, các dạng học liệu số trong dạy học Sinh học nói chung và dạy học Sinh học vi sinh vật và virus – Sinh học 10...
Adaptive Neuro-Fuzzy Systems
Adaptive Neuro-Fuzzy Systems
Fuzzy logic became the core of a different approach to computing. Whereas traditional approaches to computing were precise, or hard edged, fuzzy logic allowed for the possibility o...

