Security and Communication Networks

Volume 2017, Article ID 2459780, 8 pages

https://doi.org/10.1155/2017/2459780

## The Performance Evaluation of an IEEE 802.11 Network Containing Misbehavior Nodes under Different Backoff Algorithms

^{1}Posts and Telecommunications Institute of Technology, Hanoi, Vietnam^{2}Panasonic R&D Center, Hanoi, Vietnam

Correspondence should be addressed to Trong-Minh Hoang; nv.ude.titp@hnimgnortgnaoh

Received 11 July 2016; Revised 25 October 2016; Accepted 29 December 2016; Published 14 February 2017

Academic Editor: Francesco Gringoli

Copyright © 2017 Trong-Minh Hoang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Security of any wireless network is always an important issue due to its serious impacts on network performance. Practically, the IEEE 802.11 medium access control can be violated by several native or smart attacks that result in downgrading network performance. In recent years, there are several studies using analytical model to analyze medium access control (MAC) layer misbehavior issue to explore this problem but they have focused on binary exponential backoff only. Moreover, a practical condition such as the freezing backoff issue is not included in the previous models. Hence, this paper presents a novel analytical model of the IEEE 802.11 MAC to thoroughly understand impacts of misbehaving node on network throughput and delay parameters. Particularly, the model can express detailed backoff algorithms so that the evaluation of the network performance under some typical attacks through numerical simulation results would be easy.

#### 1. Introduction

IEEE 802.11 based wireless networks are presented as one of the most widely deployed wireless technologies in the world to provide many applications for both special and commercial domains nowadays. The original IEEE 802.11 MAC layer employs the carrier sense multiple access/collision avoidance (CSMA/CA) protocol with binary exponential backoff algorithm to get fair multiple access [1]. To enhance the performance, several alternative backoff algorithms were proposed in recent years. Among them, the Exponential Increase Exponential Decrease (EIED) backoff algorithm can be substituted for BEB in some scenarios due to its good performance [2, 3].

Likely characteristics of common wireless networks, network performance of the IEEE 802.11 based network can be violated by several native or smart attacks from both inside or outside aspects. Particularly, the backoff procedure in a node can be affected by attacks that make a normal node become a malicious node, in which, backoff freezing problem comes to the serious issue while stopping backoff process of several nodes around the malicious node. However, to the best of our knowledge, previous analytical models did not consider the backoff freezing problem and EIED simultaneously. Furthermore, the performance of different backoff algorithms in IEEE 802.11 MAC layer misbehavior has been never compared in literature.

This paper proposes a novel analytical model to analyze and validate a saturated IEEE 802.11 wireless network employing BEB or EIED backoff algorithm in case of existing misbehavior nodes. Particularly, the numerical results of network performance for both BEB and EIED backoff algorithms are presented to compare in main parameters. The paper is organized as follows: In Section 2 we briefly review the state of the art of related studies. Section 3 presents our proposed analytical model. We adopt some main simulation results with our analysis in Section 4 and our conclusions and future works are drawn in Section 5.

#### 2. Related Work

Network performance of IEEE 802.11 MAC is the interesting aim of recent studies because it is a base step to evaluate and improve the standard in varying application environments. To approach this, using analytical model is a traditional method due to its clarity. However, the accuracy and complexity of a model strongly depend on precise assumptions. Hence, the simple and accurate model proposed by Bianchi [4] has been initiated to number of papers which enhance more conditions for compensating accuracy such as the backoff freezing issue that has been fully considered in [5–7]. However, the previous proposals are focused to analyze the binary exponential backoff algorithm only.

An Exponential Increase Exponential Decrease backoff algorithm was proposed in [2] which has got several interesting characteristics. Numerical results in [2, 3] show that throughput improvement of IEEE 802.11 saturated network with EIED backoff algorithm overcame BEB backoff algorithm in the same conditions. Unfortunately, backoff freezing phenomenon in these studies has not been mentioned.

IEEE 802.11 MAC layer misbehavior can be caused by naive attack or smart attack in [8] and several attacks modify the backoff algorithm as declared in [9]. To the best of our knowledge, several proposals are based on the Markov chain [4] to validate network performance parameter for the case of having misbehavior nodes [10–12]. However, these models ignored the backoff freezing issue and investigated the BEB algorithm only. Hence, our analytical model is proposed to compensate a lack of previous studies for evaluating influenced performance under common attacks in terms of throughput and delay parameters.

#### 3. The Proposed Analytical Model

##### 3.1. The Backoff Algorithm State Model

Consider a single-hop IEEE 802.11 wireless network in saturated traffic condition. The network contains two kinds of node as normal node and misbehavior node, which contained cheating backoff rules. The number of nodes in the network is* n*, and the number of misbehavior nodes is . The IEEE MAC layer is employed by BEB or EIED backoff algorithm in all normal nodes. Let be transmission probability and collision probability for two kinds of node, respectively. Note that any formula without notation (BEB) or (EIED) indicates that it is used for both cases of network using BEB and EIED algorithm. Denote by the transmission probability of normal node when it employed BEB backoff algorithm and for a normal node employing EIED backoff algorithm. Assume all channel in the network is no prone error and there is no hidden terminal problem.

The backoff state of a node employing BEB algorithm is modelled by a 2-dimension Markov chain [5]. Two stochastic processes are presented to backoff stage and backoff time counter value . For convenience, let , , denote , , and in the* j*th retry/retransmission, is the maximum backoff stage, and is the maximum retry limit. The contention window size of BEB algorithm is illustrated as follows:

in which and . The transmission probability of a normal node using BEB algorithm is given by

The EIED backoff algorithm was introduced in [2], where the contention window size is doubled after every unsuccessful transmission and is halved after each successful transmission. Whenever the retry counter reaches the limit value, the CW is kept and not reset to zero. It can be described as follows.

After a successful transmission, the contention window decreases as a constant value :

After an unsuccessful transmission, the contention window increases as a constant value :

In this paper, we focus on a special case of EIED algorithm, where . A backoff state model of a node based on two-dimension Markov chain is illustrated in Figure 2. The state indicates that a node has a successful transmission, and the state indicates that a node stays in backoff process when a transmission is failed. Due to the anomalous slots, we can consider a window to include the first idle backoff slot after a successful transmission [5].

Denote by the stationary probability of backoff state (*j, k*). The probability that a node transmits during a generic slot time is equal to the sum of all stationary states with . The transition probabilities of the Markov model are given as follows:

The first and second lines of (5) demonstrate that the backoff counter is decreased by 1 in duration times and . Four remaining equations in (5) show that the backoff stage is reduced by* 1* after a successful transmission and increased by* 1* after an unsuccessful transmission. Owing to the chain regularities, there is a simple relation between all states belonging to the same row (corresponding to the same stage ):

The equations in (7) and in (8) modelled the horizontal relation between all states that backoff counter is equal to zero:

At the first and the last stage, we have

By using an inductive method for this calculation, we obtain that

Denote . Since all the states can be expressed as a function of the probabilities, by imposing the normalization condition, we can solve the Markov chain:

The transmission probability of a normal node when using EIED algorithm is

Given the transmission probability of normal node and misbehavior node , we can express a conditional collision probability of a normal node through a probability that a tagged node gets a transmission, which is originated by at least one of the contending nodes:

In common case, a misbehavior node always has an initiated backoff window smaller than in normal node. When misbehavior node has a fixed contention window mechanism, the contention window size is not changed in every backoff stage. Let be equal to the contention window size of misbehavior node plus 1. The backoff counter of a selfish node is chosen randomly from* zero* to . The transmission probability of a misbehavior node can be reduced from (1) as

The collision probability of misbehavior node isThen, we can solve by using numerical method based on (8), (11), (12), (13), and (14) in the case of EIED algorithm.

##### 3.2. Channel State Model

To model the IEEE 802.11 MAC in wireless multihop fashion, we clarify states around of a node by a channel state model. A backoff freezing process is modelled by a channel state model based on Markov chain in Figure 1. The four states are* Wait, Success 1, Success 2*, and* Contention*.* Wait* state is channel state in idle state;* Success 1* is channel state which presents a successful transmission of a normal node*; Success 2* is channel state which presents a successful transmission of a misbehavior node; and* Contention* is a channel state which presents a channel in collision process.