Mobile Information Systems

Volume 2016, Article ID 9586893, 19 pages

http://dx.doi.org/10.1155/2016/9586893

## Signaling-Free Max-Min Airtime Fairness in IEEE 802.11 Ad Hoc Networks

^{1}School of Electrical Engineering and Computer Science, Seoul National University, Seoul 08826, Republic of Korea^{2}Department of Information and Communication Engineering, Dongguk University, Seoul 04620, Republic of Korea

Received 23 December 2015; Revised 11 April 2016; Accepted 28 April 2016

Academic Editor: Jose Juan Pazos-Arias

Copyright © 2016 Youngsoo Lee 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

We propose a novel media access control (MAC) protocol, referred to as* signaling-free max-min airtime fair* (SMAF) MAC, to improve fairness and channel utilization in ad hoc networks based on IEEE 802.11 wireless local area networks (WLANs). We introduce* busy time ratio *(BTR) as a measure for max-min airtime fairness. Each node estimates its BTR and adjusts the transmission duration by means of frame aggregation and fragmentation, so that it can implicitly announce the BTR to neighbor nodes. Based on the announced BTR, each of the neighbor nodes controls its contention window. In this way, the SMAF MAC works in a distributed manner without the need to know the max-min fair share of airtime, and it does not require exchanging explicit control messages among nodes to attain fairness. Moreover, we successfully incorporate the hidden node detection and resolution mechanisms into the SMAF MAC to deal with the hidden node problem in ad hoc networks. The simulation results confirm that the SMAF MAC enhances airtime fairness without degrading channel utilization, and it effectively resolves several serious problems in ad hoc networks such as the starvation, performance anomaly, and hidden node problems.

#### 1. Introduction

The recent explosive proliferation of mobile devices such as smartphones and tablet PCs has accelerated the demand for wireless Internet access. The wireless local area network (WLAN) based on IEEE 802.11 standard [1] is one of the most prevailing wireless communication technologies thanks to its ease of deployment and low installation cost. At the same time, Wi-Fi Alliance has recently released a standard for peer-to-peer communication based on WLAN (called Wi-Fi Direct) to support file sharing, multimedia streaming, content synchronization, and printing between Wi-Fi devices without the aid of an infrastructure device [2]. Furthermore, the recent development of the Internet of Things has facilitated and promoted the interconnectedness of many diverse devices and has driven the production of various applications [3, 4]. It is expected that new services based on ad hoc networks will emerge and increase rapidly. Thus, it is imperative in the operation of ad hoc networks to provide fair service and efficient channel utilization.

The current media access control (MAC) protocol in IEEE 802.11 WLANs provides fair services in terms of channel access opportunity, when all the nodes are located within the carrier-sensing range of each other and they are homogeneous in terms of transmission power, rate, and/or range, which is not a typical condition for an ad hoc network to satisfy. Therefore, the standard MAC protocol of IEEE 802.11, DCF (distributed coordination function), cannot provide fairness in ad hoc networks [5]. It is challenging to design a MAC protocol that works well in ad hoc networks and such attempts have hitherto not been very successful. This is due to several intrinsic characteristics of wireless channel (e.g., time-varying channel quality, transmission failure due to interference or channel error) and ad hoc networks (e.g., dynamic network topology, multihop transmission, and existence of hidden and exposed terminals) and the distributed nature of the MAC protocol (e.g., location-dependent contention, asymmetry in carrier sensing, and random backoff mechanism). The main objectives in designing a MAC protocol for ad hoc networks are to provide fair channel sharing among neighbor nodes, to maximize network capacity, and to minimize transmission delay and/or energy consumption of nodes. These objectives usually conflict with each other, so one has to find an acceptable compromise. This paper primarily focuses on providing fairness without degrading overall channel utilization.

Extensive studies have been conducted to provide fairness in ad hoc networks [6–15]. The approach in [6] calculates the max-min fair share of a node by constructing a flow contention graph and controls the contention window size to provide max-min fairness. This approach can be modified to support proportional fairness by considering airtime usage [7]. In order to achieve both fairness and maximization of channel utilization, the distributed scheduling algorithm in [8] emulates the fair queuing mechanism by assigning start and finish tags for each packet. The weighted fair queuing mechanism can also be implemented by controlling interframe space of IEEE 802.11 WLANs [9]. The protocol described in [10] makes each node build a scheduling table by overhearing the packet priority (i.e., arrival time) of a neighbor node that is piggybacked in the control and/or data frames and then exploits the priority information in the backoff procedure to improve fairness. Also, the protocol in [11] aims to avoid the unfairness problem; the sender and/or receiver initiates information exchange among neighbor nodes before data transmission and establishes a neighbor table and a flow table. In [12], each node periodically broadcasts the estimate of its attainable throughput using a control frame, which is used to adjust the contention window of neighbor nodes. These mechanisms, which are based on the flow contention graph in [6, 7], require that each node should be aware of the local network topology of its neighbor nodes and that the flow contention information should be exchanged among neighbor nodes. Similarly, the tag-based fair scheduling mechanisms in [8–10] and the protocols in [11, 12] require that some information should be included in the frame header or in the control frame so that it can be overheard and exchanged among neighbor nodes.

Another approach available is topology-independent and works without information exchange [13–15]. The study in [13, 14] defines a fairness index that accounts for the fair share of channel occupied by each node and by its neighbor nodes and proposes to adjust the contention window size depending on the estimated value of the fairness index. In [15], each node measures the mean number of consecutive idle slots between two transmission attempts and controls the transmission opportunity based on the number of idle slots to attain fairness. This protocol is efficient in single hop networks but not suitable in ad hoc networks.

All these approaches cannot simultaneously and successfully handle various aspects of a network such as max-min fairness, network utilization, and problems originating from an ad hoc network with the hidden/exposed nodes and multihop transmission. Moreover, most of these studies, designed to provide max-min fairness in ad hoc networks, have to solve two major problems: (i) exchanging necessary information among neighbor nodes and/or (ii) computing the max-min fair share of a node. Firstly, the approach based on control messages may fail to ensure fairness among the nodes that are located within the carrier-sensing range, but out of the transmission range of each other, because they cannot obtain necessary information from the control messages. Furthermore, this approach may degrade the efficiency of channel usage due to signaling overheads, especially in dynamic environments where the control messages need to be frequently updated and exchanged (or propagated). Secondly, the calculation of the max-min fair share is not practical in ad hoc networks, because it is difficult to calculate the fair share correctly in a timely and distributed way, and the max-min fair share should be recalculated in response to the change in the network configuration such as the number of nodes, network topology, traffic load, and/or transmission rate. Moreover, the max-min throughput fairness may not be desirable when each node has a different transmission rate. Since a lower-rate node occupies a wireless channel longer than a higher-rate node to transmit a frame, the max-min throughput fairness results in the decrease of total network throughput, referred to as a performance anomaly in multirate networks [16].

This study is designed to resolve the drawbacks of the previous studies addressed above. In this paper, we propose a novel MAC protocol, called* signaling-free max-min airtime fair* (SMAF) MAC, in order to improve max-min fairness and channel efficiency in ad hoc networks. In the SMAF MAC, each node firstly estimates the ratio of its airtime usage with respect to the total busy time of the channel, defined as* busy time ratio* (BTR). This estimation can be simply performed by the carrier-sensing mechanism that is already implemented for channel access, and it does not require any information on network topology. Secondly, each node implicitly conveys its BTR to the neighbor nodes that are located within its carrier-sensing range and compete for the shared airtime. For this purpose, we adopt the idea in our previous work [17] that the transmission duration is adjusted to announce necessary information without resorting to any explicit control messages. Lastly, by sensing the transmission duration of neighbor nodes, each node estimates the BTR of neighbor nodes and compares it with its own BTR. Based on this simple comparison result, a node adjusts its contention window to improve fairness without the need to know its max-min fair share. The main contributions of the SMAF MAC compared to the previous studies are summarized as follows.(i)The SMAF MAC announces the BTR information in a signaling-free manner by encoding it into the transmission duration of a data frame. Therefore, the SMAF MAC does not require any additional control frames or fields in the frame header, which induces a high overhead and decreases the effective capacity of the network. In addition, the SMAF MAC is effective in announcing the information to neighbor nodes even though they are located out of the transmission range of each other, and it is resistant to transmission collision or interference.(ii)It operates in a distributed manner based on the BTR without requiring the explicit information of the max-min fair share that depends on the network configuration. The SMAF MAC is also effective in dealing with the problems of starvation and performance anomaly [5, 16], because each node adjusts its transmission attempt probability based on the BTR of neighbor nodes so that the airtime can be shared fairly and efficiently among neighbor nodes.(iii)The framework of the SMAF is extended to effectively handle the hidden node problem. It selectively utilizes the hidden node resolution mechanism, that is, ready-to-send and clear-to-send (RTS/CTS) exchange mechanism, based on the estimation of the achievable throughput. Also, it supports the virtual extension of CTS frame coverage, by which the hidden node problem can be further mitigated.

The rest of this paper is organized as follows. Section 2 presents the theoretical background on max-min airtime fairness in ad hoc networks. The basic operation of SMAF MAC and its implicit signaling mechanism based on frame aggregation and fragmentation are described in Sections 3 and 4, respectively. Section 5 discusses several issues related to the SMAF MAC in practical environments. Section 6 validates the performance of SMAF MAC via ns-2 simulations [18]. Finally, Section 7 concludes this paper.

#### 2. Max-Min Airtime Fairness in Ad Hoc Networks

In this section, we first define max-min airtime fairness in ad hoc networks. We are primarily interested in ad hoc networks where each node is a* saturated* node (i.e., a node that always has backlogged packets to transmit) and its transmission affects the transmission/reception of some of the other nodes directly or indirectly. When there are two sets of nodes in a network where each node in a set affects only the communications between the nodes within this set, but not the communication between the nodes in the other set, we consider them as two independent ad hoc networks. Ad hoc networks consisting of saturated and unsaturated nodes will be discussed in Section 5. We also introduce several measures related to fairness and derive a condition to achieve fairness.

The notion of max-min fairness was introduced in wired networks [19, 20]. However, it cannot be straightforwardly applied to ad hoc networks since the capacity of a wireless channel is time-varying and the channel resource is shared in a contention-based way and spatially reused. The channel resource can be considered from various aspects such as throughput, airtime, and/or transmission opportunity. In this paper, we consider airtime as the channel resource that should be shared among transmitting nodes in a fair manner, because airtime is suitable for dealing with time-varying capacity, spatial reuse of wireless channel, and the unfairness problem due to different transmission rates of nodes. Note that the max-min fairness in terms of airtime is equivalent to the proportional fairness in terms of throughput [7, 21, 22]. We consider an ad hoc network where node shares its channel resource with neighbor nodes. We call node a neighbor node of node if it can sense the transmission by node . We denote the set of nodes consisting of node and its neighbor nodes as . Let be the channel occupation time of node during the time interval of (for the time being, is assumed to be fixed for ease of explanation; however, it may vary and may be different from each node; details will be given in Section 3), and let be the normalized airtime of node . Also, we define as the time duration in which node senses the channel busy due to the transmission of its neighbor nodes during the interval of and introduce as . Here, can be measured by means of a clear channel assessment (CCA) mechanism defined IEEE 802.11 standard. When measuring , node does not have to identify the transmitting node. Any unsuccessful transmission time, as well as successful transmission time, is taken into account in and , because even an unsuccessful transmission by a certain node consumes the channel resource shared with its neighbor nodes and the neighbor nodes must defer their channel access during this transmission time. We will simply refer to and as the airtime of node and its neighbor nodes if no confusion arises, respectively. We define that the airtime vector is* feasible* if for all , where is the total number of transmitting nodes in the network. Note that there can exist node such that ; that is, the airtime can be spatially reused among at least two neighbor nodes of node that are located out of the carrier-sensing range of each other.

*Definition 1 (max-min airtime fairness). *An airtime vector is max-min airtime fair if is feasible and it is impossible to increase the airtime of a node without decreasing the airtime of the other node that has smaller airtime.

Note that the max-min airtime fairness in this definition is different from the conventional max-min fairness in wired networks unless all the nodes in the network share a common carrier-sensing coverage. Next, we introduce a measure for fairness. Jain’s fairness index [23] is a common measure for fairness in wired networks. However, it is not appropriate to evaluate the fairness in an ad hoc network because each node has different neighbor nodes and the number of neighbor nodes and their transmission rates may be different from one to another. Thus, we define new fairness indices for an ad hoc network.

*Definition 2 (per-node and network-wide fairness indices). *By modifying Jain’s fairness index, one defines the airtime fairness index for node aswhere is the number of transmitting nodes in ; that is, . Also, one defines the network-wide airtime fairness index as

Note from (1) and (2) that becomes one only when for all and that implies for all since for any .

From these fairness indices, we will derive the condition for max-min airtime fairness in an ad hoc network.

Proposition 3 (condition for max-min airtime fairness). *An airtime vector is max-min airtime fair if and only if is feasible and have the maximum value under the constraint of .*

* Proof. *(i) (If part) since , each node has the same airtime as its neighbor nodes; that is, , for all . Let be the maximum airtime that a node can achieve under the constraint of . Then, for all . Assume that is not max-min airtime fair. This assumption means that there exists another airtime vector (). However, this contradicts the definition of , confirming that is max-min airtime fair.

(ii) (Only if part) this is the case when each node in the network increases its airtime from zero until there exists a node that cannot increase its airtime any more. Let be the airtime value of each node at this point. In this case, if some nodes try to increase their airtime from , there must exist a node that has to decrease its airtime from , and consequently, the minimum airtime value of a node in the network decreases from . Therefore, is the max-min airtime value that each node can attain, and .

#### 3. Signaling-Free Max-Min Airtime Fair (SMAF) MAC

In this section, we firstly introduce a new variable,* busy time ratio* (BTR), which plays a key role in enhancing max-min airtime fairness in the SMAF MAC, and present how it can be practically measured. Next, we propose a method to adjust the contention window to achieve fairness in a distributed way without explicit signaling messages.

##### 3.1. Condition of the BTR for Max-Min Airtime Fairness and Its Estimation

Proposition 3 in Section 2 provides a clue for max-min airtime fairness, and we need to develop a practical mechanism to realize this objective in a distributed way. For this purpose, we define the BTR as follows.

*Definition 4 (busy time ratio). *The busy time ratio of node , , is defined as the fraction of the channel busy time due to the transmission by node with respect to the total channel busy time measured by node ; that is,

We assume that the network is saturated; that is, there exist nodes that have a sufficient number of packets to transmit and try to fully utilize the available channel resource. This assumption is reasonable because there is no hindrance to fair channel sharing as long as the total demand for channel access of all the nodes can be met by the available resource (i.e., airtime). (The case involving unsaturated nodes will be handled in Section 5.3 and will be investigated via simulation in Section 6.3.)

Proposition 5 (condition of BTR for max-min airtime fairness). *Airtime vector is approximately max-min airtime fair if and only if nodes and greedily access the channel to maximize and while satisfying the constraint of , for all .*

*Proof. *With the assumption of saturated network, the channel is occupied for most of the time by node and/or its neighbor node ; that is, , and from (3). Therefore, the constraint of results in , and from (1) and (2). Consequently, as given in Proposition 3, is max-min airtime fair if and only if is maximized under the constraint of .

Now, we present how each node estimates using only the measurable MAC layer statistics. Figure 1(a) illustrates the timeline from the viewpoint of node where node and its neighbor nodes compete and occupy the channel according to IEEE 802.11 DCF. The time interval consists of backoff slots (i.e., idle slots), transmission time of node , channel busy time due to neighbor nodes’ transmission, and several overhead times (i.e., short interframe space (SIFS), acknowledgement (ACK) transmission time, and distributed interframe space (DIFS)). We define a* transmission instant* as the time period consisting of data frame transmission time, SIFS, ACK frame transmission time, and DIFS, as shown in Figure 1(a). For the sake of simplicity, we can abstractly draw transmission instants of node and its neighbor nodes as black circles and white squares, respectively, as shown in Figure 1(b). We define the* estimation period* of node as the time between two transmission instants of node . More specifically, it starts from the end of its current transmission instant and terminates at the end of its next transmission instant if it senses at least one transmission of its neighbor nodes between these two transmission instants. This case corresponds to the th estimation period in Figure 1(b). If the transmission instants of node happen to occur successively, for example, the ()th estimation period in Figure 1(b), the start time of the ()th estimation period is still the end of the th estimation period, but its end time is extended to the end of the later transmission instant of node until node senses the channel busy due to the transmission by neighbor nodes. For example, if there is no transmission of neighbor nodes during the ()th period in Figure 1(b), then the ()th and ()th estimation periods should be merged into the th period. It is noteworthy that the estimation period may differ from one node to another and that it does not have to be synchronized for all the nodes. Therefore, there is no difficulty in a node’s ability to measure its own estimation period.