An efficient admission control model based on dynamic link scheduling in wireless mesh networks
 Juliette Dromard^{1}Email author,
 Lyes Khoukhi^{1} and
 Rida Khatoun^{1}
https://doi.org/10.1186/168714992013288
© Dromard et al.; licensee Springer. 2013
Received: 28 February 2013
Accepted: 23 November 2013
Published: 20 December 2013
Abstract
Background
Wireless mesh networks (WMNs) are a very attractive new field of research. They are low cost, easily deployed, and a highperformance solution to lastmile broadband Internet access. In WMNs, admission control (AC) is one of the key traffic management mechanisms that should be deployed to provide quality of service (QoS) support for realtime traffic.
Results
In this paper, we introduce a novel admission control model, based on bandwidth and delay parameters, which integrates a dynamic link scheduling scheme. The proposed model is built on two different methods to access the medium: on a contentionbased channel access method for control packets and on a dynamic time division multiple access (DTDMA) for data packets. Each time a new flow is admitted in the network, the WMN’s link scheduling is modified according to the flows’ requirement and network conditions while respecting the signaltointerferenceplusnoise ratio (SINR); this allows establishing collisionfree transmissions.
Conclusions
Using extensive simulations, we demonstrate that our model achieves high resource utilization by improving throughput, establishing collisionfree transmission, as well as respecting requirements of admitted flows in terms of delay and bandwidth.
Keywords
1 Introduction
Wireless mesh networks (WMNs) are autonomous networks, made up of mesh routers and mesh clients, where mesh routers have minimal mobility and form the backbone of WMNs. The bridge between the backbone mesh and other networks (e.g., Internet, cellular and sensor networks, etc.) is achieved through gateways. Mesh routers relay the data injected by mesh clients in a multihop ad hoc fashion until reaching a gateway. WMNs are low cost, easily deployed, selfconfiguring, and selfhealing and enable ubiquitous wireless access. Indeed, they can extend Internet access in areas where cable installation is impossible or economically not sustainable such as hostile areas, battlefields, old buildings, rural areas, etc. [1].
However, due to a lack of centralized management, unfairness between flows created by the contentionbased channel access, and unreliable wireless channels, the capacity of WMNs is limited. The capacity of a node tends to decrease with the number of hops which separates it from the gateway; this fact compromises the scalability of WMNs. Indeed, the throughput of each node decreases as O(1/n), where n is the total number of nodes in the network [2]. That is why many applications with very strict constraints (e.g., VOIP and video streaming) cannot be deployed easily.
In order to solve the deployment issues of flows with very strict requirements in WMNs, several admission control (AC) schemes have been proposed in the literature [3–7]. AC schemes aim at guaranteeing flows’ constraints in WMNs by accepting a new flow on the backbone mesh only if the latter is able to guarantee its quality of service (QoS) and the QoS of previously accepted flows. Note that most existing ACs in WMNs are based on a contentionbased channel access method, carrier sense multiple access with collision avoidance (CSMA/CA) [6]. CSMA/CA was originally built for infrastructure wireless networks and turns out to be inappropriate in a multihop wireless network as it leads to low throughput and unfairness between nodes in WMNs [8].
To overcome the above limitations, several articles (e.g., [9–12]) propose to replace CSMA/CA by TDMA in WMNs. As TDMA is not a competition access scheme, it does not need methods to avoid collision (such as the backoff algorithm used in CSMA/CA) and can, thus, gain in throughput [12, 13]. Furthermore, as it divides the access to the channel in time in order to avoid collisions, it also enables to limit packet loss rate [14]. However, in most existing TDMA schemes in WMNs, the applied link scheduling is fixed at the design stage of the network and does not evolve according to the traffic load; this may lead to network congestion. To alleviate these limitations, this paper proposes a novel admission control model based on dynamic link scheduling, which integrates bandwidth and delay as parameters. We note that a preliminary version of this work was published in [15]. Our model includes the following contributions:

The use of the signalinterferenceplusnoiseratio (SINR) as the interference model in our AC. While most existing AC models (e.g., [4, 5, 16]) rely on either hopbased or distancebased interference model, our AC considers a more accurate interference model [17]: the SINRbased interference model. This allows establishing interferencefree transmissions and increasing network throughput.

An analytical formulation which allows computing the delay of any flow, knowing its scheduling over links it crosses. Integrated in an AC scheme, the latter accepts a flow only if its link scheduling respects the required delay. Furthermore, our analytical formulation can be integrated in the IEEE 802.11s mesh coordination function controlled channel access (MCCA) [18] in order to ensure that the flows’ delay requirement is respected.

A heuristic algorithm which allows dynamic link scheduling which respects traffic constraints in terms of delay and bandwidth. While most existing link scheduling solutions based on the SINR model do not consider dynamic traffic load in the network and propose fixed link scheduling, our algorithm updates link scheduling dynamically according to flows’ requirements and traffic load; this prevents the network from congestion.
To the best of our knowledge, it is the first time that an AC scheme considers dynamic link scheduling based on the SINR interference model. Our solution allows to overcome the lack of throughput of existing ACs in WMNs as well as the lack of traffic adaptation according to the network’s load and traffic types. The rest of the paper is organized as follows. In Section 2, we survey recent works related to link scheduling and admission control schemes and underline the necessity to integrate AC and link scheduling schemes into a unique solution. In Section 3, we present our system model and formulate our problem. In Section 4, we detail our proposed model. Section 5 evaluates the proposed admission control via simulations. Finally, Section 6 concludes this paper.
2 Related works
Several papers have addressed the problem of link scheduling to guarantee collisionfree transmission. To deploy a link scheduling scheme in a WMN, the nodes must be synchronized and time must be divided into frames split into slots. Link scheduling schemes aim at selecting for each link in the network the slots in a frame during which the link is periodically activated while ensuring interferencefree transmission and a maximum throughput in the network [14]. To avoid collisions, a link scheduling scheme should employ an interference model in order to establish which set of links can be activated simultaneously without causing any interference issue. The problem of link scheduling with the objective of maximizing the network throughput is known to be NPhard, even with a simple interference model [12]. Thus, most existing works propose heuristic algorithms which produce closetooptimal (suboptimal) solutions. The efficiency of a suboptimal algorithm is typically measured in terms of computational complexity (run time) and approximation factor (performance guarantee) [14]. Link scheduling schemes can be classified according to the interference model they are based on, which can be either a hopbased (e.g., [12, 19]), a distancebased (e.g., [20, 21]), or a SINRbased interference model (e.g., [10, 22–24]).
In [10], the authors formulate the khop interference model as a kvalid matching problem in a network graph. They propose a scheduling scheme based on a greedy algorithm which computes sets of independent maximum kvalid matchings in the network graph. A maximum kvalid matching is the maximum set of edges which are at least khops from each other and which can be activated simultaneously during some slot(s). The algorithm searches for maximum kvalid matchings in order to optimize the network throughput. However, this solution can only be deployed to a limited number of topology.
The authors in [19] consider the problem of scheduling the links of a set of routes in a WMN while respecting the hopbased interference model and maximizing the network throughput. In their approach, an undirected graph G is built where each node represents a link to schedule; an edge may exist between two nodes if the links represented by these nodes interfere with each other when they are activated simultaneously. The authors show that the problem of scheduling the links of a set of routes can be considered as a problem of multicoloring the nodes of the graph G. They introduce two multicoloringbased heuristics in order to schedule the links of the WMN and study their performance. However, the scalability aspect is not respected in their approach because they only study WMNs made up of a few nodes.
In [21], the authors propose new methods for computing upper and lower bounds on the optimal throughput for any given network and workload. They also introduce a conflict graph model based on the distancebased interference model to represent clearly interferences between links. In their proposed conflict graph F (V,E), each node v_{ i }∈ V of the conflict graph represents a direct link in the network. The model assumes that there exists an edge e = (v_{ i }, v_{ j }) with e ∈ E which joins up two links represented by nodes v_{ i }and v_{ j }, if these two links interfere with each other when they are activated simultaneously according to the distancebased interference model. The developed methods to compute upper and lower bounds on the optimal throughput assume that packet transmissions at the individual nodes can be finely controlled and carefully scheduled by an omniscient and omnipotent central entity, which is clearly unrealistic.
In [24], the authors investigate the problem of finding the link scheduling for a set of paths in a WMN relying on the distance basedinterference model. They represent the issue by a mixed integernonlinear problem and propose heuristics based on Lagrangian decomposition to compute suboptimal solutions. They show that their solution is suboptimal and can be rapidly computed in large WMNs. However, the interference model used in their solution is not the most realistic one and may lead to interference issues [26].
The SINRbased interference model assumes that a receiver successfully receives data if its SINR is greater than or equal to a certain threshold whose value can be given as physical layer properties of the network card [14]. The SINRbased model is not a local concept; indeed, any far away node can be involved in corrupting a transmission [23]. So the SINRbased model is less restrictive and more accurate than both the hopbased or distancebased models; however, it is more complex.
In [10], the authors present a centralized polynomial time algorithm for link scheduling using the SINRbased interference model. This algorithm schedules link by link; each link is scheduled at slots such that the resulting sets of scheduled transmission are feasible. To maximize the network throughput, this algorithm looks at minimizing schedule length (i.e., finding the shortest frame which enables to schedule every link). The authors formally prove, under uniform random node distribution, an approximation factor for the length of the schedule relative to the shortest schedule possible under the SINRbased interference model. In their solution, the authors assume that flows’ demands are known a priori by the scheduling module, which is an unrealistic assumption.
In [22], the authors study the limits of the distancebased interference model and propose a conflictfree link scheduling algorithm (CFLS) based on the Matroid theory. CFLS is a low conflictfree link scheduling algorithm with high spatial reuse. The authors argue that there is no known relation between schedule length and network throughput; so to maximize network throughput, they introduce a spatial reuse metric. Furthermore, they derive upper bounds on the running time complexity of their algorithm and prove that their CFLS algorithm can be solvable in polynomial time.
We note that while the first two interference approaches (i.e., hopbased and distancebased models) enable low computation, they can accept transmissions that lead to interference and may reject other transmissions that are interference free [17]. The SINRbased model is the most accurate model (even it is more complex). However, most existing works on link scheduling are static which means that the number of slots dedicated to each link does not evolve in time and with the network load. Thus, a link is dedicated the same number of slots when it is high loaded and low loaded which can lead, respectively, to congestion issues and bandwidth losses.
Admission control schemes aim at accepting a new flow in the network only if it can guarantee its delay and bandwidth and the delay and bandwidth of previously admitted flows. To decide whether a flow can be admitted along a given path, the admission control scheme must evaluate whether every node along the path has an available bandwidth sufficient to meet the new flow requirements. If it is the case, it accepts the new flow along this path; otherwise, it rejects it. The available bandwidth of a node can be defined as the maximum amount of bandwidth that a node can use for transmitting without depriving the reserved bandwidth of any existing flows [4] and so without causing any interference; thus, it depends mainly on the interference model considered. Furthermore, as AC schemes mainly differ from each other in their method of computing the available bandwidth of nodes [6] and so in the interference model they are based on, the choice of the interference model used to evaluate the bandwidth is of central importance in AC. In the AC models developed in [3–6], the authors reported that the available bandwidth of a node is mainly based on the channel idle time ratio (CITR). In the CITRbased scheme, the available bandwidth of a node is equal to the fraction of the idle time of its carrier sensing range multiplied by the capacity of its channel. Thus, CITR assumes that an interference occurs only when a node transmits simultaneously with another node situated in its carrier sensing range. So this scheme relies on the distancebased interference model. However, when a node senses its channel, it does not imply that it hears all nodes situated in its carrier sensing range as some nodes may be hidden. Thus, a node which applies CITR to compute its available bandwidth may not apply precisely the distancebased interference model due to the hidden node problem.
To overcome this issue, the authors in [3] propose a probabilistic approach to estimate the available bandwidth of a node which does not trigger any overhead. This approach is based on CITR and considers the impact of hidden terminals in WMNs. Upon this available bandwidth estimation, the authors design an admission control algorithm (ACA) which differentiates QoS levels for various traffic types.
In [5], the authors propose an admission control scheme which computes the available bandwidth of a node while considering its CITR and the spatial reuse issue. Indeed, as mentioned in [14, 20], the distancebased interference model can be, in some situations, too cautious and can prevent some nodes from sending in parallel even though there is no risk of interference. Thus, to overcome this issue, the authors propose to compute the available bandwidth through passive monitoring of the channel and to improve the bandwidth estimation accuracy using a formula that considers possible spatial reuse from parallel transmissions. This solution can be integrated in networks with multirate nodes.
In CACP [4], the authors differentiate two types of bandwidth: the available local available bandwidth of a node based on the CITR which considers interference issue and the available bandwidth of a node which considers both blocking and interference issues. A blocking issue occurs when a node cannot continue to send a flow which has been previously admitted. To avoid this problem, the authors compute the available bandwidth of a node as the smallest available local bandwidth of all nodes situated in its carrier sensing range and itself.
In [27], the authors propose a fuzzy decisionbased multicast routing resource admission control mechanism (FAST). In this solution, once every node on a flow path has accepted the flow in terms of bandwidth, the source has the final decision to accept or not the flow according to the decision made by the fuzzy decision scheme. This intelligent method selects among all the flows, which the source wants to send and which have been previously accepted by the nodes of their path, the optimal one in terms of delay, jitter, packet loss, and bandwidth. Once the optimal flow is chosen, the source can start sending it. The validation of this solution shows good results; however, the authors do not specify how the used values of the parameters (bandwidth, delay, jitter, packet loss) are obtained.
In [7], the authors propose an interferenceaware admission control (IAC) for use in WMNs. The originality of their work lies in a dual thresholdbased approach to share the bandwidth between neighbors; this sharing is essential to compute the available bandwidth of nodes. However, the IAC solution cannot deal with multirate nodes and does not consider the possibility of parallel transmissions which may lead to underestimation of the nodes’ available bandwidth.
The AC schemes presented above, as most existing AC schemes, are based on CSMA/CA which is known to lead to poor throughput [10]. Indeed, CSMA/CA triggers interference and dedicates a huge amount of time to avoid collision (via backoff algorithm and RTS/CTS mechanism). To overcome these issues, the IEEE 802.11s standard [18] proposes the protocol MCCA which takes advantage of both admission control and link scheduling schemes in WMNs. In MCCA, nodes can reserve future slots in advance for their flows. To reserve a slot for a transmission, a node must first check if no node situated at two hops from it or from its receiver has already reserved the slot. Thus, MCCA is based on the twohop interference model. However, MCCA may suffer from collisions due to hidden node problems [28] and does not specify any link scheduling algorithm [29]. In a previous work [15], we have proposed to integrate link scheduling in an admission control. However, in this previous work, the link scheduling scheme is totally distributed and integrates the distancebased interference model. A flow is admitted when there exists a path where every node is able to compute a link scheduling for this flow while respecting its requirements in terms of bandwidth. However, this solution generates an important overhead due to the broadcast of advertisement packets and lacks accuracy as it does not rely on a SINRbased interference model. Furthermore, it does not integrate the delay parameter in the admission control which can prevent the deployment of multimedia flows.
3 System model
In this section, we present our system model and formulate our link scheduling problem. The system model includes the interference model, the time division model, the link scheduling in terms of bandwidth, and the link scheduling in terms of delay. We also present and prove two theorems upon which our new method computes the delay of any flow knowing its scheduling. We then formulate our problem of admitting or rejecting a new flow along a certain path according whether there exists a scheduling over this path which respects the bandwidth and the delay of the new flow and of previously admitted flow, or not. In our work, we only consider the WMN’s backhaul made up with mesh routers. Thus, the router to which a client is directly connected is considered as the sender of the flow. Every client wants to access the Internet, so no flow is directly exchanged between clients and every flow is sent via a gateway on the Internet. Mesh routers, also called nodes or stations in our paper, have all the same radio parameters.
3.1 Interference modeling
However, we assume that a link transmission is successful when both data and acknowledgment packets are received successfully. So a transmission on a link (u_{ j },v_{ j }) is successful if v_{ j }receives the data sent by u_{ j }and then u_{ j }receives the acknowledgment sent by v_{ j }. In our model, a transmission (i.e., data packet and its acknowledgment) on a link:

Starts only at the beginning of a time unit (TU) interval (see Figure 3).

Lasts the time of a TU (this will be detailed in the next sections, see Equation 6). Note that we assume that all data packets have the same size and thus have the same transmission time.
When a subset Γ of links are transmitting simultaneously, the transmission on the link e_{ i }= (u_{ j }, v_{ j }) ∈ Γ is successful if it respects Equations 4 and 5. Let I be the distance matrix of size V × V. The value of each element i_{ ij }of I is the Euclidean distance between node v_{ i }and node v_{ j }: when i = j, the value of the element i_{ ij }is null, as the Euclidean distance between a node and itself is null.
3.2 Time division
In our model, we consider that the nodes of our network are synchronized and the time is split in two different intervals (see Figure 4):

TU interval: a TU interval has a length equal to the time needed for the transmission of a packet. The formula to compute TU’s length is presented hereafter in the paper.

Transmission scheduling (TS) interval: a TS interval is composed of a fixed number N of TUs and is periodically repeated every N × TU.
A TS interval is made up of two periods: the first one is the period of contention access to the channel denoted T_{ c }and the second is the link scheduling period denoted T_{ s }(see Figure 4). The T_{ c }period is made up of N_{ c }TUs (with N_{ c }< N): during this period, nodes send packets using CSMA/CA, whereas the TS period is made up of N_{ s } TUs (with N = N_{ c }+ N_{ s }) : during this period, nodes transmit data packets using TDMA.
where L and H, respectively, are the size of a data packet and its header; T_{difs} and T_{sifs}, respectively, are the interframe space time of DIFS and SIFS which are defined in the IEEE 802.11 standard; T_{ack} is the transmission time of an acknowledgment; and T_{plcp} is the transmission time of the physical layer convergence protocol (PLCP) header [4]. The requirements of a flow f are expressed by the double △_{ f }= (br_{ f }, dr_{ f }), where br_{ f }represents the minimum bandwidth required by f and dr_{ f }represents the maximum delay required. The set of flows which are in process are denoted F. In the sequel, we present and prove two theorems. These theorems are at the base of our method to compute the delay of any flow knowing its scheduling.
3.3 Link scheduling in terms of bandwidth
3.4 Link scheduling in terms of delay
where V^{∗} is the set of gateways in the network. In the following, the transmitter node u_{ i }and the received node v_{ i }of a link e_{ i }possess the same index i as that of the link it belongs to. Furthermore, this index represents the link position on the path of a flow f; the index starts at 0. We denote ${p}_{j}^{f}$ the j th packet of a flow f. We define the three following delays:

Delay of a packet at a link: The delay of a packet ${p}_{j}^{f}$ at a link e_{ i }(with e_{ i }= (u_{ i }, v_{ i })) represents the time between the packet’s arrival at the link’s start node u_{ i }and its correct delivery to the link’s end node v_{ i }and is denoted by $d({e}_{i},{p}_{j}^{f})$.

Delay of a packet: It is the time that takes a packet ${p}_{j}^{f}$ to cross all links along its path. It is denoted by $d\left({p}_{j}^{f}\right)$ and can be computed as follows:$d\left({p}_{j}^{f}\right)=\sum _{\forall {e}_{i}\in {l}_{f}}d({e}_{i},{p}_{j}^{f})$(10)

Delay of a flow: The delay of a flow f is the maximum delay taken by a packet of this flow; it is denoted by d (f). So if a flow f sends n packets, the delay of this flow can be computed as follows:$d\left(\phantom{\rule{0.03em}{0ex}}f\right)\phantom{\rule{0.03em}{0ex}}=\phantom{\rule{0.03em}{0ex}}\text{max}\left\{S\right\}\phantom{\rule{0.03em}{0ex}}\text{with}\phantom{\rule{0.03em}{0ex}}S\phantom{\rule{0.03em}{0ex}}=\phantom{\rule{0.03em}{0ex}}\{\forall j\phantom{\rule{0.03em}{0ex}}\in \phantom{\rule{0.03em}{0ex}}\mathbb{N}\phantom{\rule{0.03em}{0ex}}\text{and}\phantom{\rule{0.03em}{0ex}}\forall j\phantom{\rule{0.03em}{0ex}}\in \phantom{\rule{0.03em}{0ex}}\phantom{\rule{0.03em}{0ex}}[0,n1\left]\phantom{\rule{0.03em}{0ex}}\right\phantom{\rule{0.03em}{0ex}}d\left(\underset{j}{\overset{\phantom{\rule{0.03em}{0ex}}f}{p}}\right)\}$(11)
Notations
Notation  Description 

r_{ f }(e_{ i })  Represents the set of the reserved TU_{ f }TUs during which link e_{ i }has to send packets of flow f 
tu (e_{ i })_{ j }  Position of the j th TU in the TS interval reserved by link e_{ i } 
${p}_{j}^{f}$  The j th packet of the flow f 
$d({e}_{i},\phantom{\rule{0.03em}{0ex}}{p}_{j}^{f})$  The delay at the link e_{ i }of the $\left({p}_{j}^{f}\right)$ th packet sent by flow f 
$d\left({p}_{j}^{f}\right)$  Delay of the packet ${p}_{j}^{f}$ 
t _{ j }  Time t such that t = TS × j 
d (e_{ i })_{ j }  The delay at link e_{ i }of a packet sent by e_{ i }at the (t u (e_{ i })_{ j }) th TU of a TS interval during the stable period of e_{ i } 
q (e_{ i }, t)  Length of the node u_{ i }’s queue at time t 
In the sequel, we fix the starting time (i.e., t = 0) at the beginning of the TS at which the first packet of flow f is sent over the link e_{0}. To compute the delay of any flow f ∈ F, we need to introduce the following assumptions:

The transmitter node u_{0} of link e_{0} sends a packet of flow f at each reserved TU for f.

Node u_{0} receives a packet of f just before every TU it has reserved for f. Thus, the delay of every packet of f at edge e_{0} is equal to one TU.

u_{0} sends its first packet at the first TU reserved for f, i.e., at the (t u (e_{0})_{1})th TU of the first TS interval, t u (e_{0})_{1} ∈ r_{ f }(e_{0}).

Every node u_{ i }has a FIFO queue whose length is equal q(e_{ i }, t) at time t. The length of every node’s queue is initialized at 0, i.e., q (e_{ i },0) = 0, $\forall i\in \mathbb{N}$.
Let t_{ j }be the time such that t_{ j }= TS × j, $\forall j\in \mathbb{N}$. Thus, the time t_{ j }is the beginning of the (j + 1)th TS interval. In the following, we introduce two theorems. These theorems are at the base of our method to compute the delay of any flow knowing its scheduling. The first theorem asserts that when a flow enters the network, it becomes stable at a link only after a certain time; once a flow is stable at a link, the start node of the link sends a packet of the flow at every reserved TU and has a queue’s length at the beginning of every TS of the same size. The first theorem enables to prove the second theorem. The second theorem asserts that once a flow is stable at a link, the delay of its packets at this link is periodic, of period one TS, i.e., every packet sent at the same reserved TU of any TS interval gets the same delay at this link. Thus, according to the second theorem, the delay of a packet at a link takes only TU_{ f }different values. By computing these TU_{ f }values for every link, we get the delay of every packet at every link. By adding up the delay a packet gets at every link, we can get the delay of the packet. Then, the delay of the flow is obtained by extracting the highest delay among the delay of every packet of the flow.
3.4.1 Transition and stable periods
Theorem 1. Before time t_{ i }, every link e_{ i }(∀ e_{ i }∈ l_{ f }) is in a transition period, i.e., its start node u_{ i }possesses at the beginning of each TS interval a queue whose length is less or equal to q (e_{ i }, t_{ i }) (queue’s length of u_{ i }at t_{ i }) and does not send a packet at each of its reserved TU. From t_{ i }, every link e_{ i }(∀ e_{ i }∈ l_{ f }) is in a stable period, i.e., it sends a packet at each of its reserved TU and the length of its queue at the beginning of every TS interval is fixed and equal to q(e_{ i }, t_{ i }).
Proof. Let us prove Theorem 1 by recurrence. First, Theorem 1 is valid for link e_{0} as we have previously assumed that u_{0} sends a packet at each of its reserved TU and the delay of any packet of flow f is one TU. Thus, from t_{0}, node u_{0} sends a packet at every reserved TU and has at the beginning of each TS interval a queue of length 0.
Thus, u_{ i }can send a packet at each of its z available TUs. During this second TS, u_{ i }sends a packet at each of its reserved TU and possesses at the end of the TS in its queue TU_{ f } z packet. As the queue length of u_{ i }is the same at t_{2} and t_{1}, it implies that from t_{1}, u_{ i }possesses at the beginning of each TS interval TU_{ f } z packets in its queue. We can thus conclude that if u_{i  1} had sent a packet at each of its reserved TU since t_{0}, u_{ i }would have had a queue of length TU_{ f } z at t_{i  1}. Thus, at t_{i  1}, u_{ i }possesses a queue of length q (e_{ i }, t_{i  1}) ≤ TU_{ f } z. From t_{i  1} to t_{ i }and according to Theorem 1, u_{i  1} sends a packet at each of its reserved TU. During this TS, u_{ i }first sends the q (e_{ i }, t_{i  1}) packets it has in its queue at the beginning of the TS. Thus, it has still TU_{ f } q (e_{ i }, t_{i  1}) TUs available in the current TS interval which can be represented by a subset of r_{ f }(e_{ i }) denoted ${r}_{f}^{3}\left({e}_{i}\right)=(\mathit{\text{tu}}{\left({e}_{i}\right)}_{1}^{3},\dots ,\mathit{\text{tu}}{\left({e}_{i}\right)}_{{\mathit{\text{TU}}}_{f}q({e}_{i},{t}_{i1})}^{3})$. As these TUs are the TU_{ f } q (e_{ i }, t_{i  1}) last reserved TUs of u_{ i }and that TU_{ f } q (e_{ i }, t_{i  1}) ≤ z, we get ${r}_{f}^{3}\left({e}_{i}\right)\subset {r}_{f}^{2}\left({e}_{i}\right)$; then according to Equation 13, u_{ i }can send z packets among the TU_{ f }it has received during the TS. Thus, from t_{i  1} to t_{ i }, u_{ i }does not send a packet at each reserved TU and link e_{ i }is still in transition. At t_{ i }, u_{ i }possesses TU_{ f } z packets in its queue, as we have previously seen; once it gets TU_{ f } z TUs at the beginning of a TS interval, u_{ i }then sends a packet at every reserved TU and possesses a queue of length TU_{ f } z at the beginning of every TS interval. From t_{ i }, link e_{ i }gets stable. Thus, we have proved Theorem 1 by recurrence as it is true for link e_{0} and for any link e_{ i }∈ l_{ f } {e_{0}} if its previous link e_{i  1} respects the theorem.
3.4.2 Periodicity of packet delay
Theorem 2. When a link e_{ i }becomes stable, the delay of packets at this link gets periodic, i.e., every packet of flow f that e_{ i }’s start node, u_{ i }, sends at the reserved TU t u (e_{ i })_{ j }(j ∈ [1, TU_{ f }]) of any TS has the same delay at the link e_{ i }(denoted d (e_{ i })_{ j }).
Proof. From Theorem 1, we know that every link e_{ i }= (u_{ i }, v_{ i }) after time t_{ i }is activated at each of its reserved TU and that u_{ i }has a queue of length q(e_{ i }, t_{ i }) at the beginning of each TS interval. Thus, after time t_{ i }, when node u_{i  1} sends a packet at the (tu (e_{i  1})_{ j })th TU of a TS interval, node u_{ i }forwards it at the $\left(\mathit{\text{tu}}{\left({e}_{i}\right)}_{{j}^{\prime}}\right)\text{th}$ TU such that j^{′} = (j + q (e_{ i }, t_{ i })) % TU_{ f }:

Of the current TS interval if $\mathit{\text{tu}}{\left({e}_{i1}\right)}_{j}<\mathit{\text{tu}}{\left({e}_{i}\right)}_{{j}^{\prime}}$

Of the next TS interval if $\mathit{\text{tu}}{\left({e}_{i1}\right)}_{j}>\mathit{\text{tu}}{\left({e}_{i}\right)}_{{j}^{\prime}}$
Equations 14 and 15 prove Theorem 2.
3.4.3 Packet and flow delay
3.5 Link scheduling
where tu_{ j }represents the j th TU of a TS interval. The matrix S must satisfy the following constraints:

The conflictfree constraint: Every link must check the SINRbased model, and so inequalities 4 and 5 and can only be scheduled during TUs of the link scheduling period T_{ s }.

The bandwidth constraint: For every flow f∈F, every link e_{ i }∈ l_{ f }(with l_{ f }respecting the constraints expressed in 9) must reserve TU_{ f }slots for f.

The flow delay constraint: Every flow f ∈ F must possess a delay inferior or equal to its requirements.
At the end of the transmission of f, the schedule S^{old} is updated such that all the scheduling made for f over every link e_{ i }∈ l_{ f }are deleted to form the new scheduling matrix S^{new}.
3.6 Problem formulation of the admission control of a flow
 1.
r _{ f }(e _{ i }) = TU _{ f }.
 2.
d _{ f }≤ dr _{ f }.
 3.
S ^{new} is feasible.
For a new flow, if every link on its path can find a schedule for this flow which satisfies these constraints, then the flow is admitted on the network; otherwise, the flow is rejected. Constraint 1 enforces that every link along the path respects the link requirements in terms of bandwidth. Constraint 2 enforces that the delay of the flow computed via Equation 17 respects the delay required by the new flow. Constraint 3 enforces that the new scheduling matrix of the network computed via Equation 19 is feasible, i.e., the scheduling made for previously accepted flows and the new flow are interference free.
4 Admission control
Our proposed admission control scheme is based on the reactive routing protocol AODV; however, packets have been modified in order to meet our approach requirements. The admission control takes place in three steps:

Route discovery

Link scheduling

Route selection
4.1 Route discovery
During this phase, a source node broadcasts a route request (RREQ) packet which contains the source sequence number (to uniquely identify each packet), the timetolive (TTL), the bandwidth, and the delay required by the new flow. Each node which receives the RREQ adds its identification id and rebroadcasts it if:

The TTL has not expired.

The node is not the gateway.

It is the first time the node receives the RREQ.

It succeeds the partial admission control.
The partial admission control aims at filtering flows’ requests which could not be in the sequel accepted. Every node which receives a RREQ for a flow carries out a partial admission control on the flow; it checks whether the number of available TUs per T_{ s } is superior or equal to the number TU_{ f } of TUs required by the flow. The number TU_{ av } of available TUs for a node in a T_{ s } is equal to the number of TUs during which the node is neither a transmitter nor a receiver. If TU_{ av }≥ TU_{ f }, then the node has enough available bandwidth to satisfy the flow requirements and thus the partial admission control is achieved; otherwise, the node has not enough available bandwidth and thus the RREQ is dropped.
4.2 Link scheduling
When a RREQ reaches a gateway access point, the latter starts the admission control and the link scheduling process. Note that gateways are the only nodes which possess the I matrix which enables checking whether a set of links can transmit simultaneously without interference. As link scheduling is computed at gateways and as the latter need the scheduling matrix S up to date to compute valid new link scheduling, thus, gateways have to be kept informed (for instance, via notification exchanges) about when a new flow is scheduled or stopped so that all gateways in the network have the same updated scheduling matrix. Recall that the link scheduling and the admission control are realized simultaneously. Indeed, a flow f is admitted if scheduling for every link on the flow path (i.e., ∀e_{ i }∈ l_{ f }, r_{ f }(e_{ i }) exists and satisfies the constraints cited in Section 3.3). To find a feasible link scheduling, we propose a simple greedy algorithm (Algorithm 1).
Algorithm 1 Admission control algorithm based on link scheduling
The algorithm returns null if the flow is rejected; otherwise, it returns a new scheduling matrix (which integrates the link scheduling made for the flow). The algorithm can be divided in three major steps. In a first step, we initiate the parameters used in the algorithm. We compute TU_{ f }, the number of TUs that each link must reserve to respect the flow constraints in terms of bandwidth (see Equation 7). We then compute for every link e_{ i }∈ l_{ f }its linear matrix of available TUs Av (e_{ i }); each element av (e_{ i })_{ j }indicates whether the j th TU of the TS interval of link e_{ i }is available (av (e_{ i })_{ j }= 0) or not (av (e_{ i })_{ j }= 1). An available TU for a link e_{ i }= (u_{ i }, v_{ i }) is a TU which has not been reserved by any link possessing either u_{ i }or v_{ i }or both as a receiver or a transmitter. In a second step, we reserve the TU_{ f } TUs required by the flow on every link on the flow path. First, an available TU of link e_{0} (i.e., the first link on the flow path) is randomly selected via its linear matrix of available TUs Av (e_{0}). Then, the algorithm checks if e_{0} can be activated during this selected TU (i.e., if the resulting scheduling matrix is feasible). If it is the case, the algorithm reserves for e_{0} this TU; otherwise, it chooses randomly another possible available TU till e_{0} has no more available TU in Av (e_{0}) that it has not tested. Once an available TU has been reserved for e_{0}, we select one available TU for each upcoming link on the path, such that the selected TU is the closest to the TU that the previous edge has just chosen. The idea is to get the shortest feasible delay for a packet at each link. The algorithm then checks whether the link can be activated during this TU (i.e., if the resulting scheduling matrix is feasible). If it is the case, then the algorithm goes on; otherwise, it chooses another possible available TU till e_{ i }has no more available TU. Each link on the flow path is processed till every link has reserved one TU. The algorithm uses the same method to reserve the other TU_{ f } 1 TUs required by the flow at every link along its path. If it succeeds in scheduling every link, then the algorithm enters its third step. In a third step, the algorithm checks whether the link scheduling that has just been realized respects the flow’s requirements in terms of delay. First, it computes the start node queue length of every link during its stable period. Then, it can compute for every link e_{ i }∈ l_{ f } {e_{ n }} its function ${\phi}_{{e}_{i}}:\phantom{\rule{0.03em}{0ex}}{r}_{f}\left({e}_{i1}\right)\to {r}_{f}\left({e}_{i}\right)$. Thanks to it and via Equation 14, it gets the delay of every packet at a link. Then, it computes the TU_{ f } possible delays of any packet in a stable period, thanks to Equation 16. Finally, by applying Equation 11, it gets the delay of the flow. If the delay of the flow is superior to the required delay, then the algorithm returns null; otherwise, it returns the new scheduling matrix and the flow is accepted. This algorithm can be solved in a polynomial time.
where each element s_{ ij }represents the j th TU of the TS interval of the link e_{i  1}. If s_{ ij }= Nc, the j th TU of the TS interval is dedicated to control packets. If s_{ ij }= 0, the link e_{i  1} does not send any packet at the j th TU of the TS interval. If s_{ ij }= f_{ z }, the link e_{i  1} sends a packet of flow f_{ z } during the j th TU of the TS interval. In a first step, the algorithm computes the number of TUs required by this flow per TS using Equation 7, ${\mathit{\text{TU}}}_{{f}_{2}}=1$. Then, the linear matrix of available TUs of every link on the flow path is computed: Av (e_{0}) = (1,1,0,0,0,0,0,0,1,1), Av (e_{2}) =( 1,1,0,0,0,0,1,1,1,1), and Av (e_{4}) = (1,1,0,0,0,0,1,1,1,1). The reservations of every link ${e}_{i}\in {l}_{{f}_{2}}$ are initiated at empty, i.e., ${r}_{{f}_{2}}\left({e}_{i}\right)=\varnothing $.
This algorithm is a heuristic approach which aims at solving the problem formulated in Section 4.2. If the flow is not admitted, then the admission control stops; otherwise, the algorithm returns the new scheduling matrix S from which the gateway extracts the scheduling for every link of the path. The gateway creates a route response packet (RREP) to which it adds the scheduling for every link on the flow path.
4.3 Route selection
In this step, the gateway unicasts the RREP to the source. The RREP goes through the reverse path of the RREQ. Thus, every node along the path extracts and registers the reservation that the gateway has made for the links it belongs to as either a transmitter or a receiver. Once the RREP reaches the source node, the flow transmission can begin and every node along the path knows at which TUs it must forward the flow. Note that the selected TUs for a link are released if the starting node of the link does not receive any packet of the flow during a certain period p.
5 Simulation results
Simulation parameters
Layer  Parameters  Values 

Signal propagation  Tworay ground model  
Physical layer  PLCP preamble  20 µs 
Channel capacity  54 Mbit/s  
MAC layer  TU interval  260 µs 
TS interval for the chain topology  30,160 µs  
TS interval for the grid topology  29,900 µs  
TS interval for the cross topology  30,160 µs  
Transport layer  UDP size packet  1,000 bytes 

The elimination of the RTS/CTS scheme. Indeed, as there is no risk of contention, the RTS/CTS scheme is not needed any more. Thus, the time used to send RTS or CTS packet in the IEEE 802.11 model can be exploited to send packets of data in our model.

The elimination of the backoff procedure during the scheduling period. Indeed, because of the backoff algorithm in the IEEE 802.11 model, every node must wait a certain time, each time it wants to send a packet of data. The time that a node has to wait is chosen randomly in a contention window. The maximum contention window size is 1,023 slots [34] and a slot time lasts usually 20 µs; thus, a node can have to wait till 2,046 µs for sending a packet whereas sending a packet takes, in our example, 260 µs. Thus, eliminating the backoff procedure enables gaining a lot of time for sending packets of data.

No risk of collision, thanks to the link scheduling. Because of collisions, in the IEEE 802.11 model, nodes need to resend packets; this leads to a waste of time which is avoided in our model.
Through these simulations, we can conclude that our dynamic link schedulingbased admission control model allows gaining in throughput, thanks to the dynamic time division multiple access (DTDMA) access method which enables getting rid of the backoff and RTS/CTS mechanisms. Furthermore, the delay and loss rate requirements are also satisfied compared to the IEEE 802.11 model. However, in our model, constraints are imposed, nodes must be synchronized, and gateways must know the power of the noise and the path loss of the signal at every moment.
6 Conclusion
In this paper, we have presented a new admission control scheme based on link scheduling to support realtime traffic in WMNs. We have considered both bandwidth and endtoend delay as two major criteria in the design. The link scheduling is based on the SINR interference model in order to prevent any collision in flow packets. To enable a dynamic link scheduling, we have mixed two access methods: on one hand, CSMA/CA is used to send control packets (e.g., requests for flow admission) and on the other hand, DTDMA exploited for flow packet transmission, ensuring an important gain in throughput and a collisionfree communication. Furthermore, we have introduced a method to compute the flow’s delay and prove its efficiency. We have compared our model with the IEEE 802.11 model and shown under different topologies that our solution reaches its goal to respect admitted flow requirements in terms of delay and throughput. In a future work, we plan to compute the complexity of our model and compare it to other admission control schemes. Furthermore, we plan to secure the proposed model by integrating a robust trust mechanism in the backbone mesh. The study reported in this paper leaves several avenues open for further research on this intriguing problem. In particular, the problem of finding the length of optimal schedules without considering ‘a priori routing approach’ remains open.
Declarations
Authors’ Affiliations
References
 Akyildiz IF, Wang X, Wang W: Wireless mesh networks: a survey. Comput. Netw. 2005, 47(4):445487. 10.1016/j.comnet.2004.12.001View ArticleMATHGoogle Scholar
 Jun J, Sichitiu ML: The nominal capacity of wireless mesh networks. Wireless Commun. IEEE [see also IEEE Personal Communications] 2003, 10(5):814.Google Scholar
 Shen Q, Fang X, Li P, Fang Y: Admission control based on available bandwidth estimation for wireless mesh networks. Vehicular Technol., IEEE Trans. 2009, 58: 25192528.View ArticleGoogle Scholar
 Yang Y, Kravets R: Contentionaware admission control for ad hoc networks. Mobile Comput., IEEE Trans. 2005, 4: 363377.View ArticleGoogle Scholar
 Luo L, Gruteser M, Liu H, Raychaudhuri D, Huang K, Chen S: A QoS routing and admission control scheme for 802.11 ad hoc networks. In Proceedings of the 2006 Workshop on Dependability Issues in Wireless Ad Hoc Networks and Sensor Networks, DIWANS ’06. ACM, New York; 2006:1928.Google Scholar
 Hanzo L, Tafazolli R: Admission control schemes for 802.11based multihop mobile ad hoc networks: a survey. IEEE Commun. Surv. & Tutorials 2009, 11: 78108.View ArticleGoogle Scholar
 Shila DM, Anjali T: An interferenceaware admission control design for wireless mesh networks. EURASIP J. Wireless Comm. Netw. 2010, 2010: 7:17:13.Google Scholar
 Gambiroza V, Sadeghi B, Knightly EW: Endtoend performance and fairness in multihop wireless backhaul networks. In Proceedings of the 10th Annual International Conference on Mobile Computing and Networking, MobiCom ’04. ACM, New York; 2004:287301.Google Scholar
 Papadaki K, Friderikos V: Approximate dynamic programming for link scheduling in wireless mesh networks. Comput. & Oper. Res. 2008, 35: 38483859. 10.1016/j.cor.2007.02.010View ArticleMATHGoogle Scholar
 Brar G, Blough DM, Santi P: Computationally efficient scheduling with the physical interference model for throughput improvement in wireless mesh networks. In Proceedings of the 12th Annual International Conference on Mobile Computing and Networking, MobiCom ’06. ACM, New York; 2006:213.View ArticleGoogle Scholar
 Cappanera P, Lenzini L, Lori A, Stea G, Vaglini G: Link scheduling with endtoend delay constraints in wireless mesh networks. In Proceedings of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops, 2009. WoWMoM 2009. IEEE, Piscataway; 2009:19.View ArticleGoogle Scholar
 Sharma G, Mazumdar RR, Shroff NB: On the complexity of scheduling in wireless networks. In Proceedings of the 12th Annual International Conference on Mobile Computing and Networking, MobiCom ’06. ACM, New York; 2006:227238.View ArticleGoogle Scholar
 Sundaresan K, Hsieh HY, Sivakumar R: Ad Hoc Netw.. 2004, 2: 109132. 10.1016/S15708705(03)000507View ArticleGoogle Scholar
 Gore A, Karandikar A: Link scheduling algorithms for wireless mesh networks. Commun. Surv. Tutorials, IEEE 2011, 13: 258273.View ArticleGoogle Scholar
 Dromard J, Khoukhi L, Khatoun R: An admission control scheme based on links’ activity scheduling for wireless mesh networks. In Proceedings of the 11th International Conference on Adhoc, Mobile, and Wireless Networks, ADHOCNOW’12. Springer, Berlin; 2012:399412.View ArticleGoogle Scholar
 Rezgui J, Hafid A, Gendreau M: Distributed admission control in wireless mesh networks: models, algorithms, and evaluation. 2010.Google Scholar
 Blough DM, Resta G, Santi P: Approximation algorithms for wireless link scheduling with SINRbased interference. IEEE/ACM Trans Netw. 2010, 18: 17011712.View ArticleGoogle Scholar
 IEEE: IEEE Standard for Information Technology  Telecommunications and Information Exchange Between Systems  Local and Metropolitan Area Networks  Specific Requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment 10: Mesh Networking. IEEE Std 802.11s2011. IEEE, Piscataway; 2011.Google Scholar
 Vieira FR, de Rezende JF, Barbosa VC, Fdida S: Scheduling links for heavy traffic on interfering routes in wireless mesh networks. Comput. Netw. 2012, abs/1106.1590: 15841598.View ArticleGoogle Scholar
 Xu K, Gerla M, Bae S: How effective is the IEEE 802.11 RTS/CTS handshake in ad hoc networks. In Proceedings of the Global Telecommunications Conference, 2002. GLOBECOM ’02, vol. 1. IEEE, Piscataway; 2002:7276.Google Scholar
 Jain K, Padhye J, Padmanabhan VN, Qiu L: Impact of interference on multihop wireless network performance. In Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, MobiCom ’03. ACM, New York; 2003:6680.View ArticleGoogle Scholar
 Gore A, Karandikar A, Jagabathula S: On high spatial reuse link scheduling in STDMA wireless ad hoc networks. In Proceedings of the IEEE Global Telecommunications Conference, 2007. GLOBECOM ’07. IEEE, Piscataway; 2007:736741.View ArticleGoogle Scholar
 Blough DM, Resta G, Santi P: Approximation algorithms for wireless link scheduling with SINRbased interference. IEEE/ACM Trans. Netw 2010, 18: 17011712.View ArticleGoogle Scholar
 Cappanera P, Lenzini L, Lori A, Stea G, Vaglini G: Optimal joint routing and link scheduling for realtime traffic in TDMA wireless mesh networks. Comput. Netw. 2013, 57: 23012312. 10.1016/j.comnet.2012.11.021View ArticleGoogle Scholar
 Rhee I, Warrier A, Min J: DRAND: distributed randomized TDMA scheduling for wireless ad hoc networks. In Proceedings of the 7th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc ’06. ACM, New York; 2006:190201.View ArticleGoogle Scholar
 Iyer A, Rosenberg C, Karnik A: What is the right model for wireless channel interference? Wireless Commun. IEEE Trans. 2009, 8: 26622671.View ArticleGoogle Scholar
 Bai YB, Zhu X, Shao X, Yang WT: FAST: fuzzy decisionbased resource admission control mechanism for MANETs. Mobile Netw. Appl. 2012, 17: 758770. 10.1007/s1103601203840View ArticleGoogle Scholar
 Krasilov A, Lyakhov A, Safonov A: Interference, even with MCCA channel access method in IEEE 802.11s mesh networks. In Proceedings of the 2011 IEEE Eighth International Conference on Mobile AdHoc and Sensor Systems, MASS ’11. IEEE, Washington, DC; 2011:752757.View ArticleGoogle Scholar
 Lenzini L, Mingozzi E, Vallati C: A distributed delaybalancing slot allocation algorithm for 802.11s mesh coordinated channel access under dynamic traffic conditions. In Proceedings of the 2010 IEEE 7th International Conference on Mobile Adhoc and Sensor Systems (MASS). IEEE, Piscataway; 2010:432441.Google Scholar
 Gupta P, Kumar PR: The capacity of wireless networks. IEEE Trans. Inf. Theory 2000, 46: 388404. 10.1109/18.825799MathSciNetView ArticleMATHGoogle Scholar
 The Network Simulator  ns2 . Accessed 11 Dec 2013 http://www.isi.edu/nsnam/ns/
 Lyakhov A, Pustogarov I, Safonov A, Yakimov M: Starvation effect study in IEEE 802.11 mesh networks. In Proceedings of the IEEE 6th International Conference on Mobile Adhoc and Sensor Systems, 2009. MASS ’09. IEEE, Piscataway; 2009:651656.View ArticleGoogle Scholar
 Gurewitz O, Mancuso V, Shi J, Knightly EW: Measurement and modeling of the origins of starvation of congestioncontrolled flows in wireless mesh networks. IEEE/ACM Trans. Netw. 2009, 17: 18321845.View ArticleGoogle Scholar
 IEEE: IEEE Standard for Information Technology  Telecommunications and Information Exchange Between Systems  Local and Metropolitan Area Networks  Specific Requirements  Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE Std 802.112007 (Revision of IEEE Std 802.111999). IEEE, Piscataway; 2007.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.