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This paper proposes a practical lowcomplexity MAC (medium access control) scheme for quality of service (QoS)aware and clusterbased underwater acoustic sensor networks (UASN), in which the provision of differentiated QoS is required. In such a network, underwater sensors (Usensor) in a cluster are divided into several classes, each of which has a different QoS requirement. The major problem considered in this paper is the maximization of the number of nodes that a cluster can accommodate while still providing the required QoS for each class in terms of the PDR (packet delivery ratio). In order to address the problem, we first estimate the packet delivery probability (PDP) and use it to formulate an optimization problem to determine the optimal value of the maximum packet retransmissions for each QoS class. The custom greedy and interiorpoint algorithms are used to find the optimal solutions, which are verified by extensive simulations. The simulation results show that, by solving the proposed optimization problem, the supportable number of underwater sensor nodes can be maximized while satisfying the QoS requirements for each class.
As an emerging technique, underwater acoustic sensor networks (UASN) have a wide range of applications, such as oceanographic data collection, environment monitoring, undersea exploration, disaster prevention, assisted navigation and tactical surveillance [
In this paper, we consider a UASN that has a clusterbased network topology, in which each cluster is governed by a clusterhead (or gateway node), since it makes the network scalable and can readily provide network connectivity in a harsh communication environment [
In such a network, an important problem is to maximize the number of nodes that the network can accommodate while still providing the required QoS for each class. In addition, as a related problem, when the operators deploy a UASN, they would want to know the achievable PDR value given the number of sensor nodes in the network. Intuitively, if the number of nodes in a UASN increases beyond a specific amount, the network may not be able to provide the demanded QoS, due to a high level of network traffic.
In order to address the problem of maximizing the supportable number of nodes, we focus on the MAC (medium access control) layer, since it plays a key role for providing QoS and dominates the overall performance of the network [
In this paper, we design a practical lowcomplexity QoSaware MAC scheme and an optimization formulation for maximizing the supportable number of sensors in UASNs. We first estimate the packet delivery probability (PDP) in the MAC layer. Then, based on the PDP estimation, an optimization problem is formulated for maximizing the supportable number of sensors in a specific QoS priority class. The main idea of the formulation is to find optimal values of the maximum packet retransmissions for each QoS class, such that the number of nodes in a specific QoS class is maximized and every node can achieve the required QoS.
The custom greedy and interiorpoint algorithms are used to find the solutions to the optimization problem. Furthermore, extensive simulations are performed to verify the solutions. The simulation results show that our optimization formulation can maximize the supportable number of underwater sensor nodes, while satisfying the QoS requirement for each class.
The rest of this paper is organized as follows. Section 2 presents the related studies and compares them with the proposed scheme. The system model and problem definition are described in Section 3. Section 4 first discusses the packet delivery probability approximation, then describes the optimization problem formulation. We also discuss the approximation of the background traffic. The performance analysis using various scenarios is presented in Section 5, in which we also discuss solutions and the simulation setup. Finally, Section 6 concludes the paper.
MAC protocols for UASN can be categorized into contentionfree and contentionbased protocols. The contentionfree protocols include time division multiple access (TDMA), frequency division multiple access (FDMA) and code division multiple access (CDMA), in which different time slots, frequency bands or codes are assigned to different users to avoid collisions among transmissions.
FDMA divides the available frequency band into several subbands and assigns each subband to a node. Due to the limited available bandwidth of underwater channels, FDMA is not suitable for UASNs that consist of a large number of underwater sensors.
In TDMA, in order to avoid the collision of packets from adjacent time slots, guard times are added to the time slot. The high propagation delay in underwater acoustic communication channels requires long guard times, which limit the efficiency of TDMA [
It is also known that CDMAbased protocols require a high complexity design for UASN. In addition, it is a challenging problem to assign pseudorandom codes to a large number of sensor nodes [
On the other hand, contentionbased protocols have received significant attention for UASN, due to their simplicity, acceptable throughput and energy efficiency [
It was also shown that, under the high and varying propagation delay in underwater acoustic channels, the performance of slotted Aloha becomes similar to that of pure Aloha [
Another simple yet practical Alohabased protocol, AlohaCS (Aloha with carrier sensing), was also studied and evaluated in [
The authors of [
Although our work is also based on channel contention, those studies differ from ours since they do not consider the provision of QoS or optimality.
There are few MAC protocols that address QoS provision in UASNs. However, there have been several MAC protocols that considered QoS provision for wireless sensor networks [
In particular, the authors of [
As another example, the study in [
However, the studies in [
There also have been attempts to design a QoSaware MAC protocol based on channel contention for a wireless sensor network. For example, the study in [
In this paper, we consider a clusterbased UASN, where each cluster is governed by a clusterhead (or gateway node). As shown in
It is assumed that communications in a cluster do not interfere with communications in other clusters, due to the use of different carriers, and Usensors transmit sensed data to the clusterhead using a direct acoustic channel [
Usensors in a cluster are classified into several QoS classes, each of which has a required packet delivery ratio (PDR). In this paper, required PDR values are used to determine QoS classes. Every node generates a data packet at a predetermined rate and transmits them to the clusterhead. Usensors in each QoS class are allowed to retransmit each data packet up to the maximum number of retransmissions, unless they receive the corresponding ACK packet from the clusterhead within the ACK timeout interval. Before a Usensor transmits data, it first performs carrier sensing to assure that the channel is idle. It also performs exponential backoffs when collisions occur.
The considered optimization problem is the maximization of the number of nodes in a specific QoS class, which will be selected by the operators of the network, while providing the QoS for every node in each class.
In order to facilitate discussion, suppose that a set of
Therefore, in order to achieve the objective, while providing differentiated QoS to nodes, the core problem is to determine an optimal value of
In this section, we first describe the approximation of the packet delivery probability. Then, we present the formulation of the optimization problem. In addition, we discuss algorithms for finding solutions.
We first define the packet delivery probability (PDP) as the probability that a packet is successfully delivered at the clusterhead when it can be retransmitted up to
In a UASN, the packet generation rate is usually low, due to the limited bandwidth. In such a network, very few packet losses result from the buffer overflow, since available space is likely when a new packet is generated. Consequently, PDP values can approximate PDR values in a UASN. Therefore, PDP is used in the optimization formulation for PDR.
Now, we discuss the approximation of the PDP of nodes in each class,
A Usensor node in each class,
Let
In order to verify the assumption of Poisson distribution of the packet arrival in a UASN, where a node performs carrier sensing and exponential backoffs, we conduct a simple simulation using Aloha and AlohaCS protocols. The considered cluster in the network consists of 50 Usensors and one clusterhead that are randomly deployed over an area of 1,555 m × 1,555 m. In this example, for simplicity, we assume that there is only one QoS class,
As shown in
Since each packet transmission can be regarded as an independent event based on the assumption of a Poisson process,
In the following section, we present an optimization formulation for maximizing the number of sensors in UASN, while satisfying the QoS requirement.
In this subsection, we describe the proposed optimization problem formulation that is a nonlinear optimization problem.
Recall that the nodes in each class,
The actual arrival rate of background traffic for a node in an arbitrary class,
In order to calculate the value of
Then, the constraint function in which we use the maximum arrival rate,
When we use the actual arrival rate of background traffic for calculating
According to the constraint function in
As a result, since
Therefore, we have the formulation of
Now, we describe our optimization problem formulation.
The objective of our optimization problem is to maximize the supportable number of nodes in a class,
We replace
From
Then, the optimization formulation is that, given
The constraint in
In order to find solutions to the proposed optimization formulation, we use a custom developed greedy algorithm and the interiorpoint method.
In the greedy algorithm, for each solution vector
It is also worthwhile to note that even though the greedy algorithm seems to be expensive in terms of computational complexity, it may be affordable in a practical scenario. For example, when there are 3 QoS classes and
In addition, the interiorpoint algorithm is used to find the solutions. The interiorpoint algorithm has been developed to solve linear or nonlinear convex optimization problems with inequality constraints in a short amount time. The basic idea of this algorithm is to decompose the problem into a sequence of equality constrained problems and apply Newton's method to each problem [
In this section, we first describe the simulation setup and then analyze the results of the simulations.
In order to evaluate the performance of the proposed protocol, we first consider a cluster with three QoS classes. Then, we extend our discussion to the case of four QoS classes. Finally, we consider a case where each QoS class has a different packet size.
When there are three QoS classes, the nodes in a cluster are partitioned into three QoS (in terms of PDR) classes (
In case of four QoS classes, the nodes in the cluster are divided into four QoS classes, and
In this paper, for practical simulation, we used the DESERTunderwater simulation framework [
Each node is equipped with a halfduplex acoustic transceiver that has a data rate of 14 Kbps and a transmission range of 1,100 m. The speed of the underwater acoustic signal is assumed to be 1,500 m/s. The data generation rate applies to every node in the network. The upper bound of the maximum number of retransmissions is set to seven.
In this subsection, we first present simulation results for a case with three QoS classes and discuss the results. Then, in order to show that our approach can support an arbitrary number of QoS classes, we extend our discussion to the case where a cluster has four QoS classes. Finally, we present the simulation results and analysis for a case where each of three QoS classes has a different packet size.
In this case, we consider a cluster that has three QoS classes. We first discuss the effects of the PDR requirement for a QoS class on PDR and on the maximum number of nodes in that QoS class. Then, we continue our discussion for the effects of the network load on the network performance. We assume that the PDR requirement of
In this case, every node transmits a data packet of 160 bytes to the clusterhead in every interval of
As shown in
Now, we discuss the selection of optimal
Note that our greedy algorithm tests all possible cases. In this particular case, it appears that reducing
It is also possible in some cases that the greedy algorithm selects a higher
Another point to note is that when
The results in
Furthermore, note that, as shown in
In this case, the PDR requirement for class
As shown in
In order to facilitate understanding, we show and compare
From
More specifically,
In this section, we show that the proposed scheme can support four QoS classes. The greedy algorithm is used to find the solution in this experiment.
Similarly to the case of three QoS classes, every node transmits a data packet of 160 bytes to the clusterhead in every interval of
As shown in
Note that, in some cases,
In this case, the PDR requirement for class
As shown in
In this subsection, we consider a case where each QoS class has a different packet size. The sensor nodes in class
From
Another point to note in
In this paper, we have proposed a practical and lowcomplexity MAC scheme that does not require time synchronization or scheduling overhead, for QoSaware and clusterbased underwater acoustic sensor networks (UASN). In particular, we have considered an optimization problem to maximize the supportable number of sensor nodes in UASNs that are required to provide differentiated QoS in terms of PDR. In order to address the problem, the packet delivery probability (PDP) has been estimated, and based on the estimation, an optimization formulation has been designed to determine optimal values of the maximum number of packet retransmissions for each QoS class. The greedy and interiorpoint algorithms are used to find the solutions, which are verified by simulations. The simulation results have shown that, by solving the proposed optimization formulation, the supportable number of underwater sensor nodes can be maximized, while satisfying the QoS requirements for each class.
This work was supported by the 2012 Research Fund of University of Ulsan.
The authors declare that there is no conflict of interest regarding the publication of this article.
Clusterbased underwater acoustic sensor network.
Approximation of the successful packet transmission ratio. AlohaCS, Aloha with carrier sensing.
Effects of node load on the PDR (mean +/ standard deviation) achieved from the greedy algorithm and from simulations, and the maximum number of nodes in class
The effects of node load on the PDR (mean +/− standard deviation) achieved from the greedy algorithm and from simulations and the maximum number of nodes in class
The effects of the packet delivery ratio (PDR) requirement for class









 

 
0.95  0.80  0.70  5  3  2  0.951  0.965  0.836  0.875  0.701  0.729  84 
0.95  0.80  0.72  5  3  2  0.959  0.970  0.852  0.896  0.721  0.750  78 
0.95  0.80  0.74  5  3  2  0.965  0.977  0.868  0.908  0.741  0.774  72 
0.95  0.80  0.76  5  3  2  0.972  0.982  0.883  0.919  0.761  0.803  66 
0.95  0.80  0.78  4  2  2  0.960  0.972  0.800  0.850  0.800  0.819  64 
0.95  0.80  0.80  4  2  2  0.960  0.972  0.800  0.850  0.800  0.819  64 
0.95  0.80  0.82  4  2  2  0.967  0.977  0.820  0.866  0.820  0.847  58 
0.95  0.80  0.84  5  3  3  0.953  0.981  0.840  0.913  0.840  0.904  55 
0.95  0.80  0.86  5  3  3  0.962  0.986  0.860  0.927  0.860  0.919  50 
The effects of the PDR requirement for class









 

 
0.95  0.80  0.70  4.321  2.321  1.737  0.950  0.962  0.800  0.865  0.700  0.716  87.6 
0.95  0.80  0.72  4.321  2.321  1.836  0.950  0.967  0.800  0.884  0.720  0.739  82.8 
0.95  0.80  0.74  4.321  2.321  1.943  0.950  0.970  0.800  0.896  0.740  0.751  78.3 
0.95  0.80  0.76  4.321  2.321  2.058  0.950  0.956  0.800  0.838  0.760  0.821  73.9 
0.95  0.80  0.78  4.321  2.321  2.184  0.950  0.956  0.800  0.860  0.780  0.843  69.6 
0.95  0.80  0.80  4.322  2.322  2.322  0.950  0.963  0.800  0.882  0.800  0.862  65.5 
0.95  0.80  0.82  4.322  2.322  2.474  0.950  0.970  0.800  0.891  0.820  0.881  61.5 
0.95  0.80  0.84  4.322  2.321  2.643  0.950  0.974  0.800  0.908  0.840  0.897  57.5 
0.95  0.80  0.86  4.322  2.322  2.836  0.950  0.982  0.800  0.917  0.860  0.911  53.6 
The solutions from the greedy algorithm with various node loads (



 

 
20  0.95  0.80  0.70  5  3  2  84 
25  0.95  0.80  0.70  5  3  2  60 
30  0.95  0.80  0.70  5  3  2  44 
35  0.95  0.80  0.70  5  3  2  33 
40  0.95  0.80  0.70  5  3  2  24 
45  0.95  0.80  0.70  5  3  2  18 
50  0.95  0.80  0.70  5  3  2  12 
The effects of the PDR requirement for class









 

 
0.70  5  4  3  2  0.95  0.97  0.91  0.94  0.83  0.90  0.70  0.77  46 
0.72  5  4  3  2  0.96  0.98  0.92  0.96  0.85  0.91  0.72  0.80  40 
0.74  4  3  2  2  0.96  0.97  0.91  0.93  0.80  0.84  0.80  0.83  36 
0.76  4  3  2  2  0.96  0.97  0.91  0.93  0.80  0.84  0.80  0.83  36 
0.78  4  3  2  2  0.96  0.97  0.91  0.93  0.80  0.84  0.80  0.83  36 
0.80  4  3  2  2  0.96  0.97  0.91  0.93  0.80  0.84  0.80  0.83  36 
0.82  6  4  3  3  0.97  0.99  0.90  0.95  0.82  0.91  0.82  0.91  32 
0.84  5  4  3  3  0.95  0.98  0.91  0.96  0.84  0.91  0.84  0.91  30 
0.86  5  4  3  3  0.96  0.98  0.92  0.96  0.86  0.93  0.86  0.92  25 
The effects of the PDR requirement for class









 

 
0.95  0.80  0.70  5  2  1  0.979  0.989  0.879  0.897  0.716  0.719  51 
0.95  0.80  0.72  4  2  1  0.970  0.987  0.903  0.924  0.748  0.750  42 
0.95  0.80  0.74  6  2  2  0.970  0.994  0.813  0.878  0.873  0.893  40 
0.95  0.80  0.76  6  2  2  0.970  0.994  0.813  0.878  0.873  0.893  40 
0.95  0.80  0.78  6  2  2  0.970  0.994  0.813  0.878  0.873  0.893  40 
0.95  0.80  0.80  6  2  2  0.970  0.994  0.813  0.878  0.873  0.893  40 
0.95  0.80  0.82  6  2  2  0.970  0.994  0.813  0.878  0.873  0.893  40 
0.95  0.80  0.84  6  2  2  0.970  0.994  0.813  0.878  0.873  0.893  40 
0.95  0.80  0.86  6  2  2  0.971  0.993  0.817  0.886  0.876  0.890  39 
0.95  0.80  0.88  6  2  2  0.981  0.996  0.845  0.909  0.895  0.921  32 
0.95  0.80  0.90  5  2  2  0.977  0.995  0.873  0.929  0.915  0.938  27 