General optimization framework for surface gateway deployment problem in underwater sensor networks
 Saleh Ibrahim^{1},
 Manal AlBzoor^{1}Email author,
 Jun Liu^{1},
 Reda Ammar^{1},
 Sanguthevar Rajasekaran^{1} and
 JunHong Cui^{1}
https://doi.org/10.1186/168714992013128
© Ibrahim et al.; licensee Springer. 2013
Received: 6 December 2012
Accepted: 30 April 2013
Published: 13 May 2013
Abstract
The performance of underwater sensor networks (UWSNs) is greatly limited by the low bandwidth and high propagation delay of acoustic communications. Deploying multiple surfacelevel radiocapable gateways can enhance UWSN performance metrics, reducing endtoend delays and distributing traffic loads for energy reduction. In this paper, we study the problem of gateway placement for maximizing the costbenefit of this UWSN architecture. We develop a mixed integer programming (MIP) gateway deployment optimization framework. We analyze the tradeoff between the number of surface gateways and the expected delay and energy consumption of the surface gateway architecture in the optimal case. We used an MIP solver to solve the developed optimization problem and integrated the optimal results to serve as an input for our simulations to evaluate the benefits of surface gateway optimization framework. We investigated the effect of acoustic channel capacity and the underwater sensor node deployment pattern on our solution. Our results show the significant advantages of surface gateway optimization and provide useful guidelines for real network deployment.
Keywords
1 Introduction
An important component of oceanographic studies is the collection of data from the aquatic environment. Remote sensing has long been employed as a tool to collect aquatic data in underwater monitoring and exploration activities. Recently, in the last decade to be more specific, underwater acoustic sensor networks (UWSNs) have emerged as a new alternative technology enabling underwater monitoring and exploration applications, including scientific, commercial, and military applications [1–5]. Compared to their remotesensing counterparts, UWSNs have many advantages. UWSNs can provide localized and more precise data acquisition. They can also employ a wider variety of sensors including, but not limited to, chemical, temperature, photo, and motion sensors.
UWSN technology is also replacing traditional underwater instrumentation technology. Traditionally, bulky sensor nodes equipped with datastorage capability are manually deployed in the underwater target space. Each node operates independently for the duration of the mission to collect readings according to a preset program. At the end of the mission, sensor nodes are picked up, and the collected data are retrieved and processed. UWSN technology adds networking capabilities to underwater sensor nodes so that sensor nodes can relay realtime data to an offshore or even an onshore control station for immediate analysis. The communication channel can also be used to transmit control signals from the control station to the underwater sensor nodes, which enables interactive control of the underwater sensor network deployment. UWSNs offer many advantages over traditional instrumentation techniques. First off, UWSNs add a realtime reporting functionality that enables a host of new realtime monitoring and warning systems. Another advantage of UWSN is that the sensing mission can be dynamically reconfigured without the need to physically access all the underwater nodes in order to reprogram them. While this particular feature makes the reuse of a UWSN deployment much less costly, it also provides for fixing configuration errors that compensates for unforeseen circumstances and unexpected node failures. This improves the UWSN resilience compared to traditional instrumentation techniques. Therefore, sensor node failures can be detected soon after they occur, allowing early replacement or early abortion of the mission instead of having to wait until the end of the mission only to find that it has failed.
In addition to the usual design challenges faced by terrestrial wireless sensor networks, UWSN technology has to deal with some unique challenges. It cannot use electromagnetic waves for longrange communication due to their quick absorption in water. Acoustic waves are usually considered the practical solution for UWSN communication. The dependency of UWSNs on underwater acoustic communications is particularly challenging. Factors such as the high levels of noise and the channel variability due to temperature, pressure, salinity gradients, and currentinduced turbulence add more constraints to the already small bandwidth available for acoustic communication. Moreover, when the Doppler effect (due to mobility) is added to those factors, channel encoding becomes a crucial component to the success of underwater acoustic sensor networks. However, the most limiting factor of underwater acoustic communications is the extremely low propagation speed of sound, which is roughly 1.5 km/s, subject to slight changes due to pressure, temperature, and salinity variations [6]. This is five orders of magnitude slower than the propagation speed of electromagnetic waves. Such high propagation delay can cause high endtoend delay, which could be greatly limiting for interactive applications and other monitoring applications where response time is critical.
In this paper, we study the problem of surface gateway deployment and present guidelines for deciding the number and locations of surface gateway nodes given an underwater sensor network deployment scenario. We focus on optimizing the cost of surface gateway deployment, by finding the minimum number and the locations of surface gateway nodes required to achieve a given design objective, which can be communication delay, energy consumption, fault tolerance, or a combination of them. The surface gateway deployment problem is formulated as an optimization problem modeling the routing of data packets from underwater sensor nodes to the virtual sink under link capacity and flow conservation constraints. A variety of objective functions are presented. Our framework provides an optimal gateway selection from given gateway candidate locations which are assumed to be a given. The rest of this paper is organized as follows. In Section 2, we provide a review of related work. In Section 3, the network model and assumptions regarding the surface gateway deployment problem are presented and justified, and the surface gateway deployment problem is formulated as an optimization problem. In Section 4, we evaluate our work, choosing sample problems to analyze the effect of various constraints on the deployment solution quality, problem complexity, and feasibility. Finally, in Section 5, conclusions are drawn, and a future work is presented.
2 Related work
The deployment problem for sensor nodes was studied extensively for terrestrial sensor networks [8]. The closest to our work from the placement strategies in terrestrial networks are those for relay node and multitier sensor network architecture [9–11]. However, most terrestrial deployment problems assume a static twodimensional (2D) architecture, and not much attention was received for multigateway deployment in UWSN. A triangulargrid deployment pattern for 2D UWSN was proposed in [12]. The objective is to minimize the number of sensors needed to achieve the sensing and communication coverage of a target area. An interesting attempt to formulate the 3D UWSN pointcoverage deployment problem as an integer linear program (ILP) is presented in [13]. The solution of the ILP decides relay node deployment, routing, and link scheduling. Throughout this work, it is assumed that there is a single sink for the entire UWSN deployment and only the overall power consumption is used as an optimization objective. Neither of these two studies considers the multiplesink network architecture. The only research study in the frame of multiple sinks we found is [7], in which Seah and Tan investigated the use of multisink architecture to enhance the underwater sensor network reliability. In this study, the same message is directed to more than one of the multiple sinks, with the assumption that if any of the sinks gets the message, then it is considered delivered successfully. The simulation results showed that highreliability benefits can be achieved at the cost of reasonable increase in energy consumption. The surface gateway (i.e., sink) deployment problem was not considered in this work. In a parallel research effort, [14] studied the problem of placing multiple mobile data collectors in both delaytolerant and delayconstrained underwater acoustic sensor networks. The authors defined candidate data collection stations as the maximal overlapping regions (MORs) of surface circles corresponding to underwater node communication ranges. They developed an O(n^{2} logn) algorithm for finding MORs. An earlier work in [15] was the first to address the underwater surface gateway deployment problem and formulate it as an optimization problem. The problem of surfacelevel gateway placement has been addressed by later research effort in[16]. The authors used surface gateways deployment as a mean to guarantee connectivity and survivability (tolerance to single node failure). They proposed an approximation algorithm for choosing a minimal subset of candidate locations where SGs may be deployed. An effort by the authors in [17] addressed the deployment for a mobile multiplesink architecture in UWSN. They used a predictionbased deployment strategy to cater for the mobility of underwater nodes. However, our work differs from all above by formulating the problem to find the best candidate locations that satisfies a set of flow conservation constraints, interference constraints, number of gateway constraints, and performance constraints in addition to a set of delay and energyconsumption objectives.
3 Gateway deployment optimization generalized formulation
There are two approaches to handle the surface gateway deployment problem, (1) solving the underwater deployment and the surfacelevel deployment problems jointly or (2) solving each of them separately. It is understood that solving both underwater and surface deployment problems jointly will lead to optimal solutions that are better, or at least as good as, the outcome of the twophase approach. However, since the objective of the research presented here is to analyze the effect of surface gateway deployment on the overall underwater sensor network performance, we fix the underwater deployment and therefore opt for the latter option. Thus, we assume that there is a preexisting underwater deployment that has been reached by a way or another.
The surface gateway deployment problem is formulated as a combinatorial graph optimization problem. The nodes of the graph represent underwater sensors and candidate surface gateway positions, and the problem is to find the subset of the candidate surface locations that maximizes a certain performance metric, satisfying a set of flow conservation constraints, interference constraints, and a deployment cost constraint (on the number of surface gateways) or a performance constraint (such as maximum endtoend delay or minimum reliability level).
The selection of the candidate positions is sophisticated enough to be considered as a separate problem to on its own and is discussed by our work in [18]. For the purpose of producing a generalized formulation, the set of candidate surface points is considered a given and assumed to satisfy connectivity constraints as a precondition. This means that each underwater node has to have at least one connected path to one or more candidate surface locations, taking into account the communication ranges of the involved intermediate nodes.
Associated with each underwater sensor node is a packet generation rate. Surface gateway nodes have to collect all generated data packets. The surface gateway deployment problem is formulated as a combinatorial optimization problem. The formulation consists of a basic set of constraints that can be augmented with a variety of objective functions.
3.1 Assumptions
The functionality of the UWSN considered is assumed to be mainly collecting data from underwater sensor nodes and transmitting the collected data samples at regular time intervals to a central station through one of the surfacelevel gateways. We assume that most of the traffic, therefore, will be flowing from the underwater nodes to the surface gateways. Internode communication (for purposes such as synchronization, collaborative sensing, data fusion, etc.) is assumed to be small enough to ignore.
Together, the set of surfacelevel gateways forms a virtual sink for the underwater sensors because the propagation delay, the energy consumption, and the reliability of transmitting the received packet by a gateway to the central station over a direct or multihop radio path are far superior to underwater communication links. It is reasonable, therefore, to assume that a packet delivered to one of the surface gateways is delivered to the central station with high reliability and negligible delay and energy cost.
For simplicity, we assume that the data link protocol uses only fixedlength packets and that all links have the same bit rate. Consequently, the packet transmission time is consistent throughout the network, and if the transmission scheme is slotted, the packet transmission time is conventionally called the timeslot. Our deployment formulation can be adjusted to include a variable packet length and a variable bit rate at different links without compromising the quality of the solution. This will require a preexisting knowledge of these parameters which will increase number of inputs and will result in a more complex formulation of the flow constraints. However, we are interested in providing a more generalized formulation of the gateway optimization problem to be adjusted and tuned for a more specified application that may require varying some of the inputs we assume is a fixed.
3.2 Definitions
The network is modeled as a graph, in which nodes represent the underwater sensors and surface gateways, and edges represent pairwise communication links.
3.2.1 Nodes
Let $\mathcal{U}$ be the set of all underwater sensor nodes and $\mathcal{T}$ be the set of candidate surface node positions.
where d(v,w) denotes the Euclidean distance between the two nodes u and v, and R_{ u } denotes the maximum acoustic communication range of the underwater node u.
3.2.2 Edges
3.2.3 Data generation and link flow rates
Let f_{ e } be the average flow per packet time in edge e measured in packets per packet time.
3.2.4 Gateway presence indicator
3.2.5 Link scheduling
Let T be the schedule length, i.e., the number of time slots in a single period of the schedule.
3.2.6 Performance parameters
The most important performance aspects of any network are the delay and energyconsumption characteristics.
3.2.6.0 Delay
Let υ be the average propagation velocity of sound waves in water.
Let ${\tau}_{u}^{Q}$ be the average transmission queuing time including the expected channel access delay at node u.
3.2.6.0 Energy consumption
Let ${\pi}_{e}^{S}$ be the transmission energy required for transmitting one data packet over the underwater acoustic link e(u,v), π^{ L } be the listening/sleeping average energy consumption per packet time, and ${\pi}_{v}^{R}$ be the reception energy per packet. For surface gateways, the reception power is taken to include the energy required to forward a packet to the central station over radio.
3.3 Constraints
The constraints can be classified into deployment constraints, flow conservation constraints, and interference constraints.
3.3.1 Deployment constraints

Number of surface gateways

Gateway presence indicator constraints
3.3.2 Flow constraints
The scenario considered in this work will be a monitoring network, where instrumentation data flow from underwater sensor nodes and through the network to a common sink station. Therefore, the analysis is limited to the (possibly multipath) route from each underwater sensor to the virtual sink. Control traffic flowing from the central station down to the underwater sensor nodes or internode traffic for the purpose of synchronization or localization or other functions than sensing data transfer will be ignored.

Gateway presence constraints
This means that the total flow ${f}_{t}^{I}$ into candidate gateway location t has to be zero if x_{ t }=0; otherwise, ${f}_{t}^{I}$ can grow as large as the maximum potential flow in any link in the network which is equal to the total data generation rate G in the network, thus rendering the constraint void.

Pernode flow conservation constraints

Endtoend flow conservation constraints

Flow allocation constraints
Note that since we assume the schedule is periodic, there is no need to add a constraint to enforce the reception of a packet before sending it out. If a packet is sent before being received, this means that it was stored from the previous period. It follows that the maximum perhop queuing delay will not exceed T−1 timeslots.
3.3.3 Interference constraints
This formulation is called pessimistic because that assumes that transmissions that could interfere with the reception at node v are going to happen in distinct time intervals without any overlapping. This is the worst case because it leaves the minimum possible bandwidth for v to receive its own intended transmissions. In reality, however, some of these transmissions from interfering neighbors of node v can occur simultaneously and successfully. Consider, for instance, two interfering neighbors of v, called u_{1}and u_{2}, v cannot receive any signals as long as it can hear either u_{1}or u_{2} is transmitting. Now, if u_{1} and u_{2} are sending to w_{1} and w_{2}, respectively, it is possible that both transmissions can succeed simultaneously if u_{1} is not within w_{2}’s interference range and u_{2} is not within w_{1}’s interference range.
This means that a node cannot transmit on more than one link during the same time slot.
T is the number of time slots a packet spends waiting in the transmission queuing at any node. Since the queuing delay can vary from 0 up to T−1 time slots, we estimate $L=\frac{T1}{2}$. In order to for this estimate to be accurate and in order to insure the best possible performance, a search for the minimum feasible schedule length will be necessary. We do so following the method pointed out in [13]. Namely, we formulate the problem starting from T=2 and attempt to solve the MIP problem. As long as the MIP is infeasible, we continue to increase the schedule length T until we find the minimum T for which the ILP formulation is feasible.
3.4 Objective functions
The objective function determines the goal of the deployment optimization. In general, the objective can be (1) a collective measure, such as minimizing the average endtoend delay and minimizing the total energyconsumption rate or (2) an extreme measure, such as minimizing the worst case endtoend delay or maximizing the worst case node lifetime.
3.4.1 Minimizing expected endtoend delay
where ${g}_{u}={\sum}_{t\in \mathcal{T}}{\sum}_{{k}_{i}\in {K}_{u,t}}{f}_{{k}^{i}}$.
3.4.2 Minimizing expected energy consumption
3.4.3 Minimizing worst case persource average delay
When the optimization objective is to minimize the overall average endtoend delay, some sources maybe excessively penalized with a much longer delay. In monitoring applications where uniform response time is favored, the optimization objective has to be minimizing the worst case, persource average delay. In other words, the objective is to minimize the average delay observed by packets from each source taken separately, to guarantee the best possible worst case scenario. In order to define this objective, we define a set of new flow variables, f_{e,u} to denote the portion of the flow in link e that is generated originally by node u. If no path from u to the virtual sink uses link e, this implies that f_{e,u}=0.
4 Performance evaluation
We conducted extensive simulation to evaluate our gateway deployment optimization framework. We assumed that acoustic transceivers of all nodes, both underwater nodes and surface gateways, to be homogeneous and, therefore, the communication range is assumed to be constant for all nodes. We assume that sensor nodes are either stationary or that their motion is correlated strongly enough to assume that their relative locations are fixed. We adopt the pessimistic interference model. To reduce the problem complexity, we assume a lightly loaded, i.e., such that channel access delays and queuing delays can be safely ignored. Since both channel access delay and queuing delay are functions of network load (i.e., flow), keeping the network very lightly loaded justifies the assumption that the queuing delay is constant, hence allowing the linearization of the formulation and the use of LP solvers. When the load is increased, the nonlinear formulation can be solved similarly by piecewise linearization algorithms, such as the FrankWolfe algorithm. Although the problem can be solved for a choice of optimization goals, we limit our focus on the simplest optimization goals, namely minimizing the average delay and minimizing the average power consumption. The LP solver uses a bruteforce search algorithm to find the optimal gateway deployment locations by numerating all possible candidate solutions and checking whether each satisfies the problem’s statement.
4.1 Case study
In order to evaluate the benefits and the performance of the gateway deployment optimization techniques, we used the following simulation setting.
Throughout the experiments, we fixed the packet length L = 400 bits, the underwater acoustic propagation velocity υ = 1.5 km/s, and the transmission power is set to a constant of 1 W/s per packet time. The communication range for the underwater modems for all nodes is fixed at R^{ C } = 150. We also fixed the area of deployment to a square area of 600 m × 600 m horizontal extent and fixed the candidate gateway deployment positions to a 5 × 5 square mesh of points spaced 150 m apart.
The depth of all underwater sensors is arbitrarily set to 100 m, such that each of the underwater sensors, regardless of its horizontal location, is within the communication range of at least one surface gateway candidate position, thus satisfying the connectivity requirement. This guarantees that an optimal solution can be found by setting a largeenough limit on the number of surface gateway nodes, N. Finally, the data generation rate at each underwater sensor is set to 1 packet per second. The acoustic channel effective bit rate B is varied among 5, 10, and 50 kbps. Accordingly, the packet transmission time τ=80, 40, and 8 ms, respectively, and the data generation rate g= 0.08, 0.04, and 0.008 packets per packet time, and the energy consumption per packet transfer π^{ S }= 80, 40, and 8 W, respectively.
4.2 Results
To characterize the benefits and limitations of the deployment of surface gateways, we analyze the effect of the following factors:

Number of gateway nodes
When the number of allowed surfacelevel gateway nodes increase, the performance characteristics, average delay or average power consumption, is expected to improve. To verify that, we vary the number of allowed surface nodes from 1 to 25 nodes and solve the optimization problem to get the optimal average delay or energy. Results show that an increase in the number of surface gateways can dramatically enhance performance, especially when the network is lightly loaded.

Network load
Intuitively, when the ratio of total data generation rate to the pernode channel bandwidth increases, the minimum number of surfacelevel nodes required to make the problem feasible increases. This is due to the fact that surface gateways will saturate with incoming traffic and, therefore, more nodes will be needed to handle the additional traffic corresponding to the increased data generation rate. On the other hand, increasing channel capacity reduces the network load, and consequently, our assumptions about ignoring queuing delays become more realistic. To demonstrate the effect of channel capacity on the quality of the solution, we solve the deployment optimization problem for different link capacities, namely 5, 10, and 50 kbps.

Underwater deployment pattern
If the set of candidate surface gateway positions is preset, the locations of underwater sensors and the distribution of data generation load among them are expected to affect the benefit of adding more surface gateways. If underwater sensors are clustered in groups, less surface gateways are expected to feasibly route all traffic to the surface, compared to the case when underwater sensors are spread evenly over the deployment area. On the other hand, clustering increases the odds of collision and, in the case of high traffic loads, can negatively affect delay and energy consumption. To study the effect of the deployment scheme of the given set of underwater sensors on the result of the surface gateway deployment optimization, we use two underwater deployment patterns.
4.2.1 Effect of number of gateway nodes
4.2.2 Effect of network load
4.2.3 Effect of underwater deployment pattern
5 Conclusions
In this paper, we provided a generalized optimization framework for the surface gateway deployment problem. We demonstrated how to use the formulation to find the optimal placement of surface gateways with respect to a variety of optimization goals and a set of flow conservation constraints, interference constraints, and deployment constraints. We assisted our work by incorporating the optimal gateway optimization framework results in simulating the operation of the UWSN with multiple surface gateways. Our simulation results confirmed the potential for performance improvement using multiple surface gateways, such as reducing both average delay and energy consumption. It was also shown that the effect of the added surface gateways depends on the channel capacity or the network loading level, as well as the given underwater sensor deployment pattern. Our work presented here helps to pave the way for a wide variety of future research. One possible improvement is to optimize underwater and surface deployments jointly. Another is to consider mobility of surface gateways and a static UWSN with some level of location certainty models and integrate it as a new design parameter in our framework.
Declarations
Acknowledgements
This work is supported in part by the US National Science Foundation under CAREER grant nos. 0644190, 1018422, 1127084, 115213, and 1205665.
Authors’ Affiliations
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