Mobile Information Systems

Volume 2019, Article ID 3597304, 13 pages

https://doi.org/10.1155/2019/3597304

## Selection of Optimal Deployment and Routing Configurations in Underwater Acoustic Sensor Networks for Avoiding Energy Holes

Faculty of Computing and Information Technology, Information Technology Department, King Abdulaziz University, P.O. Box 42808, Jeddah 21551, Saudi Arabia

Correspondence should be addressed to Fatma Bouabdallah; moc.liamg@halladbauob.amtaf

Received 18 September 2018; Revised 10 November 2018; Accepted 12 November 2018; Published 3 February 2019

Academic Editor: Laurence T. Yang

Copyright © 2019 Fatma Bouabdallah. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Improving the energy efficiency of underwater acoustic sensor networks (UW-ASNs) is a crucial issue due to the reduced and nonrechargeable energy resource of the underwater sensor nodes. In this work, we address the energy sink hole problem in UW-ASNs while considering the unique and harsh characteristics of the underwater channel. Our goal is to determine the optimal deployment and routing settings that surmount the energy sink hole problem and hence maximize the network lifetime. We prove that sensors can evenly consume their initial battery power provided that first they adjust their transmission power when they transmit the route through traffic and second they are appropriately placed while deployed. Mainly, we propose a deployment scheme and the corresponding balanced routing strategy that lead to uniform energy consumption among all underwater sensors subject to a predefined reliability level at the sink. Specifically, we look for the optimal deployment settings especially in terms of nodes’ separation distances that help achieving uniform energy consumption in the network while satisfying the application requirement especially in terms of desired information reliability. Jointly, at the routing layer, we assume that each sensor is provided with the possibility of dynamically adjusting its transmission power up to a given number of levels *N*. To this goal, we mainly deal with two main cases: fixed and variable nodes separation distance. For the fixed case, we suppose that any two successive nodes in the network are equally spaced, and we strive for deriving the optimal distance as well as the optimal number of transmission power levels along with optimal load weight corresponding to every possible transmission power level for every sensor node. For the variable case, we deal with two subcases: first, we suppose that the distance separating successive nodes follows an arithmetic progression, and second, we assume that the distance separating successive nodes is following a geometric sequence. Note that for both cases, namely, fixed and variable, we succeed to determine the optimal distances separating successive nodes and the optimal number *N* of transmission power levels along with the corresponding optimal load weight that overcome the energy holes problem, and hence the network lifespan is maximized while respecting the desired reliability level.

#### 1. Introduction

Underwater sensor networks are attracting an increasing interest of the research community since they enable a broad range of applications in various domains such as military, environmental, and scientific [1]. Acoustic communication is the most appropriate technology for these applications.

An underwater channel requires exclusive and tough characteristics. Indeed, the channel’s bandwidth is limited, the attenuation is frequency-dependent and significant, the propagation delays are high and variable, and the battery power is limited [2]. For these reasons, proposing network protocols specifically dedicated for the underwater environment confronts serious challenges. Moreover, the initially provided battery power for underwater sensors is limited. Even worse, the battery cannot be recharged, since it is impossible to use solar energy. In addition to that, replacing flat sensors risks to cost too much since the underwater sensors deployment is quite difficult. Even worse, acoustic underwater communications use much more energy than the terrestrial sensor networks using radio communications. Indeed, the deployment is quite sparse in underwater environment. This makes the underwater communication involve transmission over long distances. Moreover, in order to surmount the impairments of the underwater channel, more developed signal-processing solutions are needed at receivers.

For the above reasons, UW-ASNs need protocols that pertinently consume the limited initial battery power of the underwater sensor nodes. To this end, load balancing is the most efficient yet delicate technique that prolongs the UW-ASN lifetime. Its ultimate objective is to guarantee to the most possible extent the equitable and soft energy consumption among all the sensors. Note that, wireless sensor networks research community showed that the nearest sensors to the sink suffer from a harsh depletion of their initially provided battery power [3–7], as these sensors are relays of all the other sensors in the network. This may result in energy holes which cause drastic reduction of the network lifespan. Consequently, balancing the energy utilization in the network, by evenly distributing the traffic load, is the most promising energy efficient technique.

In this paper, we address the energy sink holes problem in UW-ASNs while considering the unique and characteristics of the underwater channel. Our goal is to jointly determine the optimal deployment and routing settings that surmount the energy hole problem and hence maximize the lifetime of UW-ASNs. To this aim, we strive for determining the optimal distances separating subsequent sensor nodes during deployment that helps achieving uniform energy expenditure among all sensor nodes while satisfying the desired information reliability. Jointly, we propose variable transmission range-based data forwarding task. Accordingly, each underwater sensor is allowed to dynamically adjust its transmission range (or interchangeably transmission power) among several possible levels (*N*). Indeed, the number of transmission power levels (*N*) is a key factor influencing the energy consumption. First, increasing *N* allows the network traffic load distribution to be more balanced giving a more uniform energy utilization of the network’s sensors. Second, adopting a large value for *N* augments the transmission energy utilization since the farthest nodes may be reached. Therefore, the number *N* has an optimal value for which the energy consumption is optimized, and hence the network lifetime is maximized. In this study, we propose a joint optimization problem that aims at deriving the optimal deployment configuration along with the optimal value of the number *N*’s of transmission levels and their corresponding optimal load weights for each sensor in the network that surmounts the sink hole problem, and hence the network energy efficiency is maximized. As a recap, our work is a three-fold optimization study where the optimal distances between subsequent sensors and the optimal value of power levels *N* along with the optimal load weights are jointly derived.

A recap of our contributions can be stated as follows. First, we propose a study case deployment pattern for UW-ASN for which we aim at deriving the optimal settings that surmount the energy hole problem. More precisely, considering this proposed deployment strategy, our goal is to determine the optimal distances separating subsequent sensors that help achieving uniform energy consumption through the network while satisfying the reliability requirement at the sink. Second, at the routing layer, we suppose that each sensor node is capable to dynamically tune its transmission power up to a predefined number *N* of levels while sending or forwarding data. Consequently, we determine the optimal value of *N*, with the corresponding optimal load weights that balance the energy depletion among the network’s sensors. Therefore, the network lifetime is maximized since the energy hole problem is solved. To summarize, we endeavor to determine the optimal deployment configuration along with the optimal value of *N* and their corresponding optimal load weight distribution that balance the energy depletion among sensor nodes. Our joint optimization solution is specifically tailored for the underwater environment as it considers the intrinsic features of the underwater acoustic propagation. More precisely, in this work, we adopt the underwater channel model proposed by Qarabaqi and Stojanovic in [8] and implemented in [9] which is considered in the literature as one of the most recent and pertinent mathematical analyses that meticulously describes the underwater acoustic time-varying channel model.

The rest of the paper is organized as follows. Section 2 summarizes work related to the focus of this paper. Section 3 describes the network and channel models under study. Section 4 formally states the general energy-balancing optimization problem. Different study cases are addressed in Section 5. Numerical results are provided in Section 6 for all proposed study cases. Section 7 concludes this paper.

#### 2. Related Work

Underwater acoustic networks are gaining a noteworthy interest within the research community. The unique and severe features of the underwater channel usually impose the design of dedicated strategies. Pompili et al. [10] and Climent et al. [11] present a state-of-the-art networking protocols for underwater networks. While extensive routing protocols have been conducted up to date [12, 13], much less work has been dedicated to surmount the energy hole problem, and thus there is still much room for innovation [14]. Note however that the energy hole problem has been recently investigated in industrial WSNs and the Internet of things [15–19] but rather from a coverage point of view. Indeed, authors in [15, 18, 19] focus on how to detect and localize coverage holes while taking into consideration the network energy efficiency. However, studies proposed in [16, 17] strive to overcome the coverage hole problem using mobile sensor nodes. More precisely, they aim at optimally selecting a subset of the randomly deployed mobile sensors to overcome the coverage holes. It is worth pointing out that authors in [17] solve the coverage holes problem while taking into consideration also the network connectivity. Although the aforementioned approaches may be inspiring, some fundamental differences with our objective and context impose the design of a new approach. Indeed, in this work, rather than dealing with coverage holes, we are interested in overcoming the energy sink hole problem that may lead to sink isolation and hence network partition. Moreover, in this paper, instead of using mobile sensors to heal the energy sink hole problem, we opt for evenly distributing the traffic load inside the network while equipping the sensors with multiple transmission power levels in order to alleviate the forwarding task of the one-hop away sensors, and hence the energy sink hole problem is surmounted.

In this section, we summarize work mainly related to energy-efficient routing and energy hole problem encountered in UW-ASNs.

##### 2.1. Energy Efficient Routing in UW-ASNs

Energy efficiency is a crucial issue in UW-ASNs. Conceiving routing protocols that make wise utilization of the finite and nonrechargeable energy budget of the underwater sensor nodes is a decisive factor for the network lifetime. Many UW-ASNs routing protocols have been proposed to improve the energy efficiency but not from an energy-balancing point of view in order to surmount the energy hole problem which restrains their contributions. In [20], a geographical routing scheme was proposed for underwater acoustic networks and joined with a power-control approach in order to increase the protocol energy efficiency. Indeed, this routing strategy called FBR aims at dynamically establishing routes only on demand while preserving the scarce energy resources of the underwater sensors. In fact, by gradually and carefully increasing the transmission power, FBR tries to find the appropriate next hop toward the sink while reducing the total energy consumption. However, for its well functioning, FBR requires from every source node to know its own location as well as one of the final destinations. Another approach would rather rely on depth information in order to reduce energy consumption throughout the network, since depth information is much easier to acquire than location information in UW-ASNs. For instance, in [21], the authors propose a depth-based routing (DBR), which uses depth information to reduce the invalid broadcasts. Indeed, by only allowing nodes with lower depth to forward a packet destined to a surface sink, the number of forwarder is highly decreased, and hence energy saving is achieved. Another depth-based routing protocol called EUROP was introduced in [22]. Thanks to the use of depth sensor, EUROP will eliminate the requirement of hello messages for control purposes which improves the energy efficiency. According to EUROP, underwater nodes are divided into different layers based on the depth information such that only nodes in the same layer (namely, in the same depth) can communicate with each other. The forwarding process is straightforward and simply dictates that data packets are forwarded from deeper layers to shallower layers. In addition to that, it is assumed that nodes can move to the upper layer and back to their predefined place for successful delivery to the surface sink. Although the use of the depth sensor will completely eliminate the need for control packets which may reduce the energy consumption, the use of these costly sensors may compromise the total energy consumption as these sensors consume a large amount of energy to move from one depth to another.

##### 2.2. Sink Hole Solutions in UW-ASNs

One of the early studies on the sink hole problem in UW-ASNs was conducted by Chen et al. [23], who proposed an energy-efficient routing protocol, called REBAR, to surmount the sink hole problem. REBAR protocol supposes that by default, every generated data packet has to undergo a flooding process in order to reach the sink. Based on this, REBAR tries to highly reduce the flooding region for each source node by most importantly involving much less number of the sink one-hop away sensors while guaranteeing a high delivery rate. Therefore, the closest nodes to the sink are much less solicited especially compared to the integral flooding process. Consequently, the network lifetime is augmented. It is completely true that, thanks to REBAR protocol, the closest sensors to the sink are much less involved in the routing process especially compared to the complete flooding process, but they keep acting as relays on behalf of all other sensors, and hence keep suffering from severe energy depletion. Indeed, as it was shown by terrestrial wireless sensor networks research community, the energy sink hole problem is inevitable in static always-on sensor networks where sensors perform continuous monitoring of a given field using a nominal communication range [5, 24–28]. Consequently, using adjustable communication power was the most undertaken research strategy to make the energy consumption more uniform among the network’s sensors. Indeed, by endowing each sensor with the ability of dynamically adjusting its transmission power, the traffic load distribution among sensors is much more balanced, and thus sensors, which are in neighborhood of the sink, are eased from the relying task. In this perspective, authors in [29] have proposed a routing strategy assuming that every sensor node has two transmission ranges: the smallest one used to reach the next hop in a linear topology and the farthest one used to directly reach the sink. According to EBH, each sensor node has to alternate between the two possible transmission ranges based on the residual nodes’ energy such that the network lifetime is optimized. Indeed, accordingly, a sensor node has to keep sending to its upstream neighbor in the linear topology as long as its own residual energy is greater than the one of its neighbor. Otherwise, the node has to proceed sending directly to the sink node.

Another possible approach to surmount the sink hole problem is by using a mobile sink node such that the set of the closest node to the sink is constantly changing. In this direction, most of the research studies, especially in WSNs, will rather focus on finding the optimal sink trajectory to maximize the network lifetime. Similarly, authors in [30] consider a UW-ASN context where an autonomous underwater vehicle (AUV) is acting as a data collector and hence try to find the best trajectory by taking into consideration the underwater environment constraints. In the same way, another recent energy-efficient routing protocol proposed in [31] is MobiCast. This protocol also aims at maximizing data collection while overcoming the energy hole problem by the use of mobile AUVs acting as a data collector.

Authors in [32, 33] propose to surmount the energy sink hole problem by using multiple transmission power levels rather than the use of mobile sink. More precisely, and as a distinguishing feature, each sensor node is endowed with multiple transmission power levels in order to optimally distribute the traffic load through the network, and hence balanced uniform energy depletion is achieved among all sensors including the closest nodes to the sink. Authors in [33] strive for analytically deriving for each sensor node the optimal number *N* of transmission power levels as well as the optimal load weight corresponding to every possible transmission power such that uniform energy consumption is achieved. Indeed, in order to derive their optimal configuration settings, authors in [33] adopted the time-varying channel model proposed in [8], which closely reflects most of the underwater channel impairments such as bottom-surface reflections, frequency-dependent attenuation, and Doppler effects which are caused by random local displacements.

This paper can be seen as a continuation of [33], where we not only determine the optimal number of transmission levels *N* along with the corresponding load weights but also we seek the optimal deployment settings in terms of nodes initial positioning. In this study, we adopt the same channel model as in [33] since it is the most realistic one but we rather opt for the numerical resolution of our threefold optimization problem that aims at determining the optimal distance between two subsequent nodes as well as the optimal value of *N* along with the optimal corresponding load weights.

#### 3. Network Model and Problem Statement

##### 3.1. Recap of the Adopted Time-Varying Underwater Channel

In this study, we use the same underwater channel model adopted by [33] and initially proposed by [8]. Accordingly, the transmitter and the receiver are subject to a uniform displacement around their nominal positions by some heights and and a distance . Moreover, we similarly suppose that the water surface level can vary by some height due to water waves. In particular, the sensors’ displacements are supposed to vary according to a random uniform distribution, each within the interval For instance, assume that two sensor nodes are initially placed at a height and within a distance of from each other where the water depth equals . We suppose that each sensor will undergo an independent random independent drift from their height by and due to water current and waves. Moreover, we suppose that this pair of node will be subject to random displacement from their nominal locations, where . Furthermore, we suppose that the water surface that initially equals may randomly vary by . Figure 1 clearly depicts the different parameters with their corresponding variation ranges.