Towards Optimizing Energy Eﬃciency
and Alleviating Void Holes in UWSN
Abdul Mateen1, Nadeem Javaid1(B
), Muhammad Bilal2,
Muhammad Arslan Farooq3, Zahoor Ali Khan4, and Fareena Riaz5
1COMSATS University Islamabad, Islamabad 44000, Pakistan
2School of Computing and IT, Centre for Data Science and Analytics,
Taylor’s University, Subang Jaya, Malaysia
3Government College University, Lahore, Pakistan
4Computer Information Science, Higher Colleges of Technology, Fujairah 4114, UAE
5University of Kotli, AJK Campus, Azad Kashmir 1100, Pakistan
Abstract. Underwater Wireless Sensor Networks (UWSNs) are promis-
ing and emerging framework having a wide range of applications. The
underwater sensor deployment is beneﬁcial; however, some factors limit
the performance of the network, i.e., less reliability, high end-to-end delay
and maximum energy dissipation. The provisioning of aforementioned
factors have become challenging task for the research community. In
UWSNs, battery consumption is inevitable and has a direct impact on
the performance of the network. Most of the time energy dissipates due
to the creation of void holes and imbalanced network deployment. In
this work, a routing protocol is proposed to avoid the void holes prob-
lem and extra energy dissipation, due to which lifespan of the network
increases. To show the eﬃcacy of our proposed routing scheme, it is com-
pared with state of the art protocols. Simulations result show that the
proposed scheme outperforms the counterparts.
Keywords: GEDPAR ·Void holes ·Energy eﬃciency ·
Underwater Wireless Sensor Network (UWSNs) ·Depth adjustment ·
The planet Earth, on which we live our lives, consists of 70% water. Whereas,
the oceans hold more than 90% of total water. This much quantity shows the
importance of the water medium. To explore the underwater medium for get-
ting and sharing the important information, a network is deployed in a speciﬁc
region. Information transmission using Underwater Wireless Sensor Networks
(UWSNs) is one of the emerging technologies and is used for the betterment
of ocean observation systems. Applications of UWSNs range from aquaculture
Springer Nature Switzerland AG 2019
L. Barolli et al. (Eds.): EIDWT 2019, LNDECT 29, pp. 516–527, 2019.
to oil industry; instrument monitoring to climate recording; pollution control
to predictions on natural disasters; search and survey purposes to submarine
The UWSN consists of several sensor nodes and these nodes acquire informa-
tion and transmit it towards the next node closer to the sink1. This sink may
be the onshore data center or simple sensor node over the water surface. In data
forwarding procedure, the source2node generates data packets and communi-
cate with its neighbors to ﬁnd the potential node. Afterwards, potential neighbor
node ﬁnds the next potential node from its neighbors and transmit data packet
towards that potential node. To ﬁnd the potential neighbor from the forwarder
node, some criteria and routing procedure are deﬁned. This criterion may base
upon eﬃcient energy utilization or alleviation of void holes.
The design of routing protocols has paramount importance in UWSN. These
protocols indicate the routing path for data from the source node at bottom
towards the sinks node at the surface of ocean. Expressly, these protocols face the
diﬀerent challenges which are associated with the underwater medium, e.g., lim-
ited battery resources, interference, noise, reliable Packet Delivery Ratio (PDR),
high propagation delay, movements of sensors and void holes.
Eﬃcient energy usage is the most important while designing a routing pro-
tocol. As sensor nodes in water have limited resources (already discussed). The
batteries are non-removable and have limited energy storage. This issue provides
a strong base for the eﬃcient utilization of batteries. Mostly, energy dissipates
during the processes of data packet transmission and reception. The eﬃcient
energy usage depends upon various factors. For instance, the initial position and
number of anchor nodes; sensor nodes and the way in which nodes are deployed.
The deployment of a network must be one of the two types (1) sparse deployment
and (2) dense deployment. The sparse deployment leads toward the creation of a
void hole and dense deployment results in an excessive amount of sensors failure.
The energy and network stability have a direct relation. As, more will be the
energy of sensors, longer will be the stability of the network and vice versa. Void
holes are areas within the transmission range of a network where a node cannot
ﬁnd its next neighbor or forwarder. The void holes creation has following reasons
(1) node becomes dead due to a lot of energy usage and (2) no forwarder node.
Localization of sensor network in underwater is indispensable. The gathered
data is useless until it is not correlated with the speciﬁed position of the sensor
node. Localization in UWSNs is very important as it has many useful applica-
tions, e.g., target tracking, underwater environment monitoring, pollution con-
trol and geographic routing protocols. Nevertheless, UWSNs cannot use Global
Positioning System (GPS) due to high energy dissipation and high attenuation
of RF signals  and .
In this work, we proposed GEographic and opportunistic routing with Depth
and Power Adjustment Routing (GEDPAR) as a routing technique. GEDPAR is
1Sink: This word is alternatively used for sink node, sonobuoy, destined node and
2Source: The words source node and initial node are alternatively used for source.
518 A. Mateen et al.
compared with GEographic and opportunistic routing with Depth Adjustment
Routing (GEDAR) and Layered Multi-path Power Control (LMPC). Simulations
are performed in order to check the eﬀectiveness of our proposed scheme.
The remainder of the paper is organized as follows: Sect. 3provides the
brief overview of state of the art work. Problem statement elaborates in Sect. 3.
Section 4represents the proposed system model. Discussion on the simulations
is given in Sect. 5. Finally, Sect. 6summarizes the paper.
2 Related Work
In this section, we review and compare some recent works on the base of covering
a speciﬁc area of UWSNs. The papers which cover the energy eﬃciency and void
holes are compared in Sect. 2.1. Additionally, the papers that cover the concept of
localization or geographic routing are compared in Sect. 2.2. Moreover, Sect. 2.3
presents the comparison of topological control based schemes. In the end, the
concept of a void hole is presented in Sect.2.4.
2.1 Energy Eﬃciency Based
The papers [1–4] propose diﬀerent schemes and protocols to enhance the energy-
eﬃciency. The papers  and  both are using multi-hops techniques. The
paper  is focusing on network reliability, mobility management, PDR and
energy eﬃciency. On the other hand, the paper  is only focusing on energy
eﬃciency. Both the papers  and  achieve their objectives; however, end-to-
end delay is compromised. The authors in papers  and  mainly focus on
reliability by covering one-hop from the forwarder node. The proposed scheme
EBLE from the paper  aims to minimize the energy dissipation with packet
size management. The objective is successfully achieved on the cost of delay. The
cooperative routing is used in paper  for data reliability and mobility manage-
ment, while PDR and eﬃcient energy usage are main aims. The objectives are
achieved successfully; however, the network performs poorly in sparse network
The works [8–11] are also using energy eﬃciency techniques. The works 
and  provide the reliability. Both of works discuss the concept of multi-
hoping. The proposed scheme in the work  is beneﬁcial for a large amount of
data packets; however, this proposed technique does not performed well in sparse
network deployment. The MLPR from  looks toward the eﬃcient path for
routing by utilizing minimum energy. For the implementation of MLPR, more
memory is required for the extra operations at each node. The energy dissipa-
tion schemes; SDVF and EBULC are proposed in works  and , respectively.
Both schemes consider mobility management for decreasing the energy consump-
tion in UWSNs. Results show that end-to-end delay in the works  and is
The energy eﬃciency is focused in the works [12–15]. In , some data col-
lection methods are discussed which used minimum energy for data transmission
from source to the destination. In both  and , mobility management is
considered, while in the , reliability and packet size management is not con-
sidered. Nevertheless, the works [13–15] focus on the reliability of the network.
Additionally,  considers both types of forwarding strategies; single-hop and
multi-hop. While [12,14] and  only focus on single-hop from the current node.
Moreover, the work in  considers the security issues of UWSNs. While in ,
the authors discusses the problems of getting route information. In , the com-
plexity of the network is a major challenge. Additionally, paper  works for
energy eﬃciency by managing the size of data packet.
2.2 Localization Based
The authors in [7,16–18] and  discuss the geographic or localization-based
routing. The work in  and  review the works in which the concept of
localization based routing is used. Both of these above, discuss reliability and
none of them work on mobility management or packet size management. More-
over, in  and , the concept of single-hop and multi-hop is devised. The
challenges which are discussed in these works are: high interference, limited bat-
teries of sensor nodes, low bandwidth and malicious attacks. The work in 
achieves the higher PDR by ﬁnding the locations of alive nodes. Afterwards,
the data packets are sent to these alive nodes, accordingly. The challenges dis-
cussed in  are: localization, feasible hardware, relevant simulation tools and
low power gliders.
2.3 Topology Control Based
In [2,5,6] and  proposed topology control based solutions. TCEB and
GARM schemes are proposed for controlling the topology of UWSNs in 
and , respectively. In addition, the  classiﬁes diﬀerent topological proto-
cols. From , reliability and mobility is discussed. The work  focuses on
single-hop and multi-hop while the work  only focus on next forwarder node.
The challenges that discussed in [2,5,6] and  are: high attenuation, mobility
of sensor nodes, energy eﬃciency, low bandwidth, connectivity loss, high bit rate
error, high deployment cost, complexities and optimal location of glider. Using
dynamic topological strategy, work in  achieves energy eﬃciency and the work
in  enhances both PDR and energy eﬃciency. In , mobility management is
a major consideration using EEL and the concept of multi-hoping. In addition,
the work  achieves better simulation results from compared ones.
2.4 Void Hole Based
The concept of a void hole is presented in . Void holes are the regions within
the network range from where further data delivery is not possible. In other
words, if a forwarder node does not have any further node for data packet trans-
mission then this node is called void node and the area where transmission is
520 A. Mateen et al.
not possible in called void holes. TORA is presented in  in order to avoid the
void holes. The proposed scheme uses the concept of multi-hoping to avoid void
holes and to improve energy eﬃciency. Nevertheless, reliability and complexity
of this scheme are not discussed.
3 Problem Statement
In UWSN, each sensor has limited resources and requires eﬀective utilization
of these resources. Eﬃcient energy consumption has a major contribution to
stabilize the network for long term communication. In UWSNs, the packet is
sent from the source node to the sink node using diﬀerent relay nodes. If a node
cannot ﬁnd a forwarder node in its transmission range, it causes hindrance in
the network during communication.
In order to avoid the void holes in UWSNs, a routing protocol namely
GEDAR presented in . GEDAR addresses the issue by adjusting the depth
of nodes; however, the process of depth adjustment consumes lots of energy.
In , LMPC routing technique addresses the eﬃcient data transmission by
making the binary tree from route node. However, binary tree generation con-
sumes high energy and lead towards the transmission overhead. To overcome the
aforementioned problems, a routing protocol namely GEDPAR is proposed for
avoiding the void holes and eliminating the extra energy consumption.
Fig. 1. Proposed system model
In this section, our proposed system model is presented in Fig. 1. The system
model consists of source nodes, relay nodes and sonobuoys. Source node forwards
data packets toward the destined sonobuoys during transmission. The proposed
protocol follows multi-hoping feature for packets transmission. Source and relay
nodes only use acoustic signals while radio waves are used for communication
among sink node, submarine, satellite, base station and the main processing unit.
In the proposed system model, sensor nodes are randomly deployed in under-
water medium. Nevertheless, sink nodes are deployed at the sea surface. The
same transmission range and energy are assigned to each sensor node. Moreover,
each sensor node has also the ability to adjust their depth from the lower layer to
the upper layer. During depth adjustment, nodes only move in vertical direction.
The process of depth adjustment occurs in the case when a node cannot ﬁnd
its next forwarder even by increasing the transmission range. There are three
diﬀerent cases that are elaborated through the proposed system model.
GEDAR is an opportunistic and depth adjustment-based routing protocol. In
GEDAR, each packet is sent to the forwarding set which consists of several
neighbors. Algorithm 1shows the procedure of periodic beaconing in GEDAR.
This procedure requires S and D. Where, κrepresents beacon messages. Lines
(4–16) elaborate on the overall procedure for distance and neighbor calculations.
Lines (8–11) add neighbors to the neighbor list. Line 6 shows that this procedure
repeats for each and every source node.
In LMPC, multiple layers are made vertically by dividing the network for eﬃcient
transmission. As, working of LMPC is totally depending on the layers and we
have already mentioned that noise in deep water is less than the shallow water.
So, the size of a layer in deep water is high and vice versa for shallow water.
This size of a layer has an inverse relation with noise, greater the attenuation of
noise lower will be the layer size and vice versa.
GEDPAR is our proposed routing protocol. For this protocol, GEDAR and
LMPC are taken as benchmark schemes. In GEDPAR, layering concept is taken
from the LMPC and depth adjustment is taken from the GEDAR. GEDPAR
takes transmission enhancement step on the appearance of void holes. Transmis-
sion enhancement takes some extra energy; however, most of the void holes are
removed in this process. If a node cannot cover the void hole even by increasing
the transmission range then depth adjustment takes place for that node. The
procedure for periodic beaconing is same as in GEDAR (see Sect. 4.1).
522 A. Mateen et al.
Algorithm 1. Periodic beaconing
1: node (S, D)
2: network deployment
3: κ: beacon message
4: if beacon is timed out then
5: κ.coordinates = distance (node)
6: if node Nthen
7: for sSdo
8: if λs=0then
9: add in κneighbor list (s.id, x-coordinates, y-coordinates)
11: end if
12: end for
13: end if
14: broadcast λ
15: set new timeout
16: end if
Algorithm 2. Void hole recovery
2: if current node is void = 1 then
3: stop beacon messages
4: end if
5: ν=∅: no neighbor node
6: ν: set of next forwarder nodes
7: Δ: set of void nodes
8: nv: is current void node
9: if |ν|>0then
10: enhance transmission radius
11: dist =(xv−xu)2+(yv−yu)2
12: if dist ≤rcthen
13: goto (23)
15: for nuνdo
16: dist =(xv−xu)2+(yv−yu)2
17: if dist ≤rcthen
20: end if
21: end for
22: nvmoves to new calculated depth
23: end if
24: current node is void = 0
25: end if
Algorithm 2involves the steps for the recovery of the void hole. First of all,
value for the current node is set to “1” for its identiﬁcation and stop beacon
messages. The symbol ∅shows that current node has no neighbor. In other
words, it is a void node. νis set that contains the record of next forwarder
nodes. Δand nvare the symbolic representations of void nodes set and current
void node, respectively. The distance for each forwarder near the current void is
calculated. Afterwards, this distance is compared with the transmission range. If
the distance is less then the transmission range, it means that the next forwarder
node is within the range of the current forwarder node and vice versa. In case, if
no forwarder node exists within transmission range then depth adjustment takes
place and the status for the void node is set “0” from “1”.
5 Simulation and Discussion
Simulations are performed in order to check the eﬀectiveness of the proposed
scheme. The results of our proposed technique are compared with GEDAR and
LMPC. GEDPAR is greedy opportunistic routing protocol in which next for-
warder node is selected on the criteria of minimum distance from the current
node. In the proposed protocol, ﬁrstly, current node enhance transmission range
when it ﬁnds no neighbor in its transmission range. After that, if current for-
warder still not able to ﬁnd any node in its range then it executes depth adjust-
ment. During depth adjustment, the node moves from deeper layer to the shallow
5.1 Network Parameters Setting
The network is deployed over the area of 1500 m ×1500 m ×1500 m. The number
of nodes and sinks are 100 and 45, respectively. Initially, nodes are deployed
randomly. The initial transmission range of each node is 245 m and nodes can
transmit up to 270 m using some extra energy. This happens only when current
forwarder cannot ﬁnd the next node in its transmission area. The initial energy
of each node is 100 J. The velocity of acoustic waves and bandwidth for the
network is considered 1500m/s and 3000 kHz, respectively. Transmission energy,
reception energy and idle time energy is considered as 2 W, 0.1 W and 10 ×
10−3W, receptively. Size of hello packet is 100 bytes while the size of all other
packets is 150 bytes.
5.2 Simulation Results
Figure 2depicts the depth adjustment of nodes. We can see from the Fig. 2that
most of the depth adjustment is done during the start of network deployment.
Once the network is deployed and initial depth adjustments are done then there
exist only a few occasions on which depth adjustment is required. A large amount
of energy is dissipated during the process of depth adjustment. So, we make sure
that the depth adjustment only occurs when it is necessary. Otherwise, try to
524 A. Mateen et al.
0 50 100 150
Number of depth adjustments
Fig. 2. Depth adjustment
avoid the nodes by enhancing the transmission range. It is clear from the Fig.2
that in GEDPAR routing protocol nodes require fewer depth adjustments as
compare to GEDAR. This step further involved in less energy dissipation.
The throughput of proposed routing protocol is compared with GEDAR and
LMPC. Figure 3shows this comparison and assure the eﬃciency of proposed
scheme. According to simulation results, LMPC performs better than GEDAR
while GEDPAR outperforms both GEDAR and LMPC. The eﬃciency of the
proposed scheme is better than LMPC and GEDAR by the percentage of 13%
and 37%, respectively.
Fig. 3. Throughput
Figure 4depicts the total energy consumption of network when diﬀerent rout-
ing protocols are implemented. GEDPAR consumes less energy as compared to
the GEDAR and LMPC. GEDAR consumes more energy because it focuses on
depth adjustment during the void hole avoidance. Depth adjustment takes 15 J
energy for one meter while transmission range enhancement takes less energy
than depth adjustment. LMPC uses multiple transmissions for one packet which
becomes a major cause in energy dissipation. Our proposed routing protocol
consumes less energy because it tries to cover the void hole by increasing the
transmission range. GEDPAR only change its depth when no forwarded node
is found even by increasing transmission area. According to Fig.4,GEDPAR
outperforms GEDAR and LMPC.
GEDAR LMPC GEDPAR
Energy Consumption (J)
Fig. 4. Total energy consumption
In current work, imbalance and unnecessary energy dissipation is avoided by
covering the void hole in an eﬃcient way. We propose a routing protocol namely
GEDPAR for void hole recovery. In order to show the productiveness of the pro-
posed protocol, comparative analysis is performed with the existing state of the
art protocols: GEDAR and LMPC. Simulation results show that GEDPAR out-
performs GEDAR and LMPC in terms of throughput by the percentage of 13%
and 37%. However, the proposed protocol minimizing the energy consumption
at the cost of delay.
526 A. Mateen et al.
1. Khasawneh, A., Latiﬀ, M.S.B.A., Kaiwartya, O., Chizari, H.: A reliable energy-
eﬃcient pressure-based routing protocol for the underwater wireless sensor net-
work. Wireless Netw. 24(6), 2061–2075 (2018)
2. Hong, Z., Pan, X., Chen, P., Su, X., Wang, N., Lu, W.: A topology control with
energy balance in underwater wireless sensor networks for IoT-based application.
Sensors 18(7), 2306 (2018)
3. Wang, H., Wang, S., Zhang, E., Lu, L.: An energy balanced and lifetime extended
routing protocol for underwater sensor networks. Sensors 18(5), 1596 (2018)
4. Khan, A., Ali, I., Rahman, A.U., Imran, M., Mahmood, H.: Co-EEORS: coopera-
tive energy eﬃcient optimal relay selection protocol for underwater wireless sensor
networks. IEEE Access (2018)
5. Ahmed, F., Wadud, Z., Javaid, N., Alrajeh, N., Alabed, M.S., Qasim, U.: Mobile
sinks assisted geographic and opportunistic routing based interference avoidance
for underwater wireless sensor network. Sensors 18(4), 1062 (2018)
6. Sher, A., Khan, A., Javaid, N., Ahmed, S., Aalsalem, M., Khan, W.: Void hole
avoidance for reliable data delivery in IoT enabled underwater wireless sensor net-
works. Sensors 18(10), 3271 (2018)
7. Nayyar, A., Puri, V., Le, D.-N.: Comprehensive analysis of routing protocols
surrounding Underwater Sensor Networks (UWSNs). In: Balas, V., Sharma, N.,
Chakrabarti, A. (eds.) Data Management, Analytics and Innovation, pp. 435–450.
Springer, Singapore (2019)
8. Wu, F.-Y., Yang, K., Duan, R.: Compressed sensing of underwater acoustic signals
via structured approximation l0norm. IEEE Trans. Veh. Technol. 67(9), 8504–
9. Khosravi, M.R., Basri, H., Rostami, H.: Eﬃcient routing for dense UWSNs with
high-speed mobile nodes using spherical divisions. J. Supercomputing 74(2), 696–
10. Gomathi, R.M., Manickam, J.M.L.: Energy eﬃcient shortest path routing protocol
for underwater acoustic wireless sensor network. Wireless Pers. Commun. 98(1),
11. Hou, R., He, L., Hu, S., Luo, J.: Energy-balanced unequal layering clustering in
underwater acoustic sensor networks. IEEE Access 6, 39685–39691 (2018)
12. Iwata, M., Tang, S., Obana, S.: Energy-eﬃcient data collection method for sensor
networks by integrating asymmetric communication and wake-up radio. Sensors
18(4), 1121 (2018)
13. Muhammed, D., Anisi, M.H., Zareei, M., Vargas-Rosales, C., Khan, A.: Game
theory-based cooperation for underwater acoustic sensor networks: taxonomy,
review, research challenges and directions. Sensors 18(2), 425 (2018)
14. Jan, M.A., Tan, Z., He, X., Ni, W.: Moving towards highly reliable and eﬀective
sensor networks (2018)
15. Yildiz, H.U., Gungor, V.C., Tavli, B.: Packet size optimization for lifetime maxi-
mization in underwater acoustic sensor networks. IEEE Trans. Industr. Inf. (2018)
16. Khalid, M., Cao, Y., Ahmad, N., Khalid, W., Dhawankar, P.: Radius-based mul-
tipath courier node routing protocol for acoustic communications. IET Wireless
Sens. Syst. (2018)
17. Latif, K., Javaid, N., Ahmad, A., Khan, Z.A., Alra jeh, N., Khan, M.I.: On energy
hole and coverage hole avoidance in underwater wireless sensor networks. IEEE
Sens. J. 16(11), 4431–4442 (2016)
18. Wang, H., Wen, Y., Lu, Y., Zhao, D., Ji, C.: Secure localization algorithms in
wireless sensor networks: a review. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi,
M. (eds.) Advances in Computer Communication and Computational Sciences,
pp. 543–553. Springer, Singapore (2019)
19. Yuan, Y., Liang, C., Kaneko, M., Chen, X., Hogrefe, D.: Topology control for
energy-eﬃcient localization in mobile underwater sensor networks using Stackel-
berg game. arXiv preprint arXiv:1805.12361 (2018)
20. Rahman, Z., Hashim, F., Rasid, M.F.A., Othman, M.: Totally Opportunistic Rout-
ing Algorithm (TORA) for underwater wireless sensor network. PloS ONE 13(6),
21. Heidemann, J., Stojanovic, M., Zorzi, M.: Underwater sensor networks: applica-
tions, advances and challenges. Phil. Trans. R. Soc. A 370(1958), 158–175 (2018)
22. Javaid, N., Majid, A., Sher, A., Khan, W., Aalsalem, M.: Avoiding void holes
and collisions with reliable and interference-aware routing in underwater WSNs.
Sensors 18(9), 3038 (2018)
23. Coutinho, R.W.L., Boukerche, A., Vieira, L.F.M., Loureiro, A.A.F.: Geographic
and opportunistic routing for underwater sensor networks. IEEE Trans. Comput.
65(2), 548–561 (2016)
24. Xu, J., Li, K., Min, G., Lin, K., Qu, W.: Energy-eﬃcient tree-based multipath
power control for underwater sensor networks. IEEE Trans. Parallel Distrib. Syst.
23(11), 2107–2116 (2012)