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Towards Optimizing Energy Efficiency and Alleviating Void Holes in UWSN


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Underwater Wireless Sensor Networks (UWSNs) are promising 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 problem and extra energy dissipation, due to which lifespan of the network increases. To show the efficacy of our proposed routing scheme, it is compared with state of the art protocols. Simulations result show that the proposed scheme outperforms the counterparts.
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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 ·
Transmission range
1 Introduction
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[18]. 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 [7] and [19].
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
destination node.
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 [14] propose different schemes and protocols to enhance the energy-
efficiency. The papers [1] and [2] both are using multi-hops techniques. The
paper [1] is focusing on network reliability, mobility management, PDR and
energy efficiency. On the other hand, the paper [2] is only focusing on energy
efficiency. Both the papers [1] and [2] achieve their objectives; however, end-to-
end delay is compromised. The authors in papers [3] and [4] mainly focus on
reliability by covering one-hop from the forwarder node. The proposed scheme
EBLE from the paper [3] 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 [4] 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 [811] are also using energy efficiency techniques. The works [8]
and [10] provide the reliability. Both of works discuss the concept of multi-
hoping. The proposed scheme in the work [8] is beneficial for a large amount of
data packets; however, this proposed technique does not performed well in sparse
network deployment. The MLPR from [10] 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 [9] and [11], respectively.
Both schemes consider mobility management for decreasing the energy consump-
tion in UWSNs. Results show that end-to-end delay in the works [9] and [11]is
The energy efficiency is focused in the works [1215]. In [12], some data col-
lection methods are discussed which used minimum energy for data transmission
from source to the destination. In both [12] and [13], mobility management is
considered, while in the [12], reliability and packet size management is not con-
sidered. Nevertheless, the works [1315] focus on the reliability of the network.
Additionally, [13] considers both types of forwarding strategies; single-hop and
multi-hop. While [12,14] and [15] only focus on single-hop from the current node.
Moreover, the work in [13] considers the security issues of UWSNs. While in [12],
the authors discusses the problems of getting route information. In [14], the com-
plexity of the network is a major challenge. Additionally, paper [15] works for
energy efficiency by managing the size of data packet.
2.2 Localization Based
The authors in [7,1618] and [21] discuss the geographic or localization-based
routing. The work in [17] and [18] 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 [17] and [18], 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 [16]
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 [21] are: localization, feasible hardware, relevant simulation tools and
low power gliders.
2.3 Topology Control Based
In [2,5,6] and [19] proposed topology control based solutions. TCEB and
GARM schemes are proposed for controlling the topology of UWSNs in [2]
and [6], respectively. In addition, the [5] classifies different topological proto-
cols. From [5], reliability and mobility is discussed. The work [5] focuses on
single-hop and multi-hop while the work [2] only focus on next forwarder node.
The challenges that discussed in [2,5,6] and [19] 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 [2] achieves energy efficiency and the work
in [6] enhances both PDR and energy efficiency. In [19], mobility management is
a major consideration using EEL and the concept of multi-hoping. In addition,
the work [19] achieves better simulation results from compared ones.
2.4 Void Hole Based
The concept of a void hole is presented in [20]. 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 [20] 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 [23]. GEDAR addresses the issue by adjusting the depth
of nodes; however, the process of depth adjustment consumes lots of energy.
In [24], 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.
4.2 LMPC
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 (, x-coordinates, y-coordinates)
10: λ=1
11: end if
12: end for
13: end if
14: broadcast λ
15: set new timeout
16: end if
Algorithm 2. Void hole recovery
1: LMPC(node)
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 =(xvxu)2+(yvyu)2
12: if dist rcthen
13: goto (23)
14: else
15: for nuνdo
16: dist =(xvxu)2+(yvyu)2
17: if dist rcthen
18: (xvxu)2+(yvyu)2+(zvzu)2
19: ν=νUzv
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 ×
103W, 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
Time (msec)
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.
Energy Consumption (J)
Fig. 4. Total energy consumption
6 Conclusion
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.
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Full-text available
Underwater Wireless Sensor Networks (UWSNs) are promising and emerging frameworks 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 the aforementioned factors has become a 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, two routing protocols are proposed to avoid the void hole and extra energy dissipation problems due to which lifespan of the network will increase. To show the efficacy of the proposed routing schemes, they are compared with the state of the art protocols. Simulation results show that the proposed schemes outperform their counterpart schemes. By keeping in mind the emerging security issues in sensor networks, we have proposed a blockchain based trust model for sensor networks to enrich the security of the network. Additionally, this model provides security along with data immutability. We have used a private blockchain because it has all the security features that are necessary for a private sensor network. Moreover, private blockchain cannot be accessed by using the Internet. In the proposed trust model, the Proof of Authority (PoA) consensus algorithm is used due to its low computational power requirement. In PoA consensus mechanism, a group of the validator is selected for adding and maintaining blocks. Moreover, smart contracts are used to validate and transfer cryptocurrency to service providers. In the end, transaction and execution costs are also calculated for each function to testify the network suitability.
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Sparse node deployment and dynamic network topology in underwater wireless sensor networks (UWSNs) result in void hole problem. In this paper, we present two interference-aware routing protocols for UWSNs (Intar: interference-aware routing; and Re-Intar: reliable and interference-aware routing). In proposed protocols, we use sender based approach to avoid the void hole. The beauty of the proposed schemes is that they not only avoid void hole but also reduce the probability of collision. The proposed Re-Intar also uses one-hop backward transmission at the source node to further improve the packet delivery ratio of the network. Simulation results verify the effectiveness of the proposed schemes in terms of end-to-end delay, packet delivery ratio and energy consumption.
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As part of the IoT-based application, underwater wireless sensor networks (UWSN), which are typically self-organized heterogeneous wireless network, are one of the research hot-spots using various sensors in marine exploration and water environment monitoring application fields, recently. Due to the serious attenuation of radio in water, acoustic or hybrid communication is a usual way for transmitting information among nodes, which dissipates much more energy to prevent the network failure and guarantee the quality of service (QoS). To address this issue, a topology control with energy balance, namely TCEB, is proposed for UWSN to overcome time-delay and other interference, as well as make the entire network load balance. With the given underwater network model and its specialized energy consumption model, we introduce the non-cooperative-game-based scheme to select the nodes with better performance as the cluster-heads. Afterwards, the intra-cluster and inter-cluster topology construction are, respectively, to form the effective communication links of the intra-cluster and inter-cluster, which aim to build energy-efficient topology to reduce energy consumption. With the demonstration of the simulation, the results show the proposed TCEB has better performance on energy-efficiency and throughput than three other representative algorithms in complex underwater environments.
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Underwater acoustic sensor networks (UASNs) are used extensively in activities such as underwater data collection and water pollution detection. A UASN consists of acoustic sensors that use batteries as their power supply. Because of the complex underwater environments in which UASNs are employed, replacing these batteries is difficult. Prolonging the battery life of UASNs by reducing their energy consumption (improving their energy efficiency) is one means of mitigating this problem. This paper proposes an energy-balanced unequal layering clustering (EULC) algorithm that improves the energy efficiency of acoustic sensors. The EULC algorithm designs UASNs with unequal layering based on node depth, providing a solution to the “hot spot” issue through the construction of clusters of varying sizes within the same layer. Simulation results show that the EULC algorithm effectively balances the energy in UASN nodes and thereby prolongs network lifetime.
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Underwater Wireless Sensor Network (UWSN) has emerged as promising networking techniques to monitor and explore oceans. Research on acoustic communication has been conducted for decades, but had focused mostly on issues related to physical layer such as high latency, low bandwidth, and high bit error. However, data gathering process is still severely limited in UWSN due to channel impairment. One way to improve data collection in UWSN is the design of routing protocol. Opportunistic Routing (OR) is an emerging technique that has the ability to improve the performance of wireless network, notably acoustic network. In this paper, we propose an anycast, geographical and totally opportunistic routing algorithm for UWSN, called TORA. Our proposed scheme is designed to avoid horizontal transmission, reduce end to end delay, overcome the problem of void nodes and maximize throughput and energy efficiency. We use TOA (Time of Arrival) and range based equation to localize nodes recursively within a network. Once nodes are localized, their location coordinates and residual energy are used as a matrix to select the best available forwarder. All data packets may or may not be acknowledged based on the status of sender and receiver. Thus, the number of acknowledgments for a particular data packet may vary from zero to 2-hop. Extensive simulations were performed to evaluate the performance of the proposed scheme for high network traffic load under very sparse and very dense network scenarios. Simulation results show that TORA significantly improves the network performance when compared to some relevant existing routing protocols, such as VBF, HHVBF, VAPR, and H2DAB, for energy consumption, packet delivery ratio, average end-to-end delay, average hop-count and propagation deviation factor. TORA reduces energy consumption by an average of 35% of VBF, 40% of HH-VBF, 15% of VAPR, and 29% of H2DAB, whereas the packet delivery ratio has been improved by an average of 43% of VBF, 26% of HH-VBF, 15% of VAPR, and 25% of H2DAB. Moreover, the average end-to-end delay has been reduced by 70% of VBF, 69% of HH-VBF, 46% of VAPR, and 73% of H2DAB. Furthermore, average hope-count has been improved by 57%, 53%, 16% and 31% as compared to VBF, HHVBF, VAPR, and H2DAB, respectively. Also, propagation delay has been reduced by 34%, 30%, 15% and 23% as compared to VBF, HHVBF, VAPR, and H2DAB, respectively.
The characteristics of mobile Underwater Sensor Networks (UWSNs), such as low communication bandwidth, large propagation delay, and sparse deployment, pose challenging issues for successful localization of sensor nodes. In addition, sensor nodes in UWSNs are usually powered by batteries whose replacements introduce high cost and complexity. Thus, the critical problem in UWSNs is to enable each sensor node to find enough anchor nodes in order to localize itself, with minimum energy costs. In this paper, an Energy-Efficient Localization Algorithm (EELA) is proposed to analyze the decentralized interactions among sensor nodes and anchor nodes. A Single-Leader-Multi-Follower Stackelberg game is utilized to formulate the topology control problem of sensor nodes and anchor nodes by exploiting their available communication opportunities. In this game, the sensor node acts as a leader taking into account factors such as 'two-hop' anchor nodes and energy consumption, while anchor nodes act as multiple followers, considering their ability to localize sensor nodes and their energy consumption. We prove that both players select best responses and reach a socially optimal Stackelberg Nash Equilibrium. Simulation results demonstrate that the proposed EELA improves the performance of localization in UWSNs significantly, and in particular the energy cost of sensor nodes. Compared to the baseline schemes, the energy consumption per node is about 48% lower in EELA, while providing a desirable localization coverage, under reasonable error and delay.
Localization plays a vital role in understanding the application context for wireless sensor networks (WSNs). However, it is vulnerable to various threats due to the open arrangement area, the nature of radio broadcasting, and resource constraint. Two kinds of attacks on localization process need to be investigated. On the one hand, adversaries may capture, impersonate, replicate, or fabricate nodes to mislead the target to an incorrect position. On the other hand, adversaries may tamper, interfere, replay, or modify jointly localization information to disturb the localization. This paper describes two kinds of localization attacks and gives a systematic survey of existing secure localization solutions, including the design ideas, application scopes, and limitations. Next, several novel secure localization schemes for special WSNs applications, including Underwater Wireless Sensor Networks (UWSNs), Wireless Body Area Network (WBAN) and three-dimensional (3D), are analyzed. Finally, many open problems for secure localization are presented.
The underwater wireless sensor networks enable the telemonitoring and communications technologies of underwater information play important roles in the gathering process of scientific data in collaborative monitoring missions. However, to design such a system, several aspects must be considered to make fewer resources required, such as energy efficiency, miniaturization, the required functionality, etc. Conventional methods of data compression and reconstruction fail in energy efficiency. Different from the conventional compression methods, compressive sampling (CS) provides a new perspective to compress huge data with low energy consumption. Unfortunately, the recordings of underwater acoustic signal (UAS) are nonsparse in time domain. Hence, the current CS methods cannot be used directly for compression and reconstruction of UAS. This study adopts the wavelet-transform-based dictionary matrix to build a framework for sparse representation; then, introduces an approach based on structured approximation $l_0$ (SAL0) norm, which is designed by exploring and exploiting the correlation structure of UAS. The proposed method searches the optimal sparse solution via steepest descent method and then projects the solution to its feasible set. Combing with the compression matrix and dictionary matrix, the estimation of SAL0 method is used for reconstructing nonsparse UAS. Experimental results confirm its recovery quality for practical use, and a clear improvement than the traditional methods including $l_1$ (L1) and orthogonal matching pursuit methods.