Grid Based Priority Routing Protocol for UWSNs

International Journal of Computer Networks & Communications (IJCNC) Vol.9, No.6, November 2017
DOI: 10.5121/ijcnc.2017.9601 1
GRID-BASED PRIORITY ROUTING PROTOCOL FOR
UWSNS
Faiza Al-Salti, N. Alzeidi, Khaled Day, Bassel Arafeh and Abderezak Touzene
Department of Computer Science, Sultan Qaboos University, Oman
ABSTRACT
In this paper, we devise and evaluate a new Grid-Based Priority Routing (GBPR) protocol for Underwater
Wireless Sensor Networks (UWSNs). GBPR utilizes a 3D logical grid view of the monitored area to deliver
data packets to sink nodes. Particularly, data packets are forwarded on a cell-by-cell-basis using elected
sensor nodes called cell-heads. The unique feature in GBPR is the classification of the neighboring cells in
different priority levels according to their distances to the sink node. Cells closer to the sink are given
higher priority to be selected as the next hop. This mechanism helps in reducing the number of hops; thus,
reducing the energy consumption and end-to-end delay, and increasing the reliability. The protocol is
evaluated and compared against EMGGR and EEF protocols available in the literature. Simulation results
show that GBPR outperforms the other two protocols in terms of energy efficiency, average delay and
packet delivery ratio.
KEYWORDS
Underwater wireless sensor networks, UWSNs, routing protocol, location-based, grid, cell-head election&
void-handling algorithm.
1. INTRODUCTION
Underwater wireless sensor networks (UWSNs) consist of a set of sensor nodes deployed in the
underwater environment. These nodes are capable of sensing, detecting, tracking and reporting
data about the monitored environment to specific nodes called sinks, which are located at the
water surface. They can measure a variety of features such as temperature, salinity and pressure;
thus, enable several applications such as environmental monitoring, undersea exploration, disaster
prevention, and assisted navigation [1]. However, UWSNs experience a number of challenges
induced by the nature of the environment and the used underwater acoustic communication.
Particularly, the speed of acoustic signals underwater is five orders of magnitude slower than the
speed of radio signals in terrestrial networks. In addition, underwater channels are severely
weakened due to the multi-path and fading effects. Moreover, underwater sensor nodes are
equipped with batteries of limited energy, and they are difficult to be replaced or recharged.
Besides, the available bandwidth is limited and inversely proportional to the transmission
distance. Furthermore, the three-dimensional (3D) deployment and the dynamic environment due
to the passive movement of sensors with water currents cause extra challenges in the development
of new schemes (e.g. routing and localization) for such UWSNs.
Several solutions have been proposed to solve different issues in UWSNs such as node’s
deployment, node’s mobility, MAC, routing, and localization. Nodes’ deployment, for example,
is an important task in UWSNs because several network services like routing protocols,
localization schemes and network topology control are built on top of it [2]. Generally, the
objectives of node deployment schemes in UWSNs are achieving high network connectivity, high
coverage, less number of nodes, low energy consumption and high delivery ratio [2]. G. Han et al.
[3] have surveyed and classified deployment strategies in UWSNs. The classification is based on
three categories namely; static deployment, self-adjustment and movement-assisted deployment
schemes.

2
Several MAC protocols have been proposed for UWSN such as those found in [4], [5], [6], [7],
and [8].According to [9], MAC protocols for UWSNs can be classified into three categories:
contention-based, contention-free and hybrid MAC protocols.
Because sensor nodes might be deployed at farther distances from the sink nodes, multi-hop
routing is necessary to manage the communications between sensors and sink nodes with efficient
use of the available resources (i.e. bandwidth, power and memory storage). General objectives of
the proposed routing protocols are high energy-efficiency, low average delay and high packet
delivery ratio. Though, these protocols face some issues like high energy-consumption in dense
networks, low packet delivery ratio in sparse networks and generally high delay. Therefore, in
this paper, we present a Grid-Based Priority Routing (GBPR) protocol for UWSNs that aims to
mitigate the earlier mentioned challenges, and to achieve high energy-conservation, low average
end-to-end delay and high packet delivery ratio under different network conditions.
GBPR utilizes a 3D grid view to deliver data packets from cell to cell via special nodes called
cell-heads that are elected using an election algorithm. The election algorithm, as described in
section 3, uses short control packets in order to reduce the network overhead. The main feature of
the proposed protocol that differentiates it from the existing grid-based protocols is the
classification of the neighboring cells into different priority levels according to their distances to
the sink node. This classification facilitates the selection of the packets’ forwarders. Basically, in
each hop, neighboring cells that are closer to the destination cell than the current cell are favored
to forward data packets.
The rest of this article is organized as follows. In section 2, different classifications of existing
routing protocols in UWSNs along with some examples are discussed. Section 3 describes the
proposed routing protocol. Evaluation and comparison results of the protocol are provided in
section 4. Finally, the paper is concluded in section 5.
2. RELATED WORK
Several routing protocols have been proposed specifically for UWSNs. These protocols can be
classified based on different criteria. For example, VBF [10], HH-VBF [11], VBVA [12], LE-
VBF [13] and DFR [14] are typical location and flooding based routing protocols that aim to
direct packets’ propagation and reduce the number of forwarders as compared to uncontrolled
flooding protocols. Thus, they reduce the number of collisions and improve the energy
consumption.
Depth-based routing protocols rely mainly on depth information for selecting candidate
forwarders. Depth information can be determined using inexpensive depth sensors [15]. Example
of such protocols is the EEF [16] routing protocol, which is developed to achieve energy
efficiency. Depth information along with residual energy and the distances to the sink node and to
the previous forwarder are used to calculate the fitness value. This value is used to determine the
best eligible forwarders. Each possible candidate should hold the packet for a certain time based
on its depth, distances and energy before forwarding it. If the period expires without overhearing
the transmission of that packet, it broadcasts the packet. Despite its simplicity, it faces void
problem in highly sparse networks and severe collisions between packets in dense networks.
Grid-based geographic routing protocols for UWSNs have been proposed in [17], [18] and [19].
They all aim to improve the energy efficiency of the networks. Mainly, MGGR [17] and EMGGR
[18] are based on gateways (i.e. elected sensor nodes) to forward data packets on a cell-by-cell
basis. They route packets over disjoint paths constructed based on the grid view rather than the
position of the nodes in order to reduce the need for paths’ maintenance due to topology changes.
However, low packet delivery ratio is achieved and high average delay is incurred in sparse
networks.

3
The GFGD and GGFGD [19] are two other grid-based protocols for UWSNs. They incorporate a
duty cycle (i.e. some nodes are scheduled to sleep for some time) mechanism, which can balance
and save energy. In addition, they use path delay, path loss and remaining energy for relay
selection. However, the protocols assume that the channel link is symmetric, which is not
practical in underwater since acoustic channels in underwater are known to be asymmetric
[10][20].
Although several routing protocols have been proposed for UWSNs, they are facing general
issues: (i) high energy consumption in dense networks due to the overhead incurred by
broadcasting a massive number of packets, (ii) low packet delivery ratio in sparse networks, (iii)
high end-to-end delay, and (iv) low performance with node mobility. Our proposed solution in
this paper strives to solve these issues as will be described and proved in the following sections.
3. THE PROPOSED ROUTING PROTOCOL
GBPR is a location-based routing protocol that advances a routed packet, in each hop, towards the
sink nodes at the surface level. It is based on viewing the network as a 3D logical grid and the
forwarding is performed in a cell-by-cell manner. Additionally, a specific set of sensor nodes,
called cell-heads, are the only nodes eligible for data forwarding at any point in time. Since
applications may employ more than one link to receive data packets, the closest link to the current
forwarder is selected as a destination target. The proposed protocol also adapts a void handling
technique when a void cell (i.e. a cell with no elected node) is encountered. The proposed
mechanisms are expected to achieve high energy-efficiency, low average delay and high delivery
ratio.
3.1 SYSTEM MODEL
3.1.1 ASSUMPTIONS
In this paper, we consider a 3D underwater wireless sensor network in which sensor nodes are
distributed in a 3D area and the sink nodes are positioned at the surface level (see Figure 1). The
following is assumed:
• The network consists of a set N = Ns∪Nnof sink and sensor nodes, where Ns is the set of
sink nodes and Nn is the set of sensor nodes. All nodes including the sinks are of equal
acoustic communication range R. The sink nodes in Ns are assumed to be stationary. In
addition, they use acoustic channels to communicate with the underwater sensor nodes and
radio channels to communicate with each other and with terrestrial stations.
• All data packets are forwarded to the sink nodes.
• Because of the rapid speed of radio propagation in the air compared to acoustic propagation
under water, data packets received by any sink are assumed to be received by other sinks in
a negligible time.
• The set Nnof the sensor nodes are assumed to have similar capabilities (e.g. transmission
range, storage space and initial energy level).
• Each sensor node i is assumed to be able to obtain its physical location information in the
Cartesian coordinates (Xi, Yi, Zi) from an existing localization service. It is also assumed to
know the location of the sink nodes.
3.1.2 CONSTRUCTION OF THE LOGICAL GRID
The geographic region is divided into 3D logical grids as shown in Figure 2. Cells are viewed as
cubes of equal volume d3
, where d is the length of the cell side. The number of cells along ani-
axis (where i is one of the three dimensions x, y, z) is known as Ki (e.g. Kx is the number of cells

along the x-axis), which can vary from one axis to another.
origin, which is assumed at the top left of the surface,
The node uses its location information,
d to determine the XYZ coordinates of its cell (x
physical location of a node i is referred to by the upper case (X
of the cell in which it is hosted are
unique ID (NID) and a cell ID (CID
belongs to, which can be determine
, ,
The transmission range R of the sensors is used to
in a cell can communicate directly
common vertex, edge or face with it
common face with the original cell) as shown i
communicating nodes are located at the farthest diagonally apart corners of the neighboring
cubes. As illustrated in Figure 4,
The 32 neighboring cells are: (x
1,y,z+1), (x-1,y+1,z-1), (x-1,y+1,z), (x
(x,y,z+1), (x,y+1,z-1), (x,y+1,z), (x,y+1,z+1), (x+1,y
1), (x+1,y,z), (x+1,y,z+1), (x+1,y+1,z
2,z), (x,y+2,z), (x,y,z-2), (x,y,z+2).
It is worth noting that a cell can communicate partially with other cell
neighbors; however, for the sake of simplicity
is used to check whether a given cell is one of the 32 neighboring cells or not.
Figure 1: 3D UWSN architecture
axis), which can vary from one axis to another. The physical location of the
, which is assumed at the top left of the surface, is denoted by (X0, Y0, Z0).
The node uses its location information, the location of the grid origin (X0, Y0, Z0) and the value of
to determine the XYZ coordinates of its cell (xi, yi, zi) as given in (1). Note that the
is referred to by the upper case (Xi, Yi, Zi), while the grid coordinates
he cell in which it is hosted are denoted by the lower case (xi, yi, zi). Every sensor node has a
CID). CID is a number that identifies the cell that the sensor node
belongs to, which can be determined from the XYZ coordinate of the cell (2).
, ,
∗ ∗
The transmission range R of the sensors is used to compute the value of cell side, such that a node
in a cell can communicate directly with any node in its 32 neighboring cells (i.e. cells that have a
with it; or cells that have a common face with those having a
common face with the original cell) as shown in Figure 3. This can be achieved when the
cubes. As illustrated in Figure 4, d should be selected to satisfy 2 √3.
The 32 neighboring cells are: (x-1,y-1,z-1), (x-1,y-1,z), (x-1,y-1,z+1), (x-1,y,z-1), (x
1,y+1,z), (x-1,y+1,z+1), (x,y-1,z-1), (x,y-1,z), (x,y-1,z+1), (x,y,z
1), (x,y+1,z), (x,y+1,z+1), (x+1,y-1,z-1), (x+1,y-1,z), (x+1,y-1,z+1), (x+1,y,z
1), (x+1,y,z), (x+1,y,z+1), (x+1,y+1,z-1), (x+1,y+1,z), (x+1,y+1,z+1), (x-2,y,z), (x+2,y,z), (x,y
2), (x,y,z+2).
It is worth noting that a cell can communicate partially with other cells apart from the
neighbors; however, for the sake of simplicity, these are not considered as neighbors.
is used to check whether a given cell is one of the 32 neighboring cells or not.
WSN architecture Figure 2: A view of 3D logical grid
4
The physical location of the grid
) and the value of
Note that the absolute
), while the grid coordinates
Every sensor node has a
). CID is a number that identifies the cell that the sensor node
1
2
, such that a node
with any node in its 32 neighboring cells (i.e. cells that have a
a common face with those having a
n Figure 3. This can be achieved when the
1), (x-1,y,z), (x-
1,z+1), (x,y,z-1),
1,z+1), (x+1,y,z-
2,y,z), (x+2,y,z), (x,y-
s apart from these 32
these are not considered as neighbors. Algorithm 1
: A view of 3D logical grid

Figure 3: A cross section of the 32 neighboring cells
Procedure: isNeighbor
//for checking if cell (Nx, Ny, Nz) is a neighbor of current cell (Cx, Cy, Cz)
x=|Cx- Nx|
y= |Cy- Ny|
z= |Cz- Nz|
if((x=0) & (y=0) & (z=0))
return false; //local cell
else if (((x≤1) & (y≤1) &
(y=2) & (z=0)) Or ((x=0) & (y=0) & (z=2)))
return true; //neighbor cell
else
return false; //neither local nor neighbor
Algorithm 1: Procedure for checking
3.2 NOTATIONS
We use the notations defined in Table 1 to describe the proposed
Table 1: Notations used in the protocol’s description
Symbol Definition
D The length of the cell side
R Node’s transmission range
Kx, Ky, Kz Number of cells along the x
Cell-head A node elected to forward data packets from the corresponding cell
Head-table A table used to store the cell
NID Unique node identifier
CID Unique cell identifier
Ε The minimum amount of energy to continue serving as a cell
Head-packet A packet sent by a node upon electing itself as a cell
Update-packet A periodic packet sent by a cell
existence
Retire-packet A packet sent by a cell
threshold
Exit-packet A packet sent by a cell head upon roaming
Update-timer A timer set by each cell
its existence by sending Update
Election-timer A timer set by non
Local cell The cell in which the node resides in
Neighbor cell A cell defined
ion of the 32 neighboring cells Figure 4: Finding the value of the cell side
Procedure: isNeighbor (Cx, Cy, Cz, Nx, Ny, Nz)
((x=0) & (y=0) & (z=0))
; //local cell
1) & (y≤1) & (z≤1)) Or ((x=2) & (y=0) & (z=0)) Or ((x=0) &
(y=2) & (z=0)) Or ((x=0) & (y=0) & (z=2)))
; //neighbor cell
; //neither local nor neighbor
Algorithm 1: Procedure for checking a neighboring cell
We use the notations defined in Table 1 to describe the proposed GBPR protocol.
: Notations used in the protocol’s description
Definition
The length of the cell side
transmission range
Number of cells along the x-axis, y-axis and z-axis respectively
A node elected to forward data packets from the corresponding cell
A table used to store the cell-heads in the neighboring cells
Unique node identifier
Unique cell identifier
The minimum amount of energy to continue serving as a cell-
A packet sent by a node upon electing itself as a cell-head
A periodic packet sent by a cell-head to inform neighbors about its
existence
A packet sent by a cell-head when its remaining energy falls below
threshold ε
A packet sent by a cell head upon roaming out of its cell
A timer set by each cell-head to periodically inform neighbors about
its existence by sending Update-packet
A timer set by non-cell head nodes to start an election process
The cell in which the node resides in
defined according to Algorithm 1 andillustrated in Figure
5
Figure 4: Finding the value of the cell side
1)) Or ((x=2) & (y=0) & (z=0)) Or ((x=0) &
axis respectively
A node elected to forward data packets from the corresponding cell
-head
neighbors about its
remaining energy falls below the
periodically inform neighbors about
election process
illustrated in Figure 3

6
3.3 DETERMINING THE NEAREST SINK CELL
Recall that there might be more than one sink node deployed for receiving data packets. In that
case, each node should construct its head-table relative to the nearest sink. A sensor node
determinesits nearest sink based on the Euclidean distances between the cell containing the sensor
node and the cells containing the sink nodes.
3.4 CLASSIFICATION OF THE NEIGHBORING CELLS
Remember that a node in a cell i can have up to 32 neighboring cells; yet, these cells differ in
their distances to the sink cell (the nearest sink). In other words, some of the cells are closer to the
sink than the cell i and some are farther from the sink. However, to achieve low average end-to-
end delay and to save the overall energy, cells that are closer to the sink cell are given higher
priority to be selected for packet relay. Thus, neighboring cells are ordered in different priority
levels according to their distances to the sink cell. To explain these levels, assume that the source
cell i, the sink cell s, the neighboring cell to be classified n have the coordinates (xi,yi,zi), (xs,ys,0)
and (xn,yn,zn), respectively. Let δmjk = |mj-mk| (where m is either x, y or z) be the distance along
the m-axis between cell j and cell k.
• Vertical positive level (group G1): This set G1 of neighboring cells includes those cells that
are closer to the sink cell along the z dimension, and closer or equal to the sink cell along
each of the x and y-dimensions. In other words, if the δzns is less than δzis, and δxns andδyns
is less than or equalto δxis and δyis, respectively, then the neighbor cell is classified as a
vertical positive neighbor cell and placed in this group G1.
• Horizontal positive level (group G2): This set G2 of neighboring cells includes those cells
that are closer to the sink cell along the x dimension or y dimension, and the remaining are
equal to that of the source cell. In othero, if one or both of δxns and δyns is, respectively less
than δxis and δyis, and the remaining of δxns, δyns and δznsare equal to the corresponding δxis,
δyis and δzis, then the neighbor cell falls in this group G2.
• Positive and negative level (group G3): This set of cells includes those cells that are closer
to the sink cells along one or more dimensions. However, it is farther to the sink than the
current cells along the other dimensions. That is if any of δxns, δyns and δzns is less than δxis,
δyis and δzis, respectively and the remaining are greater than the corresponding δxis, δyis and
δzis, then the neighbor cell falls in this group G3.
• Negative level (group G4): This set of cells includes those cells that are farther from the
sink cells along one or more dimensions while the other dimensions remain equal to that of
the current cell. In other words, if any of δxns, δyns and δzns is greater than δxis, δyis and δzis,
respectively, and the remaining are equal to the corresponding δxis, δyis and δzis, then the
neighbor cell is placed in this group G4.
Algorithm 2 further defines these levels. The head-table of each cell-head is divided into four
disjoint sub-tables according to the above priority levels. Figure 5 shows a general structure of the
head-table. It is worth mentioning that the classification of neighboring cells into the priority
levels takes place while constructing the head-tables. Particularly, when a cell-head receives a
head-packet or an update-packet (see section 3.5) from one of the neighboring cells, it checks the
priority level of that cell and accordingly stores the information in the relevant sub-table.
When a node enters a new cell, the priority levels of the neighboring cells might change. In
addition, some of the cells may no longer be neighbor cells and need to be removed from the
head-table. Furthermore, new cells are becoming neighboring cells for that node. Thus, when the
node moves out from its cell, it updates its head-table based on the new cell it enters.

Figure 6 illustrates these priority levels for
grid. To further illustrate this, assume that the source node is in the cell
is in the cell D (5,6). Figure 6 shows 12 neighboring cells for
with xy-coordinates (3,2), it is clear that t
dimension. However, the y-dimension is same as that for
in the second priority group (G2
is closer to the sink in the x-dimension, but farther from the sink than
Therefore, this cell is placed in the third priority group (
neighboring cells are shown inside each cell into the three priority le
cell in G1 in the figure since the source and sink cells are in the same z
Figure 6: Demonstration of the priority levels in a 2D grid
Figure 6 illustrates these priority levels for the 2D grid. The same idea can be extended
grid. To further illustrate this, assume that the source node is in the cell S (2,2) and the sink node
shows 12 neighboring cells for S. Considering the neighboring cell
coordinates (3,2), it is clear that the cell is closer to the sink cell than S
dimension is same as that for S. Thus the cell-head in S
G2). Another example is the cell with xy-coordinate (1,3). This cell
dimension, but farther from the sink than S in the y
Therefore, this cell is placed in the third priority group (G3). The priority levels of the 12
neighboring cells are shown inside each cell into the three priority levels. Note that there is no
cell in G1 in the figure since the source and sink cells are in the same z-level.
Figure 5: The structure of the head-table
: Demonstration of the priority levels in a 2D grid
7
can be extended to the 3D
(2,2) and the sink node
. Considering the neighboring cell
S along the x-
S puts this cell
coordinate (1,3). This cell
in the y-dimension.
). The priority levels of the 12
vels. Note that there is no

8
Procedure: classifyCell(nx, ny, nz, ix, iy, iz, sx, sy, sz)
//(nx,ny,nz) => xyz-coordinates of the neighbor cell
//(sx,sy,sz) => xyz-coordinates of the sink cell
//(ix,iy,iz) => xyz-coordinates of the current cell
δxis =|ix-sx|
δyis =|iy-sy|
δzis =|iz-sz|
δxns =|nx-sx|
δyns =|ny-sy|
δzns =|nz-sz|
if( (δxns ≤ δxis) && (δyns ≤ δyis) && (δzns<δzis))
returnG1; //vertical positive level
else if( (δxns ≤ δxis) && (δyns ≤ δyis) && (δzns==δzis))
returnG2; //horizontal positive level
else if( (δxns ≥ δxis) && (δyns ≥ δyis) && (δzns ≥ δzis))
return G4; //negative level
else
returnG3; //positive and negative level
Algorithm 2: Procedure for classifying a neighboring cell
3.5 CELL-HEAD ELECTION
We present in this section a cell-head election algorithm for electing cell-heads, which will be
responsible for forwarding data packets. Using cell-heads has the objective of reducing the
number of data packets propagated in the network. Particularly, each cell-head is responsible for
forwarding packets from its cell to a neighboring cell. In order to reduce the overhead that can be
incurred by the election algorithm, the proposed election algorithm uses only few and small
control packets. The adopted algorithm is based mainly on the residual energy of the nodes.
Basically, in a given cell each node with residual energy above a predefined threshold ε can
compete for being the head of the cell. Each node, including non-heads, records the cell-heads in
the local and neighboring cells in a table called head-table, as defined earlier. The election
algorithm consists of two phases; an initialization phase and a maintenance phase as described
below.
3.5.1.INITIALIZATION PHASE
• After deployment, sensor nodes in each cell select a random time to initiate an election
process. Each node broadcasts a head-packet <type, CID, NID>, where type indicates the
type of the packet (head-packet in this case), CID is the cell ID that the node belongs to, and
NID is the ID of the node sending the packet. In addition, the node records itself as the head
of that cell, and sets an update-timer to send an update-packet upon timer expiration.
• Each node, upon receiving a head-packet from a local or a neighboring cell, stores the head
information in its head-table. Furthermore, local nodes stop competing for being the heads
for the current period. However, they set election-timers to compete for cell-head in the next
period. If more than one head-packet is received from the same cell (due, for example, to
two nodes initiating the election process at nearly the same time), then the node
corresponding to the last received head-packet is assumed the cell-head.

9
3.5.2.MAINTENANCE PHASE
• When the update-timer of a cell-head expires, it checks its remaining energy. If it is above
the threshold ε, the node broadcasts an update-packet <type, CID, NID>, and continues
serving as a cell-head for the next period. Local and neighboring nodes update their head-
tables accordingly. Otherwise, if its energy is below the threshold, it broadcasts a retire-
packet<type, CID, NID> to inform local nodes to start a new election.
• Local and neighboring nodes upon receiving a retire-packet remove the information related
to that node from their head-tables. In addition, local nodes with energy levels above the
threshold ε start a new election process as given in the initialization phase.
• When a head node exits from its cell, it broadcasts an exit-packet <type, CID, NID>. Upon
receiving that packet, local and neighboring nodes remove the information related to the
source node of that packet from their head-tables. Also, local nodes start a new election
process as described in the initialization phase.
• When an election-timer of a node expires, the node sets itself as a cell-head and broadcasts
a head-packet. Local and neighboring nodes when receiving that packet set that node as the
cell-head. In addition, the head in the local cell, if any, cancels its update-timer.
3.6 RELAY SELECTION AND PACKET FORWARDING
Each data packet consists of current NID, next NID, crossed-cells and payload. Current NID is
the ID of the current forwarder. Next NID is the ID of the forwarder in the next hop, crossed-cells
is a sequence of CIDs of the cells that the packet already crossed, and the payload is the
information to be delivered to the sink nodes. Figure 7 demonstrates a general format of the data
packet.
When a node has a data packet to be forwarded, it looks for a cell-head in the first priority group
in a round robin fashion. It selects a cell from that group given that it has a cell-head. Once the
cell-head is selected, the node updates the packet header by setting current NID to its ID and next
NID to the ID of the selected node, and appending its CID to the end of the crossed-cell. Then, it
sends the packet to the selected node. This forwarding process continues until the packet is
delivered to the sink node. If the first group is empty (i.e. does not contain any cell-head), it
searches for a cell-head in the second group in a similar way (i.e. round robin). If there is no cell-
head in the second group, it looks for a cell-head in the third group and then in the fourth group.
Once the cell-head is selected, the node checks that the selected node is not hosted in a cell
contained in the crossed-cell of the packet. If it is the case, then the node updates the packet
header by setting current NID to its ID and next NID to the ID of the selected node and appending
its CID to the end of the crossed-cell. Then, it sends the packet to the selected node. Otherwise,
the node looks for another node to be selected. This check is used to ensure a loop-free
forwarding. The process continues until the packet is delivered to the sink node.
If at any hop, the forwarder could not find any cell-head in all its neighboring cells (i.e.
throughout the paper, this node is called a void node and its cell as a void cell), it sends a negative
acknowledgment to the previous forwarder. The previous forwarder, in turn, looks for another
cell (other than that cell) containing a node to forward the packet to it, in the same way, explained
earlier.
Current
NID
Next
NID
crossed-cells payload
Figure 7: Format of the data packet

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4. SIMULATION RESULTS
4.1 SIMULATION SETTINGS
In this section, we evaluate the performance of our proposed routing protocol against EEF and
EMGGR routing protocols (summarized in section 2) via Aqua-Sim [21] simulator. Aqua-Sim is
a simulation package developed specifically for UWSNs, and it is based on NS2 simulator.
Sensor nodes are deployed in a 3D area of size (3x3x3) km3
. Sink nodes are located at the surface
level and are assumed to be stationary. The brodcastMac protocol [10] is used in in all scenarios.
The idea of this MAC protocol is as follows: when a node has a packet to be transmitted, it senses
the channel and broadcasts the packet if the channel is idle; otherwise, it backs off. If the number
of back-off times exceeds a specified limit, the packet will be dropped. The back-off limit used in
all simulations in this research is four, which is the default value used in the simulator. Upon
receiving the packet, the receiver does not need to acknowledge the reception of the packet.
The capabilities of the sensor nodes are set as follows. The transmission range is set to 1.5 km.
The transmission, reception and idle powers are set to 8.0 W, 0.80 W and 0.008 W, respectively.
The frequency is set to 35.695 kHz, and 17.80 kbps is used for the bit rate. The bit error rate is set
to 10e-9
. This setup is similar to the specification of the real underwater acoustic sensor modem
UWM2000H[22].
The simulation type adopted in this evaluation is the terminating state where each run lasts for
2000 seconds. Results from the first 150 seconds and the last 150 seconds are discarded to
minimize the warm-up effect. In each run, 10 sensor nodes are selected randomly from the set of
deployed sensors to inject exponentially distributed traffic into the network. Sensor nodes
mobility, if not otherwise specified, is assumed to follow the 2D random walk [23][24] mobility
model which is one of the most widely used mobility model [23]. Table 2 summarizes other
system parameters.
Table 2: Additional system parameters
Parameter Value
Number of nodes 200, 300, 400
Number of sinks 1, 2, 3
Initial energy 300 J
Data packet size 150 Byte
Energy threshold 20J
Alpha for EMGGR 0.5
4.2 INVESTIGATED METRICS
The performance of the proposed routing protocol is studied and compared with the performance
of existing solutions by investigating the following performance metrics [25][18]:
• Packet Delivery Ratio (PDR): PDR is the ratio of the number of distinct data packets
delivered successfully to the sink nodes to the total number of data packets generated at the
source nodes.
• Average End-To-End Delay: The average time taken by a data packet to arrive to the
destination. It is computed from the time the packet is generated until it reaches the
destination. Only data packets that were successfully delivered to the sink nodes are
counted.
• Energy Consumption: The total energy consumed by all nodes during the simulation. It
includes the power consumed in the transmission, reception and idle modes.

11
• It is worth noting that the mentioned metrics are investigated under the effect of varying the
number of sensor nodes, traffic load and node’s mobility as follows:
• Number Of Nodes: The effect of the number of sensor nodes is investigated and assessed.
Basically, 200, 300 and 400 stationary nodes are deployed in the investigated environment.
The traffic injection rate is set to 0.07 packets/s.
• Traffic Load: The impact of traffic load is assessed by varying the packet injection rate
from 0.05 to 0.11 packets/s. In this set of experiments, 300 static nodes are deployed in the
environment.
• Node’s Mobility: According to [26], underwater objects can move at speed 3-6 km/h.
Therefore, the effect of nodes’ mobility is evaluated by varying the maximum speed of
nodes from 0 to 1.5 m/s. The minimum speed is set to 0 m/s. The number of nodes used in
this set of experiments is 300 and the traffic injection rate is set to 0.07 packets/s.
4.3 SIMULATION RESULTS
A large number of experiments have been conducted to evaluate the performance of the proposed
protocol. In the first subsection, the impacts of the number of sensor nodes, traffic rate and speed
of nodes of the three protocols are assessed under random deployment. In this set of experiments,
a single sink is used, and it is deployed at the middle of the top surface level. The second
subsection evaluates the protocol using multiple sinks. 95% confidence intervals were calculated
for all collected results. The average of 20 batch runs along with error bars are presented in the
figures.
4.3.1 GBPR VERSES EEF AND EMGGR
Impact Of The Number Of Sensor Nodes:
Figure 8.a shows the impact of the number of nodes on the total energy consumed by the nodes.
The figure reveals that the three protocols show an increase in the total consumed energy as the
number of nodes increases. This can be justified by the increase in the number of packets
propagated in the network and the number of nodes participating in the transmission and
reception of these packets. However, the increase is very sharp in the EEF protocol since each
forwarder broadcasts the packet to all its neighbors, and those with higher fitness than the
previous hop are possible eligible forwarders. Although, in GBPR and EMGGR, data packets are
forwarded hop-by-hop through cell-heads only, all nodes in the range may receive the packets due
to the shared transmission media. The energy consumption in EMGGR is a little bit higher than
GBPR due to the long paths a high number of control packets. Specifically, when the number of
nodes is 300, GBPR is better than EEF and EMGGR in saving energy by 70% and 28%
respectively.
Figure 8.b demonstrates the effect of the number of sensor nodes on the average end-to-end delay.
As revealed in the figure, the average delay of both EMGGR and EEF exhibits a similar trend as
the number of nodes increases. EEF shows the highest end-to-end delay. This is due to the fact
each node holds the packet for a certain time based on its depth, residual energy, and its distance
from the previous forwarder. In addition, since each relay broadcasts the packet to all its
neighbors, there will be a high workload and congestion in the channel, which increases the
queuing time and results in a high end-to-end delay. In contrast, GBPR and EMGGR do not
employ any waiting time because only a single forwarder is selected in each hop. In GBPR, the
increase in the number of nodes minimizes the number of voids; hence, increasing the probability
of finding forwarders that give positive progress towards the sink nodes. As a result, packets are
propagated in less number of hops. This is the justification for the decrease in the average end-to-
end delay in GBPR. With 300 nodes, GBPR is better in the average end-to-end delay than EEF
and EMGGR by 48% and 26%, respectively.

Figure 8.c illustrates the packet delivery ratio (PDR) as a function of the number of nodes. By
increasing the number of nodes, both
the PDR. The improvement can be justified by the mechanisms
to (i) reduce the number of packets propagated in the network by selecting a single forwarder in
each hop, and (ii) distribute the load among the nodes by
forwarders. These indeed reduce
encountering void cells (i.e. cells with no cell
nodes; hence, packets have better chances to be forwarded
shorter paths. On the contrary, as the number of nodes increases in EEF, less delivery ratio is
achieved due to the increase in the number of candidate forwarders (i.e. nodes with high fitness
than the previous forwarders) as the number of nodes increases. Hence, packets collide with each
other and fail to be delivered successfully to the destinations. It is worth mentioning that
outperforms both EEF and EMMGR over
number of nodes is set to 300,
respectively.
(a)
(a) Energy consumption (b)
Figure 8: Impact of the number of sensor nodes on
0
10
20
30
40
50
60
70
80
90
100
110
200 300
Energyconsumption(KJ)
Number of nodes
Energy consumption
GBPR EMGGR
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
PDR(pkts/s)
increasing the number of nodes, both GBPR and EMGGR protocols provide an improvement in
improvement can be justified by the mechanisms adopted by the protocols that try
reduce the number of packets propagated in the network by selecting a single forwarder in
(ii) distribute the load among the nodes by alternating the selection of the
reduce the number of collisions. Moreover, the probability of
encountering void cells (i.e. cells with no cell-heads) decreases by increasing the number of
nodes; hence, packets have better chances to be forwarded successfully to their next hops on
) as the number of nodes increases. Hence, packets collide with each
other and fail to be delivered successfully to the destinations. It is worth mentioning that
erforms both EEF and EMMGR overall used number of nodes. For example, when the
of nodes is set to 300, GBPR is, better than EEF and EMGGR by 13% and 31%,
(b)
(c)
nergy consumption (b) Average end-to-end delay (c) PDR
Figure 8: Impact of the number of sensor nodes on
400Number of nodes
Energy consumption
EMGGR EEF
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
200 300 400
Delay(s)
Number of nodes
Average end-to-end delay
GBPR EMGGR EEF
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
200 300 400
Number of nodes
Packet Delivert Ratio (PDR)
GBPR EMGGR EEF
12
and EMGGR protocols provide an improvement in
by the protocols that try
reduce the number of packets propagated in the network by selecting a single forwarder in
selection of the
Moreover, the probability of
heads) decreases by increasing the number of
successfully to their next hops on
) as the number of nodes increases. Hence, packets collide with each
other and fail to be delivered successfully to the destinations. It is worth mentioning that GBPR
all used number of nodes. For example, when the
is, better than EEF and EMGGR by 13% and 31%,
400
end delay
EEF

Impact Of Traffic Rate:
As revealed in Figure 9.a, the energy consumption is positively proportional to the inj
of the traffic overall evaluated protocols. However,
three protocols. The key causes of the energy efficiency of
packets employed by GBPR, the selection of a single forwarding candidate in each hop, and the
selection of the closest feasible neighboring cell to the destination cell
energy consumption between GBPR
EMGGR incurs only a small increase in the energy consumption compared to
expected since both of them select a single candidate forwarder in each hop, howe
delivers the packets on longer propagation paths. For example, with 0.09
GBPR is better in saving energy by 66% and 18% than EEF and EMGGR
Figure 9.b illustrates the average delay as a function of the packet injection rate.
traffic rates, GBPR is stable against the increase in the amount of traffic.
always favors nodes that advance the packets toward the sink node. In addition, the assumed
number of nodes (300 nodes) seems to be sufficient to
packets are propagated in the network in
average delay in EMGGR. However, in EEF increasing the traffic rate induces extra delay due to
the increased number of packets propagated in the network, which leads to collisions and high
propagation time. With 0.09 packets/s
(23%).
Figure 9.c shows the PDR as a function of traffic generation rate. Clearly
proportional to the packet injection rate, as expected. This happens because increasing the amount
of traffic increases the number of packets propagated in the network; consequently, the nodes
contend for the channel to deliver their packets. This increase in the congestion increases the
chance of packets’ collisions, which reduces the successfully received
Nevertheless, GBPR outperforms EEF and EMGGR in delivering data packets over all used
traffic rate. In addition, the decrease in the PDR in EFF is very sharp compared to the
protocol, and this proves the benefits of using a s
high traffic load) of each packet to avoid collisions between packets. For instance, when the
injection traffic rate is 0.09 packets/s
and 23%, respectively. Furthermore, the results shade some lights on the superiority of our
protocol under high traffic load conditions, and this makes it suitable for applications that require
frequent data transmissions from the deployed sensors such as
(a) (b)
0
10
20
30
40
50
60
70
80
90
0.05 0.07 0.09
Traffic rate (packets/s)
Energy Consumption
GBPR EMGGR
As revealed in Figure 9.a, the energy consumption is positively proportional to the inj
all evaluated protocols. However, GBPR is the most energy efficient among the
three protocols. The key causes of the energy efficiency of GBPR are the lower number of control
, the selection of a single forwarding candidate in each hop, and the
feasible neighboring cell to the destination cell. The difference in the
GBPR and EEF is significant overall used traffic rates. However,
EMGGR incurs only a small increase in the energy consumption compared to GBPR
expected since both of them select a single candidate forwarder in each hop, howe
on longer propagation paths. For example, with 0.09 packets/s injection rate,
is better in saving energy by 66% and 18% than EEF and EMGGR, respectively.
Figure 9.b illustrates the average delay as a function of the packet injection rate. With the used
s, GBPR is stable against the increase in the amount of traffic. This is because
) seems to be sufficient to minimize the number of void cells; thus
packets are propagated in the network in fewer hops. This also explains the reduction in the
packets/s injection rate, GBPR outperforms EEF (EMGGR) by 52%
Figure 9.c shows the PDR as a function of traffic generation rate. Clearly, the PDR is inversely
affic increases the number of packets propagated in the network; consequently, the nodes
chance of packets’ collisions, which reduces the successfully received packets at the sink node.
outperforms EEF and EMGGR in delivering data packets over all used
traffic rate. In addition, the decrease in the PDR in EFF is very sharp compared to the
protocol, and this proves the benefits of using a single forwarder (i.e. especially in networks with
packets/s, GBPR gives better PDR than EMGGR and EEF by 31%
Furthermore, the results shade some lights on the superiority of our
frequent data transmissions from the deployed sensors such as monitoring application
(a) (b)
0.09 0.11
Energy Consumption
EMGGR EEF
0
1
2
3
4
5
6
0.05 0.07 0.09
Averagedelay(s)
GBPR EMGGR
13
As revealed in Figure 9.a, the energy consumption is positively proportional to the injection rate
is the most energy efficient among the
the lower number of control
, the selection of a single forwarding candidate in each hop, and the
The difference in the
all used traffic rates. However,
GBPR. This is
expected since both of them select a single candidate forwarder in each hop, however, EMGGR
injection rate,
, respectively.
With the used
This is because GBPR
the number of void cells; thus
ns the reduction in the
outperforms EEF (EMGGR) by 52%
the PDR is inversely
affic increases the number of packets propagated in the network; consequently, the nodes
packets at the sink node.
outperforms EEF and EMGGR in delivering data packets over all used
traffic rate. In addition, the decrease in the PDR in EFF is very sharp compared to the GBPR
ingle forwarder (i.e. especially in networks with
gives better PDR than EMGGR and EEF by 31%
Furthermore, the results shade some lights on the superiority of our
monitoring applications.
0.11
end delay
EEF

(a) Energy consumption
Impact of Nodes’ Mobility:
Figure 10.a depicts the impact of nodes’ mobility on the energy consumption. While EEF shows
a little increase in the energy consumption, both
with the increase in the nodes’ maximum speed. The reason is that with the increase in the nodes’
speed, the number of void cells in
propagated to their destinations. This decrease in the number of propagated packets reduces the
amount of consumed energy. In EEF, since the packets are broadcasted to all the nodes in the
range, new nodes can enter the range with the increase in the nodes’ speed, which slightly
increases the energy consumption. Generally,
other two protocols. Specifically, when the maximum speed of nodes is set to 1m/s,
to save energy up to 72% and 20% compared to the EEF and EMGGR, resp
Figure 10.b reveals the average end
there is an inverse proportionality between the speed of nodes and the average delay.
justified as follows. In GBPR and EMGGR, increasing the speed of nodes increases the number
of voids; hence, elections are performed frequently, and the number of dropped packets increases.
In other words, only a few packets get deliver
queuing delay are incurred. In EEF, the increase in the movement of the nodes reduces the
congestions in the channel since nodes change
incurred. However, as observed in the previous experiments (
provides the best performance compared to EEF and EMGGR protocols in terms of the average
delay. It is worth mentioning that when the maximum speed of nodes is set to 1 m/s,
outperforms EEF and EMGGR by 50%
Figure 10.c demonstrates the impact of nodes’ mobility on the PDR. As the maximum speed of
nodes increases, the three protocols show a decrease in the number of successfully received
packets. EMGGR and GBPR depen
of the nodes results in moving out of their cells, which results in initiating elections frequently.
This increases the number of control packets propagated in the network as well as increasing t
number of void cells. However, the decrease is sharper in EMGGR due to the longer propagation
paths; thus, the probability of the packets being dropped increases. In EEF, each forwarder
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
PDR(paket/s)
(c)
nergy consumption (b) Average end-to-end delay (c) PDR
Figure 9: Impact of traffic rate on
a little increase in the energy consumption, both GBPR and EMGGR consume a little less energy
speed, the number of void cells in GBPR and EMGGR increases; hence, fewer data packets are
tions. This decrease in the number of propagated packets reduces the
reases the energy consumption. Generally, GBPR is the most energy efficient compared to the
other two protocols. Specifically, when the maximum speed of nodes is set to 1m/s, GBPR
to save energy up to 72% and 20% compared to the EEF and EMGGR, respectively.
Figure 10.b reveals the average end-to-end delay as a function of the nodes’ mobility. Generally,
there is an inverse proportionality between the speed of nodes and the average delay.
few packets get delivered to the destinations, and fewer congestions and
the channel since nodes change their positions; thus, the less queuing delay is
d in the previous experiments (Figure 8.b and Figure 9.b),
delay. It is worth mentioning that when the maximum speed of nodes is set to 1 m/s,
EF and EMGGR by 50% and 28%, respectively.
depend on cell-heads for forwarding the packets, and the movement
This increases the number of control packets propagated in the network as well as increasing t
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.05 0.07 0.09 0.11
Packet Delivery Ratio (PDR)
GBPR EMGGR EEF
14
and EMGGR consume a little less energy
data packets are
tions. This decrease in the number of propagated packets reduces the
compared to the
GBPR is able
end delay as a function of the nodes’ mobility. Generally,
there is an inverse proportionality between the speed of nodes and the average delay. This can be
congestions and
less queuing delay is
igure 9.b), GBPR
delay. It is worth mentioning that when the maximum speed of nodes is set to 1 m/s, GBPR
heads for forwarding the packets, and the movement
This increases the number of control packets propagated in the network as well as increasing the

broadcasts the packets to all its neighbors; hence, the probability
from the range is less. Generally,
speeds. For instance, when the maximum speed of nodes is set to 1 m/s,
deliver packets to the destinations than EEF a
(a) (b)
(a) Energy consumption (b)
Figure 10: Impact of nodes' mobility on
4.3.2 GBPR WITH MULTIPLE
The aim of this set of simulations is to study the effects of using multiple sink nodes on the
performance of the proposed protocol.
two and three sinks. 200, 300 and 4
Figure 11.a illustrates the energy efficiency of the protocol under the use of
sink nodes. Clearly, using multiple sinks provide better performance than using a single sink. The
0
10
20
30
40
50
60
70
80
90
0 0.5 1
Speed of nodes (m/s)
Energy Consumption
GBPR EMGGR
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
PDR(packet/s)
broadcasts the packets to all its neighbors; hence, the probability of all neighbors being deviated
from the range is less. Generally, GBPR outperforms EEF and EMGGR in PDR over
speeds. For instance, when the maximum speed of nodes is set to 1 m/s, GBPR is superior to
deliver packets to the destinations than EEF and EMGGR by 15% and 49%, respectively.
) (b)
(c)
(a) Energy consumption (b) Average end-to-end delay (c) PDR
Figure 10: Impact of nodes' mobility on
ULTIPLE SINKS:
performance of the proposed protocol. Specifically, the protocol is evaluated under the use of one,
00 and 400 static nodes are deployed randomly in the environment.
Figure 11.a illustrates the energy efficiency of the protocol under the use of a different number of
1.5
Energy Consumption
EEF
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0 0.5 1 1.5
Averagedelay(s)
GBPR EMGGR EEF
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 0.5 1 1.5
GBPR EMGGR EEF
15
of all neighbors being deviated
forms EEF and EMGGR in PDR overall used
is superior to
nd EMGGR by 15% and 49%, respectively.
Specifically, the protocol is evaluated under the use of one,
deployed randomly in the environment.
different number of
1.5
EEF

cause of this is that using multiple sinks allow nodes to send their packets to the closer sink;
hence, reduces the number of hops packet
the sinks is distributed, and the collisions between packets are reduced. On average, using two
sinks provides 5.3% better energy efficiency over scenarios with a single sink. However, the
improvement is just 0.3% when using three sinks
and the further increase in the number of sinks will not give much improvement in saving energy.
The reason behind this is that the number of sinks to be used depends on
area and its expansion in the horizontal plane. Basically, the larger the area, the more sinks need
to be used. Therefore, network developers need to link the number of sinks with the size of the
monitored area in order to achieve
The average end-to-end delay experienced by data packets received by the sink nodes is
demonstrated in Figure 11.b. As we can see from the figure, the scenario with three sinks
outperforms the other two scenarios with a single and tw
the packets are delivered successfully to the closer sink nod
propagation delay and back-off times. With 200 nodes, the improvement of using two sinks is
approximately 10.9% compared to the scenario with a single sink. However, the delay of three
sinks is 4.5% lower than that of two sinks. The gab in the differences reduces with the i
the number of nodes due to the resulting decrease in the number of void cells.
The PDR as a function of the number of nodes
Figure 11.c. The figure shows an increase in delivering data packets when multiple sinks are used
compared to the scenario with a single sink. Specifically, with two sin
in delivering 76% of the packets to the sink nodes even in scenarios of very sparse networks (i.e.
less than 200 nodes). Nevertheless, there is a slight increase when using three sinks compared
with the scenario of two sinks. In
is 6.6% overall used number of nodes compared with a single sink setup. However, the
improvement of scenarios of three sinks is just 1.3% over those with two sinks.
(a)
12
14
16
18
20
22
24
26
28
200 300
Number of nodes
Energy Consumption
Two sinks Three sinks
hence, reduces the number of hops packets need to make. In addition, the load on the nodes near
is just 0.3% when using three sinks over the use of two sinks. The trend is logical
further increase in the number of sinks will not give much improvement in saving energy.
The reason behind this is that the number of sinks to be used depends on the size of the monitored
monitored area in order to achieve better performance.
end delay experienced by data packets received by the sink nodes is
outperforms the other two scenarios with a single and two sinks. The justification of this is that
the packets are delivered successfully to the closer sink nodes via fewer hops, which decrease the
off times. With 200 nodes, the improvement of using two sinks is
mpared to the scenario with a single sink. However, the delay of three
sinks is 4.5% lower than that of two sinks. The gab in the differences reduces with the i
due to the resulting decrease in the number of void cells.
DR as a function of the number of nodes for a different number of sinks is examined in
compared to the scenario with a single sink. Specifically, with two sinks, the protocol succeeded
with the scenario of two sinks. In particular, the average improvement when using two sink nodes
all used number of nodes compared with a single sink setup. However, the
improvement of scenarios of three sinks is just 1.3% over those with two sinks.
(b)
400
Number of nodes
Energy Consumption
Three sinks Single sink
1
1.3
1.6
1.9
2.2
2.5
2.8
200 300 400
Delay(s)
Number of nodes
Single sink Two sinks Three sinks
16
s need to make. In addition, the load on the nodes near
two sinks. The trend is logical,
further increase in the number of sinks will not give much improvement in saving energy.
the size of the monitored
end delay experienced by data packets received by the sink nodes is
o sinks. The justification of this is that
which decrease the
off times. With 200 nodes, the improvement of using two sinks is
mpared to the scenario with a single sink. However, the delay of three
sinks is 4.5% lower than that of two sinks. The gab in the differences reduces with the increase in
is examined in
ks, the protocol succeeded
ing two sink nodes
all used number of nodes compared with a single sink setup. However, the
400
end delay
Three sinks

(a) Energy consumption
Figure 11: Impact of varying the number of sinks on
5. CONCLUSION AND FUTURE
In this paper, we proposed a new
protocol in which the packet is forwarded on
protocol is the classification of the neighboring cells into priority levels
distances to the sink nodes. Basically, neighboring cells
higher priority to be selected as the next hop. Hence,
hops, and therefore high PDR, less energy and
GBPR implements a simple election algorithm with short control packets to
responsible for forwarding data packets to the sink nodes. In addition, to efficiently conserve the
available limited resources (e.g. energy and bandwidth), data packets are forwarded to a single
cell-head in each hop and cells with
evaluation results show that the proposed protocol achieves the best performanc
energy consumption, average end
experiments. Furthermore, multiple sinks scenarios provide better performance results over single
sink counterparts. Therefore, the
detection, disaster prevention and early warning, and deep
For future direction, we aim to investigate how to adaptively order the selection of the next
forwarder according to the channel conditions (e.g. amount of traffic) in order to further improve
the performance of the protocol. Furthermore, we plan to invest
enhanced protocol with other protocols available in the literature and under different network
conditions. In addition, we aim to find the optimal number of sink nodes to be used as well as
their optimal placement that will lea
might be more powerful and more expensive than sensor nodes; therefore, finding the optimal
number and optimal placement is a valuable study.
ACKNOWLEDGMENT
This work is supported by The R
research grant number RC/SCI/COMP/15/02.
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
PDR(packet/s)
(c)
a) Energy consumption (b) Average end-to-end delay (c) PDR
Figure 11: Impact of varying the number of sinks on
UTURE WORKS
proposed a new GBPR routing protocol for UWSNs. It is a grid-based routing
which the packet is forwarded on a cell-by-cell basis. The key feature of
classification of the neighboring cells into priority levels according to their
distances to the sink nodes. Basically, neighboring cells that are closer to the sink are given
higher priority to be selected as the next hop. Hence, packets are propagated in less number of
high PDR, less energy and low end-to-end delay are achieved.
implements a simple election algorithm with short control packets to select nodes that are
ted resources (e.g. energy and bandwidth), data packets are forwarded to a single
head in each hop and cells with a positive advance toward the sink nodes are favored.
evaluation results show that the proposed protocol achieves the best performance in terms of
end-to-end delay and PDR than EEF and EMGGR over all
the GBPR can suit a number of applications such as intruder
detection, disaster prevention and early warning, and deep-water oil drilling.
the performance of the protocol. Furthermore, we plan to investigate the performance of the
their optimal placement that will lead to better performance results. This is because sink nodes
number and optimal placement is a valuable study.
This work is supported by The Research Council (TRC) of the Sultanate of Oman under the
research grant number RC/SCI/COMP/15/02.
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
200 300 400
Number of nodes
Single sink Two sinks Three sinks
17
based routing
The key feature of the GBPR
according to their
the sink are given
packets are propagated in less number of
end delay are achieved. Moreover,
elect nodes that are
ted resources (e.g. energy and bandwidth), data packets are forwarded to a single
positive advance toward the sink nodes are favored. The
e in terms of
delay and PDR than EEF and EMGGR over all
lications such as intruder
igate the performance of the
d to better performance results. This is because sink nodes
esearch Council (TRC) of the Sultanate of Oman under the

18
REFERENCES
[1] I. Akyildiz, D. Pompili, and T. Melodia, “Underwater acoustic sensor networks: research challenges,”
Ad hoc networks, vol. 3, no. 3, pp. 257–279, May 2005.
[2] G. Han, C. Zhang, L. Shu, and J. J. P. C. Rodrigues, “Impacts of Deployment Strategies on
Localization Performance in Underwater Acoustic Sensor Networks,” IEEE Transactions on
Industrial Electronics, vol. 62, no. 3, pp. 1725–1733, March 2015.
[3] G. Han, C. Zhang, L. Shu, N. Sun, and Q. Li, “A Survey on Deployment Algorithms in Underwater
Acoustic Sensor Networks,” International Journal of Distributed Sensor Networks, vol. 2013, p. 11,
November 2013, Article ID 314049.
[4] F. Bouabdallah and R. Boutaba, “A Distributed OFDMA Medium Access Control for Underwater
Acoustic Sensors Networks,” Proceedings of the 2011 IEEE International Conference on
Communications (ICC), 5-9 June 2011, pp. 1–5, Kyoto, Japan.
[5] C. Lv, S. Wang, M. Tan, and L. Chen, “UA-MAC: An Underwater Acoustic Channel Access Method
for Dense Mobile Underwater Sensor Networks,” International Journal of Distributed Sensor
Networks, vol. 2014, no. 2, pp. 1–10, February 2014, Article ID 374028.
[6] N. Chirdchoo, W.-S. Soh, and K. C. Chua, “Aloha-Based MAC Protocols with Collision Avoidance
for Underwater Acoustic Networks,” Proceedings of the IEEE INFOCOM 2007 - 26th IEEE
International Conference on Computer Communications, 6-12 May 2007, pp. 2271–2275, Anchorage,
AK, Alaska.
[7] W. Lin and K. Chen, “MHM: A Multiple Handshaking MAC Protocol for Underwater Acoustic
Sensor Networks,” International Journal of Distributed Sensor Networks, vol. 12, no 5, pp. 1–12, May
2016.
[8] D. Pompili, T. Melodia, and I. F. Akyildiz, “A CDMA-based Medium Access Control for
UnderWater Acoustic Sensor Networks,” IEEE Transactions on Wireless Communications, vol. 8, no.
4, pp. 1899–1909, April 2009.
[9] K. Chen, M. Ma, E. Cheng, F. Yuan, and W. Su, “A Survey on MAC Protocols for Underwater
Wireless Sensor Networks,” IEEE Communincations Surveys & Tutorials, vol. 16, no. 3, pp. 1433–
1447, Third Quarter 2014.
[10] P. Xie, J.-H. Cui, and L. Lao, “VBF: vector-based forwarding protocol for underwater sensor
networks,” Proceedings of IFIP Networking’06, 15-19 May 2006, pp. 1216–1221, Coimbra, Portugal.
[11] N. Nicolaou, A. See, P. Xie, J.-H. Cui, and D. Maggiorini, “Improving the Robustness of Location-
Based Routing for Underwater Sensor Networks,” Proceedings of OCEANS 2007 - Europe, 18-21
June 2007, pp. 1–6, Aberdeen, Scotland.
[12] Z. S. Peng Xie, Zhong Zhou, Zheng Peng, Jun-hong Cui, “Void Avoidance in Mobile Underwater
Sensor Networks,” Proceedings of WUWNet’07, September 2007.
[13] X. Xiao, X. P. Ji, G. Yang, and Y. P. Cong, “LE-VBF: Lifetime-Extended Vector-Based Forwarding
Routing,” Proceedings of 2012 International Conference on Computer Science and Service System,
11-13 August 2012, pp. 1201–1203, Nanjing, China.
[14] D. Shin, D. Hwang, and D. Kim, “DFR: an efficient directional flooding-based routing protocol in
underwater sensor networks,” Wireless Communications and Mobile Computing, vol. 12, no. 17, pp.
1517–1527, 10 December 2012.
[15] H. Yan, Z. J. Shi, and J.-H. Cui, “DBR: depth-based routing for underwater sensor networks,”
Proceeding of the International Conference on Research in Networking, Springer Berlin Heidelberg,
5-9 May 2008, pp. 72–86, Singapore.
[16] M. Ashrafuddin, M. M. Islam, and M. Mamun-or-Rashid, “Energy Efficient Fitness Based Routing
Protocol for Underwater Sensor Network,” International Journal of Intelligent Systems and
Applications, vol. 5, no. 6, pp. 61-69, May 2013.
[17] F. Al-Salti, N. Alzeidi, B. Arafeh, "A New Multipath Grid-Based Geographic Routing Protocol for
Underwater Wireless Sensor Networks," Proceedings of Cyber-Enabled Distributed Computing and
Knowledge Discovery (CyberC), pp.331,336, 13-15 October 2014, Shanghai, China.

[18] F. Al Salti, N. Alzeidi, and B. R. Arafeh, “EMGGR: an energy
geographic routing protocol for underwater wireless sensor networks,” Wireless Networks, February
2016.
[19] J. Jiang, G. Han, H. Guo, L. Shu, and J. J. P. Rodrigues, “Geographic multipath routing based on
geospatial division in duty-cycled underwater wireless sensor networks,”
Computer Applications, vol. 59, pp. 4
[20] M. Tariq, M. ShafieAbdLatiff, M. Ayaz, Y. Coulibaly, and N. Al
Energy Efficient (DREE) Routing Protocol for Underwater Acoustic
Networks, vol. 10, no. 5, pp. 311
[21] P. Xie, Z. Zhou, Z. Peng, H. Yan, T. Hu, J.
based simulator for underwater sensor networks,” Proceedings of MTS/IEEE
Technology for Our Future: Global and Local Challenges (OCEANS 2009), 26
1–7, Biloxi, Mississippi.
[22] “LinkQuest: Underwater acoustic modem models.” [Online]. Available: http://www.link
quest.com/html/models1.htm. [
[23] V. Davies, “Evaluating Mobility Models Within An Ad Hoc Network,” Master’s thesis, Colorado
School of Mines, 2000.
[24] T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research,”
Wireless Communications and Mobile Computing, vol. 2, no. 5, pp. 483
[25] M. Xu, G. Liu, and H. Wu, “An Energy
Networks Inspired by Ultrasonic Frogs,” International Journal of Distributed S
2014, Article ID 351520, 12 pages, 2014.
[26] J.-H. Cui, J. Kong, M. Gerla, and S. Zhou, “The challenges of building mobile underwater wireless
networks for aquatic applications,” IEEE Network, vol. 20, no. 3, pp. 12
AUTHORS
MS. Faiza Al-Salti received her B.Sc. and M.Sc. Degrees in computer science from the
Sultan Qaboos University (Oman) in 2012 and 2015, respectively. She is currently a
Ph.D. candidate in the Sultan Qaboos University. Her research interest include
communication protocols, terrestrial and underwater wireless sensor networks.
Dr. Nasser Alzeidi received his PhD degree in Computer Science from the University
of Glasgow (UK) in 2007. He is currently an assistant professor of computer science and
the director of the Center for Information Systems at Sultan Qaboos University, Oman.
His research interests include performance evaluation of communication systems,
wireless networks, interconnection networks, System on Chip architectures and parallel
and distributed computing. He is a member of the IEEE.
Prof. Khaled Day received his undergraduate degree in computer science from the
University of Tunis in 1986. He was awarded
Prize’ for excellent academic performance
degree. He was then awarded from the Tunisian government a scholarship to pursue
graduate studies in the USA starting 1987.
computer science from the University of Minnesota (U
respectively. Dr. Day has worked at the University of Bahrain in the period 1992
as Assistant Professor. He then joined in 1996 Sultan Qaboos University as Assistant Professor. He was
promoted to Associate Professor in 1999 and th
Department of Computer Science at Sultan Qaboos University during the periods 2000
2010. He was appointed in January 2013 as the Dean of Research of Sultan Qaboos University. His areas of
research interest include interconnection networks, parallel algorithms, distributed systems and wireless
networks. He has published over 100 research papers in international journals and conferences. He has
received in 2000 the Abdul Hameed Shoman Prize f
IEEE.
F. Al Salti, N. Alzeidi, and B. R. Arafeh, “EMGGR: an energy-efficient multipath grid
ocol for underwater wireless sensor networks,” Wireless Networks, February
J. Jiang, G. Han, H. Guo, L. Shu, and J. J. P. Rodrigues, “Geographic multipath routing based on
cycled underwater wireless sensor networks,” Journal of Networks and
Computer Applications, vol. 59, pp. 4–13, January 2016.
M. Tariq, M. ShafieAbdLatiff, M. Ayaz, Y. Coulibaly, and N. Al-Areqi, “Distance based Reliable and
Energy Efficient (DREE) Routing Protocol for Underwater Acoustic Sensor Networks,” Journal of
Networks, vol. 10, no. 5, pp. 311–321, May 2015.
P. Xie, Z. Zhou, Z. Peng, H. Yan, T. Hu, J.-H. Cui, Z. Shi, Y. Fei, and S. Zhou, “Aqua-
based simulator for underwater sensor networks,” Proceedings of MTS/IEEE Biloxi
Technology for Our Future: Global and Local Challenges (OCEANS 2009), 26-29 October 2009, pp.
“LinkQuest: Underwater acoustic modem models.” [Online]. Available: http://www.link
quest.com/html/models1.htm. [Accessed: 08-Feb-2016].
V. Davies, “Evaluating Mobility Models Within An Ad Hoc Network,” Master’s thesis, Colorado
T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research,”
mmunications and Mobile Computing, vol. 2, no. 5, pp. 483–502, August 2002.
M. Xu, G. Liu, and H. Wu, “An Energy-Efficient Routing Algorithm for Underwater Wireless Sensor
Networks Inspired by Ultrasonic Frogs,” International Journal of Distributed Sensor Networks, vol.
2014, Article ID 351520, 12 pages, 2014.
H. Cui, J. Kong, M. Gerla, and S. Zhou, “The challenges of building mobile underwater wireless
networks for aquatic applications,” IEEE Network, vol. 20, no. 3, pp. 12–18, May 2006.
received her B.Sc. and M.Sc. Degrees in computer science from the
Sultan Qaboos University (Oman) in 2012 and 2015, respectively. She is currently a
Ph.D. candidate in the Sultan Qaboos University. Her research interest includes
communication protocols, terrestrial and underwater wireless sensor networks.
received his PhD degree in Computer Science from the University
of Glasgow (UK) in 2007. He is currently an assistant professor of computer science and
director of the Center for Information Systems at Sultan Qaboos University, Oman.
His research interests include performance evaluation of communication systems,
wireless networks, interconnection networks, System on Chip architectures and parallel
stributed computing. He is a member of the IEEE.
received his undergraduate degree in computer science from the
University of Tunis in 1986. He was awarded in 1986 the ‘President Habib Bourguiba
Prize’ for excellent academic performance upon completion of his undergraduate
degree. He was then awarded from the Tunisian government a scholarship to pursue
graduate studies in the USA starting 1987. He obtained the M.Sc. and Ph.D. degrees in
computer science from the University of Minnesota (USA) in 1989 and 1992
respectively. Dr. Day has worked at the University of Bahrain in the period 1992-1996
promoted to Associate Professor in 1999 and then to Professor in 2005. He served as the Head of the
Department of Computer Science at Sultan Qaboos University during the periods 2000-2002 and 2006
Abdul Hameed Shoman Prize for Young Arab Researchers. He is a senior member of
19
efficient multipath grid-based
ocol for underwater wireless sensor networks,” Wireless Networks, February
J. Jiang, G. Han, H. Guo, L. Shu, and J. J. P. Rodrigues, “Geographic multipath routing based on
Journal of Networks and
Areqi, “Distance based Reliable and
Sensor Networks,” Journal of
-Sim: An NS-2
Biloxi - Marine
29 October 2009, pp.
“LinkQuest: Underwater acoustic modem models.” [Online]. Available: http://www.link-
V. Davies, “Evaluating Mobility Models Within An Ad Hoc Network,” Master’s thesis, Colorado
T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research,”
502, August 2002.
Efficient Routing Algorithm for Underwater Wireless Sensor
ensor Networks, vol.
H. Cui, J. Kong, M. Gerla, and S. Zhou, “The challenges of building mobile underwater wireless
18, May 2006.
en to Professor in 2005. He served as the Head of the
2002 and 2006-
He is a senior member of

Dr. Bassel R. Arafeh received the B.Sc. in Electrical Engineering from Cairo
University, Egypt, in 1974; and the M.E. degree in Electrical Engineering and the
Ph.D. degree in Computer Science from
Texas in 1980 and 1986, respectively. Dr. Arafeh is currently an associate professor in
the Department of Computer Science, Sultan Qaboos University, Muscat, Oman. His
research interests include wireless ad hoc and
distributed computing systems. He is a member of the ACM, the IEEE and IEEE
Computer Society.
Dr. Abderezak Touzene received the BS degree in computer science from University
Algiers in 1987, MS degree in computer science from Paris
(France) in 1988 and Phd degree in computer science from Institutpolytechnique de
Grenobe (France) in 1992. He is an Associate. Prof. in Sultan Qaboos University,
Oman. Dr. Touzene’s research inc
Distributed Systems, Network-On-chip, Wireless and Mobile Networks, Performance
Evaluation, Cryptography. Dr. Touzene is a member of the IEEE Computer Society.
received the B.Sc. in Electrical Engineering from Cairo
University, Egypt, in 1974; and the M.E. degree in Electrical Engineering and the
Ph.D. degree in Computer Science from Texas A & M University, College Station,
Texas in 1980 and 1986, respectively. Dr. Arafeh is currently an associate professor in
the Department of Computer Science, Sultan Qaboos University, Muscat, Oman. His
research interests include wireless ad hoc and sensor networks, and parallel and
distributed computing systems. He is a member of the ACM, the IEEE and IEEE
received the BS degree in computer science from University
degree in computer science from Paris-SudOrsay University
(France) in 1988 and Phd degree in computer science from Institutpolytechnique de
He is an Associate. Prof. in Sultan Qaboos University,
Dr. Touzene’s research includes Interconnection Networks, Parallel and
chip, Wireless and Mobile Networks, Performance
Evaluation, Cryptography. Dr. Touzene is a member of the IEEE Computer Society.
20

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