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Chapter 26
Adaptive Transmission Based
Geographic and Opportunistic Routing
in UWSNs
Saba Gul, Nadeem Javaid, Zahid Wadud, Arshad Sher, and Sheeraz Ahmed
Abstract UWSNs are frequency selective and energy-hungry due to the underwater
acoustic communication links. We propose adaptive transmission based geographic
and opportunistic routing (ATGOR) for efficient and reliable communication.
Opportunistic routing is utilized along with geographic routing to select a set of
forwarders from the neighboring nodes instead of a single forwarder. We propose a
3D network model logically divided into small cubes of equal volume with a goal
that the sensed data is transmitted by the unit of small cubes.
26.1 Introduction
In this regard, depth-controlled routing protocol (DCR) performs depth adjustment
based topology control for void recovery [1]. The proposed protocol organizes the
network topology and the number of connected nodes in a proactive manner to
overcome the voids. Similarly, for energy efficiency, weighting depth adjustment
forwarding area (WDFAD-DBR) for UWSNs is proposed to maintain the balance
of energy consumption among the sensor nodes for prolonging network lifetime [7].
The selection of forwarder node based upon the depth leads to the selection of same
node due to which the energy of the node depletes quickly and void hole is created.
S. Gul · N. Javaid ()·A.Sher
COMSATS Institute of Information Technology, Islamabad, Pakistan
www.njavaid.com
Z. Wadud
University of Engineering & Technology, Peshawar, Pakistan
Capital University of Science and Technology, Islamabad, Pakistan
S. Ahmed
Career Dynamics Research Centre, Peshawar, Pakistan
Iqra National University, Peshawar, Pakistan
© Springer Nature Switzerland AG 2019
M. A. Jan et al. (eds.), Recent Trends and Advances in Wireless and IoT-enabled
Networks, EAI/Springer Innovations in Communication and Computing,
https://doi.org/10.1007/978-3-319-99966- 1_26
283
284 S. Gul et al.
In order to cater the void hole in the discussed existing state-of-the-art work,
we have proposed an algorithm named adaptive transmission based geographic
and opportunistic routing (ATGOR) protocol for UWSN. We have adjusted the
transmission range based on the location of the neighbor node. We will consider
depth and energy both parameters for the selection of the forwarder node in order to
ensure that cyclic selection of the forwarder node is avoided. This selection assures
that energy is efficiently utilized.
26.2 Related Work
A void aware pressure based routing technique is proposed by Noh et al. (VAPR).
VAPR utilizes geographic and opportunistic routing for transmitting the sensed data
from sensor nodes to the sonobuoys at water surface. The next-hop forwarder is
set to continue the forwarding process by selecting the forwarders in a vertical
direction towards the surface sinks based on pressure levels [4]. Noh et al. presented
another pressure based anycast routing algorithm (HydroCast) for underwater sensor
networks [3]. The next-hop forwarder selection is based on the pressure levels at
different sensor nodes. The proposed scheme performs void recovery and limits the
co-channel interference.
In [6], a depth based routing (DBR) protocol is proposed that utilizes multi-
sink architecture. DBR is a greedy routing algorithm in which sensor nodes select
the next-hop forwarder based on the depth of neighboring sensor nodes. Jor et al.
propose focused beam routing (FBR) protocol that is suitable for both static and
mobile sensor nodes [2]. In vector based forwarding (VBF) data packets are routed
along a virtual pipeline of fixed radius [5]. The radius of the virtual pipeline is
calculated based on the source, local distribution of sensor nodes, and the destination
position location.
26.3 System Model
We assume that “i” number of sensor nodes are random uniformly distributed over
a3Dnetwork field forming a cube having volume “V.” Network field is logically
divided into uniform ‘’M” small cubes volume “v,” denoted as C1,C
2,··· ,C
M
(Fig. 26.1).
26.4 The Proposed Transmission Scheme
In this section we describe the adaptive transmission based geographic and oppor-
tunistic routing (ATGOR) in detail.
26 ATGOR 285
Sensor
Node
Satellite
Monitoring
Center
Sink
Fig. 26.1 Network model
Enhanced Periodic Beaconing The periodic beacon message of each sink includes
the sequence number , its ID, and its Xand Ylocation. The sequence number
of beacon message is used to identify the most recent beacon of the sink. The
value of Zcoordinate of sinks is omitted because the sinks are deployed over the
surface and the vertical movement of the sinks is negligible. Likely, each sensor
node embeds a sequence number , the corresponding CID, node’s ID, and X,
Y, and Zposition. Each node includes the sequence number ,ID, and Xand Y
coordinate of its reachable sinks. The sequence number of the beacon message
is incremented periodically after a fixed periodic interval of 30 s. Each entry is
refreshed upon receiving the most recent beacon message based on the sequence
number.
Determine the Next-Hop Small Cube The process of small cube selection is
shown in Algorithm 1.
While choosing a forwarder set selection, when a forwarder is selected from the
ENN other nodes suppress their communication on overhearing the packet transfer.
If the highest priority node is failed to forward the packet, then the rest of the low
priority nodes transmit the packet. Algorithm 2shows all the steps of forwarder set
selection.
286 S. Gul et al.
Algorithm 1 Election of ENC
1: Node nireceives packet from node nj
2: Acquires its CID
3: Find ENC in its ETR
4: if nihas found an ENC then
5: Acquire the ENC’s CID
6: else if There is a void cube then
7: Choose another transmission level from Tmax
8: Go to 5
9: end if
Algorithm 2 ENN set selection
1: ENN forwarder set selection;
2: Find the number of nodes within the CID of the elected ENC
3: Acquires the coordinates of nodes within the coordinates of ENC
4: Acquires its CID
5: Assign priorities to ENNs according to the distance with the nearest sink
26.5 Simulation Results and Discussion
In the simulation, we deploy 150–450 sensor nodes randomly in 1500 m×1500 m ×
1500 m region and the number of sinks is 25. Transmission ranges are set to be
150 m, 200 m, 250 m, 300 m, 350 m, 400 m, and 450 m. Each sensor node is assigned
an initial energy of 10 W. In all experiments the packet size is 150 bytes and the
data rate is 50 kbps. The energy consumption of transmission, receiving, and idle
state are 2 W, 0.1 W, and 10 mW, respectively. We determine the performance of
proposed protocol according to the following parameters: packet delivery ratio
(PDR), fraction of void nodes, and energy consumption.
26.5.1 Results and Analysis
Scenario I Figure 26.2 shows the fraction of void nodes in the network. The
probability of void holes reduces as the node density increases. Our proposed
schemes perform better than the compared scheme. This is due to the adaptive
transmission range of sensor nodes. If sensor nodes near the water surface fail to
find any forwarder node in greedy strategy, then depth adjustment is performed
which causes high energy consumption. While in ATGOR sensor nodes overcome
the void hole by adaptively adjusting their transmission range to find the nearest
sink. Our proposed adaptive transmission range strategy proves to be useful to avoid
the void holes. Figure 26.3 depicts the PDR of the network. It can be seen that the
PDR increases as the node density increases. The increase in node density results
in reduced void nodes due to availability of high number of neighbors. The fixed
transmission range causes packet failure, thus adaptive transmission range reduces
26 ATGOR 287
150 200 250 300 350 400 450
Number of nodes
0
0.05
0.1
0.15
0.2
0.25
Fraction of local maximum nodes
GEDAR
ATGOR
Fig. 26.2 Fraction of void nodes
Fig. 26.3 Packets received at
the sinks
150 200 250 300 350 400 450
Number of nodes
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Packet delivery ratio
GEDAR
ATGOR
the packet failures. Energy consumption per packet per node is shown in Fig.26.4.
In order to avoid the void holes in GEDAR energy consumption per packet per node
is high than our proposed schemes.
Scenario II Figure 26.5 shows the impact of different transmission levels on the
PDR. Transmission ranges 150 m, 200 m, 250 m, 300 m, 350 m, 400 m, and 450 m
are represented by T1,T2,T3,T4,T5,T6, and T7, respectively. It can be seen that
PDR increases as the transmission range increases. Increased transmission range
overcomes the void areas in the source to destination route. In this way, it is
ensured that the packet generated from the source reaches the destination. Fraction
of local maximum nodes at different transmission levels is shown in Fig. 26.6.High
288 S. Gul et al.
Fig. 26.4 Energy
consumption per packet per
node
150 200 250 300 350 400 450
Number of nodes
0
1
2
3
4
5
6
Energy consumption per packet per node (J)
GEDAR
ATGOR
Number of nodes
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Packet delivery ratio
T1=150
T2=200
T3=250
T4=300
T5=350
T6=400
T7=450
150 200 250 300 350 400 450
Fig. 26.5 Packets received at the sinks
transmission levels overcome the void areas and greater node density reduces the
voids due to more number of neighbors. Energy consumption in the network per
packet per node is shown in Fig. 26.7.
26.6 Conclusion
Our proposed scheme selects the next-hop small cube based on the distribution of
neighboring nodes. In case of a zero node in the neighboring cube, ATGOR adap-
26 ATGOR 289
150 200 250 300 350 400 450
Number of nodes
0
0.02
0.04
0.06
0.08
0.1
0.12
Energy per data packet per node (J)
T1=150
T2=200
T3=250
T4=300
T5=350
T6=400
T7=450
Fig. 26.6 Energy consumption per packet
Fig. 26.7 Fraction of void
nodes
150 200 250 300 350 400 450
Number of nodes
0
0.2
0.4
0.6
0.8
1
Fraction of local maximum nodes
T1=150
T2=200
T3=250
T4=300
T5=350
T6=400
T7=450
tively adjusts its communication range and avoids the void nodes. Our simulation
results demonstrate that the concept of adaptive transmission along with geographic
and opportunistic routing lead to the improvement of network performance in terms
of data delivery ratio, fraction of avoiding the local maximas and minimum energy
consumption.
290 S. Gul et al.
References
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3. Noh, Y., Lee, U., Lee, S., Wang, P., Vieira, L. F., Cui, J. H., et al. (2016). Hydrocast: Pressure
routing for underwater sensor networks. IEEE Transactions on Vehicular Technology, 65(1),
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