WIRELESS COMMUNICATIONS AND MOBILE COMPUTING
Wirel. Commun. Mob. Comput. 2008; 8:1061–1073
Published online 10 July 2008 in Wiley InterScience
(www.interscience.wiley.com) DOI: 10.1002/wcm.658
PAS: probability and sub-optimal distance-based lifetime
prolonging strategy for underwater acoustic sensor
networks
Jinfeng Dou1∗,†, Guangxu Zhang1, Zhongwen Guo1 and Jiabao Cao2
1
Department of Computer Science, Ocean University of China, 238 Songling Road, Qingdao 266100, China
2
Alcatel-Lucent Technologies Systems, Qingdao 266061, China
Summary
Lifetime prolonging is one of the most significant issues in the research on underwater acoustic sensor networks
(UASNs). Unbalanced energy consumption influences greatly the network lifetime. First this study discusses a
probability-based energy balance (PEB) scheme. The sensor nodes report the data to the sink by single-hop direct
transmission (DT) or by multi-hop transmission (MT) under the probabilities. A centralized probabilities finding
algorithm (PFA) can find a set of transmission probabilities to better balance the energy consumption. Then, a suboptimal distance (SOD)-based data transmission scheme is proposed which is a distributed scheme and operates on
each sensor node. It optimizes the slice width and selects the relays near the optimum transmission range. Simulations
show that the two schemes can save more energy and prolong the network lifetime efficiently. Copyright © 2008 John
Wiley & Sons, Ltd.
KEY WORDS: underwater acoustic sensor networks; network lifetime; energy balance
1.
Introduction
Recent advances in the acoustic communication, sensor
networks, and ad hoc networks have motivated the
development of underwater acoustic sensor networks
(UASNs) [1]. UASNs can be envisioned to enable
applications for oceanographic data collection, ocean
sampling, environmental and pollution monitoring,
offshore exploration, disaster prevention, tsunami and
seaquake warning, assisted navigation, distributed
tactical surveillance, mine reconnaissance, and autonomous underwater vehicles (AUVs) management.
UASN communication links are based on the acoustic
wireless technology, therefore the design of UASNs is
particular due to the rigorous underwater environment,
such as limited bandwidth capacity, severely attenuated
channel, long propagation delay, high bit error rates,
and limited battery power [2]. The new protocols
are expected in terms of the unique character in
underwater acoustic channels [3] such as the protocols
in References [4–6].
Since it is more difficult to replace or recharge the
battery of sensor nodes in underwater scenarios [7],
it is more important to achieve lifetime prolonging of
*Correspondence to: Jinfeng Dou, Department of Computer Science, College of Information Science and Engineering, Ocean
University of China, 238 Songling Road, Qingdao 266100, China.
†E-mail: jinfengdou@ouc.edu.cn
Copyright © 2008 John Wiley & Sons, Ltd.
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J. DOU ET AL.
UASNs comparing to the terrestrial sensor networks.
The main problem on the lifetime of UASNs is that the
network will collapse because of a few nodes’ dying out
and collecting no enough information even if there may
still be nodes with significant amount of energy. This
problem can be described as the unbalance of energy
consumption among the sensor nodes. In the traditional
direct transmission (DT) communication scenario, the
fringe nodes die out earlier for its excessive energy
consumption in long distance communications. And
sensor nodes closer to the sink tend to run out of their
energy early in multi-hop transmission (MT) scenario,
since they pass all the data collected from the network
to the sink. This paper combines DT and MT in order
to balance the energy consumption in the network.
For UASNs, this paper proposes a probability and
sub-optimal (PAS) distance-based lifetime prolonging
strategy which includes a probability-based energy
balance (PEB) scheme and a sub-optimal distance
(SOD)-based data transmission scheme. The PEB
scheme is to balance the energy consumption of
sensor nodes in two-dimensional UASNs. The scheme
assumes a circular area where the UW-sink is located
at the center and the sensor nodes are uniformly
distributed around the sink. The field is divided into
many circular slices and the nodes in each slice can
use multi-hop (through the next slice) or single-hop to
report data to the sink. A probabilities finding algorithm
(PFA) can find a set of probabilities for each slice
to decide the nodes’ transmission mode. Then, the
SOD scheme introduces an optimal transmission range
(OTR) and optimizes the slice width under the OTR.
The most important is that SOD scheme provides a
data transmission protocol to successfully select relays
near the OTR. It guarantees the previous analysis.
Here, PFA is a centralized algorithm and SOD is a
distributed one.
The contributions of this paper are: (1) the PEB
scheme solves the problem of the unbalance of energy
consumption among sensors nodes with the factor of
probabilities. The PFA is more efficient to obtain a
set of probabilities to minimize the average energy
consumed per node. (2) The SOD scheme makes the
real transmission distance approximately equal to the
OTR in the network model and saves more energy.
The remainder of the paper is organized as follows.
Related works are discussed in Section 2. Section 3
proposes the models and assumptions in this paper.
Section 4 illustrates the PEB. The design of SOD
is described in details in Section 5. Simulations are
presented in Section 6. Conclusions are discussed in
Section 7.
Copyright © 2008 John Wiley & Sons, Ltd.
2.
2.1.
Related Work
Energy Balance Algorithms
Some energy balance protocols have been presented to
prolong the network lifetime for the terrestrial sensor
networks. Singh et al. defined the energy balanced
property of networks [8] for the single-hop sensor
networks and proposed energy optimal and energy
balanced routing. Multi-hop protocol is considered in
Reference [9]. The authors selected the node with
maximum remaining energy level in the next cell as
a relay and balanced the energy consumption in the
local cell region. An energy balanced chain (EBC)
[10] was used, which can efficiently prolong the
network lifetime by actively controlling the node’s hop
distances. The nodes with higher traffic had a longer
hop distance than the nodes with lower traffic. The
analysis focused on the linear sensor networks. Stephan
Olariu and Ivan Stojmenovic studied the various
emission range of nodes for the different slices [11].
They tried to find the appropriate values of the emission
range to maximize the networks lifetime. Their studies
mainly considered the energy balancing problem in the
local slice. A non-uniform node distribution strategy
[12] was proposed to tackle the unbalanced energy
consumption problem in MT, closer to the sink the
nodes are, higher the node density is. Powell proposed
a spreading technique [13] to balance the energy
consumption per sensor in the same slice. Wang et al.
[14] introduced a mobile relay to prolong the network
lifetime. The mobile relay needs to stay within two
hops away from the sink. They also propose two joint
mobility and routing algorithms which are capable of
realizing the stated results. A method [15] considering
the sink to be mobile in the network was proposed to
prolong the network lifetime, while it limits the hop
count to one or two hops. With a mobile sink, the nodes
close to the sink would change over time, thus avoiding
energy holes around the sink. They proved that the best
position for the sink in a circular sensor network is the
center of the network. The authors also demonstrated
that using a mobile sink is beneficial and in this case,
the mobility trajectory should follow the periphery of
the network.
Other routing protocols had been proposed
to balance energy consumption by using hybrid
transmission. The disadvantages of DT and MT modes
were analyzed in Reference [16]. Two methods using
hybrid transmission [16] were then employed to
balance energy consumption: time division combining
protocol and statistical hybrid protocol. The first
Wirel. Commun. Mob. Comput. 2008; 8:1061–1073
DOI: 10.1002/wcm
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