![](bg2.png)
haveenough energy for communication. To overcome
this problem Gatzianas et al., (Gatzianas and Geor-
giadis, 2008) have considered the use of single mo-
bile sink. The mobile sink collects the information
from the sensor nodes during its round trip time i.e.,
time during which mobile sink visits entire network
in predetermined positions.
Motivation. In static sink sensor network, nodes
closer to the sink depletes their energy fast, though
other nodes in a network have enough amount of
energy for communication. The single mobile sink
keeps moving to predetermined positions and stays
for a specific period of time to collect the data. The
sensitive data moving from the source sensors may
loose their importance due to the nonavailability of
the single mobile sink. This is due to the delay in
the arrival of the single mobile sink to that position.
This problem is addressed in this paper by implement-
ing distributed algorithm with multiple mobile sinks
and thus sensitive information reaches the sink with-
out delay.
Contribution. The main contribution of this paper
is the development of an efficient distributed algo-
rithm using Multiple Mobile Synchronized Sinks of-
fering an alternative to the single mobile sink. A dis-
tributed algorithm for computing the maximum life-
time of a wireless sensor network, which routes data
to the nearest mobile sink by imposing flow con-
servation to all positions with respect to sinks. An
interference-free sensor network with a multiple mo-
bile synchronized sinks reduces delay, uses less Band-
width, consumes lower energy and increases the life-
time of the WSNs.
Organization. The rest of the paper is organized as
follows: Related work and Background work are dis-
cussed in Section 2 and Section 3 respectively. Sys-
tem Model and Network Architecture are explained
in Section 4. Problem Definition and Mathematical
Model is formulated in Section 5. Algorithm is de-
veloped in Section 6. Simulation and Performance
parameters are analyzed in section 7. Conclusions are
presented in Section 8.
2 RELATED WORK
Gatzians et al.,(Gatzianas and Georgiadis, 2008) ad-
dressed the maximization of lifetime of a mobile sink
WSNs in-terms of energy constraint. A distributed
Synchronous ε -relaxation algorithm based on the
subgradient method is presented to minimize the re-
quired time to route data from other nodes of the net-
work to a mobile sink. The system is restricted to
semi-deterministic settings resulting in considerable
delay.
Michail et al.,(Michail and Ephremides, 2003)
discussed the routing connection-oriented traffic in
wireless sensor networks with energy efficiency. Min-
imization of data transmission cost with limited band-
width resources have been considered. Real-time con-
straints in the system and the restriction of nodes to
the boundary of location leads to long routing paths
between end to end nodes.
Xiao et al.,(Xiao et al., 2004) focused on link
based optimal routing in wireless data networks. They
have exploited a Simultaneous Routing Resource Al-
location (SRRA) problem and capacitated multicom-
modity flow model to describe the data flows in the
WSN. Joint link scheduling, routing and power allo-
cation are not emphasized in this work.
Chang et al.,(Chang and Tassiulas, 2000) consid-
ered flow augmentation, flow redirection algorithm to
balance the energy among the nodes in proportion to
their reserve energy. The robustness of Shortest path
routing to maximize the lifetime of a network is not
discussed in this work.
Madan et al.,(Madan and Lall, 2006) formulated
a distributed algorithm to compute an optimal routing
scheme. The algorithm derived the concept of convex
quadratic optimization & time constraint to maximize
the lifetime of network. They have not considered
asynchronous sub-gradient algorithm.
Ritesh et al.,(Madan et al., 2005) discussed the
mixed integer convex program to maximize the life-
time of network. Non linear class of interference
free Time Division Multiple Access for load balanc-
ing, Multihop routing frequency reuse & interference
mitigation are utilized to increase lifetime of net-
work. The work is restricted for non-distributed low
topologised model with lower bound. Shashidhar et
al.,(Gandham et al., 2003)proposed a flow based rout-
ing protocol to minimize the energy consumption in
the sensors of WSNs.
Weiwang et al., (Weiwang and Chua, 2005) have
used mobile relays to prolong the lifetime of Wireless
Sensor Networks. The lifetime of the dense sensor
network with mobile sink and mobile relays are al-
most same as that of mobile sink.
Branislav et al.,(Kusy et al., 2009) have developed
an algorithm for data delivery in mobile sensor net-
works. Mobility patterns in the network, enables the
algorithm to maintain an uninterrupted data stream.
Scalability and communication cost are not consid-
ered.
Huang Zhi et al., (Zhi et al., 2010) have developed
routing strategies for Dynamic WSN with single sink
and multiple sinks. DWSN with single static sink is
MULTIPLE MOBILE SYNCHRONISED SINKS (MMSS) FOR ENERGY EFFICIENCY AND
LIFETIMEMAXIMIZATION INWIRELESS SENSOR NETWORKS
77