In this paper, a mobile base station is used to reduce the energy consumption. Clustering is done by the fuzzy method based on different priorities. In first clustering is done by energy and cluster centric priorities then the base station has moved with fuzzy logic approach on a predetermined paths for collecting data. In second, clustering is done by distance, energy and cluster centric priorities. Lastly, clustering is done based on distance and energy priorities. Simulation results show that network lifetime increases when the base station is moving and clustering is done by energy and cluster centric priorities

Javadi, Shahram

Toulabi, Mozhgan

Journal of Wireless Sensor Network (WSN)

JWSN 2013, 1, 1-0003 
 
Journal of Wireless Sensor Networks 
ISSN: 2001-6417 
www.wsn-journal.com 
 
 
Energy-saving in Wireless Sensor Networks based on Sink Movement Control 
using fuzzy logic 
Mozhgan Toulabi
1
, Shahram Javadi
2
 
1. Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, 
Iran, E_mail: t_mozh@yahoo.com 
2. Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran 
E_mail: sh.javadi@iauctb.ac.ir 
 
  
Received: 18 July 2013 / Accepted: 22 August 2013 / Published: 24 August 2013 
 
Abstract: In this paper, a mobile base station is used to reduce the energy consumption. Clustering is 
done by the fuzzy method based on different priorities. In first clustering is done by energy and cluster 
centric priorities then the base station has moved with fuzzy logic approach on a predetermined paths 
for collecting data. In second, clustering is done by distance, energy and cluster centric priorities. 
Lastly, clustering is done based on distance and energy priorities. Simulation results show that network 
lifetime increases when the base station is moving and clustering is done by energy and cluster centric 
priorities. 
Keywords: wireless sensor network lifetime, fuzzy method, mobile sink 
 
1. Introduction 
A sensor network consists of large number of sensor nodes distributed in an environment widely and 
collect data from the environment. Necessarily, there is no pre-determined and specified place for the 
sensor nodes in environment. This feature makes it possible to take them into dangerous places or 
unreachable. Therefore, the sensors are randomly scattered in places that are inaccessible to humans. 
So it is almost impossible to replace or recharge the energy source.The energy management in 
networks is Attractive for researchers. Sensors in the transmission path consume more energy therefore 
lose its power and networks will soon be down if not replenished. 
The remainder of the paper is organized as follows. In Section 2, we describe some related work in the 
area. In Section 3, we discuss the proposed method and the threat model. In Section 4, we present 
simulation parameters. Sections five summarize simulation results, and we conclude the paper in 
Section 6. 
 
 
 
JWSN.2013, 1 2 
 
 
2. Related work 
The work has been carried out for increasing network lifetime generally fall into two categories: The 
first category has a fixed base station and Network lifetime is calculated. The authors propose a novel 
two-phase clustering (TPC) scheme for energy-saving and delay-adaptive data gathering in wireless 
sensor networks. The proposed scheme partitions the network into clusters in phase I each with a 
cluster head, forming a direct link between cluster member and cluster head. In phase II, each cluster 
member searches for a neighbor closer than the cluster head within the cluster to set up an energy-
saving data relay link. The sensors use either the direct link or the data relay link for their sensed data 
forwarding depending on the requirements specified by the users or applications [1]. In this work, the 
authors aim to find the best way to relocate sinks inside buildings by determining their optimal 
locations and the duration of their sojourn. Therefore, the authors propose an Integer Linear Program 
for multiple mobile sinks which directly maximizes the network lifetime instead of minimizing the 
energy consumption or maximizing the residual energy, which is what was done in previous 
solutions?. They evaluated the performance of our approach by simulation and compared it with others 
schemes. The results show that our solution extends significantly the network lifetime and balances 
notably the energy consumption among the nodes. In the second category, base station moves for 
gathering the data from the sensors in the network [2]. The base station may move randomly [2-4]. In 
[5] the base station moves on cross path. Simulation results of this work show that this scheme has 
more lifetime as compare to wireless sensor networks with stationary base station or it may fly over the 
network [6], or on the specified path moves [7]. In some of the work done, a collector moves for 
collecting data in a networked [7, 8]. In the case of the sink moving accidentally, a sensor may be in 
the direction of network that sink is not so gradually dies without sending data, and this will reduce the 
lifetime of the network. In the presented work in this paper, data sending will be by all sensors and 
clustering is dynamically. In [9], a mobile base station approach is used to reduce the energy 
consumption of cluster heads by enclosing the base station to them while a fuzzy logic is applied to 
manage the base station move.  
A Critical Degree is assigned to each cluster heads by the fuzzy system based on the input parameters 
such as, energy, proximity to the base station and size of cluster. Then, it makes the base station move 
toward the cluster head with the most Critical Degree so that it can save much more of its energy. 
According to the simulation results, the proposed scheme has proved its efficiency in the network 
lifetime, residual energy of network and load distribution. The proposed scheme also proved to have 
considerable efficiency in different scenarios. In this paper, the Base station will move towards the 
cluster heads that have both lower energy and distance from the base station. And, thus the sensor will 
send its data to the base station before dying. And, lost data will be less. 
3. The proposed method 
In this method, clustering has been done with the fuzzy clustering method and based on the energy 
sensor and distance sensor from the sink. Cluster head has been defined by Fuzzy logic. The cluster 
heads are determined by three methods, results of the network lifetime are compared. At each step, the 
mobile base station will move towards the cluster head with higher priority. The base station controller 
has been with a fuzzy controller. In the first stage, cluster heads is determined with energy priorities, 
the distance to the sink and the centric cluster. The second, cluster heads with energy priorities and the 
centric cluster and in the third, cluster with energy priorities and distance to the sink is determined. 
Fuzzy rules to determine the clusters are given in Tables (1), (2) and (3). Membership functions of the 
fuzzy system parameters to determine the clusters are shown in Figures 1 (a),(b) and (c). Direction of 
move the base station is on a circular path, on the Square path, on the triangular path and on the 
hexagonal path these paths are considered the default. And the base station to move in this direction 
collects data from the cluster head. The base station is controlled by using fuzzy logic based on fuzzy 
JWSN.2013, 1 3 
 
 
priorities. Fuzzy controller Inputs are the residual energy of sensor and distance from the base station. 
Movement of the base station will be towards cluster head that has the highest priority. For example, a 
cluster that has a greater distance from the base station and less residual energy has the highest 
priority. Fuzzy rules to the motion control of base station are shown in Table 1. 
 
 
 
 
(a)       (b) 
 
 
 
 
(c) 
Figure 1: (a) Membership function of energy (b) Membership function of distance(c) Membership 
function of the centric of cluster 
 
Table 1: Fuzzy rules for moving the base station 
 
 
 
 
 
 
 
 
 
Table 2: Fuzzy rules for determining cluster head with priorities, the 
energy and centric cluster 
 
 
 
 
 
 
 
 
 
 
 
 
Very 
Far 
Far Medium Near Very 
near 
distance 
energy 
VH VH VH H H Very  low 
VH VH H H M Low 
H H M M L Medium 
M M M L VL High 
L L VL L VL Very High 
Very 
High 
Hig
h 
Mediu
m 
low Very 
low 
Number of 
neighbors 
 Energy 
VL L M M M Very low 
VL L M M M Low 
M L M H H Medium 
M M H H VH High 
M M H VH VH Very high 
JWSN.2013, 1 4 
 
 
Table 3: Fuzzy rules for determining cluster heads with priorities the  residual energy and distance 
from the base station 
 
 
 
 
 
 
 
 
Table 4: Fuzzy rules for determining cluster heads with priorities, energy, the centric cluster and 
distance from the base station. 
If distance=low 
 
 
 
 
 
 
 
 
If distance= Medium 
 
 
 
 
 
 
 
If distance= far 
 
 
 
 
 
 
 
 
4. Simulation parameters 
Simulations are performed using Matlab software. Energy model used for the sensors is in accordance 
with the following formula. In this simulation, 200 sensors were randomly distributed in a square area 
of 200 to 200. 
Very Far Far Medium Near Very 
near 
Distance 
Energy 
VL L M M M Very  low 
VL L M M M Low 
M L M H H Medium 
M M H H VH High 
M M H VH VH Very High 
Very High High Medium low Very low Number of  
neighbors 
   Energy 
VL L M M M Very low 
VL L M M M Low 
M L M H H Medium 
M M H H VH High 
M M H VH VH Very high 
Very High High Medium low Very low Number of 
neighbors 
   Energy 
VL L M M M Very low 
VL L M M M Low 
M L M H H Medium 
M M H H VH High 
M M H VH VH Very high 
Very High High Medium low Very low Number of 
neighbors 
   Energy 
VL L M M M Very low 
VL L M M M Low 
M L M H H Medium 
M M H H VH High 
M M H VH VH Very high 
JWSN.2013, 1 5 
 
 
                    
              
 
                        
              
 
ETX = Transfer Energy  
Eelect= The activation energy for electronic circuits 
d0 = Energy threshold  
l= bit length to send 
d=data sending distance 
 
5. The simulation results 
First, clustering has been by fuzzy method, and then clusters head are specified according to the fuzzy 
rules as listed in Tables (1) to (4). The results of the lifetime of the network for fixed and mobile 
stations are in Table 5. As can be seen in Table 5, when the cluster heads, is defined by three 
parameters: the cluster centric, the energy and when the main station is also considered constant, the 
lifetime of the network is 943 rounds, when the cluster heads is based on the distance, energy and the 
cluster centric, the lifetime of the network is 895 rounds and when the cluster heads is based on the 
distance and energy, the network lifetime is 807. If the base station is moving for different paths, 
Circular, square, triangular and hexagonal as well as a set of clusters head with different priorities, 
network lifetime is compared in Table 5, respectively is 1234,1324,1546, 1432 for the second is 1087, 
1150, 1000, 1114. In the third stage, network lifetime is 907, 1021, 1056, and 1032. Figures 2, show 
network lifetime comparison between various states have been performed for fixed and mobile 
stations. As can be seen from Figure 2, when the base station is mobile, network lifetime increased 
compared to the fixed station. The network lifetime when cluster heads selected with energy priorities 
and clusters centric, is compared with other priorities for the selection of the cluster head, network 
lifetime is increased. Movement the base station on square path has had a greater increase in network 
lifetime. Figure 5, show location of fixed stations in the networks center. In Figure 3 (a), (b), (c), (d), 
and (e) is shown the base station movement on the path square, triangle, hexagonal and circular. 
According to the simulation results shown in Table 5. When the base station moves on the default 
paths the network lifetime increases compared to the fixed station. Also, when clustering is done by 
energy and cluster centric priorities and the base station is also being made to move, network lifetime 
is higher compared with other clustering. 
Table 5: Network lifetime Simulation results for static station and mobile station with different 
priorities for the cluster heads 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
different priorities for the 
cluster heads 
Network lifetime 
Base 
station 
Shape of  base station movement path 
Circular Square Triangular Hexagonal 
energy & cluster centric 943 1432 1546 1324 1234 
distance , energy& cluster 
centric  
895 1114 1000 1150 1087 
distance and energy 807 1032 1056 1021 907 
JWSN.2013, 1 6 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2: (a), (b) comparison lifetime in fixed station and movable with different clustering 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
(a) (b) 
(a) (b) 
(c) (d) 
(e) 
JWSN.2013, 1 7 
 
 
Figure 3: path simulation for moving sink and fixed sink a: static base station b: movement path is 
Square c: movement path is Circular d: movement path is Hexagonal e: movement path is Triangular. 
 
6. Conclusion 
In this paper, clustering has been by fuzzy method, and then clusters head are specified. In first 
method, clustering is done by energy and cluster centric priorities then the base station has moved with 
fuzzy logic approach on predetermined paths for collecting data. In second time, clustering is done by 
distance, energy and cluster centric priorities. Lastly, clustering is done by distance and energy. The 
base station motion control is done with fuzzy logic, network lifetime is compared in three types of 
clustering. When clustering is done by energy and cluster centric priorities and the base station is also 
being made to move, network lifetime is higher compared with other clustering. In future works can be 
route optimization techniques to optimize moving path of the base station. 
 
 
7. References  
 
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2. Leila Ben Saad, Bernard Tourancheau ,“Towards an Optimal Positioning of Multiple Mobile 
Sinks in WSNs for Buildings” International Journal On Advances in Intelligent Systems 2, 4 
(2009) 411-421. 
3. S. Jain, R. C. Shah, G. Borriello, W. Brunette, and S. Roy, “Exploiting mobility for energy 
efficient data collection in sensor networks,” in Proc., 2nd IEEE/ACM Workshop on Modeling 
and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt), Cambridge, UK, Mar. 
2004. 
4. L. Tong, Q. Zhao, and S. Adireddy, “Sensor networks with mobile agents,” in Proc., IEEE 
MILCOM 2003, vol. 22,no. 1, Boston, MA, USA, Oct. 2003, pp. 688–693. 
5. Abhijeet Alkesh, Ashutosh Kumar Singh, N. Purohit, “ A Moving Base Station Strategy Using 
Fuzzy Logic for Lifetime Enhancement in Wireless Sensor Network”, IEEE International 
Conference on Communication Systems and Network Technologies, pp. 198-202,2011 
6. S. J. Rahul C. Shah, Sumit Roy and W. Brunette, “Data MULEs: Modeling a three-tier 
architecture for sparse sensor networks,” in Proc., IEEE Workshop on Sensor Network 
Protocols and Applications (SNPA), Anchorage, Alaska, USA, May 2003, pp. 30–41. 
7. Chakrabarti, A. Sabharwal, and B. Aazhang, “Using predictable observer mobility for power 
efficient design of sensor networks,” in Proc., 2nd Int. Workshop on Information Processing in 
Sensor Networks (IPSN), Palo Alto, CA, USA, Apr. 2003, pp. 129–145, also in Lecture Notes 
in Computer Science, Vol. & (NO) (2634), pp. 129-145. 
8. Kansal, M. Rahimi, W. J. Kaiser, M. B. Srivastava, G.J. Pottie, and D. Estrin, “Controlled 
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9. Torghabeh, N.A. ;Totonchi, M.R.A. ;  Moghaddam, M.H.Y.” Mobile base station management 
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© 2013 by the authors; licensee JWSN. This article is an open access article distributed under the 
terms and conditions of the Creative Commons Attribution license. 


Energy-saving in Wireless Sensor Networks based on Sink Movement Control using fuzzy logic

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Energy-saving in Wireless Sensor Networks based on Sink Movement Control using fuzzy logic

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