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This research deals with an energy efficient network for military mobile base station placement. The proposed method is based on minimizing the energy loss of military communication networks where the base station is moving along a preset path and the users are constantly moving in an independent speed and direction. It takes into account the free space loss and the knife edge effect for the energy loss to establish a path weight for the shortest path model. Then, it evaluates the neighboring points to the base station for the energy loss of the network in order to find the position at which the minimum energy loss occurs. The results show a clear energy saving advantage when compared to Lloyd-Max's method.
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International Journal of Wireless & Mobile Networks (IJWMN) Vol.2, No.4, November 2010
DOI: 10.5121/ijwmn.2010.2408 95
E
NERGY
E
FFICIENT
M
ILITARY
M
OBILE
B
ASE
S
TATION
P
LACEMENT
Thabet Mismar, Khaled Elleithy, and Saikat Ray*
Department of Computer Sceince and Engineering
University of Bridgeport
{tmismar, elleithy} @bridgeport.edu
*raysaikat@gmail.com
A
BSTRACT
This research deals with an energy efficient network for military mobile base station placement. The
proposed method is based on minimizing the energy loss of military communication networks where the
base station is moving along a preset path and the users are constantly moving in an independent speed
and direction. It takes into account the free space loss and the knife edge effect for the energy loss to
establish a path weight for the shortest path model. Then, it evaluates the neighboring points to the base
station for the energy loss of the network in order to find the position at which the minimum energy loss
occurs. The results show a clear energy saving advantage when compared to Lloyd-Max’s method.
K
EYWORDS
Energy Efficiency, Mobile Base Station Placements, Wireless Network, Mobile Network
1. I
NTRODUCTION
Mobile and wireless communication have advanced greatly in the past two decades. Base station
placement is the objective of many researchers in the field of wireless sensor networks and the
field of mobile networks. Previous research dealt with optimizing the network placement and
coverage while considering either the base stations or the users as the moving elements.
The demand for energy efficient communication networks is rising and needed by the concerned
users; military demand is not an exception. Energy efficiency is a research objective for
environmental and performance reasons, using less energy for a greener planet and enhancing
the energy usage of a wireless sensor or a mobile device without the need for recharging.
Mobile base station placement research has mainly considered coverage. It does not emphasize
on energy efficiency partly because the focus is on large scale networks and partly because
civilian rechargeable mobile devices are not restricted to limited energy. Although there has
been a lot of research in the field of base station placement [1- 4], there are only a few that
focused on mobile base station [5-7].
The situation of both the base station and the users being in constant movement arises in
military communication, where energy efficiency is an important issue for most of in-the-field
military communication networks.
Extensive studies have been made to develop the best base station placement algorithms. Base
station placement algorithms for mobile networks were proposed by [2-4, 8]. Although these
algorithms minimize dead zones and increase SNR, they do not take the users’ limited energy
resources into account. However, energy efficiency is a critical factor for in-the-field military
communication networks.
International Journal of Wireless & Mobile Networks (IJWMN) Vol.2, No.4, November 2010
96
Fixed base station placement algorithms for wireless sensor networks were proposed by [1, 9].
Although these algorithms take users into account, they assume a fixed base station for the
network which overloads and drains the closest user (sensor) to the base station.
Mobile base station placement was discussed in [5-6, 10]. In [5] the mobile base station
placement algorithm was based on the boundary of the transmission range for sensors. This
technique uses the limited transmission range for the sensors which does not serve in-the-field
military networks.
Algorithms that are based on the transmission range of the sensors and the boundary of the
sensors’ field were proposed by [6, 10]. These algorithms perform satisfactorily for sensor
networks when the boundary is clearly defined.
Lloyd-Max’s method [11] was modified by [12] and used by [7, 13] to develop base station
placement algorithms. In [7] the mobile base station placement was based on maximizing
network coverage.
The objective of this research is minimizing the energy loss of military communication
networks where the base station is moving along a preset path and the users are constantly
moving in an independent speed and direction.
The proposed method takes into account the free space loss and the knife edge effect for the
energy loss to establish a path weight for the shortest path model and uses Dijkstra’s algorithm
[14] to determine the shortest path. Then, it evaluates the neighboring points to the base station
for the energy loss of the network in order to find the position at which the minimum energy
loss occurs.
Lloyd-Max’s method for base station placement where the base station is basically centered in
the middle of a group of users [7-13] is considered for the purpose of comparing the results. The
random waypoint model [15] is used to provide a user movement model where each user is
moving around in an independent speed and direction.
2. E
NERGY
E
FFICIENT
M
OBILE
B
ASE
S
TATION
P
LACEMENT
In modern warfare, one of the most important elements of winning the war is communication
between soldiers and the base command and among the soldiers themselves.
Due to the fact that building bases is not practical, soldiers are constantly moving and
construction resources are expensive, a need for mobile base stations arises. Placing the base
stations on the battle field in an appropriate place is crucial because the soldier’s communication
device has a limited energy source and it is essential for it to operate as long as possible.
The objective is to find the new locations of the base stations (LBi+1) that minimize the total
energy lost (TE) in communication, given a set of base stations (BS) and a set of users (MS) at
certain locations (LBi) and (LM), consecutively.
One way is just assigning the base station to a random point, which will mean a high energy loss
for a few user stations. Another approach is fixing the mobile station to the middle of a group;
this approach was first conceived as the Lloyd-Max method and used in [7] which is later
compared to the proposed method of mobile base station placement.
The flowchart in Figure 1 shows the evaluation process of the total energy loss of the whole
network. The packet routing model uses the user and base station locations in the channel
energy loss model to determine the new locations for the base stations. The new base stations
locations are then used with the new locations of the users, from the user movement model, in a
new round to determine the total energy loss for the whole network in the final round.
International Journal of Wireless & Mobile Networks (IJWMN) Vol.2, No.4, November 2010
97
3. S
IMULATION
At the beginning the maximum speed for the base station was set to 60 m/s [16]. The possible
jumps were set to 16 points in order to find the optimum point for the next mobile base station
location.
The models, which are given in this section, clarify the steps taken to move the users, to route
the messages to the base station, and to calculate the total energy loss for the users after each
possible jump. The downlink communication is ignored for simplicity.
The flowchart in Figure 2 shows the technique used to move the users and the base stations
based on the user movement model and the total energy lost in that round. The user locations,
the base station locations and the area elevation are the input of Shortest Path First (SPF) which
outputs the total energy loss for a certain base station location. The main output of the whole
process is the total energy loss for the whole network, which is collected from the total energy
losses of each round.
A model for node movement was needed to simulate a large network movement scheme. The
model must allow each node to move freely at its own speed to an independent destination.
Consequently, the random waypoint model [15] was utilized for user movement.
In the random waypoint model, each node sets a destination and moves to it with a
predetermined speed chosen from a minimum-maximum speed range. The users are assumed to
be limited in movement by an area of 940m x 320m. Likewise, the speed is chosen randomly to
be between 0.4m/s and 1.4m/s [17].
After reaching its destination the node pauses for a period of time, then it sets a new destination
with a new speed. A maximum pause period of 5 minutes is assumed.
Packet routing is very important when talking about energy efficient communication systems.
Dijkstra’s algorithm is an excellent tool to find the shortest path tree for packet routing.
Figure 1. The Problem Flowchart
User
Locations
Base Station
Locations
Packet Routing Model
User
Movement
Model
Channel Energy Loss Model
Base Station
Location with
Minimum Energy
Total Energy Loss of the Whole Network
after a Certain Period of Time
International Journal of Wireless & Mobile Networks (IJWMN) Vol.2, No.4, November 2010
98
Assuming that the energy loss to be the edge weight in the algorithm helps achieve energy
efficient routing.
The untested paths are set to infinity, and their weights are set as a percentage of energy lost
compared to the total energy of the user. The base station energy loss is not taken into account
since it is assumed that the base station has infinite energy reserve and the downlink paths are
set to zero, unused paths. After applying Dijkstra’s algorithm the path matrix consists of ones
and zeros. Zero means the path was not used and one means the path was used to route the
packet. A dummy base station is used to eliminate the need for base stations to connect.
The flowchart in Figure 3 shows the technique used to obtain the total energy loss based on
Dijkstra's algorithm. The inputs that feed into Free Space Path Loss (FSPL) are the user
location, the base station locations and the area elevation. FSPL outputs the path energy losses
which are used in Dijkstra’s algorithm as the path weights. The output of Dijkstra’s algorithm is
a spanning tree with the lowest energy path for packet routing, which in turn enables the
calculation of the total energy loss for that run.
As mentioned previously, the expected energy losses for the users are used as path weights. The
heights of the user and base station antennas are assumed to be 1.5m and 100m, respectively.
The free space propagation model is used instead of the two-ray ground reflection model since
the distance restriction does not apply in the assumed case [18], i.e.
Figure 2. The Flowchart for the Main Program for the Proposed Method
(Main.m)
Compare Energy
SPF
Tested all
Possible
Locations?
User
Locations
Base Station
Locations
Area
Elevation
Change Base Station
Location
Total Energy Loss
User
Movement
Model
Yes
No
Energy Loss
International Journal of Wireless & Mobile Networks (IJWMN) Vol.2, No.4, November 2010
99
rt
hhd <<
(1)
where d is the distance between the transmitter and receiver, h
t
is the antenna height of the
transmitter, and h
r
is the antenna height of the receiver.
The following values are assumed for no specific reason:
1) Temperature = 35
o
C
2) Channel capacity = 1 Mbps
3) Bandwidth = 5 MHz
4) SNR = 10 db
The carrier frequency was assumed to be 2.4 GHz due to the fact that the communication
channel is based on 802.11. The packet size was set to 4000 bits [19] and the antenna gain for
both the transmitter and receiver was set to unity [18].
The knife edge loss is assumed to happen once. Given the current locations of the users and the
locations of the base stations, the knife edge loss is calculated by taking pairs of transmitters and
receivers and finding the obstructions in the way of the signal between them. Since it is assumed
that the knife edge loss happens once, the search is terminated once the first obstruction is found
and the loss is calculated using the following equations:
)(log20)(
ν
FdBG
d
= (2)
21
21
)(2
dd
dd
h
λ
ν
+
=
(3)
where h is the effective height of obstruction, d
1
and d
2
are the distances from the obstruction to
the transmitter and receiver, and λ is the wavelength.
In each round, each base station has J possible future positions or jumps. For each jump, the
expected energy losses for each user (E
i
) are calculated by
Figure 3. The Flowchart for the Packet Routing Program (SPF.m)
User
Locations
Area
Elevation
Base Station
Locations
FSPL
Dijkstra’s Algorithm
Path Energy
Losses
Total Energy Loss
International Journal of Wireless & Mobile Networks (IJWMN) Vol.2, No.4, November 2010
100
c
fC /=
λ
(4)
BTKN **
=
(5)
SNRNP
Rx
*=
(6)
knifeRxTx
LDPP /)/*4(*
λπ
=
(7)
pTxi
CbPE /*=
(8)
where λ is the wave length, C is the speed of light, f
c
is the carrier frequency, N is the white
noise, K is Boltzmann’s constant, T is the temperature in Kelvin, B is the Bandwidth, P
Rx
is the
received transmitted power, SNR is the signal to noise ratio, P
Tx
is the transmitted power, D is
the distance the signal travels from the transmitter to the receiver, L
knife
is the knife edge loss, b
is the number of bits sent, and C
p
is the channel capacity.
The energy losses E
i
is then used in Dijkstra’s algorithm as the path weights. After applying
Dijkstra’s algorithm, an estimation of the total network energy loss for a specific possible jump
is obtained. This is done for each base station and for all possible jumps in order to take the
combination of base station jumps with minimum total energy loss.
The flowchart in Figure 4 shows the technique used to obtain the transmission power. The user
locations and the base station locations are used to obtain the distance matrix, which is basically
a matrix whose values are the distances between pairs of users or users and base stations. The
distance matrix is then used to calculate the transmission power needed before considering the
knife edge loss. The knife edge loss is calculated by using the distance matrix and the area
elevation. The transmission power and the knife edge loss are finally used to calculate the final
transmission power.
Figure 4. The Flowchart for the Channel Energy Loss Program
(FSPL.m)
User
Locations
Area
Elevation
Base Station
Locations
Distance Matrix
Knife Edge Loss
Transmission Power
without Knife Edge
Transmission Power
International Journal of Wireless & Mobile Networks (IJWMN) Vol.2, No.4, November 2010
101
4. R
ESULTS
The simulation mainly focused on varying three parameters; the number of base stations, the
number of users, and the average number of packets sent per second.
For each parameter changed, the total energy used by users was calculated for the proposed
method and Lloyd-Max’s method.
The number of base stations was varied between 1 base station and 5 base stations. Figure 5
shows that the total energy lost by implementing the proposed method and Lloyd-Max’s method
decreases as the number of base stations increases. This result is expected because as the
number of base stations increases the distance between the base station and the users decreases,
thus the power needed for transmission is decreased.
The number of users was varied between 20 users and 100 users. Figure 6 shows that the total
energy lost by implementing the proposed method and Lloyd-Max’s method increases as the
number of users increases. This is due to the increase in the number of packets sent through the
network.
The number of average packets sent per second was varied between 1 packet and 5 packets per
second. Figure 7 shows that the total energy lost by implementing the proposed method and
Lloyd-Max’s method increases as the number of average packets sent per second increases. This
result is expected because the energy lost in transmission is proportional to the number of
packets sent.
Figure 5. Total Energy Loss for Both Methods. Number of Users = 40, Average
Packets Sent = 1.
International Journal of Wireless & Mobile Networks (IJWMN) Vol.2, No.4, November 2010
102
5. C
ONCLUSION
This research achieved an algorithm for an energy efficient network for military mobile base
station placement. The proposed method is based on minimizing the energy loss of military
communication networks where the base station is directed to move along an estimated path and
the users are constantly moving in an independent speed and direction. The achieved results for
the number of base stations, users, and average packets sent indicate that the proposed method is
Figure 6. Total Energy Loss for Both Methods. Number of Base stations = 3, Average
Number of Packets = 1.
Figure 7. Total Energy Loss for Both Methods. Number of Base stations = 3, Number
of Users = 40.
International Journal of Wireless & Mobile Networks (IJWMN) Vol.2, No.4, November 2010
103
suitable for in-the-field military communication networks. Furthermore, the results show a clear
average energy saving advantage of 25% when compared to Lloyd-Max’s method.
6. R
EFERENCES
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Authors
Mr. Thabet Mismar is a Ph.D. student at the University of Toledo.
Thabet Mismar received his M.Sc. degree in Electrical Engineering from
the University of Bridgeport. He received his B.Sc. degree of Electrical
Engineering from the University of Jordan. He has research interests in
wireless and mobile communications.
Dr. Khaled Elleithy is the Associate Dean for Graduate Studies in the
School of Engineering at the University of Bridgeport. He has research
interests are in the areas of network security, mobile communications, and
formal approaches for design and verification. He has published more than
one hundred twenty research papers in international journals and
conferences in his areas of expertise.
Dr. Elleithy is the co-chair of the International Joint Conferences on
Computer, Information, and Systems Sciences, and Engineering (CISSE).
CISSE is the first Engineering/Computing and Systems Research E-Conference in the world to
be completely conducted online in real-time via the internet and was successfully running for
four years.
Dr. Elleithy is the editor or co-editor of 10 books published by Springer for advances on
Innovations and Advanced Techniques in Systems, Computing Sciences and Software.
Dr. Elleithy received the B.Sc. degree in computer science and automatic control from
Alexandria University in 1983, the MS Degree in computer networks from the same university
in 1986, and the MS and Ph.D. degrees in computer science from The Center for Advanced
Computer Studies in the University of Louisiana at Lafayette in 1988 and 1990, respectively.
Dr. Saikat Ray received his B.Tech. degree from Indian Institute of
Technology, Guwahati, India in Electronics and Communications
Engineering in 2000, and M.S. and Ph.D. degrees from Boston University
in Electrical Engineering in 2002 and 2005, respectively. He spent the
summers of 2001 and 2003 as interns in Fujitsu Network Communications
(Acton, MA, USA) and Microsoft Research (Cambridge, UK),
respectively.
Dr. Ray was a post-doctoral researcher in the Department of Electrical and Systems Engineering,
University of Pennsylvania from September 2005 to December 2006. From January 2007 to
December 2008 he worked as an Assistant Professor at University of Bridgeport. Currently he is
a software engineer in the beautiful San Francisco Bay area working for Redback Networks
(Ericsson).
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Due to energy constraints in individual sensor nodes, extending the lifetime is an essential objective in wireless sensor networks (WSNs). Several proposals have aimed at that objective by designing energy efficient protocols at the physical, medium access, and network layers. While the proposed protocols achieve significant energy savings for individual sensor nodes, they fail to solve topology-related problems; an example of such problems is that sensor nodes around the base station become bottlenecks and deplete their battery energy much faster than other nodes. A natural solution to such a problem is to have multiple mobile base stations so that the load is distributed evenly among all nodes. Only few proposals have followed that direction. In this paper we propose a mobile base station placement scheme for extending the lifetime of the network. In our scheme the life of the network is divided into rounds and base stations are moved to new locations at the beginning of each round. While previous work has focused on placing the base stations at predefined spots (e.g., the work in [1]) or at the boundary of the network (e.g., the work in [2]), we define and solve a more general problem in which a base station can be placed anywhere in the sensing field. We formulate the problem as an Integer Linear Program (ILP) and use an ILP solver (with a constant time limit) to find a near-optimal placement of the base stations and to find routing patterns to deliver collected data to base stations. Our experiments show that our scheme makes significant extension to the lifetime of the network.
Article
It has long been realized that in pulse-code modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit as the number of quanta becomes infinite, the asymptotic fractional density of quanta per unit voltage should vary as the one-third power of the probability density per unit voltage of signal amplitudes. In this paper the corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy. The optimization criterion used is that the average quantization noise power be a minimum. It is shown that the result obtained here goes over into the Panter and Dite result as the number of quanta become large. The optimum quautization schemes for 2^{b} quanta, b=1,2, cdots, 7 , are given numerically for Gaussian and for Laplacian distribution of signal amplitudes.
Article
The primary purpose of a programming language is to assist the programmer in the practice of her art. Each language is either designed for a class of problems or supports a different style of programming. In other words, a programming language turns the computer into a ‘virtual machine’ whose features and capabilities are unlimited. In this article, we illustrate these aspects through a language similar tologo. Programs are developed to draw geometric pictures using this language.
Conference Paper
Base station location has significant impact on network lifetime performance for a sensor network. For a multi- hop sensor network, this problem is particular challenging as we need to jointly consider base station placement and data routing strategy to maximize network lifetime performance. This paper presents an approximation algorithm that can guarantee (1 - epsiv) optimal network lifetime performance for base station placement problem with any desired error bound epsiv > 0. The proposed (1 - epsiv) optimal approximation algorithm is based on several novel techniques that enable to reduce an infinite search space to a finite-element search space for base station location. The first technique used in this reduction is to discretize cost parameters (with performance guarantee) associated with energy consumption. Subsequently, the continuous search space can be broken up into a finite number of subareas. The second technique is to exploit the cost property of each subarea and represent it by a novel notion called "fictitious cost point," each with guaranteed cost bounds. This approximation algorithm offers a simpler and in most cases practically faster algorithm than a state-of-the-art algorithm and represents the best known result to this important problem.