Int. J. Communications, Network and System Sciences, 2010, 3, 750-754
doi:10.4236/ijcns.2010.39100 Published Online September 2010 (http://www.SciRP.org/journal/ijcns)
Two Slot MIMO Configuration for Cooperative Sensor
Network
Ibrahim Mansour, Jamal S. Rahhal, Hasan Farahneh
Electrical Engineering Department, University of Jordan, Amman, Jordan
Received July 1, 2010; revised August 3, 2010; accepted September 4, 2010
Abstract
Sensor networks are used in various applications. Sensors acquire samples of physical data and send them to
a destination node in different topologies. Multiple Input Multiple Output (MIMO) systems showed good
utilization of channel characteristics. In MIMO Sensor Network, multiple signals are transmitted from the
sensors and multiple sensors are used as receiving nodes. This provides each sensor multiple copies of the
transmitted signal and hence, array processing techniques help in reducing the effects of noise. In this paper
we devise the use of MIMO sensor network and array decision techniques to reduce the noise effect. The
proposed system uses a transmission time diversity to form the MIMO system. If the number of sensors is
large then groups of sensors will form the MIMO system and benefited from the diversity to reduce the re-
quired transmitted power from each sensor. Enhancing the BER reduces the required transmitted power
which results in longer battery life for sensor nodes. Simulation results showed an overall gain in SNR that
reaches 11 dB in some sensor network scenarios. This gain in SNR led to the opportunity of reducing the
transmitted power by similar amount and hence, longer battery life is obtained.
Keywords: Wireless Sensor Networks (WSN), Cooperative Sensor Network (CSN), MIMO, Diversity
1. Introduction
Wireless Sensor Network (WSN) is defined as spatially
distributed autonomous sensors to cooperatively monitor
physical or environmental conditions. The development
of wireless sensor networks is motivated by military ap-
plications and is used in many industrial and civilian
application areas, such as environmental, pollutants,
medical, vehicles, energy management, inventory control,
home and building automation, homeland security and
others [1-3].
A collection of sensors, actuators, controllers or other
elements that communicate with each other and are able
to achieve, more or less autonomously, a common goal
are defined as cooperating objects. Thus, sensors and
actuators form the hardware interfaces with the physical
world, where the sensors retrieve information from the
physical environment and the actuators modify the envi-
ronment in response to appropriate commands. Control-
lers process the information gathered by sensors and is-
sue the appropriate commands to the actuators, in order
to achieve contro l objectives.
Performance of WSN is measured and optimized
based on various criteria such as: capacity; bit erro r rate;
SNR; Cross-layer Optimal Scheduling; power require-
ments; security and robustness.
Power consumption of WSN is an important issue,
because if batteries are to be changed constantly, a lot of
potential applications will be lost and widespread adop-
tion will not occur. The power con sumption must be mi-
nimized when the sensor node is designed. The power
consumption can be reduced by, reduce the amount of
data transmitted through the use source encoding, com-
pression, lower the transceiver duty cycle and the repeti-
tion rate of data transmissions, reduce the frame over-
head, managing power by using power-down and sleep
modes. Implement an event-driven transmission strategy;
only transmit data when a sensor event occurs. Turn
power on to sensor only when sampling, Turn power on
to signal condition ing only when an event occurs. Lower
sensor sampling rate to the minimum required by the
application.
Virtual MIMO has been studied in a wide range in re-
cent years, in order to advise energy-efficient schemes,
constrained by allowed physical size and battery. An
individual sensor is allowed to contain only one antenna.

I. MANSOUR ET AL. 751
Previous studies showed that if these individual sensors
jointly form a MIMO system, tremendous energy is
saved while satisfying the required performance. How-
ever the disadvantages of the virtual MIMO are the in-
creased complexity and the cost of multiple Radio Fre-
quency (RF) chai ns.
Since wireless transceivers usually consume a major
portion of battery power [3], it is critical to improve their
power efficiency. Nevertheless, one of the major diffi-
culties comes from the harsh communication environ-
ment with multipath propagation and severe fading [4,5].
Sophisticated and yet computationally efficient tech-
niques is used for reliable and efficient signaling [6].
Moreover, optimization techniques have been used to
solve problems arising in wireless networks. Achievable
rate combinations were computed in [7,8]. Also, cross-
layer optimizations to maximize throughput have been
considered in [9].
In this work, we consider a wireless sensor network in
which nodes are distributed in a certain region; each
node can vary its transmission power to maintain the
energy-constraint. A group of sensors may sample one
physical quantity forming multi input to the transmission
channel and at the Central Node (CN) receiver we em-
ploy multiple antenna elements to form the MIMO sys-
tem. The use of MIMO system creates parallel channels
that can be used for independent transmissions [10,11].
This will provide a promising solution to enhance the
received signal quality and hence reducing the BER that
leads to power saving.
2. System Description
In this paper we devise a solution to implement a MIMO
system in wireless sensor networks by having a group of
sensor nodes repeat transmitting the same signal that
originally initiated by some sensor and another group of
nodes acts as a multiple receivers. This architecture of
cooperative sensor network will enhance the received
signal error rate and hence, improves the network per-
formance. The main idea is that; each sensor will trans-
mit its own signal and repeats other sensors signals. The
sensor selects the best K signals received and retransmits
them once again as shown in Figure1.
Transmitting the same signal twice from the same
node is not allowed, and hence a transmission conver-
gence is reached.
Node 4 will receive the signal from node 1 and repli-
cas from nodes 2, 3 and 5. Then at node 4 multiple ver-
sions of the signal produced from node 1 will arrive,
each from different direction and goes different channel
conditions. Node 5 will receive node 1 signal from node
1, 2 and 4. Node 2 will receive the signal from nodes 1, 4
Figure 1. Cooperative sensor network as MIMO system.
and 5. Node 3 also receives node 1 signal from two paths
(node 1 and node 4) but this node does not receive the
signal from nodes 5 and 2, therefore, it is not in the first
group.
To form MIMO system we first need to define the
nodes forming each group, these nodes will have wire-
less connectivity among each others. This can be seen
by forming the connectivity matrix as follows:
12345
1
2
3
4
5
11111
11011
101 10
11111
11011
00000 1
T
T
N
N
CCCCCC
C
C
C
C
C
C





























(1)
When the entry ji=1 it means an RF channel from
node Ci to node Cj is possible. A fully connected group
will have all ones in its corresponding sub matrix as the
case of C1, C2, C4 and C5. A partially connected group
will have ones in most of its sub matrix elements as the
case of C11, C12,C14 and C15. A MIMO system can be
constructed from a fully connected group, for example
the group G having N points fully connected forms an
N-1 × N-1 MIMO system. That means each sensor
transmits its data to N-1 other sensors. This transmission
will be fully available at the second transmission interval,
since each sensor will repeat all other sensors signals
after it receives them. If the destination node for the data
is not a member of the current group, the border nodes
(C1, C4 and C5) (C11, C12 and C14) will be associated with
other groups and hence transfer the data to the next
group.
The whole network will be constructed from many
groups; each group will be formed from a sub matrix that
contains all ones. This can be done by omitting some
rows and columns in the matrix as well as performing
some permutation to create groups in the matrix. Usually,
2
4
5
1
7
6
3
8
9
10
12
11
13
14
15
Fully Connected GroupPartially Connected Group

752 I. MANSOUR ET AL.
sensors forming a subgroup are close to each others in
space.
For each group the data is transferred by group coop-
eration and the signal from node Ci will be transmitted in
the ith time slot. Each sensor will store the signals cor-
responding to all group members and then process the
received signals to obtain the best detection. The signals
n
j
R arrived at sensor j corresponding to sensor n data
can be written as:
1
1
1
n
j
n
j nn
j
ji jini ji
n
jN
r
r
Rwhererhxw
r








 (2)
where n
j
i
r is the signal arrived at sensor j corresponding
to sensor n from sensor i, xni is the nth sensor signal
transmitted by sensor i, hij is the channel coefficient from
sensor i to sensor j and wji is an iid N(0, 2
w

) white
Gaussian noise. In matrix form we can write all the ar-
rived signals at sensor j as:
jjnj
RHXW
(3)
where:
11
22
11
1
2
1
00
00
00
jn
jn
jn
jN nN
j
j
j
jN
hx
hx
HX
hx
w
w
and W
w


 
 
 



 












 


(4)
At the jth sensor a Weighted Least Square (WLS) detec-
tor can be used to recover the data for each transmitting
sensor as: 1
11
ˆTT
nj wwjj wwj
X
HV HHV R



 (5)
Vww is the noise covariance matrix.
An example of a fully connected group from the network
shown in Figure 1 will be:
(1,2,4,5), (4,5,6), …, (11,12,14), (11,12,15). And a
partially connected groups could be: (4,6,8,9), (6,7, 9,
13), …, (11,12,14,15). Data propagation is done via the
boundary nodes, where the decision is made.
In the following we consider an example of a 5 nodes
sensor network, the close sensors will have communica-
tion channels between each other and groups are formed
to propagate data between different sensors. Each sen-
sor will repeat the data once, and the boundary sensors
will make a decision when the data is fully available for
all sensors.
Example:
Figure 2 shows a 5 nodes simple sensor network, if
Figure 2. A simple 5 sensor example.
sensor 1 sends a packet to sensor 5, it transmits its signal
first to its neighbour ing sensors (2, 3 and 4). Then in the
second transmitting interval sensors 2, 3 and 4 resends
the signal received from sensor 1 again and after the
second signalling interval sensor 2 will have the follow-
ing received signals (corresponding to sensor 1)
11 1
21 2324
,rrandr , sensor 3 will have the following re-
ceived signals11 1
31 3234
,rrand r and sensor 4 will have
the following received signals11 1
41 4243
,rrand r. The
combined signals at sensors 2, 3 and 4 form a 3x3 MI-
MO system. The received signals at sensor 2 are given
by:
1
21 211221
1
232313 23
1
2424 1424
ˆ
00
ˆ
00
ˆ 00
rh xw
rhxw
rhxw


 


 



 


 

 

(6)
where;
1213 311441
ˆˆ ˆ
x
xx hxandx hx

 (7)
x is the transmitted signal from sensor 1. We can com-
bine equations 10 and 11 for each sensor as:
1
21 2121
1
2323 3123
1
2424 4124
00
00
00
rh xw
rhh xw
rhhxw


 


 



 


 

 

(8)
1
31 3131
1
3232 2132
1
3434 4134
00
00
00
rh xw
rhh xw
rhhxw





 






 




(9)
1
41 4141
1
4242 2142
1
4343 3143
00
00
00
rh xw
rhh xw
rhhxw









 


 




(10)
Sensor j will decide for the received signal ˆj
x
using
equations 3 and 8. The network creates a diverse trans-
mission system. This diversity will enhance the BER
performance of the over all data tr ansmission. If a diver-
sity order of Ld is used then the BER will be reduced
exponentially by a factor of Ld [15]. In the proposed
structure and for a group of N nodes, we retransmit the
signal N times (lets call it power repetition Lp=N) and the
diversity order is

2
1
d
LN

. The power repetition is
the cost we pay for retransmitting the signal and the gain
we achieve is the reception diversity Ld. For the above
2
3
4
1
5

I. MANSOUR ET AL. 753
example we have Ld = 9 and Lp = 4. As N grows larger
we can achieve better gain compared to one transmission
scenario. The overall diversity gain we achieve using the
proposed structure can be written as:

2
1dB
d
N
GN

 (11)
This means that, for a preset BER performance we can
reduce the average transmitted power from each sensor
by a factor related to Gd.
The performance of this network is calculated by BER
and average power transmitted from each sensor. The
main goal is to achieve minimum BER at minimum
transmitted power from each sensor. The power con-
straint imposed in equation 6 makes sure that each sensor
will remain under its maximum allowable transmitted
power and hence, maximizes its battery life. The BER
performance depends on the MIMO sub groups formu-
lated in the network; therefore, a simulation program is
used next to determine the BER performance under the
power constraint.
3. Simulation Results
In this section we used a MATLAB routine to simulate
different sensor networks and results was obtained at
different SNR’s. The simulation flow is implemented as
follows:
1) Initialize the network topology, the power con-
straint and calculates the groups.
2) Generate random data for each sensor.
3) Transmit the first packet from each sensor, setting
the second transmission to all zeroes.
4) Receive the packets at each sensor, append new da-
ta to the received packet and retransmit it again (The
receiving is done by using equation 3 and the detection is
done by using equation 5, then the BER performance is
calculated).
5) Repeat (4) until all data is transmitted.
The MIMO part is constructed from the received
second transmission from other sensors and the current
received signal from the current sensor. This means that
we construct the multiple output from the received signal
vector arrived from other sensors and the multiple input
from the transmitted signals arrived from other sensors.
Four sensor networks were simulated for 5, 10, 15 and
20 sensors. Each simulation uses 1 million runs to calcu-
late the BER performance for the proposed system and
the system without MIMO construction. Figures 3 to 5
shows the BER vs SNR for both with and without MI-
MO. The system with MIMO has better BER in all
cases but to calculate the overall gain in power we select
the required BER and th e overall gain is found as:

10log( 1)
TMIMOav
GSNRSNRN dB  (12)
where the last term represents the average extra power
needed to be transmitted to form the MIMO system in
Figure 3. BER vs SNR performance for the proposed net-
work with 5 sensors (cont. line) and one transmission net-
work (dotted line).
Figure 4. BER vs SNR performance for the proposed net-
work with 10 sensors (cont. line) and one transmission net-
work (dotted line).
Figure 5. BER vs SNR performance for the proposed net-
work with 15 sensors (cont. line) and one transmission net-
work (dotted line).
the proposed solution.
As the number of nodes increases, the overall gain in-
creases also. This can be seen in Table 1 where we cal-
culate the overall gain at different network sizes.
The shown results suggests that the sensor power can be
reduced to smaller values even with signal repetition and
still get the same BER performance as without repetition.
100
10-1
10-2
0 1 2 3 4 5 6 7 8 9
100
10-1
10-2
10-3
10-4
0 1 2 3 4 5 6 7 8 9
100
10-1
10-2
10-3
10-40 1 2 3 4 5 6 7 8 9

754 I. MANSOUR ET AL.
Tabel 1. Total Gain as a Function of Network Size.
NT SNRMIMO SNR Nav G
T
5 0 dB 6 dB 4 1.2 dB
10 0 dB 13 dB 5 6.0 dB
15 0 dB 19 dB 6 11.2 dB
4. Conclusions
The proposed system has showed an opportunity to en-
hance the wireless sensor network life by constructing a
MIMO system from signal repetition emitted from each
sensor. In MIMO structure we can use statistical detec-
tion techniques. This provides better signal detection and
at the same time makes sure that the transmitted power
from each sensor does not exceed a certain preset value.
The proposed method requires more signal processing
and it will delay the reception by one packet time inter-
val.
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