1. Chapter 1
Analysis of Energy-efficient
Topology control for WSN’s
using Online Battery
Monitoring
1.1 Introduction
Wireless Sensor Networks (WSN) offers a broad range of applications, from
industrial surveillance over security to medical monitoring. Another emerg-
ing application of WSN systems is the monitoring of perishable or sensitive
freight in logistics. Energy is a scarce resource for this class of devices.
Mostly running on batteries as energy source the improvement of energy-
efficiency and power management are becoming ever important research
topics. Because the power of sensor network for communication among the
nodes depends on the battery energy of the node. As the node battery
power is very limited so it is very important to utilize the battery power of
the sensor node and also life time of the sensor network s depends on the
battery power.
1.2 Architecture for Wireless Sensor Network sys-
tems
The basic elements of Wireless Sensor Networks are the network nodes or
mainly referred to as motes. These motes consist of an RF-transceiver, a
microcontroller for protocol processing and sensor interfacing, sensors for
measuring.
The use of flash memory allows the remote nodes to acquire data on com-
mand from a base station, or by an event sensed by one or more inputs to
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2. the node. Furthermore, the embedded firmware can be upgraded through
the wireless network in the field.
The microprocessor has a number of functions including :
• Managing data collection from the sensors.
• Performing power management functions.
• Interfacing the sensor data to the physical radio layer.
• Managing the radio network protocol.
A key feature of any wireless sensing node is to minimize the power consumed
by the system. Generally, the radio subsystem requires the largest amount
of power. Therefore, it is advantageous to send data over the radio network
only when required.
Figure 1.1: Wireless sensor node functional block diagram
1.3 Energy consumption of WSN components
A hardware platform based on the commercially available TmoteSky sys-
tem by Moteiv Corporation is used here. These systems use the IEEE
802.15.4-compliant RFtransceiverCC2420 and the 16-bit low-power micro-
controllerMSP430F1611, both from Texas Instruments. The following mea-
surements were made for the sending of a data message (41 bytes) and the
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3. reading of the internal temperature sensor in the microcontroller. The re-
sults are shown in Table below : From the table We can say that adaptively
Figure 1.2:
tuning the output power of the transceiver and decreasing communication
overhead may drastically improve energy efficiency of a single mote and this
may increase the network lifetime.
1.4 Battery technologies for WSN systems
In this case Batteries are the common energy sources for motes. Although
energy harvesting may be an option for certain application where there is
enough energy in the surroundings of the WSN system. When sensor nodes
are surrounded by air ,light etc then there is enough energy. Considering
logistics as an application the amount of energy being harvested is much too
low to power a mote sufficiently. If we consider the discharge curves of the
most common types of secondary cells, NiCd and NiMH cells behave almost
equally with almost flat discharge behaviour while Li-Ion batteries have a
constant negative slope.
There is a advantage of this feature of the Li-Ion cells discharge character-
istic offers the advantage of computing the remaining capacity of the bat-
tery when the actual load and the temperature of the battery are known.
Li-Ion batteries are more favourable then using Ni-based batteries because
the small slopes of Ni-based batteries impose higher demands on the mea-
surement of the voltage as small changes in output voltage result in larger
changes in discharge capacity. Therefore, I-Ion cells were selected due to
their higher energy density and their behaviour towards discharge model-
ing.
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4. Figure 1.3: Li-Ion and NiCd/NiMH battery behaviour.
1.4.1 Battery monitoring
There are some advantages using Li-Ion batteries. These kind of batteries
are more favourable for modelling due to their higher negative slope. This
enables the usage of the internal AD-converter of microcontroller of the
mote. It offers a sufficient resolution of 12 bits for the measurement of the
battery voltage.
1.5 Circuitry and Measurements
In order to measurement setup it is required minimum additional hardware
for the mote system. To verify this setup a series of measurements is taken
under controlled temperature using a constant load. As selected battery is
The Panasonic CGA103450A cell with a nominal output voltage of 3.6V.
The results for 10 C and 10 C are also shown in figure given below and
reflect the temperature dependency of the discharge behaviour.
1.6 Modelling
By using these measurements, a simple numeric model is generated by divid-
ing the discharge curve into two phases: first is normal operation and second
is post-cut-off. One can get the normal operation for the slightly decreasing
slope. Post-cut-off is given just before the battery is empty. Both phases can
be described by a simple linear model as a set of four numerical parameters
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5. Figure 1.4: Measurement setup and resulting graphs for 10 C and 10 C.
Figure 1.5: Approximated discharge characteristics with Varying Tempera-
ture
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6. c0 to c3, the temperature v and the output voltage of the battery Vout in
the following equation :
C = (c0 + c1 v) + (c2 + c3 v) Vout. (1) Furthermore a cut-off capacity
is defined which clearly marks the border of the two operational phases.
Ccutoff = Cbase + c4 .v........................................................... (2)
Here Cbase is a numerically found base capacity and constant c4 is the
slope of the cut-off capacity with varying temperature ’v’.The switch-over
between these phases is realized by using a different parameter set (c0 to c3)
for Eq. (1). The maximally usable capacity can be calculated by defining an
end-point voltage which is used in adding Eqs. (2) and (1) with the cut-off
parameters.
The endpoint voltage dependable. For example it is dependent on the used
voltage regulator type and the minimum system supply voltage. It can be
obtained by simply subtracting the current capacity from this maximum
capacity, the residual capacity is calculated.
1.7 Conclusion
It is an emerging field of research and that is the need for information on
residual energy in wireless sensor networks. The temperature behaviour of
batteries has strongly affect the residual energy and thereby the performance
of the network. Based on a standard platform, a simple measurement setup
and acomputationally simple linear model was developed which is capable
of calculating the residual energy as a function of temperature directly on
the mote.
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7. Bibliography
[1] Advances in Radio Science Adv. Radio Sci., 5, 205208, 2007, www.adv-
radio-sci.net/5/205/2007/
[2] Wireless Sensor Networks: Principles and Applications Chris
Townsend, Steven Arms MicroStrain, Inc.
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