Wireless sensor networks are being used widely for applications like environmental monitoring, security, and object tracking. They suffer from energy consumption issues as sensor nodes require continuous power but are often deployed in remote locations. To address this, energy harvesting techniques capture ambient energy sources like radio waves, solar power, thermal gradients, wind, and human motion and convert it to electrical energy to power the sensor nodes. Common techniques include radio frequency, solar, thermal, and mechanically-driven harvesting. Harvesting ambient energy helps extend the lifetime of wireless sensor networks.
Energy Harvesting Techniques in Wireless Sensor Networks
1. 1
Abstract – Now a days, Wireless Sensor Networks (WSNs) are
attractively being used for multiple purposes due to their
extensively beneficial nature. A WSN contains sensor nodes, a
sink node and uses electromagnetic radio waves to establish
connection with internet or to form any other network. Most
commonly WSNs are being used to sense atmospheric
conditions, movement recognitions, security purposes, object
tracking, fire detection etc. Currently, WSNs are suffering from
energy consumption issues. Since they are deployed in
abundance at very unfriendly and difficult places so in case of
any loss of energy supply it is very hectic task to re-supply or
recharge the batteries that are being used to supply energy to
the Wireless Sensor nodes. As the sensor nodes need to be more
functional so to achieve it, the life of the WSNs and their sensor
nodes need to be extended either by reducing the energy
consumption factor by disabling all the subsystems in certain
mode or by introducing some mechanisms to harvest energy
either from external sources or ambient surroundings.
Disabling the subsystems is also proven not so beneficial hence
a need to introduce an appropriate energy harvesting system
aroused. Harvesting Systems are basically subdivided into two
types. One in which ambient energy is converted to required
electrical energy directly without any storage and the other is
where storage of converted energy is required before supplying.
So for these sub-systems different energy harvesting techniques
are proposed which are Radio Frequency based, solar based,
thermal based, flow based from source of ambient environment
and from external sources of mechanical based & human based.
Flow based are further classified into wind based and hydro
based. Each energy harvesting technique’s source has its own
capability to harvest energy and can effectively overcome the
issues of energy consumption.
Index Terms – Micro electro-Mechanical Systems (MEMS),
Mechanical Harvesting, piezoelectric harvesting, Radio
Frequency (RF), Seebeck Effect, Solar & Thermal Harvesters,
Wind and Hydro Harvesters, Wireless Sensor Networks
(WSNs).
I. INTRODUCTION
ecently, the wild usage of sensing and wireless based
communication technology has enabled the development
of networks of this technology. WSNs are attractively being
used for multiple purposes due to their extensively beneficial
nature, mostly because of their rapidly growing use in Micro
electro-Mechanical Systems (MEMS) [1], which introduced
Farwa A. Hannan was born in Faisalabad and is now a student of MSCS
in Fast National University of Computer and Emerging Sciences, CFD
campus, Pakistan (Email: f169006@nu.edu.pk)
smart sensors that are small in size, have limited processing
and computing resources, not so expensive, can sense,
measure and collect information from the surroundings and
send the sensed data to the user [2]. Wireless Sensor
Networks are of two types – structured and unstructured. In
Structured one, sensor nodes are deployed randomly in ad
hoc way in abundance and then left unattended to do
monitoring while in the later one, some/fewer sensors are
deployed according to pre-defined plan and due to less
abundance of sensor nodes, maintenance and management
costs are reduced.
A WSN contains a collection of sensor nodes (few tens to
thousands), a sink node which is also known as Base station
that provide a human interaction platform which can be
accessed remotely by the user, electromagnetic radio waves
to establish connection with internet or to form any other
network and a remote location to send the processed data as
illustrated in Fig. 1.
Wireless sensor nodes contain limited storage and
computational capacity and require low energy consumption
to let themselves work for longer time span [3]. Smart sensor
node contains memory, power supply, sensor (thermal,
biological, chemical, mechanical, magnetic), processor, radio
and an actuator which is used to control the different
components of a system like controlling/actuating different
sensing devices [2]. Most commonly WSNs are being used to
sense movement, atmospheric conditions, for security
purposes, object tracking, fire detection etc. WSNs have large
number of applications like surveillance, military target
tracking[4], hazardous environment exploration, natural
disaster relief [5], biomedical health monitoring [6], [7] and
detection of earthquakes/volcanos or other vibrations of the
earth etc. [8]. Multimedia WSNs have also been introduced
in order to track or monitor any event in the form of
audio/image/video etc. [9]. Fig. 2 gives the overview of the
WSN’s applications.
Fig. 1 General Architecture of a WSN
Energy Harvesting Techniques in Wireless
Sensor Networks – A Survey
Farwa A. Hannan, Fast-NUCES
R
2. 2
Fig. 2 Overview of WSNs Applications [2], [10]
Currently, WSNs are suffering from energy consumption
issues. Since Wireless Sensor nodes are containing three
subsystems each – sensing subsystem, processing subsystem
and wireless communication subsystems [11] so they
consume efficient amount of power supply/energy and are
deployed in abundance at very unfriendly and difficult places.
Hence in case of any loss of energy supply it is very hectic
task to re-supply or recharge the batteries that are being used
to supply energy to the Wireless Sensor nodes. As the sensor
nodes need to be more functional so to achieve it, the life of
the WSNs and their sensor nodes need to be extended either
by reducing the energy consumption factor by disabling all
the subsystems when the Wireless Sensor nodes are in sleep
mode/inactive mode or introducing some mechanisms to
harvest energy either from external sources or ambient
surroundings. Disabling the subsystems is also proven not so
beneficial because mostly sensor nodes require continuous
power supply for processing data hence a need to introduce
an appropriate energy harvesting system aroused[12].
Schemes to make the sensor nodes of a network less energy
consuming are introduced in upcoming paragraphs.
In [13] WSNs are made energy efficient by introducing
energy efficient routing scheme. As the sensor nodes have
limited energy supply so they need to be working in a low
power operation mode. As the nodes route data to each other
in order to sync it at sink node so according to this scheme
when a node is in low energy mode it alerts other nodes to
stop routing data to it until it reaches the maximum level of
energy again.
The authors in [14], [15] proposed Energy Efficient
Routing in Wireless Sensor Networks Through Balanced
Clustering by introducing Equalized Cluster Head Election
Routing Protocol (ECHERP) scheme. In very large WSNs,
nodes are divided/ combined in clusters and a cluster head is
selected which gathers the data of its cluster so in proposed
ECHERP and Energy Efficient Clustering Scheme (EECS)
schemes a cluster head is selected randomly or the one having
maximum amount of energy which is enough to perform
further operations. Hence in this way energy consumption is
reduced by making routing energy efficient.
In [16] to reduce energy consumption in WSNs, schemes
of Duty Cycling, Data driven and Mobility are introduced. In
duty cycling approach the radio transceiver is turned off when
it is not in duty/operational mode. As the radio transceiver is
used to send or receive data between nodes so when data
transferring or receiving is not required, radio transceiver is
put in sleep mode which will resume its working when the
new data is ready to send or receive. This kind of mechanism
is termed as duty cycling. While data driven scheme is related
to stop the transmission/ reception of redundant information.
Eliminating the transmission/ reception of redundant
information or such information which is not required, lead
to less energy consumption. Introducing the mobile sensor
nodes reduce energy consumption to a great extent. In the
static sensor network, data packets are transferred by
following a multi-hop path which can lead to produce an
increased load on some nodes and hence more energy is
consumed but by introducing mobile nodes, the node's load
can be reduced because in this scenario mobile nodes will be
able to collect data directly from the static ones and send it to
3. 3
Fig. 3 Classification of WSNs' Techniques for better Energy Consumption
the Sink node or base station.
Using these techniques no doubt energy consumption will be
reduced but still one day the power supply will be completely
consumed and will be needed to recharge. So in order to
supply a continuous and long lasting power supply to the
sensor nodes energy needs to be harvested from some cheap
and continuous sources.
Energy Harvesting is actually a mechanism of generating
energy from the concerned network’s immediate
surroundings to provide an un-interrupted or continuous
power supply to the sensor nodes of the WSN. For this
purpose Energy Harvesting Systems are basically subdivided
into two types. One where ambient energy is directly
converted to required electrical energy and the other is where
storage of converted energy is required before supplying. So
for these sub-systems different energy harvesting techniques
are proposed namely Radio Frequency based Energy
harvesting (RFEH), solar based Energy harvesting (SEH),
thermal based Energy harvesting (TEH), Electric-field
energy harvesting (EFEH), Magnetic Field Energy
Harvesting (MFEH), flow based Energy harvesting (FEH)
from source of ambient environment and from external
sources of mechanically driven Energy harvesting (MDEH)
& human based Energy harvesting (HEH). Flow based
Energy harvesting techniques are further classified into wind
based Energy harvesting (WEH) and hydro based Energy
harvesting (Hydro EH) [12]. All these techniques are
classified in Fig. 3.
This paper explores the all the techniques (previous and
latest) to harvest energy from ambient environmental
sources, efficient elements for energy buffers, the sensor
nodes/harvesters of each technique and the major open
research challenges as well, which collectively make this
paper unique. Until now only one latest survey paper related
to multiple energy harvesting techniques exists which covers
the research work mainly related to the harvesters of the
energy harvesting techniques. It was accepted on 12
November 2015 and was written by Faisal Karim Shaikh and
Sherali Zeadally.
The rest of the paper is assembled as follows. Section II
gives an explanatory review of the energy harvesting
techniques. Section III discusses the appropriate storage
elements for the appropriate energy harvesting technique.
Section IV points out some major research challenges that are
still unaddressed while Section V is related to results and
discussion. Final in Section VI I've presented some
concluding remarks as well. At the end, Section VII, VIII and
IX is related to future work, acknowledgement and references
respectively.
II. TYPES OF ENERGY HARVESTING TECHNIQUES IN WSNS
To harvest energy, different sources can be chosen by
keeping in mind the requirement of energy and to generate
power, suitable voltage level needed to be considered also.
A. RFEH TECHNIQUE
Harvesting Energy from Radio Frequency doesn’t involve
any battery usage. This technique, by using some receivers
(easily added to Circuit Board) like Powercast’s
Powerharvester, harvest/receive electromagnetic waves or
radio waves which are being broadcast from radio
transmitters like smart phones, TV or radio broadcast
stations, WIFI etc. and convert it into Direct current (DC)
power supply [17] which after some conditioning/cleaning is
continuously supplied to active sensor nodes/WSNs, in a
defined schedule or when it is required. This technique gives
the advantage of power distribution in a one to many fashion
in which the power can be supply from one node to all other
nodes that can communicate or sense the transmitter. If the
power being harvest isn’t up to the required level then some
power boosters like Multistage Villard Voltage Multiplier,
Multistage Dickson Charge Pump etc. or multiple antennas
4. 4
are used to increase the amount of energy being harvest. For
RF energy harvesting, RF Identification (RFID) system
which read and captures information by using Radio Waves,
is a RF energy harvesting (EH) solution which is available in
the market [12]. This RF based mechanism can be used
indoors and outdoors. Fig. 4 demonstrate the RF energy
harvesting system for wireless sensor networks.
This harvesting process starts with a given antenna that can
fetch radiation waves/frequencies and harvest power from
them. As this technique is capable of transferring the received
radiation frequency signals into electricity/Direct current so
it is a very effective, encouraging and favorable alternative
solution to provide power supply to the energy constrained
WSNs. The energy harvested by using RF Harvester can be
utilized in recharging any low power device. With the
increasing number of the wirelessly operating transmitters
the RF based power/energy availability is also increasing.
The RF based energy harvesting networks are now
enormously being used in Wireless Body area Networks
(WBANs), WSNs and wirelessly charging Systems (WCSs)
Now a days most of the wireless portable devices are
largely dependent on the battery power especially the nodes
of WSN which require constant battery supply. Since TV,
radio, cellphones and many other electric devices eject Radio
frequencies/waves in the air continuously. Hence by utilizing
these waves as an electric power supply one can easily
recharge any device in any place. RFID technology is an
example of the RFEH technique. In [18] the experimental
results of the RF energy harvesting from the Radio Frequency
electromagnetic waves are presented. The system design for
this particular experiment consists of an antenna, voltage
multiplier circuit, storage capacitor and a tuning circuit.
Monopole, Micro strip patch and loop antennas are used for
this experiment. The worst results were obtained using
monopole antenna using which the system produced 10mV
of storage capacitor in 4 days. While using Loop antenna the
results were the best. The system using loop antenna
produced 1V in 0 seconds and 1.25V in 20 seconds.
Harvesting energy from RF waves has led to the development
of the Ultra-low power wireless devices that can be
developed for low maintenance applications like remote
monitoring [19].
Fig. 4 RF based Energy Harvesting System Mechanism for WSNs
The radio frequency based energy harvesting mechanism
uses radioactive signals as a medium to take power/energy in
Electromagnetic Radiation form, with a frequency range of
300GHz to 3 kHz. The other techniques to transfer energy
wirelessly are Magnetic Resonance Coupling (MRC) - based
on the usage of Evanescent Field/Wave coupling to produce
electricity and transfer it between resonators and Inductive
Coupling (IC) - based on Magnetic Field Coupling that
transfers generated electrical energy between coils that
produce electrical or mechanical resonance at the same
frequency [20]. But both of these techniques shown in Fig. 5,
work in the near region wireless transmission not in the
farther ones so the coils or resonators used in these two
techniques need to be placed as close to the transmitters &
receivers as possible which is the reason of preferring RF
based energy harvesting mechanism in case of mobile and
remote charging due to the extra effort requirement on the
alignment of the resonators/coils at each receiving and
transmitting end in both the other mentioned techniques.
Hence Radio Frequency based energy harvesting mechanism
can be called as the Far-Field energy harvesting/transferring
technique as well. The readers can refer to [21], [22] for more
detailed explanation of wireless energy transfer techniques.
The comparison between these three techniques is shown in
Table 1 also elaborated in [23].
Fig. 5 Magnetic Resonance and Inductive coupling
TABLE 1 COMPARISON OF DIFFERENT RF BASED WIRELESS ENERGY
TRANSFER TECHNIQUES
Wireless
Energy
Transfer
(WET)
Technique
s
Apps Field Efficiency
% in
respective
Applicatio
n Field
Nature Year
Radio
Frequency
Based
[12], [21],
[22], [23]
WSNs,
WBAN
s
Far-
Field/
Regio
n
Over 50%
at -5
decibel-
mill watts
Radioactiv
e
2012
,
2014
,
2016
Inductive
Coupling
[20], [23],
[24]
RFID Near 57% at
508kHz
Non-
radioactive
2011
,
2014
,
2016
5. 5
Magnetic
Resonance
Coupling
[20], [23],
[24]
plug-in
hybrid
electric
vehicle
(PHEV)
&
Smart
phone's
chargin
g
Near 30% at
distance of
2.25m
90% at the
distance of
0.75m
Non-
radioactive
2011
,
2014
,
2016
B. MDEH TECHNIQUE
By utilizing electro-magnetic waves, Mechanical energy
can be obtained from sources like vibrating structures i.e.
vibrations produced by pressure, flow of air or water and can
be termed as wind or hydro-electric energies. Generally,
generation of electric energy from mechanical ones is
accomplished by using piezoelectric or electromagnetic or
electrostatic mechanisms. The mechanical strain can be
converted to electrical voltage by utilizing piezoelectric
effect. The mechanical strain can be obtained from seismic
vibrations of low frequency, human motion etc. This effect
can be obtained from human walking also [12].
Piezoelectricity is a crystal’s ability to produce electricity by
applying mechanical stress. By the application of the
mechanical stress upon crystal, the alignment of the electric
charge of the dipoles occurs which lead to the electric
polarization and produces electricity. Recently piezoelectric
based heel strike units were developed in which small electric
generators are installed that uses piezoelectric material to
convert mechanical motion to electric power [24].
Piezoelectric mechanism can harvest electrical energy
from mechanical energy of pressure, vibrations or force
sensed by the sensors. By straining a piezoelectric material
electrical energy converted from mechanical energy can be
obtained. Piezoelectric harvester has a cantilever like
structure. Its piezoelectric beam has a seismic mass attached.
In the piezoelectric harvester, the piezoelectric material's
strain do the charge separation resulting in the electric field
and the required voltage/current/power which varies
according to the change in strain and time [26]. Piezoelectric
energy harvesting mechanism has lots of benefits like simple
structure/less complexity, low cost, no interference of electro
magnetism, efficiency of electromechanical conversion etc.
[27].
In electrostatic energy harvesting technique required
energy is produced by varying the capacitance of capacitor
which depends upon vibrations. This harvester unlike
piezoelectric harvester provides an initial voltage to the
capacitance of the capacitor and as a result vibrations are
produced by the external voltage application which lead to
the change of the quantity of charges stored in capacitor. This
process leads to the generation of electric current.
Electromagnetic energy harvesting mechanism works on
the electromagnetic induction principle of Faraday's law. By
using a stationary magnet a magnetic field will be created and
then the electricity can be produced by passing a magnetic
mass through this field. But due to the enhanced size of the
magnetic materials, it is a very hectic task to implement them
in the sensor nodes [26], [27].
A magnetic spring based electromagnetic energy harvester
consisting of a hollow tube with two magnets fixed at both
ends and a magnetic stack moving inside is presented in [28]
along with its design, modeling and experimental evaluation.
A lot of electromagnetic generators have been introduced to
harvest energy from the vibrations produced by the human
motion. The authors in [28] mentioned that Rome et al.
introduced an electromagnetic suspended load backpack
which converted the mechanical energy obtained from the
vertical movement of the carried load to electrical energy
during the state of walking and an electromagnetic generator
introduced by Saha et al. which can be placed in the rucksack
and it had the capability of generating 300 microwatt to 2.46
mW as shown by the experimental results. Similarly Ylli
introduced multi coil topology harvester that using a set of
coils accelerate a magnetic stack upon the swing motion of
the foot and produces 0.84mW.
Vibrational energy is available in our surroundings in
abundance like from different types of industrial &
commercial machines, vehicles, railway structures etc. The
ideology of vibration to electric energy production was
proposed by Williams and Yates in 1996. Since then it is an
attractive technique of producing energy and powering low
power devices. In [29] using multiple nonlinear techniques a
piezoelectric vibration energy harvester is presented. Using
these techniques the shape of the piezoelectric material or
cantilever is changed to nonlinear form to generate energy
from varying vibrational frequency and the bandwidth is
broaden. The multiple nonlinear effects including Duffing-
spring effect, impact effect, pre- load effect, and air elastic
effect can be achieved in this type of Harvester.
In [30] a study on the feasibility of the Piezoelectric
Harvesters such as MEMs, for Low-Level Vibrations in
Wireless Sensor Networks is presented. According to this
study piezoelectric harvester is such a harvester which
transfers the mechanical energy to electrical energy by
straining/deforming a piezoelectric material. This strain
produces a charge separation across the device, which led to
the production of an electric field that is proportional to the
applied stress.
C. HEH TECHNIQUE
Living bodies have great resources of energy in the form
of chemical, thermal and mechanical energies. These
energies can be utilized to overcome the battery/rechargeable
energy consumption problem so that long-term processing of
the sensor nodes or wearable devices can be achieved [31].
WBAN is the most active application of wireless sensor
networks which involve the sensor's implantation inside the
human body for the betterment of health and quality of life.
Apps of WBANs are shown in Fig. 6. WBAN is an interesting
evolution of wireless sensor networks.
In human based energy harvesting mechanism, Wireless
Body Area Networks (WBANs) [32] are used that are
basically implanted inside the human body and can generate
energy through the change/movement of the finger position,
6. 6
blood circulation, body temperature, frequency/pitch of
voice, walking/jogging/running movement – by using shoe
mounted rotary harvesters [33] etc. Due to their installation
inside the human body the lifetime of these sensor networks
can be longer than the others and hence the reason that human
based energy harvesting mechanism is more preferable [12].
Hence various techniques to harvest energy can be
implemented and utilized to generate more energy in order to
compensate energy consumption issues.
Fig. 6 Applications of WBANs/HEH [34], [35]
D. SEH TECHNIQUE
Another source to harvest energy for WSNs is from
sunlight or solar energy which is available in abundance and
is affordable. Hence its utilization for generating electrical
energy is a clean source. In this technique semiconductor
materials like crystalline silicon (c-Si) in solar cells or solar
panels, are used that harvest the solar rays into the direct
current power supply. When the photons of sun light falls on
the solar panel the separation of electron and holes occur due
to the special manufacturing mechanism of the Solar Panel
and electrons are monitored towards the energy storage
through the input regulator. Then after the separation of holes
and electrons, they are separately allowed to collide at a
junction point by moving in the direction opposite to each
other and their collision produces spark/current/ power. Then
at the end of the process using output regulator this power is
supplied to the sensor nodes. This single solar system can
produce power from microwatt to megawatt depending upon
the size of solar panel. A generalized solar energy harvesting
system is shown in Fig. 7.
This technique of energy harvesting by using the
photovoltaic (PV) effect of converting the light into current
using some semiconductor materials, generates the highest
energy density as compared to the mechanical and thermal
Energy harvesters [36].
Solar energy harvesting technique also known as
Photovoltaic energy harvesting technique, is suitable for
large systems but its power generation process is strictly
dependent on the conditions of environment and the light
availability. For energy to be supplied in a continuous
manner, during night hours energy is stored in the energy
storage or energy buffer. For energy storage Lithium Ion
secondary cell is the most commonly used energy storage
battery/buffer. It has remarkable storage capacity for energy,
very small internal resistance and prolonged lifetime [37].
The most salient element of the solar energy harvesting
model is the load by which the harvested energy is utilized
and this component consists of I/O regulators, processing
units and transceivers. It is also known as a small sensor node.
The load's transceiver - that can transmit and receive
communications, is basically the most energy utilizing part
[38].
Photovoltaic energy conversion mechanism produced
higher energy levels as compared to other energy harvesters
and is suitable for large energy harvesting systems. Its
generated power and efficiency depend upon the light
availability and environmental/weather conditions. The type
of material used for the photovoltaic cell also effects the
system's efficiency and power level. Fleck [39], Enviromote
[40], Trio [41], Everlast [42], and Solar Biscuit [43] are the
commonly known implementations of the solar energy
harvesting sensor nodes.
Fig. 7 Solar Energy Harvesting System for WSNs
A lot of research work has been done to increase the life
time of a wireless sensor network. This work includes the
major factor of data size reduction but reducing data size
increased the delay time and the waiting time to collect a
maximum amount of data for compression. So in [44] the
authors mentioned that since solar energy is denser and heat
enriched energy so it is more preferred source to harvest
energy for WSNs. But the amount of energy harvested using
this technique can be in excess that is more than the energy
required for a particular operation. Yang et al. proposed a
WBAN'sApplications
Blood pH
measurments
ECG
Heart Beat Rate
Recording Breathing
Activity
Insulin Pump
Motion Sensor
Glucose level
Blood Pressure
EEG etc.
7. 7
model to determine the amount of excessive energy for a
given period of time. M. Kang, S. Jeong, I.Yoon et al.
proposed an energy adaptive selective compression scheme
(EASCS) which calculates an energy threshold that
determines if there is any excessive or surplus energy. If it is
not found then the data is compressed to reduce its energy
consumption and transferred otherwise it is transferred
without compression to reduce delay & waiting time for
further data. It prevents the blackout time in which the sensor
node stop working without any warning.
In [45], Solar Castalia, a Solar Energy Harvesting Wireless
Sensor Network Simulator is presented that can surpass
various solar energy harvesting sensor systems by
configuring the solar panel types and size and the
rechargeable batteries. It can also simulate the
season/weather when the target wireless sensor node is about
to operate.
E. EFEH TECHNIQUE
Electric-field energy harvesting (EFEH) technique is an
emerging technique which act as an alternative for next
generation wireless sensor networks. It is a promising
technique to stop the wastage of energy, minimize loss and
increase operational efficiency. In [46] authors proposed a
multi-layer harvesting model and a practical, general use
implementation model that support a vast range of network
topologies by assigning different voltage levels. In this
technique whenever the power is on, the sensing nodes sense
the presence of electric field in the overhead power supplies
and keeps on storing it in the storage capacitor until the max
power level is obtained and the electric power is supplied to
the needy sensor node.
Experimental results in [46] deduce that EFEH technique
is a better solution to build an energy efficient WSN with long
life time, more robustness, greater throughput and improved
flexibility which lead to the deployment or distribution of
large number of sensors.
F. MFEH TECHNIQUE
Another technique to harvest energy for wireless sensor
networks is Magnetic Field Energy Harvesting Technique. In
this technique from the electromagnetic field created around
the current carrying conductor magnetic field is separated out
and by using current transformers electric energy is collected
from the magnetic field. Since magnetic field is produced due
to alternating current so a max amount of current flow is
required in the conductor [46].
S. Yuan el al. in [47] proposed a magnetic field energy
harvester which is more efficient and free standing and to
lessen the demagnetization factor the path of magnetic flux
in helical core can be increased leading to the increased
magnetic flux density and hence sufficient amount of power
will be produced.
G. TEH TECHNIQUE
Thermal energy is generated by using either thermoelectric
energy harvesting technique or pyroelectric energy
harvesting technique [48]. Thermal based energy harvesting
technique utilizing thermoelectric generators (TEG)
produced electric energy by using heat energy which is then
converted to electrical energy by the use of Seebeck effect
[49].
The TEG utilizes hot and cold plates with semiconductor
thermocouples between them. Upon heating, electrons and
holes are separated. Holes are represented by positive signs
while electrons by negative signs. A semiconductor material
carries carrier electrical charges, electrons (-) and holes (+).
Higher the temperature is provided at hot plate, more current
will be produced. While the other plate/sink contains lower
temperature due to which electrons and holes move towards
it having the low level of heat. They move from higher to
lower temperature to obtain equilibrium. Hence more the
electrons and holes are in number, greater the
potential/temperature difference will be created leading to
generation of more voltage as shown in Fig. 8. Then the
Thermal DC converter produces DC power utilizing these
electrons and holes and then after power conditioning energy
is supplied to WSNs/Sensor nodes [12], [50].
Fig. 8 Seebeck Effect’s Mechanism
Recently with the advancement and development in
thermoelectric materials up to 10% energy efficiency is
achieved. 0.14 micro-watt/mm2
power density is achieved for
a 700mm2
device at the temperature dif. of 5 K. The
phenomenon of Thermal energy Harvesting is described
visually in Fig. 9.
Electric energy can be produced by heating Pyroelectric
materials which do not require a continuous temperature
difference unlike the one required by thermocouples in TEGs
but they require time varying temperature changes. These
temperature changes alter the alignment of atoms in
material’s crystalline structure which lead to the production
of voltage or electricity. The leakage of generated voltage
will occur if the continuous temperature change is not applied
to the pyroelectric material. With pyroelectric energy
harvesting techniques (PEHT) maximum efficiency can be
achieved as compared to thermoelectric Harvesting technique
(TEHT) because PEHT can harvest energy from high
temperature sources while TEHT can harvest higher amount
8. 8
of energy as compared to PEHT but its efficiency is limited
as it is dependent upon the temperature difference ∆T. This
dependency is because the TEGs can’t have efficiency η
greater than the Carnot Cycle (∆T/Th) which has greatest
efficiency and four processes of reversible isothermal gas
expansion, adiabatic gas expansion, isothermal gas
compression and adiabatic gas compression [51] and is the
ratio of temperature difference and the maximum/highest
temperature that can be applied [52], [53].
In [54] Hybrid Indoor Ambient Light and Thermal Energy
harvesting mechanisms are proposed to extend the lifetime of
the WSN's node that using only one power management
circuit does the conditioning of output harvested power.
Fig. 9 Thermal Energy Harvesting System for WSNs
H. FEH TECHNIQUE
For addressing power consumption problem using ambient
source, the technique is flow based energy harvesting that
utilizes rotors and turbines and with their rotational effect
electrical energy is produced either by using wind or water.
Flow based energy harvesting technique is further subdivided
into Wind energy and hydro energy.
Thermal and flow energy harvesters are Ambimax,
SPWTS TEG [12], [55], Wearable TEG [56], Flex TEG [12],
[57], Commercial Hydrogen Crator, VAWT [58], AFII etc.
Room Heater TEG are made by heating one side of its
thermoelectric module and cooling the other face letting an
electric current to be produced and it has long life cycle,
simplicity and high reliability and no moving parts [59].
Wind and Hydro energy harvesting is considered as
dynamic fluid energy harvesting mechanisms. A Flow energy
scenario is shown in Fig. 10.
Fig. 10 Flow energy Harvesting Scenario
a) WEH Technique
Wind energy, like solar energy is also freely available and
is affordable. It is a process of converting wind or air flow
into the current or electricity. In this process of producing
current, a wind turbine of right size is used to harness the
actual motion of air to produce electricity. By utilizing this
technique alternative energy/power supply will be provided.
In this technique gear/motors/generators move the turbines &
rotors and rotor’s frequency is then pass on to the FV
(frequency-voltage) convertor which as the name suggested
converts this frequency into voltage/current which is supplied
to the Sensor nodes.
To harvest energy from wind or air sources, micro wind
turbine systems or micro wind harvesters and
electromagnetic wind generators are used [60]. In [60] for the
purpose of wind turbine a plastic four bladed horizontal axis
wind turbine is used which has a diameter of 6.3cm and
length of 7.5cm. A large number of generators are attached to
its shaft that let it to harvest a great amount of power even at
low speed. The most commonly used method is the
conventional wind turbine mechanism for harvesting wind
energy but with the increase in size its efficiency reduces due
to the increased frictional loss’ effect and the reduced surface
area of the blades [27] . In [61], based on the piezoelectric
cantilever, a new piezoelectric flow energy harvestor is
introduced and also that the piezoelectric devices offer the
potential as a flow energy harvester in the absence of the
electromagnetic induction. The output power of the micro
wind belt generators is very high at the high wind speed but
it significantly reduces with the decrease in the wind speed
and these generators can be extremely noisy [27] . In [62], a
small wind generator for wireless sensor applications
consisting of an aerofoil connecting to a cantilever spring is
presented. The air force on the aerofoil make the cantilever
to bend and when it is bend the air force is reduced so that it
can move back to its original shape. This process of applying
airflow is repeated multiple times and the movement of the
aerofoil produces a change in the magnetic flux and generated
electric power.
b) Hydro EH Technique
In Hydro based technique which utilizes water power and
generates energy through the use of Hydro-generators. These
hydro-generators produce power around about 18 mili-watts.
In Seawater batteries, Microbial Fuel Cell can also be used
for underwater energy harvesting mechanism. By using this
technique 1200mWh per day can be produced.
Furthermore, energy can be obtained from some external
sources like mechanical wastes and human based energy.
Energy from the mechanical wastes can be harvested using
vibration to electricity conversion mechanism. This type of
energy harvesting was initially introduced for low power
generators. Using three basic mechanisms of
electromagnetic, electrostatic and piezoelectric
transductions, vibration to electricity conversion is
processed. [63].
9. 9
III. EFFICIENT ENERGY BUFFER ELEMENTS
An ideal energy buffer would be the one that can save
energy in abundance without being affected by it and can
keep it save in energy buffer even when it is not in use.
So considering all these factors, energy buffers can be
selected out either from rechargeable batteries or from the
supercapacitors [52] and their respective best elements are
also mentioned.
a) Rechargeable batteries
Rechargeable batteries use electrochemical cell to store
energy. These batteries consist of a cathode, an anode and an
electrolyte. There are some important technologies that'll be
used in the construction of the rechargeable batteries. Some
mostly used ones are: Nickel Cadmium (Ni-Cd), Nickel
Metal Hydride (NiMH), Lithium Ion (Li+
), Lead Acid (Pb
Acid), Lithium Iron Phosphate (FeLiO4P) and Lithium
Polymer (Li Po). The anode's ability to release negatively
charged particles in oxidation reaction and cathode's ability
to attract them in reduction reaction will lead to the
determination of the voltage being produced from a cell. Due
to these abilities the reactions of both the anode and cathode
are known as reduction reactions due to the hydrogen
electrode and is termed as standard electrode potential or
standard reduction potential (E0
) given by unit of Voltage.
Some E0
for commonly used batteries are listed in Table 2.
TABLE 2 SOME E0
FOR COMMON BATTERY TECHNOLOGIES
Technologies
for Batteries
Reactions E0
in Volts Year
Li [4], [45], [52] Li+
+ e-
Li(s)
-3.05 2011,
2014,
2015
Ni-Cd[45], [52] 2NiOOH +
2H2O + 2e-
2Ni(OH)2 +
2OH-
+0.48 2014,
2015
Ni-Cd[45], [52] Cd(OH)2 + 2e-
Cd + 2OH-
-0.82 2014,
2015
Pb Acid[52] -2
4+ SO2PbO
-
+ 2e+
+ 4H
O2+ H4PbSO
+1.70 2014
Pb Acid[52] -
+ 2e4PbSO
-2
4Pb + SO
-0.35 1014
b) Supercapacitors
A supercapacitor, formerly was known as electric double
layer capacitor (EDLC) now sometimes also called as ultra-
capacitor, is an electrochemical capacitor with high capacity.
It also have very much higher capacitance values as compare
to other capacitors which fills up the gap between
rechargeable batteries and electrolytic capacitors.
The power density of the commercial supercapacitor is
~5Wh/kg while that of Li-ion batteries is above 200Wh/kg.
They are of 3 types: double layer capacitors, pseudo-
capacitors and hybrid capacitors shown in Fig. 11.
Fig. 11 Types of Super capacitor
Porous carbon based electrodes produced from burnt
coconut shell, are used by the double layer capacitors and are
put within the electrolyte and upon applying voltage, charges
in the electrolyte will attract to the carbon electrodes. While
in pseudo-capacitors, capacitance is produced when the
electrolyte’s ions perform reaction with the electrode's atoms
and these electrodes are made of metal oxides. Hybrid
capacitors are the mixture of both of the previous ones.
IV. ENERGY HARVESTERS
A sensor node is something that sense something
depending upon the program fed in to it. It is also known as
a Mote (mostly in North America). It has a sensing
subsystem/unit, processing unit/subsystem, a power source
and a transceiver unit/ information processing or wireless
communication unit. A basic architecture of a general Sensor
node is elaborated diagrammatically in Fig. 12.
Fig. 12 General Architecture of a Sensor Node
An Energy Harvester is also basically a sensory node that
sense the presence of its source/input material and utilize it in
the process of energy harvesting.
A. RF SENSOR NODE/ENERGY HARVESTERS
In [64] Dickson's charge pump circuit’s design is presented
with a multi resonant loop antenna for Radio Frequency
energy harvesting. This circuit enables the RF Harvester to
process efficiently at both outdoors and indoors. The
proposed energy harvester gains sensitivity of -21.2 dBm and
-17.1 dBm at 900 MHz & 2.4 GHz respectively and at power
10. 10
level of 10MΩ output load at 1V output voltage shows
working rate having the efficiency of 25.7%. The authors
presented dual band operational processing results of RF
harvester by using a single loop antenna whose impedance
facilitates simple impedance transformations at both
mentioned frequencies resulting in reduced loss in matching
networks and they built Dickson charge pump using HSMS-
285C Schottky Diodes due to which manufacturing cost is
reduced as compared to integrated ones. This design
technique can facilitate the dual frequency band energy
harvesting using loop antenna.
The Texas Instruments’ (TI) analog experts have discussed
multiple RF Sensor Node Development Platforms for
6LoWPAN and 2.4 GHz Applications in [65]. Some of these
platforms are CC2520, MSP430F5438A, TMP106 and
TPS22901 etc.
A dual-path CMOS rectifier with adaptive control for
ultra-high frequency (UHF) RF energy harvesters is
introduced in [66]. It includes both low power path and high-
power path. The dual path rectifier is included in the 65nm
CMOS process along with an adaptive circuit. The power
convention efficiency can be achieved above 20% with an
11dB input range from -16 to -5 dBm. The sensor nodes
sensitivity of -17.7 dBm is achieved with 1V voltage level.
Majdi M. Ababneh, Samuel Perez, and Sylvia Thomas in
[67] introduced a power management circuit which improves
the efficiency of the DC-DC converter by using particle
swarm optimization technique in which fitness function
generated from the converter's efficiency is used and inductor
and on time are selected as optimized parameters. This design
technique improves the efficiency level of the power
management circuit to 9.25%. This technique can be utilized
in portable apps to increase the battery life.
RFEF Technique is immensely being popular in green
technology because of the excessive deployment of mobile
phone base stations, television base stations, Wi-Fi,
Bluetooth etc. So to increase the low RF energy obtained
from the receiving antenna from AC to DC focusing on low
RF input, the authors in [68] presented a 1.8 GHz and 2.4
GHz Multiplier Design for RF Energy Harvester in Wireless
Sensor Network. For this purpose Dickson Multipliers are
also optimized by using advanced Design System which
gives efficiency of 6.5 % at 0.96 V and 5 % at 0.76 V and at
0 dBm for 1.8 GHz and 2.4 GHz respectively.
With the introduction of the applications that exploits
industrial, civil, and aerospace infrastructure, it is mandatory
for sensor nodes to be robust and power efficient especially
in locations that are difficult to access. For this purpose,
microwave energy is examined for powering wireless sensor
nodes that are deployable. Hence a prototype micro strip
patch antenna was introduced in [69] to operate in the 2.4
GHz ISM band and to gather directed RF energy for
powering up a Wireless device. The power was then used
further to charge the sensor node to 3.6V in 27s which was
enough to two piezoelectric sensors.
Apart from these mentioned sensor nodes and harvesters
multiple RF harvesters have been introduced in the past
named as ST Microelectronic Modules [70], Texas
Instruments eZ430 - RF2500 [71], Powercast’s
Powerharvester [17], [72], Ambient Radio Frequency based
Harvester nodes and multiple antenna based RF Harvester
nodes.
B. SEH SENSOR NODE/ENERGY HARVESTERS
Using an on-chip solar cell an ultra-compact single chip
solar energy harvesting integrated circuit for biomedical
implant applications is introduced in [73]. The authors used
an on-chip charge pump along with the parallel connected
photodiodes which improves the efficiency to 3.5 times as
compared to conventional stacked photodiode. In order to
improve the area efficiency a photodiode assisted dual startup
circuit also used which enhanced the startup speed by 77%
and a low startup voltage of 0.25 is obtained by using an
auxiliary charge pump with zero threshold voltage devices in
parallel with main charge pump. To improve the energy
harvesting efficiency they utilized a synthetic charge pump
and solar cell area optimization technique. Experimental
results show that a maximum efficiency of 67% is achieved
by utilizing this harvester.
Due to the change of oil prices and growing degree of
pollution led to the alternative and renewable energy sources
that are less oil consuming and free of factors that enhances
pollution. Hence the advancement of technology led to the
usage of photovoltaic systems which are pollutant free and
non-oil consuming and usage of converters in these systems
enhances the efficiency rates and reduces cost. In [74] a
photovoltaic system with maximum power point tracking
facility is introduced. Generally maximum power point
tracking control is very challenging due to the conditions that
calculates the quantity of sun energy and transfer it into the
photovoltaic generator. The power generated by the solar
energy harvester in [74] that utilize photovoltaic generator, is
maximized by using sliding mode controller that handles the
boost converter connected between the photovoltaic
generator and the load. After designing and modeling this
system is tested under MATLAB/SIMULINK environments.
For this processing the SMC-MPPT algorithm is divided into
two steps. In first step the actual reference voltage level at
which the maximum power level is achieved, is calculated
while in the second step the SMC PVG voltage regularization
is done at the reference voltage. The stability of the proposed
SMC MPPT system is analyzed and verified using the
Lyapunov theory. The MPPT algorithm ensures the
robustness and high tracking performance rates.An advanced
form of technology is WSN using which one can monitor
environmental or physical conditions such as light intensity,
temperature, pressure etc. Sensor nodes in a wireless network
are generally powered by two AA size batteries which have
limited life time and needed to be replaced whenever they
expired. Hence this energy harvesting system of solar power
is introduced along with other solutions.
A simple and low cost energy harvester is developed and
introduced in [75]. Due to the cheap, maximum and natural
availability and high power density of solar power in the
11. 11
environment, sunlight energy is selected as an energy
harvesting source. Though solar energy have some
drawbacks that it is available only in daytime and is
dependent upon the sunlight density but still it is much better
and cheaper as compared to others. The authors in [75]
proposed an energy harvester that contains 4 major
components of solar panel, batteries, output regulators and
wireless sensor network node/mote. While selecting the type
of solar panel several factors of open circuit voltage, short
circuit current, maximum power point (MPP) and the IV
characteristic curve need to be considered and for building up
this harvester a MSX-005F solar panel is considered which
has maximum power of 0.5W. Due to the seasonal cycle
availability of the sunlight energy an energy buffer/storage is
required to provide power un-interrupted. Hence three types
of batteries of lithium-ion, nickel cadmium and nickel metal
hydride are considered but later the lithium ion battery is
discarded due to its long lifespan and low discharge rate,
expensiveness and due to the fast discharge rate of nickel
cadmium battery, it is also discarded and Nickel metal
hydride (NiMH) battery is selected due to its higher energy
density, lower life cycle and efficient rates of energy charging
and discharging. An output voltage regulator was needed to
boost up the voltage supplied by the battery to the desired
voltage level. Hence for this purpose a DC-DC boost
converter was used and the used model is MAX1724EZK33
from Maxim Integrated because it requires a minimum
number of external components. Since two NiMH batteries
are used having a capacity of 1500mAh and a voltage supply
of 2.5V so energy supplied by the battery will be
B = 1500 mA × 60 × 60 x 2.5V = 13500 J
And the duration of the battery power supply to sensor
node would be
Tb= B/E = 13500/7897.82 = 1.7 days
The average harvested energy produced by the solar panel
is 12231 Joules within the time span of 1.1 days. Therefore
from experiments it can be concluded that solar panel
requires only 1 day to be fully charged while batteries can
power nodes for 1.7 days.
In [76] an efficient solar energy harvesting single axis sun
tracking system with dynamic offset parabolic mirrored
reflector dish to enhance the efficiency of solar energy
harvesting system is introduced and its performance
compared with static solar panel and solar panel with static
mirrored reflector system is better. It can harvest more energy
as compared to others. The parabolic mirrored reflector dish
can reflect radiations of light and can harvest energy
efficiently by focusing the reflected light at its focal point
where solar panel is placed. For rotation of dish in the
direction of sun's motion, clock tracking system has been
used and an Arduino UNO controller has been used for dish's
rotation by an angle of 6.25 degree at 30 minutes periodic
interval during 6am to 6pm. This harvester can generate
average output voltage of 7.822 volts and 6.144 mA and is
capable of showing 81% more efficiency rates as compared
to static solar panels and 48.26 % more efficient than solar
panel with static reflector system. This harvester
autonomously operate the motor driven dish by harnessing a
portion of harvested solar energy which is stored in
rechargeable lead acid battery.
Moreover different harvesters of solar energy for WSNs
are IRN [77], BLSH [78], MSIL [79], LTSN [80], Hydro
Watch [81] and Heliomote [82] etc.
C. TEH SENSOR NODE/ENERGY HARVESTERS
In [83] a cheaper source to harvest thermal energy than
thermoelectric generator is provided by the Electrochemical
thermocells which by utilizing the temperature dependence
of electrode potential, produces electrical energy and redox
mediators for transmission of charge in the electrolyte. The
electrolyte in these thermocells acts like semiconductors in
conventional thermoelectric generators if the charge transfer
resistance and the thermal and ionic transport occur by
thermal convection of the electrolyte.
In [84] B. Gusarova, E. Gusarovac, B. Viala et al. proposed
a unique thermal energy harvester. This harvester does
coupling of both pyroelectric and piezo- electric effects of
polyvinylidene fluoride (PVDF) with shape memory effect of
TiNiCu alloy. To harvest, the small and quasi-static
temperature changes and the superior flexibility of the
polyvinylidene fluoride is combined with the great
temperature induced strain of the shape memory alloy and the
harvester of a size of post stamp obtained 0.41 mJ/cm3 per
event of temperature variation of 20◦C energy density. At
70◦C along with 4 polyvinylidene fluorides the harvester can
immediately charge the sensor node without storing any
energy in the storage unit.
An electret based unsteady Thermal Energy Harvester
using Potassium Tantalite Niobate Crystals is presented by
H. Xie et al. in [85]. This harvester is composed of three parts:
external load, electret acting as a permanent voltage source
and temperature sensitive dielectric capacitor. As ambient
temperature the permittivity of dielectric changes varies,
producing the change in the amount of the induced charges
which led to the production of external current by fluctuating
temperature.
Global environmental challenges and energy crises has led
to the utilization of the ambient energy. A lot of chemical
reactions that are frequently being used/ processed in
industries and laboratories lead to the production of large
amount of un-needy heat, almost every 2 hours. For example
combustible reactions of natural gas and coal, different
methods of waste water treatment in industries etc. produces
great quantity of heat which has temperature below 1300
C.
This heat can be recycled to produce electric power. Many
methodologies has been proposed for this purpose including
stirling engines which can convert thermal energy into
electrical energy [86] Organic Rankine cycles use
refrigerants and hydrocarbons to harvest heat up to 200–
3000
C [87] but due to the small Carnot efficiencies these
techniques are not suitable for the low grade heat. In paper
[88] a pyroelectric device/harvester is presented that utilizes
carbon nanotube(CNT)/PVDF/CNT sandwich as a potential
approach for harvesting heat from chemical process. For this
12. 12
purpose pyroelectric device is attached to the outer side of the
beaker having various chemical exothermic reactions. This
way heat is converted to electricity and an output voltage 9.1
V with impedance of 100MOhm and short circuit current 95
nA is noticed when the reaction of sodium hydroxide and
hydrochloric acid occurs in the beaker.
Energy harvesting is the most demanding
technology/methodology to power up any sensor node or
rechargeable things. In [89] an approach to harvest human
body heat for powering wearable devices is presented and this
work focused on the optimization process of power
conversion efficiency from human body to the application.
Using this approach the micro TEG produces up to 65%
higher output power per area in a laboratory testbed and 1–
15% in a real world experiment on the human body
depending on physical activity and environmental conditions.
Furthermore SPWTS TEG [12], [55], Wearable TEG [56],
Flex TEG [12], [57] are some TEG based thermal energy
harvesters.
D. MDEH SENSOR NODE/ENERGY HARVESTERS
Since mechanical energy is basically obtained from
vibrational effect, piezoelectric effect etc. So some of its
energy harvesters are mentioned below.
In [29] using multiple nonlinear techniques a piezoelectric
vibration energy harvester is presented. Vibrational energy is
available in our surroundings in abundance like from
different types of industrial & commercial machines,
vehicles, railway structures etc. The ideology of vibration to
electric energy production was proposed by Williams and
Yates in 1996. Since then it is an attractive technique of
producing energy and powering low power devices. In this
technique the shape of the piezoelectric material or cantilever
is changed to nonlinear form to generate energy from varying
vibrational frequency and the bandwidth is broaden. The
multiple nonlinear effects can be achieved in this device
including Duffing-spring effect, impact effect, pre- load
effect, and air elastic effect.
Dedicated sensor nodes are frequently being used in
everyday life aspects of security, tracking, monitoring and
measuring performance metrics etc. In [90] the authors
presented an electromagnetic vibrational energy harvester to
enable sensor nodes to power themselves up. This harvester
is basically a unique conception of the electromagnetic, proof
mass based energy harvester which tends to enhance the
vibrational velocity of any vibrating body twice. Due to the
large power capacity and reliability an electromagnetic
design is selected for this harvester. As the electromagnetic
generator's output voltage is directly proportional to the
squared relative velocity between the magnets and the coils
and the squared magnetic field strength through the coils. To
amplify the velocity of the magnet and coil, a unique frame
of spring steel compliant mechanism was utilized. The
vibrations from the planted machinery induced proof mass
and the shape of the frame enhances the relative motion
between magnets and coils of the electromagnetic energy
harvester. At normal frequency operation, the harvester
shown up to 0.91V AC open voltage and a max power of
2mW.
Vibrational energy harvesting is actually a phenomenon of
converting mechanical energy from ambient sources to the
electrical energy to supply power to remote sensor nodes.
This harvesting mechanism work well as compared to the
linear resonator that works very poorly at a farther distance
from their natural frequency source while nonlinear energy
harvesters work much better because they utilize vibrational
effect over a wide range of spectrum. Hence the authors in
[91] presented a hybrid non-linear energy harvester that
merges the bi-stability with internal resonance to increase the
frequency bandwidth. The presented harvester contains a
piezoelectric cantilever beam, the output voltage and the
movable magnet. Nonlinear vibration energy harvesting with
internal resonance contains a spring and a movable magnet
with a piezoelectric beam inside the two of them and a fixed
magnet. The beam is displaced in sideways to produce
voltage. The larger the displacement, the larger the
piezoelectric output voltage. The numerical techniques of
Runge Kutta Method of long time integration and the
shooting methodology were utilized for the verification of the
analytical results.
A feasible study of impact based piezoelectric road energy
harvester for WSNs in smart highways is presented in [92].
The main goal of this study is to design and examine the
impact based piezoelectric road energy harvesters as energy
sources for different sensor nodes and smart highways. The
harvester discussed in this research work is much better than
the existing ones. The output power of this harvester is first
measured using a machine known as Universal testing
machine that does the application of the axial load with a
controlled loading frequency then a mobile loading simulator
simulate the real world traffic load on a lab scale. At the end
the result of the maximum output power of mentioned energy
harvester is achieved which is 483 mW and 21.47 W/m2
.
Hence a basic piezoelectric road energy harvester is build up
that can be used for powering up the WSN's sensor nodes.
AEM MEEG [93], Piezoelectric MEEG and micro
Piezoelectric MEEG [94] are also the electric energy
harvesters/generators of Mechanical energy harvesting
mechanism.
E. HEH SENSOR NODE/ENERGY HARVESTERS
Human energy harvesting sensor nodes can be powered up
by utilizing the energy harvested from the human walking
motion. The continuous development in the wearable energy
harvesting technology has led to the discovery or introduction
of the more advanced devices that deliver increased power
outputs which can be used to provide un-interrupted energy
supply to body sensors to get the energy autonomous WSNs.
In [95] a wearable energy harvesting powered wireless
sensing system is introduced which contains 4 parts of a
magnetically plucked wearable knee-joint energy harvester
(Mag-WKEH) to harvest energy from knee-joint motions
during human walking motion, a power management module
(PMM) with a maximum power point tracking (MPPT)
13. 13
function, an energy aware interface (EAI) in order to deal
with the dissimilarity between the energy generated and
energy required and the fourth one is an energy aware
wireless sensor node (WSN) for data sensing and data
transmission. Experimental test bed on the human beings
walking on the treadmill while wearing this proposed system
at different speed levels, showed that the energy power output
of Mag-WKEH with the increase in the walking motion from
3 - 7 km per hour, increased from 1.9 ± 0.12 to 4.5 ± 0.35
mW. This out energy/power supply was sufficient for WSNs
and within the active time period of 2.0 ± 0.1s they can do
their processing in duty cycle from 6.6 ± 0.36% to 13 ± 0.5%.
M. Geisler, S. Boisseau, M. Perez et al. in [96] introduced
a technique for the optimization of electromagnetic energy
harvester and converting the low frequency of the body
motion in to the usable power supply and use it to power up
the WBAN's sensor nodes. This methodology for the targeted
human being's running activity, optimizes the nonlinear
inertial energy harvester and an electrical power supply of
4.95mW in a resistive load and 3mW in a 2.4V NiMH
rechargeable battery is produced.
A Wireless power transfer system for a human motion
energy harvester is presented in [97] by P. Pillatsch et al.
They presented a methodology to actuate a rotor wirelessly
in the rotational piezoelectric energy harvester by utilizing a
magnetic circuit's reluctance coupling property. This
coupling is done with the external running rotor which has
multiple magnet stacks permanently attached to it. This
phenomenon led to the possibility of recharging a battery or
super capacitor even in the absence of human motion.
F. FEH SENSOR NODE/ENERGY HARVESTERS
FEH Sensor Node/Energy Harvesters are further
categorized to WEH and Hydro EH Sensor nodes/Energy
Harvesters
a) WEH Sensor Node/Energy Harvester
WSN is now becoming more and more advanced
technology from which we can say that in near future
everything we use will be internet connected. But in spite of
WSN's thousands of advantages they are suffering from
energy consumption issue due to which energy harvesting
mechanisms are introduced. In [98] a wind energy harvester
for autonomous WSNs is presented which will improve the
prediction of weather/wind conditions. In this work the wind
is exploited as an environmental energy source and uses a
small wind turbine. The weather forecast is also used along
with the actual wind state and at the end the correct predicted
value is selected on the basis of the error between the real
wind state and the previously predicted value. For
experimental analysis a Powwow platform connected with
wind turbine and rotation per minute circuit is used and
different wind conditions are created by using three different
fans at different distances from the harvester and the
configuration is maintained for ten minutes. The node sends
the rotation per minute value to the receiver during the
process after every minute and this value is compared with
ten different values and at the end average speed is
determined. Then the noted or selected rpm value is
converted to harvested energy.
In [99] a wideband topology and a design optimization
technique is presented for piezoelectric wind energy
harvester to maximize the wind energy, Hence the obtained
maximum power density, maximum efficiency, and cut-in
wind speed of the harvester are 0.59 mW/cm3, 24%, and 2.1
m/s, respectively. Instead of WSNs wind energy harvesters
are also used for autonomous embedded systems [100] and
can be deployed as building skin based on zinc oxide [101].
b) Hydro EH Sensor Node/Energy Harvester
With the passage of time and the advancement of
technology especially wireless technology, energy harvesting
techniques are becoming the alternative of the conventional
batteries. For energy harvesting process humans have already
used the technology in the form of the windmill, watermill,
solar energy and geothermal energy etc. These harvesting
mechanisms provide power level of [102] kW or MW and the
energy harvesting process is limited to micro energy
harvesting. Piezoelectric material can be used for small
devices. This material has a capability of converting the
dynamic pressure into required electric energy hence the
dunamic pressure or load can be human motion, water, wind,
tides, rainfall etc. The proposed system model in [98]
contains a water tank, set of nozzle to increase the velocity
factor of the fluid, pipe to circulate the water flow. This
model can be used at location swhere maximum water supply
is available such as river etc. If the existing water is not
enough then the water can be stored in the reservoir tank and
water can be circulated using the pump and recirculation of
water can be done using the electric motor.
For the transportation of any type of resources underwater
pipelines are mandatory and it is also important to do
automatic monitoring of pipelines using WSNs and for longer
lifespan of WSNs continuous energy supply is required. So
for this purpose a methodology for near optimal piezoelectric
energy harvester design is presented in [103] to enable the
wireless sensor node self-powered for in pipe monitoring by
utilizing kinetic energy of water flow. The Turkish Cyprus
water pipeline project tis considered for experimental
purposes and the designed energy harvester can produce
energy between 820microWatt to 12.3 mW, with an average
velocity of 1.4m/s and with a negligible head loss of 1.5mm.
More Flow based energy harvesters are Ambimax,
Commercial Hydrogen Crator, VAWT [58], AFII etc.
Since EFEH technique and MFEH technique are so novel
and are still under research so their energy harvesters or
sensor node have not yet been surveyed or researched out.
V. OPEN RESEARCH CHALLENGES
Some research challenges that have not been addressed yet
are:
A. GENERALIZATION OF HARVESTERS
One of the research challenges of the WSN’s energy
14. 14
harvesting scenario is to harvest as many energy as possible
from multiple sources. So this requirement leads to the
implementation and deployment of a generic harvester that
can be used to harvest energy from multiple sources.
B. FAULT TOLERANT WSN
Another key challenge of the WSN’s energy harvesting
scenario is to make the WSNs fault tolerance so that they can
keep on generating energy and supply continuous power to
the needy sensor node. So building a WSN that can’t be
effected by the failure of any energy buffer will lead to the
continuous power supply mechanism. A WSN must not be
affected by the failure of any node or anything connected to
the WSN. This is a reliability concern. Fault tolerance is the
ability to maintain the functionalities of network without any
interruption even if any node fails or energy buffer failure.
C. EFFICIENT COMMUNICATION SYSTEMS AND SENSORS
For a WSN to be working more efficiently and for the
betterment of the energy consumption process it is necessary
to work on the development of low power and low cost
communication systems and sensors so that WSNs can
perform much better.
D. BETTER RECEPTION OF SIGNALS
Another key challenge is to improve the mechanism of
receiving signals because if a node remain busy in getting a
distorted signal much energy will be lost/consume so there is
a need to introduce different methods to bring improvement
in the reception of signal so that less energy can be consumed
and process can be fasten up.
VI. DISCUSSION
From the wireless sensor network’s point of view and by
keeping in mind the detailed elaboration of the Energy
Harvesting techniques, it can be pointed out that RF based
energy harvesting technique is very promising and the solar
energy harvesting technique and thermal energy harvesting
techniques by utilizing the photovoltaic cell and
thermoelectric generators respectively will also work very
effectively because the energy sources in both the techniques
are available in abundance and very cheaply. But in case of
flow based energy harvesting mechanisms like wind and
hydro, no doubt have their own capabilities but are not a
source of maximum and continuous power generation and are
little expensive as compared to others because in order to run
turbines or rotors continuously, additional power supply is
required. While for mechanical and human based energy
harvesting, the resources to generate energy like oil, gas, coal,
nuclear power plants etc., are limited and expensive since
they do not exist in abundance. The description about energy
harvesters is summarized in Table 3.
V. CONCLUSION
WSNs have now become very attractive in lots of fields of
technologies and are attracting many stakeholders and it
seemed that they are suffering from energy consumption
issues but in this work lots of energy harvesting techniques to
overcome the issue of energy consumption factor have been
surveyed out. These techniques will gradually advance
towards the implementation and deployment level, from
which it is concluded that in near future WSNs can
completely overcome the ragging issue of energy
consumption. Then some appropriate storage elements for
different techniques that support storage of harvested energy,
are also surveyed out. I found out that in this domain of
WSNs the current state of art in the modeling of appropriate
technique is still immature and is mostly at research level.
Lastly some open research challenges that have not been
addressed yet, need to take into consideration in near future.
VI. FUTURE WORK
It is obvious that gradually there'll be a situation when no
energy will be left to harvest. Considering the solar energy,
this problem will occur during night time. This problem can
be minimized by utilizing an energy storage/buffer that can
provide continuous power supply even when there is no
energy left in the environment, to harvest. Using the elements
mentioned in the section III such an energy buffer can be
made. In the future the basic focus will be upon the
development of autonomously self-charging energy buffers
without using any sort of harvesting system and in near future
all these techniques will be on implementation level.
Furthermore a generalized harvester to implement any of
these mentioned techniques on a single harvester will be
deployed and future concerns will be related to the
development of fault tolerance WSNs along with the
betterment in the reception of signals and efficient
communication systems and sensors as well.
TABLE 3 SUMMARY
Harvesters Probable Power
Produced per
Area or Energy
density
Efficiency
Rate
Pros Cons
Solar Energy Harvester
[22], [36]–[38], [45], [58]
15 mW/cm3
Around
about 40%
[52]
Limit-less energy in daytime Fragmentary supply
Thermo-electric Harvester Depends upon the 10 to 15 % Continuous power supply can be Very High temperature is required
15. 15
[12], [48]–[50], [53]–[57],
[59]
temperature
difference and most
commonly 40
µW/cm3
[52] possible
Radio Frequency Based
Energy Harvester [17]–
[19], [21], [104]–[106]
Depends upon
frequency being used,
approximately about
50 nW/cm2
Above 70%
[52]
Continuous power supply can be
possible
Abrupt Decrement in power in case of
greater distance from transmitter
Mechanical Energy
Harvester [12], [25], [26],
[27], [52]
330 µW/cm2
nil Mechanical power makes a direct,
immediate impact wherever it will be
used and generation of this energy is
quick also.
Sending mechanical power over long
distances is inefficient because friction
in ropes, gears and other mechanisms
turn much of the useful mechanical
energy into heat before it reaches its
destination.
Flow Based
Energy
Harvester
WEH
[12],
[58],
[107]
10.4 mW/cm3
About 30 to
40 % [12],
[58],
The production of wind energy is
“clean”. Unlike using coal or oil,
creating energy from the wind
doesn’t pollute the air or require any
destructive chemicals. Wind is free.
In the event that you live in a
geological area that gets a lot of wind,
it is ready and waiting. As a
renewable asset, wind can never be
drained like other regular, non-
renewable assets [107].
Wind doesn’t generally blow reliably,
and the wind turbines usually work at
about 30% wind capacity or more. If
the weather is not favorable then the
energy can’t be produced. The wings
of wind turbines can be unsafe to
natural life, especially birds and other
flying creatures which is a threat to
wildlife [107].
Hydro
EH
[26],
[27],
[36],
[108],
[109]
0.35 mL at 3.43 m/s
speed produces 30.67
µW/cm2
nil Water is a freely available energy
source. Another pro of the Hydro
Energy Harvesting is that additional
power plants can be added to the
existing dams instead of building
bigger or additional dam sites,
whenever needed. Hydro energy
harvesters do not pollute the air,
water or land like other power plants
do. Hydro energy harvesters do not
lead to global warming or acid rain
[108].
Dams can break and cause havoc with
flooding, endangering human and
animal life. The building of dams can
change flow of water or rivers causing
short of water to neighborhood and
sometimes leading to the cutting off, of
the water supply to local areas. Also
that the construction costs are huge and
the energy production is mostly
dependent on rainfall and can be
effected by drought [108].
Human Based Energy
Harvesting [63]
1.2 µW/cm2
nil Easy to harvest and can be
implemented in multiple places
within a single body. Also very useful
for health care.
Very costly and maintenance is also
very costly
Electric-Field Energy
Harvesting Technique [46]
nil max No need for current conversion,
available most of the time and easy to
implement
Unwanted side effects such as burning
of power plants or transformers
Magnetic Field Energy
Harvesting Technique [46]
150 µW/cm3
nil easy to implement, non-complex
structure
Requires high current flow, safety
vulnerability
ACKNOWLEDGEMENT
I, Farwa Abdul Hannan, after Allah Almighty, would like
to express my profound gratitude to my teacher Dr. Rana Asif
Rehman for his exemplary guidance, monitoring and constant
encouragement throughout this research. I am highly thankful
to him for providing necessary information and material
regarding this research project and for making it possible.
I would like to express my gratitude to my parents also for
their support and encouragement which helped me in the
completion of this research work.
REFERENCES
[1] L. University, An Introduction to MEMS (Micro-
electromechanical Systems), no. January. 2002.
[2] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network
survey,” Comput. Networks, vol. 52, no. 12, pp. 2292–2330, 2008.
[3] A. Ali, A. Khelil, F. K. Shaikh, and N. Suri, “Efficient predictive
monitoring of wireless sensor networks,” Int. J. Auton. Adapt.
Commun. Syst., vol. 5, no. 3, pp. 233–254, 2012.
[4] K. Maraiya, K. Kant, and N. Gupta, “Application based Study on
Wireless Sensor Network,” Int. J. Comput. Appl., vol. 21, no. 8,
pp. 9–15, 2011.
[5] M. Castillo-Effer, D. H. Quintela, W. Moreno, R. Jordan, and W.
17. 17
Electron., vol. 58, no. 9, pp. 4424–4435, 2011.
[55] Y. Shi et al., “A novel self-powered wireless temperature sensor
based on thermoelectric generators,” Energy Convers. Manag.,
vol. 80, pp. 110–116, 2014.
[56] Z. Wang, V. Leonov, P. Fiorini, and C. Van Hoof, “Realization of
a wearable miniaturized thermoelectric generator for human body
applications,” Sensors Actuators, A Phys., vol. 156, no. 1, pp. 95–
102, 2009.
[57] V. Leonov, T. Torfs, P. Fiorini, and C. Van Hoof, “Thermoelectric
converters of human warmth for self-powered wireless sensor
nodes,” IEEE Sens. J., vol. 7, no. 5, pp. 650–656, 2007.
[58] R. Morais et al., “Sun, wind and water flow as energy supply for
small stationary data acquisition platforms,” Comput. Electron.
Agric., vol. 64, no. 2, pp. 120–132, 2008.
[59] S. Yang, “Thermal energy harvesting for WSNs,” 2010 IEEE Int.
Conf. Syst. Man Cybern., pp. 3045–3052, 2010.
[60] D. Carli, D. Brunelli, D. Bertozzi, and L. Benini, “A high-
efficiency wind-flow energy harvester using micro turbine,” in
SPEEDAM 2010 - International Symposium on Power
Electronics, Electrical Drives, Automation and Motion, 2010, pp.
778–783.
[61] X. Gao, W.-H. Shih, and W. Y. Shih, “Flow Energy Harvesting
Using Piezoelectric Cantilevers With Cylindrical Extension,”
IEEE Trans. Ind. Electron., vol. 60, no. 3, pp. 1116–1118, Mar.
2013.
[62] Dibin Zhu, S. Beeby, J. Tudor, N. White, and N. Harris, “A novel
miniature wind generator for wireless sensing applications,” in
2010 IEEE Sensors, 2010, pp. 1415–1418.
[63] L. B. Kong, T. Li, H. H. Hng, F. Boey, T. Zhang, and S. Li, Waste
Energy Harvesting, vol. 24, no. I. 2014.
[64] A. Azam, Z. Bai, and J. S. Walling, “A Low-cost , Dual-band RF
Loop Antenna and Energy Harvester,” pp. 33–36, 2017.
[65] T. I. Designs, “RF Sensor Node Development Platform for
6LoWPAN and,” no. March, 2014.
[66] Y. Lu et al., “A wide input range dual-path CMOS rectifier for RF
energy harvesting,” IEEE Trans. Circuits Syst. II Express Briefs,
vol. 64, no. 2, pp. 166–170, 2017.
[67] M. M. Ababneh, S. Perez, and S. Thomas, “Optimized Power
Management Circuit for RF Energy Harvesting System,” 2017.
[68] Y. E. Sun and N. M. Mahyuddin, “A 1.8 GHz and 2.4 GHz
Multiplier Design for RF Energy Harvester in Wireless Sensor
Network,” Springer, Singapore, 2017, pp. 501–510.
[69] K. M. Farinholt, G. Park, and C. R. Farrar, “RF Energy
Transmission for a Low-Power Wireless Impedance Sensor
Node,” IEEE Sens. J., vol. 9, no. 7, pp. 793–800, 2009.
[70] “M24LR-DISCOVERY - Discovery kit for CR95HF Reader
& M24LR04E Dynamic NFC / RFID Tags with energy
harvesting - STMicroelectronics.” [Online]. Available:
http://www.st.com/en/evaluation-tools/m24lr-discovery.html.
[Accessed: 31-May-2017].
[71] “RF & Microwave ICs | Overview | TI.com.” [Online].
Available: http://www.ti.com/lsds/ti/analog/rf-
microwave/overview.page. [Accessed: 31-May-2017].
[72] “Powercast Lifetime Power® Energy Harvesting Kit Europe.”
[Online]. Available:
http://eu.mouser.com/new/powercast/powercastlifetimepower/.
[Accessed: 31-May-2017].
[73] Z. Chen, M.-K. Law, P.-I. Mak, and R. P. Martins, “A Single-Chip
Solar Energy Harvesting IC Using Integrated Photodiodes for
Biomedical Implant Applications,” IEEE Trans. Biomed. Circuits
Syst., vol. 11, no. 1, pp. 44–53, Feb. 2017.
[74] M. Farhat, O. Barambones, and L. Sbita, “A new maximum power
point method based on a sliding mode approach for solar energy
harvesting,” Appl. Energy, vol. 185, pp. 1185–1198, 2017.
[75] L. J. Chien, M. Drieberg, P. Sebastian, and L. H. Hiung, “A simple
solar energy harvester for wireless sensor networks,” in 2016 6th
International Conference on Intelligent and Advanced Systems
(ICIAS), 2016, pp. 1–6.
[76] M. Singh, J. Singh, A. Garg, E. Sidhu, V. Singh, and A. Nag,
“Efficient autonomous solar energy harvesting system utilizing
dynamic offset feed mirrored parabolic dish integrated solar
panel,” in 2016 International Conference on Wireless
Communications, Signal Processing and Networking (WiSPNET),
2016, pp. 1825–1829.
[77] A. Hande, T. Polk, W. Walker, and D. Bhatia, “Indoor solar energy
harvesting for sensor network router nodes,” Microprocess.
Microsyst., vol. 31, no. 6, pp. 420–432, 2007.
[78] D. Brunelli, C. Moser, L. Thiele, and L. Benini, “Design of a Solar-
Harvesting Circuit for Batteryless Embedded Systems,” IEEE
Trans. Circuits Syst. I Regul. Pap., vol. 56, no. 11, pp. 2519–2528,
Nov. 2009.
[79] H. Yu and Q. Yue, “Indoor Light Energy Harvesting System for
Energy-aware Wireless Sensor Node,” Energy Procedia, vol. 16,
pp. 1027–1032, 2012.
[80] P. Corke, P. Valencia, P. Sikka, T. Wark, and L. Overs, “Long-
duration solar-powered wireless sensor networks,” in Proceedings
of the 4th workshop on Embedded networked sensors - EmNets
’07, 2007, p. 33.
[81] Jaein Jeong, Xiaofan Jiang, and D. Culler, “Design and analysis of
micro-solar power systems for Wireless Sensor Networks,” in
2008 5th International Conference on Networked Sensing Systems,
2008, pp. 181–188.
[82] V. Institute of Electrical and Electronics Engineers., A. Kansal, J.
Hsu, J. Friedman, and M. Srivastava, IPSN 2005 : 2005 Fourth
International Symposium on Information Processing in Sensor
Networks : 25-27 April 2005, UCLA, Los Angeles, California,
USA. Institute of Electrical and Electronics Engineers, 2005.
[83] L. Zhang et al., “High Power Density Electrochemical
Thermocells for Inexpensively Harvesting Low-Grade Thermal
Energy,” Adv. Mater., vol. 29, no. 12, p. 1605652, Mar. 2017.
[84] B. Gusarov et al., “Thermal energy harvesting by piezoelectric
PVDF polymer coupled with shape memory alloy,” Sensors
Actuators A Phys., vol. 243, pp. 175–181, 2016.
[85] H. Xie, K. Morimoto, and Y. Suzuki, “Electret-based Unsteady
Thermal Energy Harvester using Potassium Tantalate Niobate
Crystal,” J. Phys. Conf. Ser., vol. 773, no. 1, p. 12023, Nov. 2016.
[86] D. G. Thombare and S. K. Verma, “Technological development in
the Stirling cycle engines,” Renew. Sustain. Energy Rev., vol. 12,
no. 1, pp. 1–38, 2008.
[87] B.-T. Liu, K.-H. Chien, and C.-C. Wang, “Effect of working fluids
on organic Rankine cycle for waste heat recovery,” Energy, vol.
29, no. 8, pp. 1207–1217, 2004.
[88] T. Zhao et al., “Flexible pyroelectric device for scavenging
thermal energy from chemical process and as self-powered
temperature monitor,” Appl. Energy, vol. 195, pp. 754–760, 2017.
[89] M. Thielen, L. Sigrist, M. Magno, C. Hierold, and L. Benini,
“Human body heat for powering wearable devices: From thermal
energy to application,” Energy Convers. Manag., vol. 131, pp. 44–
54, 2017.
[90] J. A. Klein and L. Zuo, “Design and experimental study of a
velocity amplified electromagnetic vibration energy harvester,”
2017, p. 101642O.
[91] W. Yang and S. Towfighian, “A hybrid nonlinear vibration energy
harvester,” Mech. Syst. Signal Process., vol. 90, pp. 317–333,
2017.
[92] C. H. Yang et al., “Feasibility study of impact-based piezoelectric
road energy harvester for wireless sensor networks in smart
highways,” Sensors Actuators A Phys., 2017.
[93] W. Wang, A. Vinco, N. Pavlov, N. Wang, M. Hayes, and C.
O’Mathuna, “A rotating machine acoustic emission monitoring
system powered by multi-source energy harvester,” in
Proceedings of the 1st International Workshop on Energy Neutral
Sensing Systems - ENSSys ’13, 2013, pp. 1–6.
[94] G. Chen, Q. Meng, H. Fu, and J. Bao, “Development and
experiments of a micro piezoelectric vibration energy storage
device,” Mech. Syst. Signal Process., vol. 40, no. 1, pp. 377–384,
2013.
[95] Y. Kuang, T. Ruan, Z. J. Chew, and M. Zhu, “Energy harvesting
during human walking to power a wireless sensor node,” Sensors
Actuators A Phys., vol. 254, pp. 69–77, 2017.
[96] M. Geisler et al., “Human-motion energy harvester for
autonomous body area sensors,” Smart Mater. Struct., vol. 26, no.
3, p. 35028, Mar. 2017.
[97] P. Pillatsch, E. M. Yeatman, A. S. Holmes, and P. K. Wright,
“Wireless power transfer system for a human motion energy
harvester,” Sensors Actuators A Phys., vol. 244, pp. 77–85, 2016.
[98] A. Jushi, A. Pegatoquet, and T. N. Le, “Wind Energy Harvesting
for Autonomous Wireless Sensor Networks,” in 2016 Euromicro
Conference on Digital System Design (DSD), 2016, pp. 301–308.
[99] N. Rezaei-Hosseinabadi, A. Tabesh, and R. Dehghani, “A
18. 18
Topology and Design Optimization Method for Wideband
Piezoelectric Wind Energy Harvesters,” IEEE Trans. Ind.
Electron., pp. 1–1, 2015.
[100] D. Brunelli and Davide, “A High-Efficiency Wind Energy
Harvester for Autonomous Embedded Systems,” Sensors, vol. 16,
no. 3, p. 327, Mar. 2016.
[101] L. Du, Z. Fang, J. Yan, and Z. Zhao, “Enabling a wind energy
harvester based on ZnO thin film as the building skin,” Sensors
Actuators A Phys., vol. 260, pp. 35–44, 2017.
[102] P. Rani and D. Chhabra, “Piezoelectric Energy Harvesting from
Fluid Flow Dynamism using PVDF,” Int. J. R&D Eng. Sci.
Manag., vol. 4, no. 1, pp. 23–36, 2016.
[103] F. U. Qureshi, A. Muhtaroglu, and K. Tuncay, “Near-Optimal
Design of Scalable Energy Harvester for Underwater Pipeline
Monitoring Applications With Consideration of Impact to Pipeline
Performance,” IEEE Sens. J., vol. 17, no. 7, pp. 1981–1991, Apr.
2017.
[104] H. Ostaffe et al., “The Top 5 RF Essentials,” Microw. RF, vol. 1,
no. 2, pp. 4–7, 2014.
[105] “RF Energy Harvesting- Batteries Not Included.pdf.” .
[106] “The Realities of RF Power Harvesting.pdf.” .
[107] “7 Pros and Cons of Wind Energy - Conserve Energy Future.”
[Online]. Available: http://www.conserve-energy-
future.com/pros-and-cons-of-wind-energy.php. [Accessed: 18-
Apr-2017].
[108] “Hydropower Pros and Cons List | OccupyTheory.” [Online].
Available: https://occupytheory.org/hydropower-pros-and-cons-
list/. [Accessed: 18-Apr-2017].
[109] “16 Main Pros and Cons of Hydropower | Green Garage.”
[Online]. Available: https://greengarageblog.org/16-main-pros-
and-cons-of-hydropower. [Accessed: 19-Apr-2017].
[110] A. Alemdar and M. Ibnkahla, “Wireless sensor networks:
Applications and challenges,” in 2007 9th International
Symposium on Signal Processing and Its Applications, 2007, pp.
1–6.
[111] U. Prathap, P. D. Shenoy, K. R. Venugopal, and L. M. Patnaik,
“Wireless Sensor Networks Applications and Routing Protocols:
Survey and Research Challenges,” in 2012 International
Symposium on Cloud and Services Computing, 2012, pp. 49–56.