Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP) - Survey and challenges
ABSTRACT Wireless sensor networks (WSNs) research has pre-dominantly assumed the use of a portable and limited energy source, viz. batteries, to power sensors. Without energy, a sensor is essentially useless and cannot contribute to the utility of the network as a whole. Consequently, substantial research efforts have been spent on designing energy-efficient networking protocols to maximize the lifetime of WSNs. However, there are emerging WSN applications where sensors are required to operate for much longer durations (like years or even decades) after they are deployed. Examples include in-situ environmental/habitat monitoring and structural health monitoring of critical infrastructures and buildings, where batteries are hard (or impossible) to replace/recharge. Lately, an alternative to powering WSNs is being actively studied, which is to convert the ambient energy from the environment into electricity to power the sensor nodes. While renewable energy technology is not new (e.g., solar and wind) the systems in use are far too large for WSNs. Those small enough for use in wireless sensors are most likely able to provide only enough energy to power sensors sporadically and not continuously. Sensor nodes need to exploit the sporadic availability of energy to quickly sense and transmit the data. This paper surveys related research and discusses the challenges of designing networking protocols for such WSNs powered by ambient energy harvesting.
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ABSTRACT: Energy provisioning trend in Wireless Sensor Net-works (WSNs) is shifted towards alternate sources by utilizing available ambient energy, of which solar irradiance harvesting is considered a viable alternative to fixed batteries. However, the en-ergy storage buffer for harvested solar energy should be adaptive to the sporadic nature of the diurnal solar radiation availability. We believe that the typical fixed battery models no longer apply in harvesting enabled sensors. Therefore, we propose a random walk based stochastic model namely; Trinomial Random Walk (TRW) model for the storage capacity of harvesting enabled sensors. We then apply the proposed model on a comprehensive solar radiation data set of four different locations around the globe. Our performance evaluation demonstrates that the proposed model better analyze the sporadic nature of the diurnal solar radiation availability for estimating the required storage capacity. We further investigate an optimal power consumption value for a given energy store size, such that the utilization of harvested energy is maximized and the probability of energy depletion is minimized. For a given energy harvesting scenario, our model better approximates the optimal load with probability of up to a maximum of 98%, compared to a maximum of 37% for the binomial random walk model.PIMRC 2014, Washington D.C; 10/2014
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ABSTRACT: Energy provisioning trend in Wireless Sensor Net-works (WSNs) is shifted towards alternate sources by utilizing available ambient energy, of which solar irradiance harvesting is considered a viable alternative to fixed batteries. However, the en-ergy storage buffer for harvested solar energy should be adaptive to the sporadic nature of the diurnal solar radiation availability. We believe that the typical fixed battery models no longer apply in harvesting enabled sensors. Therefore, we propose a random walk based stochastic model namely; Trinomial Random Walk (TRW) model for the storage capacity of harvesting enabled sensors. We then apply the proposed model on a comprehensive solar radiation data set of four different locations around the globe. Our performance evaluation demonstrates that the proposed model better analyze the sporadic nature of the diurnal solar radiation availability for estimating the required storage capacity. We further investigate an optimal power consumption value for a given energy store size, such that the utilization of harvested energy is maximized and the probability of energy depletion is minimized. For a given energy harvesting scenario, our model better approximates the optimal load with probability of up to a maximum of 98%, compared to a maximum of 37% for the binomial random walk model.PIMRC 2014, Washighton D.C; 09/2014
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ABSTRACT: a b s t r a c t As the wireless sensor networks (WSNs) technology has great advancement, small and smart WSN systems now can be used for more complicated and challenging applications. WSNs investigation has primarily believed the use of a convenient and inadequate energy source for empowering the sensors. A sensor becomes useless in the absence of energy and becomes unable to contribute to the utility of the network as a group. Therefore, extensive efforts have been used in finding energy-efficient networking protocols for increasing the life span of WSNs. However, there are promising WSN applications where the sensors are obligatory to work for a long time after their deployments. In these cases, batteries are tough or impractical to replace/recharge. Although, a little amount of power is required for these applications, the useable lifetime of WSNs is decreased by the gradual degradation of the batteries. With the motivation of raising the usable WSNs around us and to value a number of economic and environmental limitations, researchers are looking for new green and theoretically unlimited energy sources. Harvesting of energy from the ambient energy is the basement of these new sources. Energy harvesting devices efficiently and effectively capture, accumulate, store, condition, and manage this energy and supply it in a form that can be used to empower WSNs. This harvested energy can be an alternative energy source for adding-on a principal power source and thus increase the consistency of the whole WSN by preventing the disruption of power. A great deal of research has been reviewed and specific ranges of applications have been found. Though there are challenges to overcome, different researchers have taken different approaches to solve those. In this review, we have emphasized on different scopes, challenges, ideas and actions of energy harvesting for WSNs.Renewable and Sustainable Energy Reviews 07/2014; 38:973-989. · 5.63 Impact Factor
Wireless Sensor Networks Powered by Ambient
Energy Harvesting (WSN-HEAP) – Survey and
Winston K.G. Seah∗, Zhi Ang Eu†and Hwee-Pink Tan∗
∗Networking Protocols Department
Institute for Infocomm Research, Singapore
†NUS Graduate School for Integrative Sciences and Engineering
National University of Singapore, Singapore
Abstract—Wireless sensor networks (WSNs) research has pre-
dominantly assumed the use of a portable and limited energy
source, viz. batteries, to power sensors. Without energy, a sensor
is essentially useless and cannot contribute to the utility of the net-
work as a whole. Consequently, substantial research efforts have
been spent on designing energy-efficient networking protocols to
maximize the lifetime of WSNs. However, there are emerging
WSN applications where sensors are required to operate for
much longer durations (like years or even decades) after they
are deployed. Examples include in-situ environmental/habitat
monitoring and structural health monitoring of critical infras-
tructures and buildings, where batteries are hard (or impossible)
to replace/recharge. Lately, an alternative to powering WSNs is
being actively studied, which is to convert the ambient energy
from the environment into electricity to power the sensor nodes.
While renewable energy technology is not new (e.g., solar and
wind) the systems in use are far too large for WSNs. Those
small enough for use in wireless sensors are most likely able
to provide only enough energy to power sensors sporadically
and not continuously. Sensor nodes need to exploit the sporadic
availability of energy to quickly sense and transmit the data.
This paper surveys related research and discusses the challenges
of designing networking protocols for such WSNs powered by
ambient energy harvesting.
The greatest problem faced by wireless sensor networks
is energy. When a sensor is depleted of energy, it can no
longer fulfill its role unless the source of energy is replenished.
Therefore, it is generally accepted that the usefulness of a
wireless sensor expires when its battery runs out. Much of
the research on wireless sensor networks has assumed the use
of a portable and limited energy source, namely batteries, to
power sensors and focused on extending the lifetime of the
network by minimizing energy usage. Portable energy sources
like batteries will experience current leakages that drain the
resource even when they are not used; furthermore, any flaws
in the packaging due to long term wear and tear can result
in environmental problems. A wireless sensor network that
is not dependent on a limited power source (like a battery)
essentially has infinite lifetime. Failure due to other causes
(like structural hardware damage) can be overcome by self-
organization and network re-configuration. This has motivated
the search for an alternative source of energy to power WSNs
especially for applications that require sensors to be installed
for long durations (up to decades) or embedded in structures
where battery replacement is impractical. In this paper, we
first provide a brief survey of research on energy harvesting
wireless sensor networks. We then define the ideal WSN that
is powered solely by energy harvesting which we refer to
as Wireless Sensor Network Powered by Ambient Energy
Harvesting (WSN-HEAP) and then discuss the challenges in
designing networking protocols for such WSNs arising from
the characteristics of the energy source.
II. RESEARCH ON ENERGY HARVESTING
The use of renewable energy to generate electricity is not
a new concept. Renewable energy that is being harvested
to generate electricity today includes solar, wind, water and
thermal energy. Harvesting energy for low-power (and pos-
sibly embedded) devices like wireless sensors presents a new
challenge as the energy harvesting device has to be comparable
in size (i.e. small enough) with the sensors. There are complex
tradeoffs to be considered when designing energy harvesting
circuits for WSNs arising from the interaction of various
factors like the characteristics of the energy sources, energy
storage device(s) used, power management functionality of
the nodes and protocols, and the applications’ requirements.
Currently, the main sources of ambient energy considered
suitable for use with WSNs are solar, mechanical (vibration or
strain) and thermal energy. In the following subsections, we
briefly survey research efforts in these areas.
Solar power is the most common and matured among the
different forms of energy harvesting. However, it has the
disadvantage of being able to generate energy only when there
is sufficient sunlight or artifical light; furthermore, existing
systems were not designed for use with low power WSNs,
prompting new research efforts. With the envisioned indoor
WSN applications, a system has been developed to address the
needs of WSNs deployed in indoor environments (e.g. hospital
and industrial) where lights are operational at close to 100%
duty cycle . To ensure that energy is not unnecessarily lost
during the transfer from the harvester to the wireless sensor, a
low-power maximum power point tracker (MPPT) circuit 
has been proposed to efficiently transfer the harvested solar
energy to rechargeable batteries even in non-optimal weather
conditions. The Heliomote  project focused on developing
a plug-and-play solar energy harvesting module for use with
Crossbow/Berkeley motes. Another effort conducted empirical
and mathematical analysis of two micro-solar power systems
and used the results to propose design guidelines for micro-
solar power systems for WSNs .
Vibrational, kinetic and mechanical energy generated by
movements of objects can also be harvested. Vibrations are
present all around us and especially prominent in bridges,
roads and rail tracks. One method of harvesting vibrational
energy is through the use of a piezoelectric capacitor while
kinetic energy can be harvested using a spring-loaded mech-
anism. In , a vibration-based harvesting micro power gen-
erator is used to scavenge environmental vibrations for use
in a sensor node. Traffic sensors can also be solely powered
by the short duration vibrations when a vehicle passes over
the sensor . Experimental results have shown that when
a piezoelectric pushbutton is depressed, sufficient energy is
harvested to transmit two complete 12-bit digital word infor-
mation wirelessly . Similarly, a system that harvests energy
from the forces exerted on a shoe during walking has been
demonstrated  and indoor locations, like staircases, are
potential locations to harvest vibrational energy for powering
wireless environmental sensors, as shown in .
Current is generated when there is a temperature difference
between two junctions of a conducting material. Thermal
energy harvesting uses temperature differences or gradients
to generate electricity, e.g. between the human body and
the surrounding environment. Devices with direct contact to
the human body can harvest the energy radiated from the
human body by means of thermogenerators (TEGs) . To
address the needs of telecommunications and other embedded
applications, design of microstructured thermoelectric devices
has been proposed in . Due to the lack of moving parts
in thermal energy harvesting devices, they tend to last longer
than vibration-based devices.
D. Commercial Energy Harvesters
Energy harvesting devices are becoming available com-
mercially. E.g., a Solar Energy Harvesting development kit,
produced by Texas Instruments (http://www.ti.com), can be
used to create perpetually powered wireless sensor net-
works based on their ultra-low power components. Advanced
Cerametrics (http://www.advancedcerametrics.com) produces
vibration-based energy harvesters that can replace batteries
and be used to power wireless sensor nodes. Details on other
commercially available energy harvesters for sensor nodes are
available in .
III. RESEARCH IN ENERGY HARVESTING WSNS
While there has been extensive research on wireless sensor
networks, those specific to energy harvesting WSNs are just
emerging. With energy (or the lack of) being the key issue, it
is unsurprising that the focus of research has been on power
A. Power Management
In this respect, most of the efforts have proposed the
use of energy harvesting to supplement the on-board bat-
tery. Therefore, efficient power management is important to
maximize the benefits of having the extra harvested energy.
A consequence of using energy harvesting devices in sensor
motes is that traditional metrics such as residual battery level is
no longer usable in power management. Instead, information
about future energy availability is required to make optimal
routing decisions . To achieve this, an environmental en-
ergy harvesting framework (EEHF)  has been proposed to
adaptively learn the energy environment and make use of this
information to more efficiently exploit the energy resources in
order to improve the performance of the sensor network. A
power management system, incorporating an analytical model
for predicting various performance metrics, adaptive duty
cycles and other related aspects, has also been developed .
Another approach assumes that sensors have two transmission
modes that allow them to tradeoff energy consumption with
packet error to maximize performance .
B. Data Delivery Schemes
Delivering data from a sensor across multiple wireless hops
to the sink involves generally two key tasks: accessing the
medium and forwarding the data to the next hop towards the
sink. Energy conservation has been and remains as the key
objective in the design of networking protocols for WSNs. A
recent survey on WSN protocols can be found in . Here,
we focus our discussion specifically on works that are related
Many ultra-low power medium access control (MAC) pro-
tocols have been proposed for WSNs. However, any scheme
that involves some form of backoff and retransmission is very
likely to be non-optimal because timing schedules cannot
be strictly enforced when a node has no energy to operate,
and the amount of harvested energy may not be sufficient
for retransmissions. A polling-based MAC protocol has been
proposed for use in sensors powered by ambient vibrations but
the scheme has not been shown to be optimal . Cooperative
transmission protocols, an active area of research in wireless
communications, have also been proposed for use in energy
harvesting wireless sensor nodes .
A straightforward approach to data forwarding involves
modifying existing WSN routing protocols. Directed Diffu-
sion, one of the early WSN routing protocols, has been
modified to incorporate information on whether the node
is running on solar or battery power , and the results
have shown that the solar-aware variant performs better than
shortest path routing. Similarly, a solar-cell energy model
is incorporated into geographic routing to improve network
performance. Since nodes may harvest different amounts of
energy, their duty cycles may not be the same. In WSN-HEAP,
maximizing the network throughput is the main consideration
since energy can be replenished. However, the amount of
power provided by the environment is limited, therefore we
need to have a routing algorithm that takes into consideration
actual environmental conditions . The main idea is to
model the network as a flow network and obtain the solution
by solving the maxflow problem to maximize throughput.
Another solution incorporates the energy replenishment rate
into the cost metric when computing routes . Geographic
routing can also be modified to consider the amount of energy
that can be harvested from the environment.
C. Topology and Connectivity
Power control is important to maintain connectivity through
topology control. If the rate of harvested energy is not enough
to power the sensor node continuously, this means that nodes
have to go to sleep to charge up the battery, and this alters the
network topology and therefore connectivity. The performance
of different sleep and wakeup strategies based on factors such
as channel state, battery state and environmental conditions are
analyzed in  and game theory is also applied to find the
optimal parameters for a sleep and wakeup strategy to tradeoff
between packet blocking and dropping probabilities . An-
other study presented an analytical framework  to estimate
various statistical properties of the system (e.g. expected
downtime) given specific system parameters, such as, energy
harvesting rate, buffer capacity, etc. It has also been shown that
clustering in sensor networks can be improved by considering
the characteristics of the energy harvesting process .
D. Energy Storage Technology
Besides being difficult to replace in sensors embedded
in structures such as buildings and bridges, batteries also
have limited recharge cycles such that they cannot be further
recharged beyond a threshold. For self-powered sensors with
energy harvesting capabilities to be sustainable, an alterna-
tive form of energy storage is necessary – supercapacitors.
Supercapacitors, which are recharged by energy harvesting
devices, can replace batteries as the energy storage device. A
supercapacitor can be recharged for more than half a million
charge cycles and has a 10-year operational lifetime before the
energy capacity is reduced to 80% . The main difference
between capacitors and supercapacitors lies in their energy
storage density. Supercapacitors can store energy at higher
energy density, and therefore its small form factor is more
suitable for sensor nodes than using a capacitor.
E. State of Commercial Technology
Energy harvesting powered sensor nodes for specific
dustrial environmental control, are commercially avail-
by any energy harvester are produced by Ambiosystems
(http://www.ambiosystems.com). The sensor nodes developed
by Microstrain (http://www.microstrain.com) harvest energy
from two sources ; the first method uses tiny solar cells to
convert solar energy while the second method uses piezoelec-
tric materials to convert mechanical energy into electric en-
ergy. Another company, EnOcean (http://www.enocean.com),
produces sensing systems that can power themselves by har-
vesting ambient energy from the environment.
IV. WSN-HEAP NODE CHARACTERISTICS
Using energy harvesting to supplement batteries does not
eliminate the problem of having to replace the batteries when
they run out. The process merely delays the inevitable. In
applications like structural health monitoring of civil infras-
tructures, the sensors need to be installed in-situ (possibly
embedded and out-of-reach after they are installed) and op-
erate for long durations, from years to decades or even longer.
Combining low-power electronics, energy harvesting devices,
and supercapacitors, it is possible to implement WSNs that
rely solely on energy harvesting to operate, i.e. WSN-HEAP.
Although WSN-HEAP is very promising for solving the
energy constraints of traditional WSN, the power levels avail-
able from state-of-the-art energy harvesting devices is in the
order of tens to thousands of µW or several mW (1% to 20%
of operating power) which is not enough to power the sensor
node continuously. Using the energy harvesting rates presented
in , we estimate the duty cycle achievable by the Crossbow
MICAz based on the power consumption requirements of
83.1mW in receive state and 76.2 mW in transmit state.
We base our computation of the harvesting rate on a 10cm2
material which is about the same size as the mote, and the
results are shown in Table I.
ACHIEVABLE DUTY CYCLE BY MICAZ WITH 10CM2HARVESTING
Vibration - electromagnetic
Vibration - piezoelectric
Vibration - electrostatic
Solar - direct sunlight
Solar - indoor
Without the sustained energy supply, the exact sleep and
wakeup timings are unknown. Therefore, the operating char-
acteristics of WSN-HEAP as compared to battery-operated
WSN can be simply illustrated as shown in Figure 1. In WSN-
Fig. 1.WSN-HEAP vs Battery-powered WSN
HEAP, each sensor node is equipped with a small processor,
a radio transceiver for communication, one or more energy
harvesting devices, a capacitor/supercapacitor to store the
harvested energy and a sensor. The key differentiating feature
of a WSN-HEAP node (as compared to a battery-operated
WSN node) is the energy source which is a combination
of energy harvesting device (s) and supercapacitor(s) instead
of batteries. The sink is powered by an external source and
remains on all the time.
V. DESIGN CHALLENGES
Instead of focusing on energy-efficient networking protocols
to maximize the lifetime of sensor networks, the main objec-
tive is to maximize the information or data collected from the
sensor network given the rate of energy that can be harvested
from the environment. In the following subsections, we briefly
discuss the networking-related research issues.
A. Topology Control
Topology control schemes can exploit transmission power
control to increase the probability of successfully delivering
the data to the next hop . Larger transmission power means
that more energy is required to be harvested before the node
can receive or transmit data packets, thereby decreasing the
duty cycles of the node. This may be necessary if a node’s
neighbours have not harvested sufficient energy to operate.
Therefore, transmission power control is crucial in optimizing
the performance of WSN-HEAP. This also influences the
logical topology and deployment strategies .
Typically, MAC protocols designed for WSNs aim to reduce
energy usage and prolong network lifetime at the expense
of longer delays. In the case of WSN-HEAP, it makes more
sense to find a means of efficiently using the harvested energy
to maximize throughput and minimize delays. Furthermore,
unnecessary waiting (to synchronize with time slots) or re-
transmissions can be counter-productive; it has been shown
in  that a slotted CSMA MAC performs worse than an
unslotted scheme because energy is consumed during the slot
synchronization process, resulting in longer harvesting periods
thereby reducing throughput.
Since the wakeup time of any sensor cannot be estimated
accurately because the exact rate of energy harvested fluctuates
with time and other environmental factors, it is very difficult
to ensure that the next-hop node is awake to receive a packet.
The uncertainty in how long it takes a node to harvest enough
energy before it can function again makes existing sleep-wake
scheduling schemes for WSNs unusable since a node may
not have harvested sufficient energy at the scheduled wake-
up time. Furthermore, if it has depleted all its energy in its
previous cycle, it may loose its timing reference when it wakes
up again. Therefore, broadcast and opportunistic schemes are
more suitable in WSN-HEAP. However, broadcasting may
result in many duplicates if many nodes are awake; therefore,
some form of duplication-suppression is needed so that the
harvested energy is not wasted on delivering duplicates. The
ideal situation would be anycast where exactly one node
(among those awake and heard the packet transmission) will
forward data packets towards the sink. This ensures that the
sink receives exactly one copy of each packet from the source.
If there are insufficient awake forwarding nodes, either because
the density of the nodes deployed is too low or the average
energy harvesting period is too long, then it becomes an
intermittently connected mobile network, where the use of
delay-tolerant network (DTN) techniques may be appropriate.
D. Reliable Data Delivery
Reliable data delivery may be required for some appli-
cations. Since the source node is not awake all the time,
it is a challenge to design reliable transport protocols as
many reliable transport protocols need to make use of positive
feedback for retransmissions. Another requirement to ensure
each flow gets its fair share of bandwidth given the amount
of energy that can be harvested from the environment. Since
energy is free because it is renewable, nodes further away from
the sink may starve the nodes nearer the sink if forwarding
packets have higher priority than the node’s own packets.
Therefore, there is a need for a transport protocol to regulate
the data flow such that any source will get its fair share of
bandwidth no matter where it is located in the network.
Wireless sensor networks that are powered by ambient
energy harvesting is a promising technology for many sensing
applications as it eliminates the need to replace batteries.
However, the current state of technology in energy harvesting
is still unable to provide a sustained energy supply to enable
WSNs continuously. Furthermore, the ability to harvest energy
from the environment is highly dependent on many environ-
mental factors and these need further research to understand
and exploit. We have provided an overview of the research in
WSNs powered by ambient energy harvesting and discussed
the challenges in designing networking protocols for WSN-
HEAP which are WSNs powered solely by energy harvesting.
A comparative summary of the key aspects of WSN and WSN-
HEAP is provided in Table II.
SUMMARY OF KEY ASPECTS OF WSN AND WSN-HEAP
Throughput and latency are usually
traded off for longer network lifetime
can be determined precisely
Energy model is
Battery-operated WSNs with Energy Harvesters
Longer lifetime achieved by supplementing
battery power with harvested energy
Sleep-and-wakeup schedules can be determined
if future energy availability is correctly predicted
Energy model can be
predicted to high accuracy
GoalMaximize throughput and minimize delay since
energy is renewable & no concept of lifetime
cannot be predicted
Energy harvesting rate varies across time, space
as well as type of energy harvester
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