These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how energy harvesters are becoming more economically feasible for the Internet of Things (IoT). Small amounts of energy can be harvested from vibrations, temperature differences, and radio frequencies using various types of electronic devices such as piezoelectric, MEMS, thermo-electric power generators, and other devices. As improvements in them occur and as the energy requirements of accelerometers, pressure sensors, gas detectors, bio-sensors, and readout circuits fall from microwatts to hundreds of nano-watts, energy harvesters become cheaper and better than are batteries. Improvements in energy harvesting are occurring in the form of higher power per area or higher power per temperature difference and improvements of about five times are expected to occur in the next 5 to 10 years. The market for energy harvesters is expected to reach $2.5 Billion by 2024. In addition to their impact on buildings and the other usual applications for IoT, they will also impact on agriculture, aircraft, and medical implants.
The Spansion Energy Harvesting family includes the MB39C811, an ultra-low-power buck PMIC with dual input that enables efficient harvesting from both solar and vibration energy; and the MB39C831, an ultra-low-voltage boost PMIC for solar or thermal. The Spansion Energy Harvesting family of devices works seamlessly with Spansion FM0+ microcontrollers (MCUs), ultra-low-power microcontrollers (based on the ARM Cortex-M0+ core) for industrial and cost-sensitive applications with low-power requirements.
Learn more: http://www.spansion.com/Products/Analog/Energy-Harvesting-PMICs/Pages/pmic-eh.aspx
Theoretical and Experimental Investigations of a Non-linear Single Degree of ...Rathish Chandra Gatti,Ph.D
There is an increasing need for sensors to be selfpowered
and hence autonomous in order to operate in remote and
inaccessible locations for long periods of time. Amongst the
various ambient sources of energy, mechanical vibration is a viable
wasted source of energy and can be found in rotating equipment
including generators, motors and compressors as well as
structures including bridges. The current research deals with
developing a novel non-linear single degree of freedom
electromagnetic vibration energy harvester using spatial variation
of the magnetic field.
Initially, approximate linear methods using Laplace transforms
and the linear state space methods were considered, where the
magnetic field and hence the coupling coefficient were considered
as constants. The linear methods were used to derive the frequency
response behavior of the system and also its eigenvalues to
determine the approximate resonant frequency range. This was
followed by more accurate non-linear single degree of freedom
electromagnetic energy harvester model simulation considering
the spatial variation of the magnetic field and hence a spatially
varying coupling coefficient. An experiment of the single degreeof-
freedom one-direction electromagnetic vibration energy
harvester (SDOF1D EMVEH) prototype was conducted for a
range of frequencies to obtain the time domain data to validate
against the theoretical data obtained from theoretical time domain
simulation.
DESIGN & ANALYSIS OF RF ENERGY HARVESTING SYSTEM FOR CHARGING LOW POWER DEVICESJournal For Research
Finite electrical battery life is encouraging the companies and researchers to come up with new ideas and technologies to drive wireless mobile devices for an infinite or enhance period of time. Common resource constrained wireless devices when they run out of battery they should be recharged. For that purpose main supply & charger are needed to charge drained mobile phone batteries or any portable devices. Practically it is not possible to carry charger wherever we go and also to expect availability of power supply everywhere. To avoid such disadvantages some sort of solution should be given and that can be wireless charging of mobile phones.[4] If the mobile can receive RF power signals from the mobile towers, why can’t we extract the power from the received signals? This can be done by the method or technology called RF energy harvesting. RF energy harvesting holds a promise able future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. RF power harvesting is one of the diverse fields where still research continues. The energy of RF waves used by devices can be harvested and used to operate in more effective and efficient way.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to show how energy harvesters are becoming more economically feasible for the Internet of Things (IoT). Small amounts of energy can be harvested from vibrations, temperature differences, and radio frequencies using various types of electronic devices such as piezoelectric, MEMS, thermo-electric power generators, and other devices. As improvements in them occur and as the energy requirements of accelerometers, pressure sensors, gas detectors, bio-sensors, and readout circuits fall from microwatts to hundreds of nano-watts, energy harvesters become cheaper and better than are batteries. Improvements in energy harvesting are occurring in the form of higher power per area or higher power per temperature difference and improvements of about five times are expected to occur in the next 5 to 10 years. The market for energy harvesters is expected to reach $2.5 Billion by 2024. In addition to their impact on buildings and the other usual applications for IoT, they will also impact on agriculture, aircraft, and medical implants.
The Spansion Energy Harvesting family includes the MB39C811, an ultra-low-power buck PMIC with dual input that enables efficient harvesting from both solar and vibration energy; and the MB39C831, an ultra-low-voltage boost PMIC for solar or thermal. The Spansion Energy Harvesting family of devices works seamlessly with Spansion FM0+ microcontrollers (MCUs), ultra-low-power microcontrollers (based on the ARM Cortex-M0+ core) for industrial and cost-sensitive applications with low-power requirements.
Learn more: http://www.spansion.com/Products/Analog/Energy-Harvesting-PMICs/Pages/pmic-eh.aspx
Theoretical and Experimental Investigations of a Non-linear Single Degree of ...Rathish Chandra Gatti,Ph.D
There is an increasing need for sensors to be selfpowered
and hence autonomous in order to operate in remote and
inaccessible locations for long periods of time. Amongst the
various ambient sources of energy, mechanical vibration is a viable
wasted source of energy and can be found in rotating equipment
including generators, motors and compressors as well as
structures including bridges. The current research deals with
developing a novel non-linear single degree of freedom
electromagnetic vibration energy harvester using spatial variation
of the magnetic field.
Initially, approximate linear methods using Laplace transforms
and the linear state space methods were considered, where the
magnetic field and hence the coupling coefficient were considered
as constants. The linear methods were used to derive the frequency
response behavior of the system and also its eigenvalues to
determine the approximate resonant frequency range. This was
followed by more accurate non-linear single degree of freedom
electromagnetic energy harvester model simulation considering
the spatial variation of the magnetic field and hence a spatially
varying coupling coefficient. An experiment of the single degreeof-
freedom one-direction electromagnetic vibration energy
harvester (SDOF1D EMVEH) prototype was conducted for a
range of frequencies to obtain the time domain data to validate
against the theoretical data obtained from theoretical time domain
simulation.
DESIGN & ANALYSIS OF RF ENERGY HARVESTING SYSTEM FOR CHARGING LOW POWER DEVICESJournal For Research
Finite electrical battery life is encouraging the companies and researchers to come up with new ideas and technologies to drive wireless mobile devices for an infinite or enhance period of time. Common resource constrained wireless devices when they run out of battery they should be recharged. For that purpose main supply & charger are needed to charge drained mobile phone batteries or any portable devices. Practically it is not possible to carry charger wherever we go and also to expect availability of power supply everywhere. To avoid such disadvantages some sort of solution should be given and that can be wireless charging of mobile phones.[4] If the mobile can receive RF power signals from the mobile towers, why can’t we extract the power from the received signals? This can be done by the method or technology called RF energy harvesting. RF energy harvesting holds a promise able future for generating a small amount of electrical power to drive partial circuits in wirelessly communicating electronics devices. RF power harvesting is one of the diverse fields where still research continues. The energy of RF waves used by devices can be harvested and used to operate in more effective and efficient way.
Various energy sources for wearable and IoT has been covered and explained.
Super-capacitor, Secondary cell and energy harvesting technology has been explained here. Thin film battery, Piezo electric energy harvesting, wireless charging and other technology has been explained.
The slides for a presentation on Energy harvesting and the state off the art designs currently taking advantage of the energy around us.
Energy harvesting (also known as power harvesting or energy scavenging) is the process by which energy is derived from external sources (e.g.solar power, thermal energy, wind energy, salinity gradients, and kinetic energy), captured, and stored for small, wireless autonomous devices, like those used in wearable electronics and wireless sensor networks.
Credits: A thanks go out to Johan Pedersen for introducing me to the subject a great workshop and use of some of his slides.
In modern days, the use of energy consumption increasing very rapidly. Fossil fuels are finite and environmentally costly. Sustainable, environmentally benign energy can be derived from nuclear fission or captured from ambient sources. Large-scale ambient energy (eg. solar, wind and tide), is widely available and large-scale technologies are being developed to efficiently capture it. At the other end of the scale, there are small amounts of ‘wasted’ energy that could be useful if captured. Recovering even a fraction of this energy would have a significant economic and environmental impact. This is where energy harvesting (EH) comes in.
Wireless sensor nodes are usually deployed in not easily accessible places to provide solution to a wide
range of application such as environmental, medical and structural monitoring. They are spatially
distributed and as a result are usually powered from batteries. Due to the limitation in providing power
with batteries, which must be manually replaced when they are depleted, and location constraints in
wireless sensor network causes a major setback on performance and lifetime of WSNs. This difficulty in
battery replacement and cost led to a growing interest in energy harvesting. The current practice in energy
harvesting for sensor networks is based on practical and simulation approach. The evaluation and
validation of the WSN systems is mostly done using simulation and practical implementation. Simulation is
widely used especially for its great advantage in evaluating network systems. Its disadvantages such as the
long time taken to simulate and not being economical as it implements data without proper analysis of all
that is involved ,wasting useful resources cannot be ignored. In most times, the energy scavenged is directly
wired to the sensor nodes. We, therefore, argue that simulation – based and practical implementation of
WSN energy harvesting system should be further strengthened through mathematical analysis and design
procedures. In this work, we designed and modeled the energy harvesting system for wireless sensor nodes
based on the input and output parameters of the energy sources and sensor nodes. We also introduced the
use of supercapacitor as buffer and intermittent source for the sensor node. The model was further tested in
a Matlab environment, and found to yield a very good approach for system design.
Harvesting Energy for the Internet of ThingsAmala Putrevu
Harvesting energy for the Internet of Things is the primary challenge that engineers of today face. Through this presentation we bring to you two models of sensors that use piezoelectric energy harvesting to generate the required power.
Self-generating devices can truly make the Internet of Things a reality.
Designing Energy Harvesting Solar Powered SensorsDan Wright, MBA
Presented 19 Nov 2014 at Energy Harvesting and Storage USA in Santa Clara California
http://www.idtechex.com/events/presentations/designing-energy-harvesting-solar-powered-sensors-005321.asp
A WSN primary outline issue for a sensor system is protection of the vitality accessible at every sensor node. We propose to convey different, versatile base stations to delay the lifetime of the sensor system. We split the lifetime of the sensor system into equivalent stretches of time known as rounds. Base stations are migrated toward the begin of a round. Our strategy utilizes a whole number straight program to focus new areas for the base stations and in view of steering convention to guarantee vitality proficient directing amid every round. We propose four assessment measurements and look at our answer utilizing these measurements. Taking into account the reproduction results we demonstrate that utilizing various, versatile base stations as per the arrangement given by our plans would altogether expand the lifetime of the sensor system.
This design involves the implementation AES 128. Inside top module, enc, dec and key_generation modules are available. Both enc and dec are controlled via respective resets. When enc executes, key_generation runs and further fills the key memory. dec unit on its execution extracts key from the same memory. Working on to test the design with Side Channel Attacks.
Various energy sources for wearable and IoT has been covered and explained.
Super-capacitor, Secondary cell and energy harvesting technology has been explained here. Thin film battery, Piezo electric energy harvesting, wireless charging and other technology has been explained.
The slides for a presentation on Energy harvesting and the state off the art designs currently taking advantage of the energy around us.
Energy harvesting (also known as power harvesting or energy scavenging) is the process by which energy is derived from external sources (e.g.solar power, thermal energy, wind energy, salinity gradients, and kinetic energy), captured, and stored for small, wireless autonomous devices, like those used in wearable electronics and wireless sensor networks.
Credits: A thanks go out to Johan Pedersen for introducing me to the subject a great workshop and use of some of his slides.
In modern days, the use of energy consumption increasing very rapidly. Fossil fuels are finite and environmentally costly. Sustainable, environmentally benign energy can be derived from nuclear fission or captured from ambient sources. Large-scale ambient energy (eg. solar, wind and tide), is widely available and large-scale technologies are being developed to efficiently capture it. At the other end of the scale, there are small amounts of ‘wasted’ energy that could be useful if captured. Recovering even a fraction of this energy would have a significant economic and environmental impact. This is where energy harvesting (EH) comes in.
Wireless sensor nodes are usually deployed in not easily accessible places to provide solution to a wide
range of application such as environmental, medical and structural monitoring. They are spatially
distributed and as a result are usually powered from batteries. Due to the limitation in providing power
with batteries, which must be manually replaced when they are depleted, and location constraints in
wireless sensor network causes a major setback on performance and lifetime of WSNs. This difficulty in
battery replacement and cost led to a growing interest in energy harvesting. The current practice in energy
harvesting for sensor networks is based on practical and simulation approach. The evaluation and
validation of the WSN systems is mostly done using simulation and practical implementation. Simulation is
widely used especially for its great advantage in evaluating network systems. Its disadvantages such as the
long time taken to simulate and not being economical as it implements data without proper analysis of all
that is involved ,wasting useful resources cannot be ignored. In most times, the energy scavenged is directly
wired to the sensor nodes. We, therefore, argue that simulation – based and practical implementation of
WSN energy harvesting system should be further strengthened through mathematical analysis and design
procedures. In this work, we designed and modeled the energy harvesting system for wireless sensor nodes
based on the input and output parameters of the energy sources and sensor nodes. We also introduced the
use of supercapacitor as buffer and intermittent source for the sensor node. The model was further tested in
a Matlab environment, and found to yield a very good approach for system design.
Harvesting Energy for the Internet of ThingsAmala Putrevu
Harvesting energy for the Internet of Things is the primary challenge that engineers of today face. Through this presentation we bring to you two models of sensors that use piezoelectric energy harvesting to generate the required power.
Self-generating devices can truly make the Internet of Things a reality.
Designing Energy Harvesting Solar Powered SensorsDan Wright, MBA
Presented 19 Nov 2014 at Energy Harvesting and Storage USA in Santa Clara California
http://www.idtechex.com/events/presentations/designing-energy-harvesting-solar-powered-sensors-005321.asp
A WSN primary outline issue for a sensor system is protection of the vitality accessible at every sensor node. We propose to convey different, versatile base stations to delay the lifetime of the sensor system. We split the lifetime of the sensor system into equivalent stretches of time known as rounds. Base stations are migrated toward the begin of a round. Our strategy utilizes a whole number straight program to focus new areas for the base stations and in view of steering convention to guarantee vitality proficient directing amid every round. We propose four assessment measurements and look at our answer utilizing these measurements. Taking into account the reproduction results we demonstrate that utilizing various, versatile base stations as per the arrangement given by our plans would altogether expand the lifetime of the sensor system.
This design involves the implementation AES 128. Inside top module, enc, dec and key_generation modules are available. Both enc and dec are controlled via respective resets. When enc executes, key_generation runs and further fills the key memory. dec unit on its execution extracts key from the same memory. Working on to test the design with Side Channel Attacks.
An Energy Efficient Protocol To Increase Network Life In WSNIOSR Journals
Abstract : Wireless Sensor Network consists of several sensor nodes, these nodes loss some of their energy after the process of communication. So an energy efficient approach is required to improve the life of the network. In case of broadcast network, LEACH protocol uses an aggregative approach by creating cluster of nodes. Now the major concern is to built such clusters over WSN in an optimized way. This work presents the improvement over LEACH protocol. Hence we have different work environments where the network is having different capacities. The proposed work shows how the life time of the network will improve when the number of nodes varies within the network. Keywords - LEACH, energy, lifetime, cluster based, WSN\
Piezo electric MEMS energy harvester-CreativeaskAshik Ask
Let me describe fabrication and characterization of a significantly improved version of a MEMS-based PZT/PZT thick film bimorph vibration energy harvester with an integrated silicon proof mass. The main advantage of bimorph vibration energy harvesters is that strain energy is not lost in mechanical support materials since only PZT is strained, and thus it has a potential for significantly higher output power. An improved process scheme for the energy harvester resulted in a robust fabrication process with a record high fabrication yield of 98.6%. Moreover, the robust fabrication process allowed a high pressure treatment of the screen printed PZT thick films prior to sintering, improving the PZT thick film performance and harvester power output reaches 37.1 μW at 1 g.r description goes here
Energy Harvesting Techniques in Wireless Sensor Networks – A SurveyFarwa Ansari
It is a Self effort done under the supervision of my Respected Supervisor Dr. A Rehman, to surveyed out all the techniques for Energy harvesting in WSNs. 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.
A STUDY OF POWER SAVING TECHNIQUE IN WIRELESS NETWORKScscpconf
Much research on wireless networks have focused on the power consumption of the wireless
nodes, while at the same time how to acquire power from ambient environment is another
direction to extend the battery lifetime. Though, mostly extending the lifetime of WSNs rely on
making the electronic circuitry power efficient by incorporating advances in node architecture,
transceivers, access protocols and on finite energy sources like batteries. In contrast, WSNs
Powered by Ambient Energy Harvesting can also prove to be useful and economical in the longterm
as they can operate for very long periods of time until hardware failure, because ambient
energy can be harvested from the environment perpetually. Although cellular networks account
for a rather small share of energy use, lowering their energy consumption appears beneficial
from an economical perspective. In the strive for lessening of the environmental impact of the
information and communication industry, energy consumption of communication networks has
recently received increased attention. The paper discusses the various techniques for increasing
the life of WSNs.
SINK RELOCATION FOR NETWORK LIFETIME ENHANCEMENT METHOD IN WSNEditor IJMTER
Recent advances in micro manufacturing technology have enabled the event of cheap, normal
power, unique functional detector nodes for the wireless communication. Sensing applications have
normal conjointly as a reality of result. These embrace environmental observation, intrusion detection,
battleground police work, and so on. In a very wireless detector network (WSN), the way the restricted
power resources of sensors to increase that to conserve the network lifespan of the WSN as long as double
whereas activity the sensing and detected knowledge news tasks, is that the most important issue within
the network style. In a WSN, detector nodes deliver detected knowledge back to the sink as multi hopping.
The detector nodes are very close to the sink can usually consume additional battery power than others;
consequently, these nodes will been drain out their battery energy quickly and short the network lifespan
of the WSN. Sink relocation have associate degree economical network lifespan extension methodology,
that could avoids an excessive amount of battery energy for a selected cluster of detector nodes. during
this paper, we have a tendency to propose a moving strategy known as energy-aware sink relocation
(EASR) for mobile sinks in WSN. These projected mechanism uses info associated with the residual
battery energy of detector nodes to be adaptively alter the transmission vary of detector nodes and
therefore the relocating theme to the sink. Some theoretical and numerical analyze area unit given to point
out that the EASR methodology will extend the network lifespan of the WSN considerably
International Refereed Journal of Engineering and Science (IRJES) irjes
International Refereed Journal of Engineering and Science (IRJES)
Ad hoc & sensor networks, Adaptive applications, Aeronautical Engineering, Aerospace Engineering
Agricultural Engineering, AI and Image Recognition, Allied engineering materials, Applied mechanics,
Architecture & Planning, Artificial intelligence, Audio Engineering, Automation and Mobile Robots
Automotive Engineering….
FTTCP: Fault Tolerant Two-level Clustering Protocol for WSNIDES Editor
In this paper, we propose an agreement-based fault
detection and recovery protocol for cluster head (CH) in
wireless sensor networks (WSNs) of two level cluster
hierarchy. The aim of protocol is to accurately detect CH
failure to avoid unnecessary energy consumption caused by a
mistaken detection process. For this, it allows each cluster
member to detect its CH failure independently. Cluster
members employ distributed agreement protocol to reach an
agreement on failure of the CH among multiple cluster
members. The detection process runs concurrently with
normal network operation by periodically performing a
distributed detection process at each cluster member To
reduce energy consumption, it makes use of heartbeat
messages sent periodically by a CH for fault detection.
Simulation results show, our protocol provides high detection
accuracy because of agreement protocol.
Chain Based Wireless Sensor Network Routing Using Hybrid Optimization (HBO An...IJEEE
In Wireless Sensor Network, due to the
energy restriction of each nodes, efficient routing is very
important in order to save the energy of the hybrid
optimization technique. The results of new protocol i.e.
hybrid have been compared with EEPB and IEEPB.
Simulation results show that the lifetime of Hybrid is better
as compared to EEPB and IEEPB.
Optimized Projected Strategy for Enhancement of WSN Using Genetic AlgorithmsIJMER
This paper put forward a new strategy for selecting the most favorable cluster head in Stable
Election Protocol (SEP). The planned approach selects a node as cluster head if it has the maximum
energy among all the available nodes in that particular cluster. It considers diverse nodes and divides
nodes among normal, transitional and advance nodes. To handle the heterogeneity of the nodes, different
optimized probability density functions are selected. First node dead time explain the network stability
period and last node dead explain the overall network lifetime. The main pressure is to increase the time
when first node dies and also when last node dies. The projected strategy is designed and implemented in
the Matlab using mathematics toolbox. The projected algorithm is also compared with the some prominent
protocols like leach, E-LEACH, SEP and extended SEP
Application of Weighted Centroid Approach in Base Station Localization for Mi...IJMER
A Wireless Sensor Networks (WSNs) consisting of sensor with strategic locations, and a base-stations (BSs) whose locations are relatively flexible. A sensor cluster consists of many small sensor nodes (SNs) that capture, encode, and transmit relevant information from a designated area. This article is focused on the topology of positioning process for BSs in WSNs. Heterogeneous SNs are battery-powered and energy-constrained, their node lifetime directly affects the network lifetime of WSNs. We have proposed an algorithmic approach to locate BSs optimally such that we can maximize the topological network lifetime of WSNs deterministically, even when the initial energy provisioning for SNs is no longer always proportional to their average bit-stream rate. The obtained optimal BS locations are under different length of area field and number of nodes according to the mission criticality of WSNs. By studying energy consumption due to space loss and amplification losses in WSNs, we establish the upper and lower bounds of maximal topological parameters of area and number of nodes, which enable a quick assessment of energy provisioning feasibility and topology necessity. Numerical results and surface plot are given to demonstrate the efficiency and optimality of the proposed topology of BSs positioning approaches designed for maximizing network lifetime of WSNs.
Spread Spectrum Based Energy Efficient Wireless Sensor NetworksIDES Editor
The Wireless Sensor Networks (WSN) is
considered to be one of the most promising emerging
technologies. However one of the main constraints which
is holding back its wide range of applications is the
battery life of the sensor node and thus effecting the
network life. A new approach to this problem has been
presented in this paper. The proposed method is suitable
for event driven applications where the event occurrence
is very rare. The system uses spread spectrum as a means
of communication.
An Adaptive Energy Efficient Reliable Routing Protocol for Wireless Sensor Ne...IDES Editor
A reliable routing protocol for wireless sensor
networks (WSN) should be capable of adjusting to
constantly varying network conditions while conserving
maximum power. Existing Routing protocols provide
reliability at the cost of high energy consumption. In this
paper, we propose to develop an Adaptive Energy Efficient
Reliable Routing Protocol (AEERRP) with the aim of
keeping the energy consumption low while achieving high
reliability. In our proposed protocol, the data forwarding
probability is adaptively adjusted based on the measured
loss conditions at the sink. So only for high loss rates, a node
makes use of high transmission power to arrive at the sink.
Whenever the loss rate is low, it adaptively lessens the
transmission power. Since the source rebroadcasts the data,
until the packet loss is minimized, high data reliability is
achieved. By simulation results we show that the proposed
protocol achieves high reliability while ensuring low energy
consumption and overhead.
A Review Study on Shortest Path in WSN to detect the Abnormal Packet for savi...Editor IJMTER
The main motive of this research is to study energy-efficient data-gathering mechanisms to
abnormal packet data for saving the energy. To detect the abnormal packet irregularities is useful for
saving energy, as well as for management of network, because the patterns found can be used for
both decision making in applications and system performance tuning. Node distribution in WSNs is
either deterministic or self-organizing and application dependant. The sensor nodes in WSNs have
minimum energy and they use their energy for communication and sensing.
Anchor Point Based Data Gathering with Energy Provisioning In Wireless Rechar...IJTET Journal
Abstract: Several studies have demonstrated in Wireless Sensor Network for reducing the energy consumption of nodes However, these benifits are dependent on the path taken by the SenCar, particularly in delay-sensitive applications, as all sensed data must be collected within a given time constraint. An approach proposed to address this challenge is to form a hybrid moving pattern in which a mobile-sink node only visits anchor points, as opposed to all nodes. Sensor nodes that are no anchor point forward their sensed data via multihopping to the nearest anchor point. The optimal TSP nearest neighbour algorithm is used for finding the optimal path taken by the SenCar . The new method is proposed for energy transfer mobile collector to sensor nodes by using WerMDG. The simulation result show that the energy consumption is reduced when comber with the existing method.
Realization Of Energy Harvesting Wireless Sensor Network (Eh Wsn) With Special Focus On The Energy Harvesting Systems Tan Yen Kheng
1. Realization of Energy Harvesting
Wireless Sensor Network (EH-WSN)
(EH-
- with special focus on the energy
harvesting systems
Presented by
Yen Kheng Tan and A/Professor S.K. Panda
Department of Electrical & Computer Engineering
National University of Singapore (NUS)
tanyenkheng@nus.edu.sg
Research Motivations
Ubiquitous/Pervasive computing (Invisible/Disappearing)
– As people find more ways to incorporate these inexpensive,
p p y p p ,
flexible and infinitely customizable devices into their lives, the
computers themselves will gradually "disappear" into the fabric
of our lives (http://www.microsoft.com/presspass/ofnote/11-02worldin2003.mspx)
– “Will we be surrounded by computers by 2010? Yes, but we
won’t know it.” Bill Gates in ‘The Economist’, 2002
Military Environment Bio-medical Healthcare
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
1
2. Research Motivations (cont’d)
Energy Harvesting/Scavenging Technology
– “The pervasiveness and near-invisibility of computing will be
p y p g
helped along by new technologies such as … inductively
powered computers that rely on heat and motion from their
environment to run without batteries.”
Bill Gates in ‘The Economist’, 2002
– “The importance of energy harvesting has motivated the
German federal government to include the topic in its €500
million (about S$1 billion) research support program.”
EE Times article, 2007
Goal: To investigate energy harvesting technologies that can
power tiny pervasive computing devices indefinitely in a smart
environment
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Architecture of Smart Environment
Reference: D.J. Cook and S.K. Das, ”Wireless Sensor Networks, Smart Environments: Technologies, Protocols
and Applications”, John Wiley, New York, 2004.
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
2
3. Design Challenges in Conventional WSN
Sensor node has limited energy supply
Q Hard to replace/recharge nodes’ batteries once deployed, due to
- Number of nodes in network is high
- Deployed in large area and difficult locations like hostile
environments, forests, inside walls, etc
- Nodes are ad hoc deployed and distributed
- No human intervention to interrupt nodes’ operations
=> Restricted resources available for collecting and relaying data
Configure and/or reconfigure sensor nodes into network
Q Network and communication topology of WSN changes frequently
- Addition of more nodes, failure of nodes, etc
Tradeoff between Energy and Quality of Service
Q Limited finite energy and demand for QoS
=> Prolong network lifetime by sacrificing application requirements such
as delay, throughput, reliability, etc
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Research Issues in WSN
Energy related matter in WSN
- Power management for sensor node
g
- Energy efficient protocols in medium access control (MAC) and
routing layers
Network performance
- Quality of Service (QoS) e.g. data throughput, reliability,
propagation delay, etc
- Network security
- Sensor network deployment
- Real-time location estimation
WSN performances highly dependent on energy supply
=> Higher performances demand more energy supply
=> Bottleneck of Conventional WSN is ENERGY
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
3
4. Typical Power Consumption of a Wireless Sensor Node
Compare battery estimated life of a Crossbow sensor
node operating at 1 % and 4 % duty cycles
Duty cycle
=1%
Duty cycle = 4 %
Longer operational lifetime => Require more energy supply =>
Higher energy storage capacity => Larger battery size
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Energy Harvesting in Wireless Sensor Network
Wireless Sensor Network (WSN) only
Energy Harvesting Wireless Sensor Network (EH-WSN)
Finite
Energy
Energy energy Sensor
manage-
Harvest source nodes
ment
-ing such as in WSN
circuit
batteries
EH + Batteries => prolong energy supply => sustainable
Batteries => finite energy supply => limited WSN lifetime
WSN lifetime
– Network failure occurs after some nodes go into idle state
– Nodes go into idle state after energyusing EH
Recharge batteries in sensor nodes supply exhausted
??? + Batteries => sustainable WSN lifetime$
– Prolong WSN operational lifetime or even infinite life span$
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
4
5. Power Aware EH-WSN Considerations
Adapted from MIT, Chandrakasan et al.
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Research Issues in EH-WSN
1. Quality of service (QoS) under constrained energy supply
– Trade-off between energy consumption in sensor node &
gy p
QoS in WSN
– Determine optimal operating point e.g. optimal sleep and
wakeup strategy => achieve highest system utility
2. Optimization of energy usage based on EH device
behaviour
– Harvested energy largely depend on ambient conditions
– Optimize energy usage to satisfy Q constraints under
p gy g y QoS
varying energy supply
3. Cross-layer optimization
– Energy optimization in WSN using EH in cross-layer fashion
e.g. energy-aware routing and MAC protocols
4. Integration with new wireless technologies
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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6. Design and Development of EH-WSN
Objective: Integrate energy harvesting systems into
wireless sensor nodes target for specific applications
g p pp
– Investigate on various energy harvesting (EH) sources
– Model and characterize the performances of energy
harvesters
– Develop suitable power/energy management circuits
between energy harvester and load
– Validate EH sensor nodes in practical applications
p pp
Energy
Finite Power/
harvest
Energy energy Energy Sensor
-ing
harvest source manage- nodes in
sources
ers such as ment EH-WSN
i.e.
batteries circuits
wind
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Energy Harvesting Sources and their Energy Harvesters
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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7. Existing Research Works
EH-WSN research
– Indoor Solar EH (SEH) wireless sensor node for smart
environment
– Outdoor Solar EH for military portable computing system
– Vibration EH (VEH) wireless sensor node for condition
based maintenance of large equipment
– Thermal EH (TEH) from human warmth for wireless
body area network
– Wind EH (WEH) wireless sensor node for remote
sensing and management of disasters
Other energy related research
– Wireless energy transfer
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Indoor SEH Wireless Sensor Node
Example of indoor testbed in
Cables
Pavoda
Issue on battery duration for
non–cabled nodes
→ even worst for large
numbers of nodes (100-1000)
Michele Zorzi, 2008
Introduce indoor solar energy
gy
harvesting for indoor nodes
Bulky size and heavy weight
Large area required by Solar panels
nonocrystaline solar panels
Dallas IEEE, 2007
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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8. Indoor SEH Wireless Sensor Node (cont’d)
Resistance Emulation using DCM boost converter to
achieve MPPT during impedance matching
g p g
i1 (t ) Conversion ratio, M
Battery-powered
Ts Emulated Resistor, R e
+ 2
d (t )Ts V 1 + 1 + 4d 2 / K
sensor node v1 (t ) T M= =
i1 (t ) T = v1 (t )
2
s
s
2 Ts Vg
Indoor Solar 2L 2L V 1 + 1 + 4 R / Re
- Re (d ) = 2 = 2 f s =
powered sensor node d Ts d Vg 2
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Indoor SEH Wireless Sensor Node (cont’d)
Voltage waveforms of DCM DC-DC boost converter
PFM from VCO
(C1)
Vsolar (C4)
1
Vinductor (C3)
2
3
V (C2)
DCMswitch DC-DC converter
boost
1 2 3
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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9. Indoor SEH Wireless Sensor Node (cont’d)
Evaluate power harvested from solar panel with MPPT for
various loading conditions (Vref = 0.93 V)
Pharvested Pharvested Difference in
Rload Vload harvested power
w/emulator@Rload w/Rload
180 Ω 1.510 V 12.67 mW 8 mW 58.4 %
270 Ω 1.836 V 12.48 mW 6 mW 108 %
470 Ω 2.412 V 12.38 mW 3 mW 312.7 %
680 Ω 2.907
2 907 V 12.43
12 43 mW 2 mW 521.5
521 5 %
1200 Ω 4.1 V 14.00 mW 1 mW 1300 %
3900 Ω 6.906 V 12.23 mW 0.32 mW 3721.9 %
Significant increase in power harvested with resistor emulator
Q Rload // Re matches with Rsolar → fs changes, Re changes
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Outdoor SEH Portable Computing System
Deployment testbed and experimental results
Experimental Testbed
Courtesy of DSO & NUS research team
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
9
10. Maximize VEH Using SCE Technique
Illustration of synchronous charge extraction circuit
Primary Circuit Secondary Circuit
Piezoelectric
generator
Switch S closed
Primary Circuit: Accumulated charges Secondary: Open-circuit
extracted from piezoelectric generator Circuit
transferred to transformer, L
Switch S Open
Primary Circuit: Open-circuit & Secondary: Stored energy in L
generated charges accumulated in Circuit gets released to
generator Cr & RL
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Maximize VEH Using SCE Technique (cont’d)
Piezoelectric generator
Vibration
SCEC energy source
Bootstrap Circuit
Latching Circuit
Accumulates sufficient energy power
Allows applications with higherin storage
Buck Converter operated intermittently, Vibration
cap, which then be
consumptions toprovide the initial startup
Regulatescontinuously voltage @5V
rather than the output
power to the control circuit. energy source
Shaker
Power consumption of control circuit ~300 μW
(60μA @5V)
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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11. Maximize VEH Using SCE Technique (cont’d)
Performance of SCE technique
Theoretical results 8.8 mW
Simulation results 6.7 mW
Experimental results 5.6 mW
Y.K. Tan, J.Y. Lee and S.K. Panda, “Maximize Piezoelectric
Energy Harvesting Using Synchronous Electric Charge
Extraction Technique For Powering Autonomous Wireless
Transmitter”, IEEE International Conference on Sustainable
Energy Technologies (ICSET 2008), 1254-1259, 2008.
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
TEH from Human Warmth for WBAN
Overview of WBAN and its TEH system
Human wrist
TEH system
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
11
12. TEH from Human Warmth for WBAN (cont’d)
Circuit design and video demonstration
D.C. Hoang, Y.K. Tan and S.K. Panda, “Thermal Energy Harvesting From Human Warmth For
Wireless Body Area Network In Medical Healthcare System”, The 8th IEEE International Conference
on Power Electronics and Drive Systems, 2009, in-progress
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
WEH Wireless Sensor Node
System-level problems to be addressed
- Fluctuating wind energy source → load energy requirement
- Min and max wind speeds available → voltage regulation and
Wind
turbine
energy storage
- Portability of wind energy harvester system → size and
Scheme 1
Scheme 2
weight
- Energy consumed by wind speed sensing and wireless
communicating
Power management
Motivation transmitter
and RF
circuits
- Self-sufficient and sustainable by wind energy source
- Compact and miniature wind energy harvester
=> Two WEH schemes implemented to power remote area wind
speed sensor in disaster management application
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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13. WEH Wireless Sensor Node (cont’d)
Video demonstrations on the wind turbine and wind piezo
harvesting systems$
g y
Scheme 1: Wind turbine Scheme 2: Wind piezo
R.J. Ang, Y.K. Tan & S.K. Panda, “Energy harvesting for Y.K. Tan & S.K. Panda, “A Novel Piezoelectric Based Wind Energy
autonomous wind sensor in remote area”, 33th Annual IEEE Harvester for Low-power Autonomous Wind Speed Sensor”, 33th Annual
Conference of Industrial Electronics Society, pp.2104-2109, 2007. IEEE Conference of Industrial Electronics Society, pp.2175-2180, 2007.
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
MEH through Inductive Coupling for WSN
Magnetic energy harvesting based on Ampere’s law and
Faraday’s law
y
Gauss
meter
AC power
source
Magnetic energy
harvesting circuit
Resistor
load Magnetic energy
bank harvesting circuit
Y.K. Tan, S.C. Xie and S.K. Panda, “Stray Magnetic Energy Harvesting in Power Lines through
Inductive Coupling for Wireless Sensor Nodes”, The Proceedings for the 2008 nanoPower Forum
(nPF’08), Darnell Group, Irvine, Costa Mesa, California, 2008.
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
13
14. Wireless Transmission of Power with
Magnetic Resonance 80
70
Efficiency (%) vs Inductance (H)
60
Source Transmitting
g Receiving Load
g
y )
n y(%
50
Coil Coil Coil Coil
fficie c
40
30
E
20
10
0
1.00E- 1.00E- 1.00E- 1.00E- 1.00E- 1.00E- 1.00E- 1.00E+0
07 06 05 04 03 02 01 0
Inductance (H)
Efficiency (%) vs Capacitance (F)
80
70
60
ffic n y )
E ie c (%
50
40
30
20
10
Transmitting Receiving 0
1.00E-15 1.00E-13 1.00E-11 1.00E-09 1.00E-07 1.00E-05 1.00E-03
end end Capacitance (F)
Efficiency (%) vs Conductor radius (m)
Efficiency (%) vs Distance (m)
100
120
90
100 80
70
ffic n y )
E ie c (%
ffic n y )
E ie c (%
80 60
50
60
40
40 30
20
20
10
0 0
0 0.5 1 1.5 2 2.5 0 0.002 0.004 0.006 0.008 0.01
Conductor radius (m)
Distance (m)
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Case Study Example
Wind Energy Harvesting Wireless Sensor Node
– Modeling and Analysis
– Design considerations
– Implementation and hardware prototype
– Live Demonstration
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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15. Wind Speed Distribution
Fire behavior on the Bor Forest Island under the FIRESCAN
fire research program
p g
Nominal daily wind speed in the deployment location over a
period of one month
Wind speed high, wind energy harvester harvests energy for
electronic circuitries and charge supercapacitor
Wind speed too low, supercapacitor acts like DC power
source to power electronic circuitries
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Functional Model and Power Equations of
Wind Turbine
Pwind = FA v =
1
ρAv 3 Pmech = Paeroη gear
2
1
Paero = Pwind C p (λ , θ pitch ) = ρπR 2v 3C p (λ ,θ pitch )η gear
2
1
= ρπR 2 v 3C p (λ , θ pitch ) Pelec = Pmechη generator = VI
2
v − v2 1
C p = 4a (1 − a ) 2 , a = = ρπR 2v 3C p (λ ,θ pitch )η gearη generator
2v 2
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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16. Characteristic of Wind Turbine
AC electrical power generated by wind turbine vs voltage
and current under varying wind source
y g
MPPT pt MPPT pt
Does not exist any voltage or current reference point for
maximum power harvesting over the range of wind speeds
di λv
Q V = I s R s + L s + k φω , where ω =
dt r
Fixed reference V and I MPPT approaches are not applicable
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Characteristic of Wind Turbine (cont’d)
AC electrical power generated by wind turbine vs load
resistance under varying wind source
y g
Maximum power extraction at optimal load resistance of 100Ω
– Low optimal resistance => high output current EH source
Deviate away from optimal loading, either very light or heavy
loads, will result in significant drop in output power harvested
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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17. Overview of WEH Wireless Sensor Node
Power
management
electronic
circuits
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Resistance Emulation Approach
Resistance Emulation (RE) is based on the concept of
impedance matching
p g
RE = REmulated by converter // RLoad
2
Vin Vo2
=
Rin Ro
Rin ⎛ 1 ⎞
=⎜⎜ (1 − D ) 2 ⎟
⎟
Ro ⎝ ⎠
where Rin = Rs ⇒ Ro b, D b
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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18. Performance of Resistance Emulator
Performance of resistance emulator for matching source
Rs = 150 Ω with dynamic load (
y (charging supercapacitor)
g g p p )
DC electrical power (mW)
urce resistance (Ω)
Ropt = 150 Ω Pmppt = 7.5 mW @3.5 m/s
Sou
Load resistance (Ω) e Duty cycle
Supercapacitor is initially uncharged, i.e. Rload = 0 Ω
As supercap is charged, Rload changes => dynamic load
Ropt = 150 Ω remains and Pmppt = 7.5 mW @3.5 m/s achieved
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Performance of Resistance Emulator
Performance of resistance emulator for matching source
Rs = 150 Ω with dynamic load (
y (charging supercapacitor)
g g p p )
ource voltage (V)
Load voltage (V)
1
Vl =
So
L
Vi
(1 − D)
Load voltage (V) Duty cycle
As supercap is charged
– Vcap increases, but Vsource remains at Vmppt = 1.07 V
– Rload changes, D changes to maintain Ropt = 150 Ω
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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19. Performance of Resistance Emulator
Performance of WEH w/ and w/o resistance emulator in
charging supercapacitor (act like a dynamic load)
g g p p ( y )
t
−
Vcap (t ) = Vcap ,max (1 − e τ )
Vmax =
For Vcap ,max = 5.5V ,
2.14 V
w/ MPPT control
1) Vcap (t = 500 sec) = 2.14V
Vmax = τ w / mppt = 1015 sec
w/o MPPT control
0.66 V 2) Vcap (t = 500 sec) = 0.66V
τ w / o mppt = 3911sec
⇒ τ w / mppt << τ w / o mppt
where τ is the charging
time constant
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Performance of Resistance Emulator
Demonstrate the effects of MPPT and WEH on the
operation of a sensor node i.e. 1 sec per transmission
p p
@ 3.6 m/s wind speed
Vo, boost
Vi, boost
Ii, boost
w/o MPPT w/MPPT w/o MPPT
w/WEH w/WEH w/o WEH
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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20. Live Demonstration
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
Conclusions
Challenges and research issues in a sustainable
WSN – energy supply is the bottleneck
Integration of energy harvesting wireless sensor
network
Design considerations for energy harvesting
systems in practical applications
Maximize energy harvesting with dedicated power
management solutions
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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21. Thank you!
Questions and Answers
National University of Singapore
Yen Kheng Tan
tanyenkheng@nus.edu.sg or tanyenkheng@ieee.org
Realization Of Energy Harvesting Wireless Sensor Network (EH-WSN) - with special focus on the energy harvesting systems
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