Smart Applications for Energy Harvested WSNs

7/31/2019 Smart Applications for Energy Harvested WSNs

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Smart Applications for Energy Harvested WSNsPrabhakar T.V.,Shruti Devasenapathy,H.S.Jamadagni

Centre for Electronics Design and TechnologyIndian Institute of Science

Bangalore,India(tvprabs,spshruti,hsjam)@cedt.iisc.ernet.in

R Venkatesha PrasadTUDelft, The Netherlands

[email protected]

Abstract A Wireless Sensor Network (WSN) powered usingharvested energies is limited in its operation by instantaneouspower. Since energy availability can be different across nodes inthe network, network setup and collaboration is a non trivialtask. At the same time, in the event of excess energy, excitingnode collaboration possibilities exist; often not feasible withbattery driven sensor networks. Operations such as sensing,computation, storage and communication are required to achievethe common goal for any sensor network. In this paper, wedesign and implement a smart application that uses a DecisionEngine, and morphs itself into an energy matched application.The results are based on measurements using IRIS motes runningon solar energy. We have done away with batteries; insteadused low leakage super capacitors to store harvested energy.The Decision Engine utilizes two pieces of data to provideits recommendations. Firstly, a history based energy predictionmodel assists the engine with information about in-coming energy.The second input is the energy cost database for operations. Theenergy driven Decision Engine calculates the energy budgets andrecommends the best possible set of operations. Under excessenergy condition, the Decision Engine, promiscuously sniffs theneighborhood looking for all possible data from neighbors. Thisdata includes neighbors energy level and sensor data. Equippedwith this data, nodes establish detailed data correlation and thusenhance collaboration such as lling up data gaps on behalf of nodes hibernating under low energy conditions. The results areencouraging. Node and network life time of the sensor nodesrunning the smart application is found to be signicantly highercompared to the base application.

Index Terms Energy efciency, Energy harvesting, Energyneutral communication.

I. INTRODUCTIONEnergy harvester entities capture small amounts of energy

over a long time from several sources such as ambient light,linear motion, temperature differential, vibration, RF energyetc. Such energies can be stored in super capacitors. Today,energy harvesting or energy scavenging is possible fromsources such as waste heat from industrial plants, vibrationsand temperature differentials in aircrafts and automobiles, andeven from human action such as walking, lifting and pressing.The power generated can be several hundreds of milliwatts,sufcient to drive low power electronics used for embed-ded applications. Table I[1]shows the power densities for afew energy sources. The gure shows that thermogenerationhas a signicantly higher power density compared to othersources. While photonic power density can be promising under

a good light condition, vibration energy can only form asupplementary source of power. The self powered WirelessSensor Network (WSN) nds its applications where batteryreplacements are difcult both when the sensor nodes are noteasily accessible, or because they are deeply embedded. Suchapplications include rail and road bridge monitoring, aircraft

health and human health care monitoring. When harvestedenergies are used to power up WSN nodes, for perpetualcommunication, the harvested energy should at least be equalto the consumed energy. The energy requirements for a sensornode include the energy consumed by the processor, energyrequired for sensing, and nally a signicantly large amountof energy for communication.

In recent years, energy harvesting has reached a certain levelof maturity due to advancements in low power electronics anddesign technologies. For instance, the standby current of atypical microcontroller such as Texas Instruments MSP430is about 160 microamperes. Photovoltaic panels have becomeeconomical in scale and under moderate light provide suf-

cient energy to power mote class devices. Several researchand commercial solutions have demonstrated that solar energyis indeed a good source of energy. The Sensorscope project[2] offers a complete weather monitoring station powered bysolar energy. We conducted power output measurements for asolar panel size of 95mm X 65mm . The panels were sourcedfrom [3]. The Voltage (V)- Current (I) characteristics indicatethat even in low light condition of about 3600 Lux lightintensity, the system can offer about 8mw of power. The V-Icharacteristics are shown gure 1. Figure 1 shows the powergenerated from the panels under laboratory conditions usingincandescent lamps. A light meter was used to measure theincident light intensity. As one can observe, signicant energygeneration of about 30 mW is possible with sufciently brightlight by applying maximum power point tracking algorithms(MPPT). Similarly, wireless light switches have started ap-pearing to free the use of extensive copper wiring for homes.We looked at Enoceans ECO 100 [4] linear motion harvesterwhich has a typical application as a wireless light switch. Theenergy generated by such switches is sufcient to signal awireless receiver at the lamp load to control the load. Vibrationenergy harvesting has seen advancements due to processingof Lead Zirconate Titanate (PZT) materials. These materials

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TABLE IPOWER DENSITIES OF DIFFERENT ENERGY HARVESTERS

3V exible solar ce ll 1000 lx 7mW/kg3V exible solar cell 10000lx 280mW/kg

Vibration generator (60 Hz) a = 0.24 m/s 2 2.78mW/kgVibration generator (60 Hz) a = 0.98 m/s 2 37mW/kg

Thermoelectric generator T=10K 8W/kgThermoelectric generator T=40K 131W/kg

have long been known to exhibit piezoelectric effects. Weconducted a V-I characterization of one such PZT based macrocomposite bers from [5]. Figure 2 shows the results of itspower viability. Figure 2 shows that several 100s of microwattsof energy generation is possible from small and light weightcomposites.

Fig. 1. A photovoltaic panel for motes

Fig. 2. PZT based MFCs power offering

I I . MOTIVATION AND GOALAlthough several harvesting sources are available to power

WSN devices, networking with harvested power is a chal-lenge because sensor nodes cannot follow a strict regime of activities. Let us take an example of an application that isexpected to: (a) periodically sense the environment, (b)encryptthe data (c) communicate the data over multiple hops to abase station. If the node is also a relay then it additionallyhas the activity to fuse data values from several sensors andthen communicate the information. This application runningout of harvested energies may be difcult to run due to thefollowing peculiarities: (1) Available power is limited although

not the energy (2) Power availability varies with time (3)Power availability might be different in different nodes (4) Nosingle node has complete knowledge of the entire networksenergy opportunity. The impact of these peculiarities affectsseveral aspects related to task scheduling. Firstly, fresh arrivingdata should be prioritized over backlogged data that was nottransmitted due to lack of instantaneous power. The nodeoperations have to be done in a suitable manner to match theenergy availability. Secondly, in the event of excess energyavailability at one of the nodes, there are several possibilitiesfor node collaboration.

In the framework we have described, our philosophy is thatnodes have to perform the maximum number of operationssubject to matching the energy available with a view that themorphed application is closest to the base application.Nodes should be in a position to decide between the differentoperations such as sense, compute, store and communicate.Accurate energy prediction together with energy budgets foroperations and coupled with operation priority rule book should show us the way to to build such a decision engine.

III. E NGINE DESIGN

As we stated earlier, the goal for the Decision Engine is tomaximize the number of operations. We have done away withbatteries; instead used low leakage super capacitors to harvestthe energy. The capacitor buffer is divided into two halves. Thelower half of the energy is used for routine activities such asneighbor discovery, route establishment, channel sensing andother housekeeping activities. The upper half energy is usedby the application, and our engine, in the rst step checks if the energy is above the lower threshold. Time is divided intoxed slots and energy arrival in a slot is used by the engineto provide its recommendations. The Decision Engine utilizes

two pieces of data to provide its recommendations. Firstly,an energy prediction model helps identify in-coming energy.The second input is the energy cost database for operationsrequired for energy budgeting. This database was built usingreal measurements. Figure 3 shows the architectural design of the engine. All operations associated with the base applicationare shown. The base application comprises of sensing twoenvironment parameters such as light and acoustic data. Sincethe read from ash R and W write to ash are performed ona longer time scale, the gure displays them separately. Whilethe light data requires to be encrypted, acoustic data can goas plain text. A forwarding node for multiple sensors has theadditional role of aggregating acoustic data. Each block in thebase application indicates a timer re to complete an activity.The energy aware Decision Engine calculates the total energyrequirements in each time slot and assisted by a Heuristicrule book decides operation prioritization. The system nallyrecommends the best possible set of operations matching thearrived energy. Broadly, the Decision Engine offers a combina-tion of the following outputs: (a) Sensing and Computation (b)Communication (c) Storage and nally, (d) provision for net-work collaboration. The Decision Engine, under good energyconditions, promiscuously sniffs the neighborhood looking for

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all possible data from neighbors. This data includes neighborsenergy level and sensor data. Equipped with this data, highenergy nodes perform detailed data analysis and establish datacorrelation to enhance collaboration. The objective is to ll updata gaps of nodes hibernating due to low energy conditions.The engine takes care of special cases where sometimes noenergy arrives soon after the capacitor is fully charged. In thesecases, apart from node collaboration, the morphed applicationis a slowed down version of the base application. For instance,temperature data if sampled every 2s in the base applicationis now sampled every 4s.

A. Energy Budgets and Heuristic rule book

One may broadly associate three dimensions to energybudgets. The rst dimension is to evaluate the percentageof energy required for a node to participate in the network.Budgets are required towards neighbor discovery, route estab-lishment and maintenance, and transaction of security keysfor secure communication. To support all these activities,

the engine has to ensure that the node never depletes itsenergy below a certain threshold. In this work, although wedo not focus on this dimension, we implicitly take care of thisrequirement by suitably partitioning the super-capacitor energylevel. The second dimension of energy budget is required tohandle workloads. Given that the operation state is differenton each node in the network, workloads can be different aswell. Such workloads could be characterized either in terms of backlogged data or backlogged operations. For instance, somenodes can have large amounts of backlogged data to transmitbecause of neighbor or parent nodes energy depletion or lossof connectivity. Furthermore, such nodes can be burdenedwith backlog of operations caused due to nodes own energy

constraint. One may have to address questions related to thequantity of data that can be scheduled and agging importantdata. Finally, the third dimension related to the communica-tion budget plays an important role. Which node among theneighbors has energy to receive data? If the nodes own energyis high enough, would it be possible to communicate directlywith other overlay high energy nodes? The Decision Engine inthis paper is an attempt to address the rst two dimensions of energy budgets. Concerning the Heuristic rule book, the rulesshown in Figure 3 are dened based on the application design.The rules are organized hierarchically. At the highest levelis Packet Communication which has a higher priority overPacket Processing. The next level has data 1 (Light data) hasto be encrypted and its transmission has a higher priority overreception. Packet Processing rule has data sensing prioritizedover computation. These rules can be entered by the user.Our chosen application requires data1 to be communicatedsecurely and data2 requires to be fused at a hop node by wayof computing the average value. Rules have to be designedin such a manner that the morphed application attempts to beas close as possible to the base application. For instance, onesuch rule could be sensor data should be sampled twice slowerthan the base application.

Fig. 3. Architectural overview of the engine

IV. RELATED WORK

Our focus is on maximizing the number of operations tomatch the in-coming harvested energy. During periods of no energy in a time slot, no operation is performed andduring periods of excess energy the nodes get proactive andacquire as much information as possible from the environment.Perhaps PUMA[6] comes closest to our Decision Engine. Itdescribes a hardware switch used for power distribution tovarious components of the hardware. An algorithm is proposedto increase the utility of harvested power, by matching powerconsumed with the power provided by the harvesting sources.Similar efforts Most other literature propose harvested energymanagement and battery based residual energy management

hybrid schemes. Current literature on energy harvesting fo-cuses on energy-neutral systems both in terms of analyticalmodels for harvesting and consuming entities and derived the-orems that characterize their achievable performance. In [7] theauthors propose a harvesting distributed framework for nodesto learn their environment energy. The learning helps in energyaware assignment of leader elections for clustering techniquesand load balancing. The authors propose new algorithmsfor scheduling and task allotment. Extensive simulations areconducted with their scheme and compared with base residualenergy schemes. In [8] an energy-conservation taxonomy isdetailed around a sensor system for residual energy baseddesign. Broadly, three vertical segments related to duty cy-cling, data driven and mobility based energy conservationschemes are surveyed. In [9] the authors propose a tool fordeveloping energy efcient software code using applicationlevel knowledge.

V. EXPERIMENTAL SETUPThe experimental setup consisted of Iris motes [10] powered

by solar energy through Heliomote [11] using two 22F super-capacitors from [12] in series as the energy storage buffer.A simple scenario of a single energy harvested node and

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Fig. 4. Self-Discharge comparison between a single capacitor and capacitorbank

base station were part of the testbed. The base station wasequipped with the MIB520 [13] base station board connectedto a PC running the serial forwarder application. The nodesran applications written using TinyOS-2.1.0[14] code.

A. Experiments with Supercapacitors

Supercapacitors have the advantage of energy density muchhigher than conventional capacitors and power density surpass-ing all known types of batteries. One of the major advantagesover batteries is that they have virtually unlimited cycle lifeand simple charging methods. Supercapacitors do not containany chemicals that may explode. Some manufacturers suchas [15] even mention they are explosion, re and smoke freewhen the polarity is accidentally reversed. Such features makesupercapacitors attractive in aerospace applications. However,one of the biggest disadvantages of these capacitors is the

self-discharge, or internal leakage current. This is found tobe higher than conventional batteries. Our application require-ment is such that energy harvested in say 12 hours should besufcient to drive the network for remaining 36 or 48 hours. If energy replenishment does not occur within this period, nodesmight switch to hibernating mode. To facilitate this require-ment, it is extremely important to ensure that self-dischargeof the supercapacitors is minimal. For our tests, we selectedcapacitors from [12]. We selected 22F, 2. 3volt capacitors sinceexperiments in [16] shows this value to be satisfactory. Sinceour voltage requirement is 2. 8 volts, we arranged the individ-ual capacitors in series combination to achieve the requiredvoltage. We then conducted self-discharge measurements on asingle 22F capacitor and the series combination capacitors. Wecharged the individual capacitor and the capacitor pair using aconstant current source to the same voltage level and let themdischarge under no load condition and conducted periodicmeasurements of the capacitor terminal voltage using a 10M impedance oscilloscope probe. Our comparison results areshown in Figure 4. As expected, due to lower capacitance,the gure shows that the series-capacitor provides a lowerself discharge compared to individual capacitor. We used thecapacitor pair for all our experiments described here.

B. The Base Application

The base application running on the harvesting nodesconsists of a combination of all the operations that a nodecan perform. Motes performing low power listening (LPL)sample four different kinds of data: light, acoustic data,super-capacitor terminal voltage and solar panel terminal volt-age. Light and acoustic sensor values are as sensed by the

MTS300[18] sensor board from Crossbow , while the solarpanel terminal voltage is from the Heliomote [11] board andthe capacitor terminal voltage is from Iris motes on-boardADC. Of these measured parameters, light data, transmittedevery 10s, is taken as data to be encrypted using a light-weightencryption algorithm known as TinyDragon [20], acoustic datais sensed and transmitted every second. If the node is a relaynode, it receives neighbor information as well and averagesall this data before transmitting it to its parent. The average of all these values is transmitted every 50s. In order to maintaina database of solar panel voltage, the values are read from theheliomote circuit and written to ash every 30 minutes. A readfrom ash operation of the solar panel voltage is performed

by the base application every 15 minutes. These operationstogether constitute the base application.In our experiments, to acquire information about the appli-cations performance, the number of operations performedby the node was transmitted with acoustic data in the samepacket, while super-capacitor voltage and solar panel voltagewere transmitted with light data in the same packet. The baseapplication packet size was 24 bytes.

C. The Energy Prediction Tool

For energy-matched applications, it is necessary to be ableto predict the amount of energy that would be availableto the harvesting node in the next time slot, when node

operations are to be performed. Time series prediction [19],is most often performed using methods such as exponentiallyweighted moving average methods and their derivatives. Inthe implementation of the Decision Engine, we used anexponentially weighted moving average (EWMA) method topredict the incoming energy. EWMA, popular for time seriesforecasting[17], assigns weights to data in the time series,assigning lower weights to older data and giving importanceto more recently acquired data. The forecast value in the timeslot t+1 is given by:

S(t+1) = X(t) + (1- )S(t)

X(t) is the measured value of the time series data in the timeslot t. Here X(t) is the solar panel terminal voltage which givesus the trend of the incoming energy. The weight is given avalue between 0 and 1. We chose a value as 0.5 after testingseveral values for least error in prediction. The value of 0. 5was chosen in [17] as well. This prediction is obtained onceevery 1s, which is the duration of the assumed time slot t.

D. Energy Budget Calculator

The other tool used by the Decision Engine is the Energybudget calculator, which looks up a table of values of energy

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TABLE IIENERGY CONSUMED PER OPERATION BY IRIS MOTES

OperationAverage of 50 samples 7.056 J

Finding peak among 50 samples 7.392 JSensing once from ADC 16.128 JWriting 1 byte to Flash 0.136 mJ

Reading 1 byte from Flash 28.224 JTransmitting @0dBm 28 bytes once 0.784 mJ

Receiving 28 bytes once 0.672mJ

consumed per operation by Iris motes. The experimentalsetup included a current probe amplier connected to anOscilloscope to measure the current consumed by a node foran operation. These experiments conducted on IRIS motes aredocumented in Table II. It can be seen that the communicationoperation together with writing to ash require signicantlyhigher energy compared to other operations. The sum of energy consumed by the next operation is calculated eachtime a scheduled operations timer res. Only if the predictedenergy inow is the greater than the energy needed to performthe scheduled operation, it is performed. If the predicted inowis lesser than the needed amount, the scheduled operation isignored and other operations that can be accommodated areperformed. To decide between multiple matches, the heuristicrule book is used to decide the priority. At the next scheduledoperation timer-re, the node checks for previously ignoredoperations and goes back to perform them if incoming energyis sufcient in this time slot.

E. Heuristic Rule Book Implementation

In order to achieve the objective of maximizing number

of operations performed, the Decision Engine uses a rulebook that helps to make decisions under low energy inowconditions. This rule books hierarchy was mentioned earlier.It contains the following rules:

1) Transmission has higher priority than reception2) Reception is performed before transmission, when back-

logged data is large3) Sensing has a higher priority over computation4) Computation is done before sensing, when backlogged

data is large5) Increase communication range when there is excess

energy

6) Slow down the application by half when there is excessenergy but no new energy comes in

These rules are specic for the chosen application and mayvary for different application areas. In this application rule (1)were enforced by turning the radio OFF, thus stopping low-power listening, and turning it on just before transmission.Rules (2) , (3) and (4) were executed by simply deferring ex-ecution with the help of setting of appropriate ag conditions.Rule (5) and (6) were done by setting the RF power in theapplication and by modifying the timer intervals respectively.

Fig. 5. Measured solar panel terminal voltage vs. predicted values

Fig. 6. Morphing of the application when energy is depleted and replenished

F. Excess energy

In our work, we dene excess energy condition as onewhere the energy harvested by the system is over 90% of

full capacity. Under excess energy, node collaboration wastriggered. We have implemented data correlation realized usingsimple mean difference for photonic sensor data. In thisexperiment, we had two nodes and a base station. When thelow energy node went into hibernation, the high energy nodewas able to predict and transmit its data.

VI. RESULTS AND D ISCUSSIONS

The results shown in this section capture several implemen-tation aspects of the Decision Engine. Figure 5 illustrates theoperation of the EWMA time series forecasting. It can be seenfrom the gure that the predicted value adapted to changesin the actual measurement. A weight of 0. 5 also ensuredsmoothing the curve as a correction for measurement noise.

Figure 6 shows a plot of energy contained in the super-capacitor and illustrates the application response to incomingenergy. The lower limit prevents energy depletion from thesuper-capacitor. We conducted all the experiments outdoorunder bright sun light. During the course of the experiment,to emulate low light conditions, we blocked the light to thesolar panels. This blocking is apparent at the times 0 to350s, 935 to 1450s and 2600 to 3770s. During periods of bright light, the node performed operations matching closely

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(a) Energy Contained in the super-capacitor

(b) Number of Operations Performed

Fig. 7. Comparison of Applications Behaviour at High Energy Inow vsLow Inow

with the base application. During low light conditions, itmodied its operation states and also stopped all operations

when the super-capacitor energy level crossed the no lightcondition threshold. When energy inow resumed, the nodeallowed for a certain amount of energy to accumulate beforeresuming energy-matched operations. Each point represents apacket transmission. The oscillatory behaviour during energyinow was found to be attributed to the heliomote. Figure6 also shows the transmitted packet count ( right Y axis)at the node. One can observe 3 distinct slopes, indicatingthe modication of the transmission interval from 2s to 4sand 5 s. The three slopes may be attributed to uctuation inenergy availability. While the rst slope indicates a fall inenergy for a shorter interval, the second and third slopes aredue to energy unavailability for signicantly longer periods.

Since the light blocking was shorter the rst time, the numberof packet transmissions adjusted to this energy availability.We nd that 500 packets were transmitted in the rst 1200seconds, compared to fewer number of packet transmissionsdue to longer intervals of light blocking.

Figure 7 shows the behaviour of the morphed application.We conducted specic experiments to study the applicationsresponse to excess energy both when energy was constantlyreplenished and otherwise. We performed a comparison of op-eration of the sensor node in 2 cases : Case (1) when the node

is ooded with incoming harvested energy and Case (2) whenthere is no incoming energy, but the super-capacitor has a highamount of stored energy. While gure 7(a) shows the energycontained in the super-capacitor in both cases throughout theexperiment, gure 7(b) illustrates the the cumulative count of the number of operations that the node performed in each case.It can be seen that in Case (2) , the application modied itself and performed lesser number of operations than in Case (1) .The super-capacitor energy level remained constant in Case (1)which clearly shows that the node performed energy matchedoperation. For example, we looked at the behaviour of theapplication between instances of time t = 200s to 300s. Thenumber of operations performed by the node in Case (1)were 165, whereas for the same interval of time, in Case (2) ,the node performed 108 operations. Of the 165 operations inCase(1) , sense and compute operations alone accounted for54 and the remaining 111 operations were transmit operations.In Case(2) however, the number of transmissions dropped to57 and the number of sense and compute operations was 51.The application found an opportunity in Case (1) to transmit

as many packets as possible compared to Case (2) in which itcontinued to operate, depleting slowly from the energy storagebuffer. A manual calculation of energy consumption againstinow veried the applications behaviour from its energyconsumption.Finally, the node performed node collaboration by snoopingon messages transmitted by neighbours, acquired informationof neighbour energy and also its sensed data. It formed acorrelation between its sensed data and that of the neighbournode. When the neighbour went into hibernation, the nodeenjoying high energy availability, transmitted the dead nodesdata with its own, thus lling gaps in data at the base station.

VII. SUMMARY AND

FUTURE

WORK

In this work we explored building smart and autonomousapplications using exciting opportunities presented by energyharvesting sources. We proposed a decision engine attachedto the application that directs it to morph according to energyavailability. We have shown that data sensing, computing,communication and storage operations are energy matched.We have also incorporated data correlation schemes to exploitexcess energy. Our results are encouraging and provide severalinteresting opportunities for further work. For instance, in theevent of excess energy, an overlay network of high energynodes that can extend their sensing and communication rangecan be explored. The Decision Engine is currently tied tothe base application. Our future efforts will be concentratedon making it a plug and play and generic solution for anyapplication to be run on an energy harvesting node. Wehave presented a prediction tool based on the EWMA andfound it suitable for solar energy harvesting. It would alsobe interesting to model prediction based on the Holt-Wintersmethod to exploit the trend and seasonality of data. Weneed to explore several other time series models suitable forenergy harvesting sources such as vibration, thermogenerationand linear motion. The current heuristic rule book does not

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incorporate any network and link impairments and possibleoperations under these conditions. For instance, if the link quality is poor, the calculation of the energy budget shouldtake into account the fact that there is a likelihood of packetretransmission. Alternately, the Decision Engine could alsoadvice a different operation set. Finally, specic harvesterelectronics is needed for super-capacitor buffers. Heliomotewas designed for harvesting systems that used rechargeablebatteries as a storage buffer. While it can be used with super-capacitors as we have shown in this paper, its operation is notoptimal. Also, since this electronics does not perform MPPTand lacks the ability to extract all harvested energy below theVbatt , our demonstrable results were limited. We think that ahardware similar to Ambimax [21] may be more suitable sinceit has a signicantly higher efciency.

VIII. C ONCLUSION

We present our application layer based multiple input De-cision Engine. It tries to recommend operations in an energymatched manner that allows the morphed application to follow

the base application as closely as possible. Since harvestedenergies can replenish storage devices, in the event of excessenergy, several new operations are explored. These includeneighbor energy level information, link quality, and neighborproxying. We present results obtained from measurementsconducted on hardware, consisting of a solar energy harvestedwireless sensor node with a super-capacitor energy buffer.

IX. ACKNOWLEDGEMENTS

This work was supported by ANRC - Project 2B.

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Smart Applications for Energy Harvested WSNs