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Zero Energy Network stack for Energy HarvestedWSNs

Akshay Uttama Nambi S. N1, Prabhakar T.V2, R Venkatesha Prasad1, Jamadagni H.S21Delft University of Technology, The Netherlands

2Department of Electronic Systems Engineering (DESE), Indian Institute of Science, Bangalore, India{Akshay.narashiman,R.R.Vekateshaprasad}@tudelft.nl{tvprabs,hsjam}@dese.iisc.ernet.in

Abstract—We present our “Zero Energy Network” (ZEN)protocol stack for energy harvesting wireless sensor networksapplications. The novelty in our work is 4 fold: (1) Energy har-vesting aware fully featured MAC layer. Carrier sensing, Backoffalgorithms, ARQ, RTS/CTS mechanisms, Adaptive Duty Cyclingare either auto configurable or available as tunable parameters tomatch the available energy (b) Energy harvesting aware RoutingProtocol. The multi-hop network establishes routes to the basestation using a modified version of AODVjr routing protocolassisted by energy predictions. (c) Application of a time seriescalled “Holt-Winters” for predicting the incoming energy. (d) Adistributed smart application running over the ZEN stack whichutilizes a multi parameter optimized perturbation technique tooptimally use the available energy. The application is capable ofprogramming the ZEN stack in an energy efficient manner. Theenergy harvested distributed smart application runs on a realisticsolar energy trace with a three year seasonality database. Weimplement a smart application, capable of modifying itself tosuit its own as well as the network’s energy level. Our analyticalresults show a close match with the measurements conductedover EHWSN testbed.

I. I NTRODUCTION

Several disruptive sensing and control algorithms proposedfor wireless sensor networks (WSNs) have remained abeyantdue to power requirements for the hardware. However, recentlyEnergy Harvesting wireless Sensor network (EHS) applica-tions are a reality due to their independence from utility powerand thus unleashing several interesting proposals in sensingand control. This possibility is because of advancements innanoelectronics and materials, increased power efficiencyofharvesting electronics and the rapid advancements in highintegrated ultra low power microcontrollers and communica-tion radios. The “ZigBee Green” [1] is specifically meant forrunning out of energy harvesting sources. Recent advance-ments in materials and MEMS research has made ThermoEnergy Generators (TEGs) and vibration harvesters (unusabletill recently) as potential energy sources. Other contributingparameters include system operating voltage & frequency ofthe microcontroller and radio, and finally the system’s severallow power modes. Operating voltages of about 1.8 volts withoperating frequencies of less than1MHz and sleep currentsof the order of100 nA are some of the recent technologytrends for system parameters. Radio communication energiesof about 10 nJ per bit [2] is fast becoming a reality. Also,energy storage in thin film batteries [3][4] and low leakagesuper capacitors [5] [6] with several thousand charge-discharge

cycles offer efficient energy storage.EHS find attractive applications wherever remote monitor-

ing and control are required and cover a wide spectrum ofscenarios. On the one end could lie an intrusion detection sys-tem deployed in a wireless tripwire paradigm for monitoringan international border, and the other end of the spectrum isasimple wireless switch application for home lighting systems.While the former application requires an energy storage buffer,the latter application requires energy generation and its usageon the fly. Also, these applications work in multi-hop andsingle-hop settings respectively. Other applications under thiswide spectrum include intelligent transportation, smart build-ings, pollution monitoring, agriculture and climate change,health care including body area networks and other similar ap-plications. For outdoor applications, while photovoltaicpanelsoffer significantly higher power compared to other harvestingsources, often sensor nodes packaged with these small panelscould physically be placed where reflected light or even apartial shade might be present. This is especially true forintrusion detection where the purpose is to detect an intruderunder a camouflage of the sensor node so as to avoid attentionfrom the intruder. Additionally, seasonal variation in sunlightplays an important role in ensuring continuous and untetheredoperation of solar powered EHS nodes. Thus, applicationsperhaps have to continuously adjust to varying instantaneouspower fluctuations and yet accomplish their primary assignedtask. Since the EHS nodes are usually wide spread and thecommunication of these nodes are limited in range, multi-hop network is required with performance deterioration withinacceptable limits. Thus, designing a multi-hop EHS sensornetwork under harsh incoming energy condition is indeed achallenge.

One key difference between battery driven WSNs and EHSnetworks concerns the optimization parameters such as: (a)energy consumption to increase the node’s battery life and (b)A network wide policy such as support a network lifetimeof about “X” (say) number of hours or support the largestpartitioned network. A large body of work in WSN is limitedto maximizing the policy subject to a given energy constraintor minimizing energy consumption to satisfy a network pol-icy requirement. In direct contrast, for an energy harvestednetwork, the objective is to maximize the network policiesand also maximize the energy consumption. In other words,minimize the energy differential between the available and

consumed energy in each discrete time slot and maximize thenetwork policy. Our work in this paper uses a multi-criteriaoptimization approach to find the optimal network policy andenergy differential curve. The significance of optimal networkpolicy-energy differential curve is to show a trade-off betweenglobal perspective of network policy and energy differential.Also, when the requirement on either network policy or energyconsumption varies, one can use the optimal network policy-energy differential curve to locate the new optimal trade-off.

Fig. 1. Energy Harvesting Sensor node stack.

In this paper, the goal is to implement a distributed smartapplication for environment monitoring applications where thesensor and communication nodes are powered with harvestedenergy. We approach this goal by building a protocol stackcalled the “ZEN Stack” as shown in Fig. 1. At the applicationlayer, we consider the application performance not only itsown energy level, but also on its neighbour and parent for-warding nodes. We use energy measurements and time seriespredictions to change the behaviour of our base applicationin a manner that energy utilization is a maximum in the timeslot. At the routing layer, since each node in the network canharvest different energy magnitudes and thus have a varyingenergy profile, finding network wide routes when energy levelson nodes vary continuously is a challenge. Hence, routingprotocols have to consider the energy harvested to decide howto route packets to the base station. At the data link layer, wepropose several enhancements to the existing Medium AccessControl (MAC) Layer. Our goal is to study the performanceof this stack in its ability to exploit the available energy tothe maximum extent possible. Thus ensuring network wide“energy neutrality” condition. The analysis followed by im-plementation results are encouraging and also show that even“near real time” applications can perform reasonably well.

II. RELATED WORK

In recent times, a large body of work is being concentratedon Energy Harvesting Wireless Sensor Nodes (EHWSN).Several energy harvesting sources like solar, thermo, acoustic,wind, RF and wave for driving low power embedded devicesare discussed in [7]. Many energy harvesting WSNs have beenimplemented in past like Trio [8], AmbiMax [9], Prometheus[10]. However, literature on Energy Harvesting systems is

mostly limited to single node networks or extensive simulationstudy. Many of the work focus on the system building,efficiency and viability of energy harvesting mechanisms. In[10] authors shows improvement in lifetime where a superca-pacitor and battery are charged together from a solar panel byadjusting their duty cycles. Recently Power management anddata rate maximization in EHWSN was discussed in [11]. Theauthors analytically find the required amount of energy to beharvested to ensure energy neutral operation in each slot.

Accurate prediction of the energy is an important factor forenergy neutral operations. Time series based energy predic-tions are commonly used for predicting solar energy. Exponen-tial Weighted Moving Average (EWMA) is commonly appliedfor future energy predictions [12], [16] and assigns weights todata in the time series, assigning lower weights to older dataand giving importance to more recently acquired data. Holt-Winters (HW) time series is widely used in stock markets trendanalysis, production planning, healthcare decisions on staffing& purchasing, demand forecasting [14] and for predictingshare prices where forecast value takes trend and seasonalvariation into account [13], [15]. In [25] a three state markovchain model is used to predict the solar energy source andtheir transition probabilities are restricted for day timeonly.Energy samples from a simulation model are also generatedfrom the model.

In the network layer, the work in [16] demonstrate theperformance benefits in using AODVjr for ZigBee meshnetworks. The work introduces an energy aware metric withfuture energy consumption using Exponential Weighted Mov-ing Average (EWMA) time series model. The work is limitedto simulation studies and also considers battery driven sensornodes. In this work, we utilize AODVjr protocol [17] asimplified version of AODV routing protocol that reducesimplementation complexity by eliminating certain featuresfrom the original AODV protocol. The work in [18] com-pares several energy harvesting based routing protocols ina simulation setup and show that R-MPRT routing protocoloutperforms the other protocols. The work considers realisticMAC protocols suitable for energy harvesting networks andis limited to extensive simulation studies. In [19], authorspropose a routing algorithm that takes into account distanceand link quality information. The algorithm requires that allnodes in the network know about the position, which usuallynot available in sensor network deployments. The authors in[20], propose asymptotically Optimal Energy-Aware Routingfor Multihop Wireless Networks named Energy-opportunisticWeighted Minimum Energy (E-WME). In E-WME the bestroute is decided based on the energy considerations, however,routing decisions should also take into account different chan-nel conditions, especially in a wireless environment in orderto minimize packet retransmissions and energy wastage.

At the data link layer, while there are several MAC pro-tocols been designed for WSN, they are not optimized forenergy harvesting sensor nodes. However there are quite afew MAC protocols designed for EHS, the work in [21]proposes dynamically adapting the duty cycle of a node by

observing deviations in energy input from an estimated modelto ensure energy-neutral operation. In [22] the authors proposeto use adaptive control theory to formulate a linear-quadraticoptimal tracking problem for maximizing task performanceand minimal duty cycle variations. In [23] the authors presentanalytical models and performance metrics for different MACprotocols. The work however is limited to simulation studiesand over single-hop network. In [24], performance of varioussleep and wakeup strategies based on channel state, batterystate and environmental factors are analysed. The authorspropose a optimal sleep wakeup strategy using game theoryapproach which provides trade-off between packet droppingand blocking probabilities.

Several optimization techniques have been discussed inliterature for battery driven multihop WSN’s in the pastwhere the objective is to maximize the network lifetimeby minimizing energy consumption [26], [27]. With a viewon utilizing the harvested energy in an optimized manner,adapting the performance of an application while respectingthe limited and time-varying amount of available power inEHWSN is discussed in [28]. A formal model that activelyadapts application parameters such as “rate of sensing” is usedto optimize the performance. The simulation study does notconsider network related parameters or network wide energylevels. A “Lazy Scheduling Algorithm” is proposed and testedfor its effectiveness by assigning several power values to thesystem in [29]. Task admittance and future energy predictionis carried out with energy variability characterization curvesgenerated by modeling the power source.

We demonstrate a distributed smart application which op-timizes the necessary parameters to ensure maximization ofapplication policy and minimization of residual energy. Con-trasting to the work in literature, we believe performing EnergyNeutral Operations based on node’s own energy is not optimal.Instead we propose to derive the optimal number of operationsand the residual energy based on the available energy on thenode, predicted energy and network energy (i.e., neighbouringnodes). Particularly, we tune the duty cycle factor, transmissionpower factor and the application morphing factor to achieveoptimal performance. Amongst the rich literature on EHWSN,inter alia, we position our work based on these attributes:(a) Harvested energy is the only available source; (b) EHWSNnodes are deployed in a testbed with MAC layer contentionand adaptive duty cycling; (c) Inter-node distance of about25 − 30ft with a realistic channel where Wi-Fi and other2.4GHz radios are present; (d) Energy prediction from physicalsolar trace data including seasonality over a three year databaseis used; and (e) A smart application which morphs itself intoseveral forms based on the energy level of the node as wellas its network in a manner where performance deteriorationis within acceptable limits.

III. E XPERIMENTAL SETUP

Our multi-hop EHWSN comprises of4 nodes placed ina tree topology as shown in Fig 2. Our custom hardwaremotes uses TI’s MSP430 microcontroller and Chipcon’s IEEE

802.15.4 standard CC2520 radio. We broadly divide the net-work into data collectingsensor nodes and data relayingrelaynodes. From Fig 2, nodes B,C are relay nodes and node Dis the sensor node. Relay nodes have the task of forwardingpackets to the base station. Node A is utility powered and con-figured as the base station or data sink node for the network.For our implementation, we used solar energy harvesters withvarying energy profile across the network. Since we requirerealistic spatio-temporal varying power across all the networknodes, the power output solar trace database available from[30] is replicated over the experimental setup. The three yearsolar trace with trend and seasonality has power output fordirect, diffused and reflected sunlight. In Fig 2, Node B runson direct sunlight profile, node C runs on reflected sunlightprofile and node D runs on diffused sunlight profile. The poweroutput from the solar panels was varied by switching “on” and“off” electrical lamps in the solar harvesters. We scaled downthe power output obtained from Lowry Range Solar Station(LRSS) to match the laboratory solar harvesters.

Fig. 2. Experimental Setup.

Each node in the network has two modes of operationnamely (a) active mode and (b) sleep mode. While, inactivemode, a node can transmit and receive packets, insleep mode,node turns off the radio transceiver to reduce the energyconsumption. Switching between these two modes dependson the sleep and wakeup strategy used by that node. In oursetup, based on the harvested energy we determine the sleepand wakeup periods. For the MAC layer channel sensing, weuse Clear Channel Assessment Mode -1 (CCA Mode 1); asimple energy detection scheme available as part of the IEEE802.15.4 standard.

IV. SMART APPLICATION - THE MODEL

We argue that node based task scheduling and pre-emptionis perhaps not best suited for multihop EHWSN. For instance,it is possible that low priority tasks located at the head ofthe queue gets executed during energy stress disregardingnetwork’s requirement and energy budgets. We propose thatapplications have to be “smart”. A typical WSN applicationcomprises of operations such as sense, compute, store andcommunicate. These operations are required to be executed

based on a policy over several time slots. Communication be-ing an integral part of a node, it typically comprises of packettransmission and reception. Packet forwarding is essentiallytransmission but refers to relaying (reception and transmission)a neighbour’s packet. Transmission power control is mandateddue to energy considerations. The smart application is es-sentially a set of policy elements. Smart application designand its successful implementation require several parameters:(a) Available energy in the storage buffer. We call this the “realenergy” (EA). (b) Predicted energy that would be harvestedin the next two or three time slots called “virtual energy”(EP ). (c) The “network energy” (EN ) which is essentially theenergy available in the neighbouring nodes (can be extendedtoenergy available along a route). Thus our smart applicationhasto exploit the energy in the slot to the maximum such that the“residual energy” between the available energy and consumedenergy in a slot is minimum. Since energy replenishmentoccurs in EHWSN, energy utilization should be such thatmaximum number of tasks or operations should be completed.As one can observe, Packet Reception Ratio (PRR) andapplication performance depends on accurate measurement ofthe above parameters. Since available energy is stored in asuper-capacitor, we calculate the real energy by sensing thevoltage across the capacitor. We use Holt-Winters model topredict the virtual energy in a node. We leverage on the RSSIvalues contained in routing messages for periodic update onthe energy level in the network. Since available energy on eachnode varies, the application morphs itself into several forms ina manner that performance deterioration is within acceptablelimits. Though our analysis and simulations can use manyenergy levels, for the purposes ease of implementation, wediscretize the available energy levels into4 levels representedby Ei, i ∈ {0, 1, 2, 3}. E0, E1, E2 and E3 correspond toLowest survivable, Minimum, Intermediate andHigh energy levels respectively. Similarly, the residual energylevels are represented byE′

i, i ∈ {0, 1, 2, 3}, where E′

0,E′

1, and E′

2, E′

3 correspond toLowest survivable,Minimum, Intermediate and Higher energy levelsrespectively. Let the stored energy, harvested energy, predicted(virtual) energy at time slotk on node ‘n’ be represented byES(k)(n), EH(k)(n) and EP (k)(n) respectively. LetEN(k)(s)

be the energy available on neighbouring node ‘s’ at time slotk and EDC(k)(n) denotes the minimum energy required forthe node for one duty cycle. We denote the maximum energycapacity of a supercapacitor asEmax. The harvested energyfor noden is EH(k)(n) ≥ 0 and it is a trivial assumption asno energy harvested can only stall the application execution.The available energy at each time slotk on node n isrepresented byEA(k)(n), EA(k)(n) = ES(k)(n)+EH(k)(n) andEA(t)(n) ≤ Emax.

A. The Base Application and Morphing

As mentioned earlier, the base application essentially con-stitutes a set of policies which would be executed if availableenergy is high. At the beginning of each time slot, the applica-tion checks the available energy, virtual energy and the policy

TABLE IENERGY CONSUMED/OPERATION BY OUR CUSTOM MOTES

Operation Energy ConsumedAverage of 50 samples 7.056µJ

Finding peak among 50 samples 7.392µJSensing once from ADC 20.30µJWriting 1 byte to Flash 1.23µJ

Reading 1 byte from Flash 0.3 µJTransmitting 128 bytes @ 0dBm 0.341 mJ

Receiving 128 bytes once 0.40 mJ

that requires to be executed. If there is sufficient harvestedenergy, the node initiates network support for its policy andprepares itself to execute the corresponding policy, else,theapplication on the node decides to morph into a slower version(lesser functionalities than the base application). In theeventof network showing lack of support, e.g., neighboring nodeshaving lesser energy, the application will beagain forced tomorph itself to a further slower version. For example, whenensuing two time slots reports the virtual energy for the nodeis low and current energy level is low, the application morphsto a lower version by partial execution of the policy with a fewoperations backlogged for later execution. A partial fulfillmentfor a specific policy is necessary to ensure that priority forcertain operations in the policy has soft guarantees.

Fig. 3. Energy level transitions on each energy harvesting node.

Fig.6 captures the time varying energy profile for a nodeand shows the energy transitions based on real, virtual andnetwork’s energy. For instance, the state transition diagramshows when the node is high on own energy and virtual energy(i.e. both the energy values are atE3), the node can completeits policy execution in the current time slot including anybacklog operations. This energy expenditure is possible onlywhen the network provides the necessary support. With itscompletion, the node can transit to eitherE1 or E0 dependingon the level of energy depletion. At the same time, if the futuretime slots predict a low energy, the base application choosesto morph to a slower form and thus deplete lesser energy andtransit to say either toE2 or E1.

B. Policy Models

1) Policy Model - Operation Set:: The operation set in-cludes the set of operations (both node and network) anywireless sensor node performs. Basic operations may includesensing, computing, communication and storage. Each elementin the operation set is associated with fixed energy consump-tion. The typical energy requirements for each operation onour custom mote are given in Table I.

2) Policy Model - Policy Set (P):: A policy setP defines theset of operations and their order of execution obtained fromanoperation vector that a sensor noden has to follow. Typically,a sensor node might be associated with two or more policies,and the specific set of policy to be executed in a particulartime slot is defined as part of the base application. Thus thepolicy set for a node can be defined as,

P ={

P1, P2, P3, ..., Px

}

. (1)

A typical policy on a sensor nodeD can bePd = {P1, P2, P3}where, P1 = {Sense, Transmit}, P2 = {Sense, Compute,Write} andP3 = {Read, Compute, Transmit}. For example,a relay nodeR may have the policy setPr = {P4, P5, P6}where,P4 = {Receiving, Forwarding}, P5 = {Read, Compute,Forward} andP6 = {Receive, Write}. At each time slot, thenode decides to execute subset of policy set denoted byP ′.The energy required for policy execution in time slotk is givenby,

ER(k) =

m∑

i=1

EiPi′,

{

m = x, P ′

i = P

m < x, P ′

i ⊂ P(2)

where,P ′ is the subset of the policy set,x the maximumnumber of policies andEi is the energy consumed forith

policy set.

V. SOLAR ENERGY PREDICTION MODEL

One of the key requirements for optimal application per-formance is accurate prediction of virtual energy on eachnode. Virtual energy availability has impact not only on node’sown performance but also on the network’s performance. Asmentioned earlier, literature on time series models for energyprediction is a well researched area. However, none till datehave performance results from a physical implementation. Inorder to study network wide efficacy of time series models,we used solar energy trace database with data collected overthree years by lowry range solar station [30] from year May2008 to August 2011. Three data sets are available from thewebsite corresponds to direct sunlight, reflected sunlight, anddiffused sunlight. Since the complete database of three yearsrequires about450 KB for each dataset, it was easily possibleto load the database on individual sensor nodes. We appliedthese spatio-temporal data sets across the complete network asshown in Fig 2. We evaluated two of the most commonly usedenergy prediction models namely Exponentially WeightedMoving Average [EWMA] prediction model, and the Holt-Winters [HW] time series prediction model for these data sets.

A. EWMA Prediction Model

EWMA is a commonly used algorithm for predicting solarenergy which assigns weights to data in time series, assigninglower weights to older data and giving importance to morerecently acquired data. The forecast value using the EWMAalgorithm is obtained from Eq. 3.

EP (t+ 1) = ǫEA + (1− ǫ)EP (t) (3)

In the Eq. 3,EP (t+1) indicates the predicted value for thetime slott+1, EA indicates the measured available energy attime slot t and0 < ǫ < 1 is the weighting factor. To predictthe incoming energy, EWMA sums the last read real valueto the previous value predicted with weightsǫ and 1-ǫ. If theweighing factorǫ is high i.e. close to 1, previous values willhave higher importance and vice versa. The best value forǫis choosen based on the least mean square error (LMSE). Wefound thatǫ value of 0.5 provides a minimum error for theEWMA algorithm which is the same as chosen in [12].

B. Holt-Winters Prediction Model

Since solar energy output can have seasonal variation,intelligent prediction algorithms should exploit the trend andseasonality available within the data sets. We applied Holt-winters time series prediction model as it is a commonly usedtime series model for forecasting when data follows a trendand seasonality. The Holt Winters algorithm computes forecastvalue based on the estimates of trend and seasonality fromthe previous data. HW algorithm uses the following set ofequations for forecasting:

EP (t) = ǫy(t)

I(t− l)+ (1− ǫ)(EP (t− 1) + b(t− 1))

b(t) = γ(EP (t)− EP (t− 1)) + (1 − γ)b(t− 1)

I(t) = βy(t)

EP (t)+ (1− β)I(t− l)

In the above equations,EP is the predicted value,y is thecurrent value,ǫ is the weighting factor,b is the trend factor,Iis the seasonal index,t is the current time slot,l is the numberof periods. The termǫ, β, γ are constants whose values areestimated such that their LMSE is minimized and estimated itto be0.906, 0.1 and0.650 respectively.

VI. M ULTI -CRITERIA OPTIMIZATION PROBLEM

We mentioned in previous sections about efficient energyutilization in each time slot where the difference betweenavailable energy and consumed energy is minimized for agiven real, virtual and network energy. The problem is to findan optimal policy execution. We cast this problem as multi-criteria optimization with two objectives: (a) Maximizingap-plication policy execution, and (b) Minimizing residual energy.

A. Maximizing application policyIn each time slotk, the objective is to maximizeP ′ policies

executed by a node. Thus the application policy utilityU isgiven by,

U =

m∑

i=1

αiPi′ s.t.0 ≤ αi ≤ 1 (4)

In Eq.(4), Pi′ is the set of policies to be executed andαi

indicates the morphing factor for theith policy set. Whenαi

= 1, the entire policy setP is executed (i.e.,Pi′ = P ).

B. Minimizing residual energyLet Xk be the total available energy in time slotk. This is

a function ofEA(k), EP (k) andEN(k). Let Yk be the energyrequired to execute the policies in time slotk as given by Eq.2 andZk is the energy required for operation of the node witha duty cycle factor ofδ. In the event ofXk <[Yk − Zk], wehave

Yk = f(Yk, αk) s.t. 0 ≤ αk ≤ 1; (5)

Zk = f(EDC , δk) s.t. 0 ≤ δk ≤ 1; (6)

Yk is the consumed energy to execute the set of policies basedon the morphing factor ‘α’ and Zk is the energy required foradaptive duty cycle based on harvested energy in each timeslot. We define the residual energy utility,V , as

V = [Xk − Yk − Zk] (7)

s.t. Xk ≤ Emax, [Xk − Yk − Zk] ≥ E′

0;

whereE′

0 is the lowest survivable residual energy level re-quired for the system to be operational. Now, our multi-criteriaoptimization problem is formulated as,

MOPT: max U =

m∑

i=1

αi ∗ Pi′ (8)

and min V = [Xk − Yk − Zk]

First we consider a single objective optimization problem fora givenV (i.e., fixing one of the objectives).

OPT(V ) max U =

m∑

i=1

αi ∗Pi′; s.t. [Xk− Yk−Zk] = V

(9)For all possible values ofV ∈ [E′

0, E′

3] we obtain theircorresponding OPT(V ), optimal points in application policyversus residual energy curve. This gives a mapping fromVto U , which is denoted asf : V → U , where for each point(U, V ), U = f(V ) is the maximum application policy utilitythat can be obtained.

VII. SOLUTIONThe solution to OPT(V ) is obtained by using the special

structure of linear programming such as the Parametric Anal-ysis (PA). The overall approach is to obtainf(V ) by solvinga finite number of linear programs to provide the morphingfactor “α”, transmission power factor and duty cycle factor“δ”. These values ensure minimization of residual energy andmaximization of application policies.

Using PA, we study the perturbation ofV and its effect onthe optimality of OPT(V ) to obtain the perturbation factorλ. We use boldface to denote matrices and vectors. For agiven V , the current optimal basis of OPT(V ) could still beoptimal when there is a perturbation onV . Thus, the range[E′

0, E′

3] can be partitioned into consecutive small intervals,each corresponding to a different optimal basis.

We use Dual simplex algorithm to find the optimal basisin each iteration. The dual simplex method is useful for re-optimizing a problem after a constarint has been added or someparameters have been changed so that the previous optimalbasis is no longer feasible.

The algorithm starts with a basic solution that is dualfeasible so all the elements of ‘row 0’ must be nonnegative.the iterative step of the algortihm first finds the variable thatmust leave the basis and then finds the variable that must enterthe basis to maintian dual feasibility.

For a givenV , let the optimal basis matrix beB and thenon-basic matrix beN. Let the optimal solution to OPT(V )is (xB,xN), wherexB and xN denote the values of basicand non-basic variables respectively. Further, letcB andcN denote the coefficient vectors of the objective functionof application policy utility U for the basic and non-basicvariables respectively.b is the vector with coefficients ofV .The corresponding canonical equations are:

U + (cBB−1

N− cN)xN = cBB−1

b

xB +B−1

NxN = B−1

b

Let the perturbation on parameterV be V + λ. Then thevectorb is replaced byb+ λb

′

with vectorcBB−1N− cN

unchanged. NowB−1b will be replaced byB−1(b+λb

′

) andaccordingly the objective becomescBB−1(b+λb

′

). As longas B

−1(b+λb′

) is non-negative, the current basis remainsthe optimal basis. The value ofλ for another basis to becomeoptimal can be determined as follows: LetS = {i : bi

′

< 0}

wherebi

′

= B−1

b′

. If S = ∅, then the current basis is optimalfor all values ofλ ≥ 0. Otherwise, let

λ = mini∈S

bi

−bi

′

. (10)

Let λ1 = λ, then the current optimal basis is optimal forλ ∈[0, λ1], wherexB = B

−1(b+λb′

) and the optimal objectiveis cBB

−1(b+λb′

). Whenλ > λ1, the basisB is no longeroptimal. Thus, we need to choose a variablexr to leave thebasis, where the minimum in Eq.(10) is attained fori = r.xs is chosen by the dual simplex method rule [33] and weupdate the canonical equations based on the new optimal basisobtained and get(U, V ) pair as defined earlier for OPT(V ).The process is repeated to find the range[λ1, λ2] over whichthe new basis is optimal.

Algorithm 1 descibes the steps to obtain the new optimalbasis for a given basis matrixB. Thus, starting fromV = E′

0,we repeat the steps iteratively to find different bases untilwe reachE′

3. The series ofλ for these bases will partition[E′

0, E′

3] into small intervals. Thus, by executing the abovesteps repeatedly, we obtain a series of(U, V ) pairs, eachcorresponding to an optimal basis. We obtain the applicationpolicy versus residual energy optimal curve by connectingthese endpoints consecutively.

Algorithm 1 Basis Updation Algorithm using Dual simplexmethod

1: Input: An optimal basis matrixB for a given V.2: ComputeN = B

−1N, b= B

−1b andbi

′

= B−1

I

3: If S = { i : bi

′

< 0 } = ∅ terminates.4: λ = mini∈S{

bi

−bi′}

5: r = argmini{bi

−bi′}

6: s= argminj{bj

Njr

,Njr < 0}

7: Let new(B) = (new(B)/ r) ∪ s andb = b+ λI.8: UpdateB based onnew(B).9: Compute V = V +λ andU = cBB

−1b

10: Output: The new basisnew(B), λ and (U,V) pair.

Fig. 4. Optimal application policy versus residual energy when availableenergy isE3

Thus for each small interval with an optimal basis, we nowshow that f(V) is linear. Suppose, interval[E′

0, E′

3] is dividedinto ’K’ small intervals [ E′

i, E′

i+1], i= 1,...,K, whereE′

1 =E′

0, E′

K+1 = E′

3 and the optimal basis for each small interval[ E′

i, E′

i+1] is Bi. Then, the objective value function f(V) canbe computesd as

f(V ) = cBB−1(b+λb

′

)

whereλ = E′

0 - E′

i Thus,

f(V ) = cBB−1(b+(E′

0 − E′

i)b′

) (11)

In Eq 11. cB, B−1, b, I are constants andE′

i is the onlyvariable and hence f(V) is a linear function.

Fig.5(I) shows the simulation results where x-axis rep-resents the residual energy (V) between the available andthe consumed energy and y-axis represents the total numberof operations executed by the node i.e.,U . Fig.5(I) showsthe optimal curve for a node when available energy isE3

and its neighbour node energy is eitherE1 or E2 or E3.Curves 1 and 2 indicate the optimal curve when the energyprediction for future slots is high and low respectively. Whenthe available energy on the node wasE3, the morphing factorα obtained were0.9890, 0.6269, 0.2797 for residual energyof E′

0, E′

1 andE′

2 respectively. Similar curves are obtained for

Fig. 5. Plot of time series residual energy versus number of operations.

other available energy levels i.e.,E2, E1 on the source node.Fig.5(II) shows the time series of residual energy against thenumber of operations performed by the source node obtainedvia simulation and implementation. The source node runs thediffused energy profile from the LRSS. We can see that. thesimulation result closely matches the implementation. Thedifference in number of operations between simulation andimplementation is around 6% due to error in prediction. Forinstance, at time slot3 of Fig.5(II), the available energy atthe source node was wrongly predicted and hence the nodeperformed more operations compared to the simulation result.

VIII. N ETWORK AND MAC L AYER PROTOCOL

ENHANCEMENTS FOREHS NODES

In EHS, because each node in the network can harvestenergy at various rates, it is necessary to use energy harvestingaware mechanisms at all layers in the protocol stack. Also, asour smart application is expected to run on a network of sensornodes, in this section we list the key objectives of “energyharvesting aware” network and MAC layer. Furthermore, sincerouting and MAC layers are well researched in literature, ourapproach is to identify candidate routing and mac protocolsand perform necessary modifications to meet our objectives.Our main focus is to design and develop a multi-node multi-hop energy harvested sensor nodes for outdoor applicationslike intrusion detection, smart buildings etc.

A. Network Layer Enhancements

Given a network wide varying energy profile, one majorchallenge in multi-hop energy harvested WSNs is to find stableroutes from the source node to the sink (or base station) overaset of communication nodes. In a typical scenario shown in Fig2, given a set of possible routes, the question is to find the bestroute from node D to node A. The selected transmission powerat the source and relay nodes should ensure that receiver gainis sufficiently high such that it overcomes channel impairmentssuch as fading. Due to this reason, transmission power controlis one of the key requirements.

Since we have chosen3 discrete energy levels, we havedivided the supported radio transmission powers also into3discrete values as shown in the figure 6. We use this mappingin the multi criteria optimization setting as well. Due to energy

Fig. 6. Selection of transmission powers based on the available energy.

fluctuations, an on-demand routing protocol is required toensure route stability inspite of nodes abruptly shutting downand powering up. We selected AODVjr, a reactive routingprotocol as a candidate. In the beginning, EHS nodes sendout a beacon packet using a fixed transmission power. Relaynodes that listen to this beacon use it as a reference receivedpower. The beacons are sent out at regular intervals to ensurethat the reference is constantly adjusted to the environmentand channel. From our measurements, RSSI reference level of-65 dBm was observed for a transmission power of0 dBmat a typical inter-node distance of about25 − 30 feet. Thuseach relay node is apriori aware of the signal strength ofpacket reception from their downstream relays in the network.To setup routes, source nodes transmit route request packetscalled the RREQ broadcast packets using a transmission powersupported by its available energy. This procedure is followedat each relay node that forwards the received RREQ from thesource node towards the base station.

We develop a modified AODVjr protocol for EHWSN.In our Modified-AODVjr case, the sink node performs theweighted sum of the link quality against each candidate routeand replies with an RREP packet that carries information aboutthe best route back to the source. The best route is the onebetween the source and destination that has the least weightedsum of the link qualities obtained from Eq. 12

C(s,d) =

n∑

i=1

µi ∗RSSIi (12)

In Eq. 12,C(s,d) indicates the cost from source node tothe destination node,RSSIi indicates the Received SignalStrength Indicator (RSSI) of node ’i’ and0 < µ ≤ 1 isthe weight assigned based on the RSSI value at each hop-node in the path from source to destination node. The termµ is obtained from extensive measurements performed on ourcustom board by varying both transmission powers and inter-node distance.

Since the weighted sum at the sink node is dependant onthe network wide energy fluctuations, another modification toAODVjr facilitates every RREQ message to be honoured even

in the middle of a data transfer. The latest RREQ messagesare used to recompute the weighted sum and if this sum isfound to be lesser compared to the previously computed value,the same is conveyed back to the source node via a RREPmessage. Unlike AODVjr, we reintroduced source sequencenumbers to RREQ messages to improve the route reliability.This modification also takes care of dropped and lost RREPpackets. Thus Modified-AODVjr compares both the sequencenumber and overall link quality of the route before sending anRREP.

B. MAC Layer Enhancements

We believe “Virtual Energy Transfer” between peer nodesis an important objective for energy harvesting aware MAClayers. This comes in two related ways: (a) In energy harvestedsystems, since harvesting rate is dependent on the environ-mental conditions, the node has to optimize its sleep andwakeup schedules based on the harvested or available energy.For instance, the idle (reception) current for CC2520 is22.3mA. Thus, one of the important methods to reduce the energyconsumption of the device is by duty cycling. However, anenergy harvesting node high on energy would perhaps increaseits duty cycle to support and respond to shorter preambles;thereby reducing the energy consumed by the sender. (b)Choice of transmission power for control and data packetsshould also be based on available energy. In CC2520, thetransmission (0dBm) and reception currents are25.8 mA &18.5 mA respectively. A node high on energy can include tableentries of other high energy nodes; thereby skip entries forlowenergy neighbours.

We perform enhancements on one of the well known andpopular MAC protocols called the BOXMAC-2 protocol [31].BOXMAC-2 is a random access MAC protocol and usesLPL with periodic check times for ongoing communication.To reduce overhearing between neighbours, a long preambleis replaced by a short preamble that consists of packetscontaining the receivers address along with a small receivecheck period between successive packets. Hence, packet-basedMAC protocol is well suited for harvesting systems due to thefact that it does not require any initial arbitration and onlythe sender and receiver pair spends energy. We have used itsability to draw cross-layer information such as the termδ toadapt the residual energy to the duty cycle.

δ =

{

0 Only system related operations

Otherwise energy transfer is enabled

In the event ofδ taking a value0, the node does only systemrelated operations as there is no energy for communication.For all other values ofδ the virtual energy is enabled withmaximum transfer to neighbours atδ = 1.

IX. RESULTS

In this section, we discuss the results pertaining to: (a)Efficacy of energy prediction models, (b) Routing layer per-formance for varying energy profiles, and (c) Adaptive dutycycling on EHS nodes based on the harvested energy.

A. Evaluation of energy prediction models

We compare the real solar data with virtual solar dataobtained from EWMA and HW prediction models for five daysof a month. The first, second and the fourth days correspondto sunny conditions and the second, fifth day represents thecloudy situations as shown in Fig 7. Since EWMA predictionalgorithm only uses values from previous days at the sametime period, if the weather condition changes for the nextday, this method presents a large error in prediction. Lookingat the Fig. 7 it can be easily noticed that EWMA algorithmfails to adapt to changes in weather on consecutive days.The HW model computes the termsǫ, β, γ based on theprevious solar data to obtain the weighting, trend and seasonalfactors. It can be clearly seen from the Fig. 7, HW predictionmodel has the ability to predict the solar values by consideringboth seasonal and weather condition changes from one dayto another. This improvement in prediction of solar energycomes from the trend and seasonal factors considered by theprediction model. From the three year datasets, we evaluatedthe maximum average error percentage of about 45% forEWMA prediction model and around 7% for Holt-Wintersenergy prediction model.

Fig. 7. Comparison of real, EWMA and HW solar energy

B. Routing Layer

Fig 8 shows the improvement of RSSI for increasing trans-mission powers. Data packets of128 bytes were transmittedevery 2 seconds. The figure also indicates energy per byterequired for transmission on a node with various transmissionpowers. Thus, increasing transmission power requires higherenergy per byte and hence node maps transmission powersto the corresponding energy levels. It is also noted from [32]that the channel coherence time is very high and hence changein RSSI is mainly due to change in transmission power. It isclear from the figure, transmission power change impacts RSSIand hence node changes the transmission power based on theenergy level which is reflected on the RSSI value. We use thisRSSI value to identify the neighbour nodes energy level andwe call this as “Faking of RSSI” based on the energy level.Our Modified-AODVjr routing protocol decides the best routeto the destination node based on the weighted sum of thelink quality. To study the performance comparison between

Fig. 8. Change in RSSI with transmit power

AODVjr and Modified-AODVjr, we implemented both theprotocols on our custom mote. Our metric for comparison isthe packet delivery ratio. The experimental setup is as shownin Fig. 2. Once the source node is powered up, it sendsRREQ message to all its neighbours with the destination nodereplying either to the first RREQ (AODVjr) or reply based onthe overall link quality of the route (Modified-AODVjr). Inboth the experiments the source node generates a data packetevery 2 seconds with a payload of22 bytes. Fig. 9 showsthe comparison of packet delivery ratio between AODVjrand Modified AODVjr. The Modified-AODVjr protocol has aaverage packet delivery ratio of72.7% whereas AODVjr hasan average packet delivery ratio of51.2 %. Thus, Modified-AODVjr routing protocol is necessary for energy harvestedsystems to improve packet delivery ratio and quick routeconvergence.

Fig. 9. Packet Delivery Ratio comparison for Aodvjr and modified Aodvjrprotocols

C. Adaptive Duty Cycling

As described previously, “Virtual Energy Transfer” betweenpeer nodes is an important objective for energy harvestingaware MAC. We utilize the optimalδ value obtained fromour optimization to set the duty cycle on EHS nodes. Fig. 10shows three cases based on theδ value (a) Theδ value of0.05 corresponds to duty cycle of (5%) due to low energyavailable on the node (b) duty cycle of15% was chosen whenthe node has intermediate energy level and the obtainedδ

value is0.15, thus decreasing the sleep periods (c) when theharvested energy level isE3, δ obtained is0.25, hence theduty cycle on the node is set to25%.

Fig. 10. Adaptive Duty Cycle on EHS nodes based on harvested energy.

In Fig. 10, the source node has to send short packets aspreamble for the duration of sleep time i.e.,100ms for (5%)duty cycle when the energy level isE1. When harvestedenergy level on the node is higher i.eE2, E3 the numberof packets sent as preamble by the source node to wakeup theneighbour should span the sleep interval of35ms and20msrespectively. Thus, reducing the number of short preamblessent by the sender and the energy spent by the source nodewhen neighbour node is high on energy. Thus, enabling“Virtual Energy Transfer” between peer nodes at the MAClayer.

D. Application

We show the performance of the distributed smart appli-cation running on sensor Node D (in Fig.2) associated witha diffused energy profile. The solar emulator placed aboveNode D generates the diffused energy profile where most ofthe time either(E1) or (E2) amount of harvested energyis available. The sensor node uses the Holt-Winters energyprediction algorithm in every time slot to obtain the virtualenergy. For generating a reference, we exposed Node D toa constant high energy profile to fixE3. Fig.11(I) showsthe behaviour of the morphed application on sensor NodeD. The plot shows the comparison for two cases:Case-1 isthe reference(E3) energy level andCase-2 when the node issubjected to the diffused energy profile.

In Case-1, the source and the neighbour’s energy wasalways high atE3 and thus Node D could perform all thepolicies associated with the energy levelE3. This can beconsidered as the best case and serve as a reference forthe distributed smart application. InCase-2, the source isassociated with diffused energy profile and the neighbournode’s energy profile is reflected. Fig.11(I) shows the resultsof application morphing under energy fluctuation. We capturedthese fluctuations as events and enumerated them as:(a) when

Fig. 11. (I) Morphed application on the source node for diffused and constantenergy profile. (II) Morphed application on the source node for various energyprofiles.

the energy of node is low and predicted energy for future timeslots is high and vice-versa;(b) when energy level of the nodeis high but the network’s energy level is low and vice-versa.In Fig.11(I), several regions namelyp, q, r, s, t show energylevel transitions as well as application morphing. The region‘p’ indicates the change in energy level fromE3 to E2 onthe source node. The region ‘q’ indicates the morphing ofthe application in one time slot before the change in energylevel on the source node. The application morphed to a slowerversion of the base application as shown in region ‘q’ wherethe number of operations performed by the source node wassignificantly less compared to the previous time slot. Thismorphing improves the available energy on the energy buffer.The region ‘r’ shows the low energy profileE1 on the sourcenode and finally region ‘s’ shows the application morphingto a slower version of the base application. This applicationmorphing event is due to source node energy level being lowand future time slot energy level is high. However, in the nexttime slot it can be clearly seen that Node D morphed to afaster version towards the base application as the future energylevel is E3 and region ‘t’ indicates the increase in numberof operations performed by the sensor Node D. Fig.11(II)shows the implementation result of application morphing onthe source node for various energy profiles obtained fromLRSS. The neighbouring node was running reflected energyprofile. As expected, it can be clearly seen that applicationmorphing was enabled even when the source node was runningon direct energy profile. In summary, the upper bound onapplication performance is dependant on source node’s ownenergy as well as network’s energy. Application performancewhen source node runs other energy profiles is also shown.

Fig. 12. (I) Number of operations performed by relay node B. (II)Numberof operations performed by relay node C.

Fig. 12 shows the number of operations performed by therelay nodes B and C in each time slot. The relay node Bruns direct energy profile and relay node C runs reflectedenergy profile. As can be observed from the figure, relaynode B supported the source node to perform network relatedoperations in most of the time slots. Figure 13 shows thenumber of operations performed by the source node withdiffused energy profile. The figure shows the number of node’sown operations and network operations performed. In timeslot 9 the node was in intermediate energy level and thenetwork returned intermediate support and hence, the sourceperformed few network and its own operations. However, inthe next time slot source node was in high energy level andnetwork returned intermediate support, hence node performedless network operations and more node’s own operations.

To study the efficacy of the optimization method, we enu-merated number of node and network operations (refer TableI) performed by each node for all energy profiles. When thesource node has diffused energy profile and neighbour hasreflected profile, the source performed354 node operations and172 network operations. The source node performed475, 656node operations and190, 212 network operations for reflectedand directed energy profiles respectively.

X. D ISCUSSIONS

In this paper, we discuss energy harvesting aware routingand MAC protocols. The prediction of virtual energy plays

Fig. 13. (I) Number of node own and network operations performed bysource node B.

an important role on the application performance. As shownin section V, we evaluated two of the most common timeseries prediction models. It can be clearly seen from Fig. 7,if the real energy level wasE3, EWMA model forecasted theenergy level asE2 whereas HW model forecasted it asE3 andwhen real energy level wasE2, the forecasted values wereE1 and E2 by EWMA and HW models respectively. Fromour datasets, we evaluated the maximum error percentage ofabout45% for EWMA prediction model. Whereas, for HWmodel it is around7%. Thus EWMA model based predictionsdeteriorate the application performance. In our experiments wefound that when HW prediction model was used, on an average64.5% of the operations were performed at the source node.The values of the parametersǫ, β andγ used in HW predictionmodel vary for different solar power traces. Concerning therouting protocol, significant energy savings were seen due toabsence of control messages such as Hello, RERR etc. Whilethe base station uses weighted sum of the link quality metricto provide a route, in reality, since power control is used bynodes, the link quality is a direct manifestation of the availableenergy. In the MAC layer, we believe “virtual energy transfer”ensures energy neutral operations for a multi-hop multi-nodenetwork setting.

XI. CONCLUSIONS

We have implemented a multihop EHWSN using solarenergy. The efficacy of the proposed optimization algorithmwas studied by evaluating node and network operations forvarious energy profiles. Clearly, the predicted, network andavailable energy contribute to the working of the distributedapplication. The smart application was able to maximize itsoperations by adjusting its application policy (rate) to satisfythe least residual energy criteria. In this work the numberof hops is limited and we have only showed possible wayof building application morphing also considering availableenergy in the nodes of the network rather than source nodealone. We are encouraged by the results and propose toextend the network to include more number of hops andentire routes to study the scalability of our scheme. To thebest of our knowledge this is the first implementation of a

multihop energy harvested WSN,albeit two hops. We believethat our results are a step forward towards larger EHWSNdeployments. We plan to generalize this work by proposinga cognitive networking stack for EHWSNs. The idea here isto provide hooks that consider EH including predictions atevery layer of the networking stack. Moreover, we proposeto study the distributed algorithms also with handles such asdynamically varying available voltage and possible frequencyscaling.

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