Medium Access Control for Thermal EnergyHarvesting in Advanced Metering Infrastructures

Madava D. Vithanage #∗1, Xenofon Fafoutis #2, Claus Bo Andersen ∗3, Nicola Dragoni #4

# DTU Informatics, Technical University of Denmark, Denmark1 s090912@student.dtu.dk, {2 xefa,4 ndra}@imm.dtu.dk

∗ Brunata A/S, Denmark3 cla@brunata.dk

Abstract—In this paper we investigate the feasibility of pow-ering wireless metering devices, namely heat cost allocators, bythermal energy harvested from radiators. The goal is to take afirst step toward the realization of Energy-Harvesting AdvancedMetering Infrastructures (EH-AMIs). While traditional battery-powered devices have a limited amount of energy, energy har-vesting can potentially provide an infinite amount of energy forcontinuous operating lifetimes, thus reducing the cost involvedin installation and maintenance. The contribution of this workis twofold. First, we experimentally identify the potential energythat can be harvested from Low Surface Temperature (LST)radiators. The experiments are based on a developed Energy-Harvesting Heat Cost Allocator (EH-HCA) prototype. On thebasis of this measured power budget, we model and analyticallycompare the currently used Medium Access Control (MAC)scheme of an industrial case study (IMR+) to a MAC schemespecifically designed for energy harvesting systems (ODMAC).Our analytical comparison shows the efficiency of the latter, aswell as its ability to adapt to harvested ambient energy.

Index Terms—Advanced Metering Infrastructure, DistributedEmbedded Systems, Medium Access Control, Wireless SensorNetworks, Thermal Energy Harvesting

I. INTRODUCTION

Automatic Meter Reading (AMR) consists of embedded de-vices which perform time domain measurements and providedata over a unidirectional remote connection from a customer[1]. Industry is gradually moving towards Advanced MeteringInfrastructures (AMI), which refers to the entire measurementand collection system, providing bi-directional communicationbetween the service provider and customer. The data fromthese systems can be used for billing purposes or as feedbackinto home automation systems [2] for intelligent regulationof energy sources. A key limitation of current meters usedin AMI systems is that they are battery powered, whichimposes a limitation on the operational lifetime of a device.Recent advancements in energy harvesting technology has ledto the possibility of harvesting ambient energy. The concept ofEnergy Harvesting Wireless Sensor Networks (EH-WSNs) isrelated to using this small-scale ambient energy to power em-bedded wireless devices. In contrast to the limited amount ofenergy in batteries, energy harvesting can potentially producean infinite amount of energy. Moreover, energy harvestingreduces the cost involved in the installation and maintenanceof the network, while any performance gain could be used

Internet

Cluster 1

Cluster n

Cluster N

Sink (Database)Mains Powered

Gateway Node (Collector)Mains Powered

Node (Meter)Battery Powered

433.92 MHz ISM BandStatic Duty Cycle

GPRSStatic Duty Cycle

WiredNo Duty Cycle

Figure 1. Existing AMR network topology

to encourage building dwellers to be more energy aware,constituting an efficient driver to cut down wasted energy [3].

In this paper we aim at taking a first step toward thedevelopment of industrial Energy-Harvesting Advanced Me-tering Infrastructures (EH-AMI). In particular, we aim atinvestigating the feasibility of harvesting thermal energy fromradiators to power the nodes (meters) of the network. The mainmotivation is the lack of state-of-the-art EH applications usedin the metering industry. Although some studies have beencarried out by academia, to the best of our knowledge wedo not know any paper that focuses on concrete applicationsusing readily available technology. To this purpose, we presenta real world case study conducted in collaboration with aleading company in the AMR sector, namely Brunata. Brunatais an independent Danish exporter of solutions for individ-ual billing of costs for heating and water, with experiencein the development and production of metering equipment.There are currently more than 20 million Brunata Heat CostAllocators (HCA) in service, that ensure costs for heating andwater are billed according to metered consumption. HCAsare mandatorily mounted on radiators in Denmark and in anincreasing number of countries worldwide, and thus the heatproduced by the radiator is an ideal source of power for theHCA. The company monitors these meters and can thereforesupply accurate and fair billing information according to heatand water consumed by individual offices or dwellings. The

TGP-751

Thermal Energy Harvester

BQ25504

Energy Storage

Capacitor / Li Battery

Power ConditionerQ(T) Pin(T) Pout(T)

Figure 2. The EH system (left) and EH-HCA mounted on a radiator (right)

existing system constitutes a multi-tier Wireless Sensor Net-work (WSN), as shown in Figure 1. Tier-1 is subdivided intoclusters of HCA (i.e. nodes) mounted onto radiators, forminga single-hop network to the collectors. Tier-2 is composedof collectors (i.e. gateway nodes), where a single gatewaynode is assigned to each cluster. The gateway nodes are wiredto each other and connected to the internet over a GeneralPacket Radio Service (GPRS) link. Lastly, tier-3 is a mixtureof workstations, servers, and databases (i.e. sinks) that aredistributed among a large geographical area.

The contribution of the paper can be summarized as follows.Firstly, we identify the energy budget of the nodes in the sys-tem by investigating the potential energy that can be harvestedfrom Low Surface Temperature (LST) radiators. We focus onthe worst case scenario in which the surface temperature of theradiators is in the range 30−40◦C. These radiators are widelyused in environments such as kindergartens and hospitals duetheir safety requirements. Significantly more power can beharvested by standard radiators that heat up to 50◦C. Basedon the findings, we also explore the possibility of harvestingthermal energy from hot water pipes in buildings, wheregateway nodes could be mounted, forming an EH-WSN in anAMI system. Furthermore, to make efficient use of this energysource, a Medium Access Control (MAC) scheme suitable forthis kind of networks is identified. The MAC scheme playsa central part in the design of energy-efficient WSNs sinceit controls the active and sleeping state of each node andin turn, the radio which dominates the energy consumptionof a node. In particular, we analytically compare the MACscheme currently used by Brunata to a state-of-the-art schemespecifically designed for EH-WSNs.

The remainder of the paper is structured as follows. In Sec-tion II, we identify the potential energy that can be harvestedby LST radiators. In Section III, we qualitatively evaluate thestate-of-the-art MAC schemes and select the most appropriatecandidate for the AMI scenario. In Section IV, we model andanalytically compare the MAC scheme used by Brunata withthe selected one. Section V discusses the results obtained inthis study, and Section VI concludes the paper.

II. THERMAL ENERGY HARVESTING

To measure the power harvested from the heat of a LSTradiator, we developed a Energy-Harvesting Heat Cost Alloca-tor (EH-HCA) prototype. The EH-HCA consists of a thermo-electric energy harvester, a power conditioner and an energy

8 10 12 14 16 18 200

5

10

15

20

25

30

35

ΔT (K)

Pow

er (μ

W)

Pin

Poutavg

Poutfit

Figure 3. Measured amount of harvested power

storage, as shown in Figure 2. The TGP-751 [4] is used asthe thermo-electric harvester. The power conditioner BQ25504[5] is responsible for providing a stable operating voltage tothe system. An electrolytic capacitor (820μF) is used to storethe harvested power in the experiment setup, while a Lithium(Li) based battery could be used for long term deployment.Describing the details of the EH-HCA prototype is beyond thescope of this paper, as it would require dedicated treatment,not feasible due to page limitation. We will thus focus on themeasurement experiment only.

The experiments were carried out in a temperature-controlled room at Brunata, with an area of approximately12m2. The surface temperature of the radiator is regulatedby a controller, adjusting the inlet and outlet water temper-ature to maintain a certain surface temperature. A ventilatorinstalled in the room ensures that a certain room tempera-ture is maintained by circulation of the air. To represent aLST radiator, the temperature on the surface of the radiatorwas varied approximately between 30◦C to 40◦C, while theroom temperature was maintained at around 20◦C, giving atemperature difference approximately ranging between 10Kto 20K. In this setup, we measured the power Pin deliveredfrom the harvester to the BQ25504 and the output power Poutavailable to the system. The experiment was conducted in thetemperature-controlled room over a time period of 22 hours.

Figure 3 shows Pin and the average power Pavgout measured

in the EH system. The experiment shows that approximately1μW to 20μW can be harvested, when the temperature dif-ference between the surface of the radiator and the room iswithin 8K to 20K respectively. It is also interesting to noticethat the Maximum Power Point Tracking (MPPT) of the powerconditioner fails below 10K, as a result of the BQ25504 sacri-ficing the state of converting maximum power to maintainingthe TGP voltage above the minimum operational level.

The average power output from the EH system can beobtained with a curve fit as described by (1), which can beused in emulating EH. It should be noted that this empiricallyobtained model is only a valid representation for the con-struction of the specific EH-HCA. In case different hardwareis used, the extracted power is expected to be at the same

order of magnitude within the specified temperature gradient,provided that a harvester of the same dimensions is used andthe efficiency of the hardware is around the same.

Pfitout =

0.15ΔT−1.45 − 0.006

− 3 where ΔT ∈ [8, 20] (1)

A conservative conclusion can be drawn, which states thatan EH-HCA can harvest between 1μW to 10μW of powerfrom LST radiators for radio communication. Furthermore, ageneral thermal energy harvesting module, TE-CORE7 [6],can be used by gateways to extract between 100μW to 1mWof power from hot water pipes in buildings.

III. MEDIUM ACCESS CONTROL

The MAC scheme is vital to form the AMI network asit is responsible for regulating access to the shared wirelesschannel, establishing communication links between nodes.Moreover, the MAC scheme plays a key role in the design ofenergy-efficient WSNs since it controls the radio component,which dominates the energy consumption, and thus the dutycycle of each node (i.e., the active and sleeping period ofeach node). In a contention-based MAC scheme this poses theproblem of deciding the rendezvous point between a senderand receiver so that a link can be established. MAC schemestake a synchronous or asynchronous approach to solve thisproblem. In the following, we qualitatively evaluate the state-of-the-art MAC schemes with the goal of selecting the mostappropriate candidate for EH-AMI networks. A comprehensivesurvey of MAC schemes can be found in [7].

In synchronous schemes, nodes organize the active and sleepstates to overlap each other. Part of the active state is used tosynchronise all the nodes to a global active/sleep schedule.When a source node has data to transmit, it waits until theactive state to initiate the data transfer. Sensor MAC (S-MAC) was a major milestone in synchronous schemes [8].Other MAC protocols that are using the synchronised approachinclude T-MAC [9], AC-MAC [10], DSMAC [11] and DW-MAC [12], to mention only a few. These MAC schemesfundamentally require a notion of a globally synchronousclock, which creates an additional overhead for EH-WSN.

Asynchronous schemes have been shown to be “energy-efficient”, usually in terms of saving energy for increasing thenetwork lifetime [13], [14]. A major strength of asynchronousschemes is the decoupling of nodes, removing the need fortime synchronization. Hence, a node is able to decide itsown duty cycle to utilize the available energy efficiently.This also makes the implementation of such schemes simpler.Asynchronous schemes evolved into two general approaches,namely sender-initiated and receiver-initiated schemes.

Sender-initiated schemes use preamble sampling, where thesender transmits a preamble to indicate that there is a pendingneed for communication. The receiver wakes up occasionallyinto the active state, to listen to such a preamble transmission.Once the preamble is detected, the receiver replies with apositive acknowledgement to the sender after the preamble

transmission stops. WiseMAC [15] is based on this pream-ble sampling technique, where all the nodes follow a staticduty cycle. Berkley MAC (B-MAC) constitutes a milestonecomplete implementation of the sender-initiated approach [13].Short Preamble MAC (X-MAC) uses a short strobed preambleto further improve upon the weaknesses of B-MAC [16]. Avariant of X-MAC is implemented in the TinyOS embeddedoperating system [17]. Currently, X-MAC is the most widelyused sender-initiated scheme. A thorough survey of sender-initiated schemes can be found in [18], which also provide aguideline to select MAC schemes for a given application.

Receiver-initiated schemes are based on the receiver-initiated paradigm. According to this paradigm, a node period-ically wakes up to check for incoming data. Immediately afterwaking up, a Clear Channel Assessment (CCA) is performedand a beacon is broadcasted if the channel is idle. If thechannel is busy, the node does a Binary Exponential Backoff(BEB) and transmits the beacon later. After the beacon hasbeen transmitted, the receiver continues to listen to the channelfor a short period of time. A node with data ready to besent enters the active state and listens to a beacon from theintended receiver. Once the beacon is received, the senderstarts transmitting data and waits for a time period to receiveanother beacon which acknowledges the reception of the data.If there is no incoming data from the sender after transmittingthe beacon, the receiver enters the sleep state. Then both senderand receiver resume the cycle of beacon transmissions.

While Receiver Initiated CyclEd Receiver (RICER) [19]introduced the receiver-initiated paradigm for duty cyclingnodes, the most widely known implementation of the paradigmis Receiver-Initiated MAC (RI-MAC) [20]. On-Demand MAC(ODMAC) represents the first receiver-initiated scheme specif-ically designed for energy harvesting WSNs [21]. It is basedupon the receiver-initiated paradigm and the Energy NeutralOperation (ENO) concept [22]. However, unlike other pro-tocols, ODMAC employs dynamic duty cycling [22]–[24],specifically for the purpose of regulating the power con-sumption, where each node can be tuned to be sustainableand assign the excess of energy for improving applicationperformance metrics [25]. Other well known receiver-initiatedMAC protocols are OC-MAC [26], RW-MAC [27], EE-RI-MAC [28] and PW-MAC [29]. The receiver-initiated paradigmsignificantly reduces the amount of time a pair of nodes occupythe channel, allowing more contending nodes to communicatewith each other, thus increasing the capacity and throughputof the network. It is efficient in detecting collisions, becauseaccess to the channel is mainly controlled by the receiver.Since receivers only wait a short period of time for incomingdata, after beacon transmission, overhearing is greatly reduced.

A key requirement of MAC schemes for EH-WSNs is theability to fully independently adjust the duty cycle of anindividual node to adapt to the energy the node can harvest.The duty cycles of nodes in a synchronous network arecoupled to each other via a global clock, making synchronousMAC schemes not suitable for EH-WSN. In the asynchronous

Time

Sender 1

Sender 2

Receiver 1

TransmittingReceiving Idle Listening Deaf Transmissions

D D

D D

DD D D

D

D

D

D

Tsense

txTtx

rx

Trx

Figure 4. IMR+ communication model

approach, the receiver-initiated scheme has been shown to bemore energy efficient than the sender-initiated scheme [20],[25]. In particular, ODMAC is the only scheme that extendsthe receiver-initiated approach specifically for EH-WSNs andsupports dynamic duty cycles. Other receiver initiated proto-cols can provide some interesting benefits. For instance, OC-MAC promotes aggressive cooperation among senders thatacts both as a collision avoidance mechanism and reduces idlelistening. EE-RI-MAC attempts to reduce idle listening of asender by introducing an alternating duty cycle when waitingfor a beacon. Since such policies can be easily incorporatedinto ODMAC, this study considers ODMAC as the state-of-the-art MAC scheme for EH-WSNs, and will be furtheranalyzed to explore the benefits over Inter-Meter Reading+(IMR+), the industrial MAC scheme used by Brunata.

A. Inter-Meter Reading+

Brunata uses a simplified adaptation of the ALOHA pro-tocol [30], called IMR+, that only supports a unidirectional,single-hop, single channel AMR network. The sender nodesonly contain a radio transmitter, while the receiving gatewaynode only contains a radio receiver. Both the senders andreceiver use a static duty cycle. Figure 4 shows an overview ofthis approach. In IMR+, Random Channel Access (RCA) isused to prevent collisions caused when senders coincidentallysynchronize. Due to this simple collision avoidance technique,small payload sizes used by Brunata, and ultra-low duty cy-cling of the senders, the probability that a collision will happenis quite low. Nevertheless, several significant sources of energywaste exist: a) Senders continuously transmit data even thoughthe receiver is in the sleep state. b) In sparse networks, thereceivers spend most of the active period listening for data,but not receiving any. c) Senders that are within range of morethan one gateway node will broadcast data to all the receivers.

B. On-Demand MAC

ODMAC [21] (Figure 5) is the first receiver-initiated MACscheme specifically optimized for EH-WSNs. It exploits inde-pendent duty cycles of nodes to define a policy for dynamicallyadjusting the duty cycle of each node. Since nodes in the net-work have a dual role of being a receiver for forwarding tasksand sender for measuring tasks, ODMAC also decouples theduty cycles of these two roles in a single node. Hence, a nodehas a beacon duty cycle and a sensing duty cycle. The beacon

Time

Sender 1

Sender 2

Receiver 1

TransmittingReceiving Idle Listening Deaf Transmissions

B

B D

B DACK

ACK

ACK

DB

B DACK

B DACK

B DACK

Ttxbeacon

tx

txbeacon

Ttxpacket

rxCCA rxdwell

Figure 5. ODMAC communication model

High Energy

Low Energy

Low Energy

High Energy

Figure 6. Opportunistic forwarding in a multi-cluster network

duty cycle controls the trade-off between energy consumptionand end-to-end delay, while the sensing duty cycle controls thetrade-off between energy consumption and throughput. Hence,ODMAC grants the network administrator the ability to decidethe trade-offs depending on the application. ODMAC uses anadaptive duty cycle mechanism based on the ENO principle,where the energy consumed by a node is less than or equalto the amount of energy harvested. All nodes in the networkdynamically adjust the beacon and sensing duty cycle, in orderto achieve and maintain an ENO-Max state, which is definedas an ENO state with maximum performance. This means thatwhen the node is consuming more energy than is harvested, theduty cycles are decreased to reduce the energy consumption.In the same manner, when the energy consumed is lower thanthe energy harvested, the duty cycles are increased.

For scenarios with multiple receivers, where one receiver ison a high beacon duty cycle due to the high amount of energyit harvests, ODMAC defines a policy for anycast routingsupport named opportunistic forwarding. Instead of waitingfor the intended receiver to wake up, a sender forwards dataafter the first beacon it receives. This mechanism requires arouting protocol that assigns each sender a list of approvedreceivers. Since the probability of receiving beacons from areceiver with surplus energy is high, this policy creates amore robust network, that is adaptive to energy changes, bymaintaining a balanced load in the network. Furthermore, theidle listening time of senders is reduced in the region wherethe clusters overlap. Figure 6 shows an example of such apolicy in a multi-cluster network, where as the state of energyof the receivers change, the nodes that are in range of boththe receivers adapt to the receiver with more energy.

C. What MAC Scheme for EH-AMI?

As discussed in this Section, the metering industry prefersto use MAC schemes that are similar to ALOHA, such as the

Wireless M-Bus [31] Mode-C1 or the previously introducedIMR+ (Section III-A) in AMI. The main motivation lies in theperceived belief that the simplistic nature of ALOHA-basedMAC schemes out performs any other MAC scheme. Themain question left open is whether these MAC schemes areappropriate to support EH-AMIs or not. To this extent, in thenext Section we will analytically compare IMR+, as represen-tative of the “industrial” ALOHA-based MAC schemes, withODMAC (representing a MAC scheme specifically designedfor EH-WSNs, thus suitable for EH-AMIs). The results of theanalysis will highlight the benefits of adopting the latter.

IV. ANALYSIS

IMR+ and ODMAC are modeled and analytically com-pared. Since models are used for comparison, the properties ofthe channel are considered to be the same for both schemes.Therefore, the models do not consider active collision avoid-ance mechanisms and retransmissions due to channel errors.Furthermore, nodes are considered to transmit only a singlepacket within a duty cycle period, and the packet size isconsidered to be constant for all transmissions over all nodes.In a single-hop topology, nodes do not relay data to each other.As a result, the model of a MAC scheme can be separated intothe sender and receiver in a single cluster. The analysis takesinto account the available power for the sender and receiverfrom the energy harvesting system described in Section II.Our experiments have shown that the power available to thenodes is very low, i.e. at the order of μW. For the given powerbudget of the senders and the receiver, the models can be usedto determine the feasible measurement period of the senders.

A. Modelling of Inter-Meter Reading +

Figure 4 shows the model used in the analysis of IMR+.a) Sender: The duration of a single transmission τtx is

the time the channel will be used by a single node and canbe expressed by (2), where D is the packet size in bytes andBR is the transmission bit rate in bits per second.

τtx =D ∙ 8BR

(2)

Equation (3) models the duty cycle period Ttx of a singlepacket transmission in seconds for a given available amountof power Psender ∈

[Pmin

sender, Pmaxsender

]in watts. Ptx is the

power consumed by the radio during transmission in watts.P s is the power consumed by the radio to enter the activestate from the sleep state in watts, and T s is the time it takesin seconds. T s

tx is the time it takes for the radio to begintransmission in seconds.

Psender =P s ∙ T s + Ptx ∙ (T s

tx + τtx)

Ttx(3)

Collisions can occur when two or more nodes transmit at thesame point in time. The probability of a collision P (c) dependson the number of nodes in the cluster N (4).

P (c) = (N − 1) ∙τtxTtx

(4)

Given the required probability P (s) of successfully deliveringat least one out of n transmissions, the number of trans-missions required to guarantee that at least one out of ntransmissions is successfully delivered, collision-free, is:

P (s) = 1 − P (c)n ⇒ n =

⌈log(1 − P (s))

log(P (c))

⌉

(5)

b) Receiver: Since the receiver cannot communicateinformation back to the senders, it has to be prepared for theworst case active time. In order to ensure that at least onevalid packet is received from all the nodes in the cluster, thereceiver should listen to the channel for at least time τrx statedby (6), where the worst case duty cycle period of the senderTmax

tx is given by (3) for Psender ≡ Pminsender.

τrx = Tmaxtx ∙ n (6)

The receiver duty cycles to ensure operation with the availablepower Preceiver ∈

[Pmin

receiver, Pmaxreceiver

]in watts. The duty

cycle period Trx can be calculated using (7). Prx is the powerconsumed by the radio during reception in watts. T s

rx is thetime it takes for the radio to enter the receive mode in seconds.

Preceiver =P s ∙ T s + Prx ∙ (T s

rx + τrx)Trx

(7)

The sensing period T sense represents how often a new mea-surement can be made by a node. Since within a single dutycycle period, the receiver has at least one valid packet fromall the nodes in the cluster, the sensing period is the sameas the duty cycle period of the receiver, T sense = Trx. Thethroughput of the receiver ρ, defined as the amount of newmeasurements received from all the senders per unit time, canbe calculated from (8).

ρ =N ∙ D ∙ 8T sense (8)

B. Modelling of On-Demand MAC

ODMAC is modelled as shown in Figure 5. While the samenotations from Figure 4 are used, new notations used in thisanalysis are described.

c) Receiver: The receiver performs a CCA to ensure thatthe channel is idle before transmitting a beacon. The timetaken to perform a CCA τcca

rx using carrier sensing, is specifiedfor the radio that is used. Once the channel is free, the receivertransmits a single beacon frame. The duration of the beaconτbtx is described by (9), where B is the beacon size in bytes.

After broadcasting a beacon, the receiver continues to listenfor a short period of time to receive a response from a sender.

τbtx =

B ∙ 8BR

(9)

The beaconing duty cycle period Tbtx, given by (10), de-

pends on the amount of power the receiver is harvestingPreceiver ∈

[Pmin

receiver, Pmaxreceiver

]in watts. After the sender

receives the beacon, it transmits the data immediately, whichis then acknowledged by the receiver with another beacon.

Preceiver =P s ∙ T s + Prx ∙ (2T s

rx + τccarx + τtx) + 2Ptx ∙ (T s

tx + τbtx)

T btx

(10)

d) Sender: The transmission duration of a single packetis given by (2). When the sender has data to exchange, inthe worst case, it has to wait for a full beacon period beforereceiving a beacon from the receiver. It then immediatelytransmits the data, and receives an acknowledgment from thereceiver. The duty cycle period of the sender T

ptx is given by

(11). The duty cycle period of the sender depends on the powerbudget Psender ∈

[Pmin

sender, Pmaxsender

]in watts.

Psender =P s ∙ T s + Prx ∙ (2T s

rx + T btx + τb

tx) + Ptx ∙ (T stx + τtx)

Tptx

(11)

A collision occurs at the receiver in ODMAC, when two ormore nodes that has data to transmit, wakes up for the samebeacon. Since beacons form time slots for communication,these senders collide when transmitting data after receiving thesame beacon. The probability P (c) of such an event happeningcan be expressed by (12).

P (c) = (N − 1) ∙Tb

txT

ptx

(12)

As described by (5), in the worst case, a sender has to transmitdata for n times to ensure that with a probability P (s), at leastone transmission is successful. The sensing period of a sender,which is the shortest time duration a sender has to wait beforeperforming a new measurement, is represented by (13).

T sense = Tptx ∙ n (13)

The throughput of the receiver can be calculated by (8).

C. Comparison

The models for IMR+ and ODMAC are used to compareperformance of both schemes using MATLAB R©. Only arelative comparison can be made, since channel errors are notincluded in the model. However, the models are sufficient todetermine the advantages and disadvantages of the two MACschemes. The harvested and consumed power levels used in theanalysis are described in Table I. The range of power levelsharvested from the heat of the radiator by the senders arebased on the experiment described in Section II. The rangeof power levels harvested from the heat of hot water pipesin buildings by the receiver are based on the TE-CORE7 [6].

0 2 4 6 8 100

5

10

15

20

25

30Packet size is 32 bytes

Psender

(µW)

Tse

nse (

hour

s)

100 µW200 µW500 µW1000 µW100 µW200 µW500 µW1000 µW

0 2 4 6 8 100

5

10

15

20

25

30Packet size is 64 bytes

Psender

(µW)

Tse

nse (

hour

s)

0 2 4 6 8 100

5

10

15

20

25

30

35Packet size is 128 bytes

Psender

(µW)T

sens

e (ho

urs)

0 2 4 6 8 100

10

20

30

40

50

60Packet size is 256 bytes

Psender

(µW)

Tse

nse (

hour

s)

Analysis of IMR+ and ODMAC for various Preceiver levels

Figure 7. Impact of harvested power on the measurement period

Table IVALUES USED FOR THE ANALYSIS

B 18 bytes BR 153600 bpsP s 1.02mW T s 5.8msPtx 132mW T s

tx 0.5msPrx 11.55mW P max

sender 10μWP (s) 99.99% P min

sender 1μWT s

rx 0.5ms P minreceiver 100μW

τccarx 100μs P max

receiver 1000μW

The power consumption levels of the radio are based on theSX1212 transceiver from Semtech [32].

The impact of harvested power on how often a new mea-surement can be performed is shown in Figure 7. Since thereceiver cannot communicate any information back to thesender in IMR+, it has to be designed for the worst case. Thereceiver cannot efficiently use the power available to increaseits performance, since the listening duration should be longenough to ensure that at least one valid packet will arrivesuccessfully from all the senders. Furthermore, IMR+ cannotbe used in an EH-WSN application where senders can affordto completely shut down when there is no energy to harvest,because the receiver would have to remain constantly active.In contrast, ODMAC demonstrates its ability to dynamicallyadjust to the energy harvested from its environment. Forvery low harvested power, ODMAC sacrifices the frequencyof measurements to keep the network stable. As soon asthe harvested power increases, the sensing period reducesexponentially, far out performing IMR+. Another observationof the analysis is the impact of the packet size. In IMR+,

0 200 400 600 800 10000

50

100

150Data successfully received

Number of nodes

Thr

ough

put (

bps)

0 200 400 600 800 10000

2

4

6

8

10

12Maximum delay

Number of nodes

Sen

sing

per

iod

(hou

rs)

0 200 400 600 800 10000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9Probability of a failed transmission

Number of nodes

Pro

babi

lity

of a

col

lisio

n

0 200 400 600 800 10000

5

10

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45At least one successful packet received with 99.99% probability

Number of nodes

Num

ber

of tr

ansm

issi

ons

32 B64 B128 B256 B32 B64 B128 B256 B

Performance analysis of IMR+ and ODMAC for various packet sizes

Figure 8. Best case: senders and receiver harvest the maximum power

the sensing period is severely affected by the increase in thepacket size, where as ODMAC is more resilient to the increasein packet size while still maintaining the same adaptivity.

The optimal network performance is achieved when bothsenders and receivers are able to harvest the maximum amountof power from the energy source, as shown in Figure 8. Theperformance of ODMAC drops as the network becomes moredense, when the sender and the receiver are fully active. This isdue to the linearly increasing probability of collisions. IMR+is more resilient and robust in this scenario. However, evenwith the problem of scalability, ODMAC out performs IMR+in a sparse network, while in a dense network it still maintainsa faster sensing frequency. While it was previously observedthat ODMAC gracefully handles an increase in packet size,it can now be seen that an increase in packet size actuallybenefits ODMAC significantly, far out performing IMR+ forlarge packet sizes. The receiver is flooded when senders arefully active and the receiver is harvesting the lowest amountof power. This scenario shows similar characteristics to thebest case, but with a very low throughput.

The network has the worst performance when the amount ofharvested energy is the lowest for both senders and receivers.Such a scenario is shown in Figure 9. For smaller packetsizes ODMAC performs worse than IMR+. However, for largepacket sizes, ODMAC out performs IMR+, and maintainsa higher sensing frequency under all circumstances. Further-more, like IMR+, ODMAC remains scalable and robust. Inthe scenario when the senders are starved of power while thereceiver remains fully active, the performance of ODMAC andIMR+ show similar characteristics, except for the throughput,which is higher than in the worst case.

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Performance analysis of IMR+ and ODMAC for various packet sizes

Figure 9. Worst case: senders and receiver harvest the minimum power

V. DISCUSSION

The previous analysis shows that the simplicity of IMR+makes it very scalable and robust. However, it is highlyunsuitable for EH-AMI due to its inability to dynamicallymanage its resources to improve performance. In contrast,ODMAC far out performs IMR+. Since ODMAC is ableto dynamically manage its resources to achieve a maximumperforming state for a given amount of energy, it is well suitedfor EH-AMIs. An interesting observation from the analysisdemonstrates the benefits of buffering. Buffering can furtherreduce the transmission period. Senders are able to performsensing tasks and store the packets in a buffer, while the MACscheme transmits aggregated data packets. As shown from theanalysis, IMR+ cannot benefit from buffering, as transmittingmore data increases the probability of collisions and thescheme will suffer from poor performance. Furthermore, dueto the lack of MAC level acknowledgments, the large datatransmissions will increase the probability of channel errors.On the other hand, ODMAC is able to utilize the benefits ofbuffering efficiently, without a large impact from additionalcollisions or channel errors.

In dense networks, ODMAC is shown to suffer from highnumber of collisions, making it less scalable. However, inour comparison we opt to disregard the collision avoidanceincluded in ODMAC [21]. An extension of the analysis withcollision avoidance schemes is considered future work. Onthe other hand, senders in IMR+ cannot be enriched withan active collision avoidance mechanism due to the lack ofa receiver, which constitutes CCA impossible. Furthermore,when additional receivers are included in the network, theanycast routing support introduced by ODMAC enables the

network to gracefully balance the load in a distributed manner.However, the addition of receivers in IMR+ would only createredundancy because of overhearing in the receiver.

The typical quantum used for billing in the metering indus-try is a day. Thus, at least one measurement every 24 hoursis required. Our analysis shows that the feasible transmissionperiod with energy harvested from LST radiators is generallyfulfilling this application requirement. In the scenario of thelowest energy harvesting levels, buffering can be used tosupport the application requirement. For instance, accordingto Figure 7, the lowest energy harvesting levels can supporttwo 32 byte packets approximately every 54 hours whichis failing to fulfill the requirement. However, buffering twopackets makes it possible to support a single 64 byte packetapproximately every 28 hours. Therefore, as long as theradiator is in use, thermal energy harvesting is able to powerthe EH-HCA for daily measurements. If the metering industryis also interested in monitoring the radiators when they are notbeing used, the EH-HCA needs to store part of the harvestedenergy for the summer months, limiting the power availableto the system. Nonetheless, enhancement mechanisms likebuffering or lower power consuming hardware would makethe operation of the system sustainable.

VI. CONCLUSION AND FUTURE WORK

We investigated the feasibility of powering wireless me-tering nodes, namely heat cost allocators, by thermal energyharvested from radiators. We experimentally identified thatthe power budget of the nodes is in the order of severalμW. The energy harvested was measured on a LST radiatorusing our prototype EH-HCA, which consists of off-the-shelfhardware with minor modifications. We stress that significantlymore energy can be extracted from standard radiators. Then,we qualitatively evaluated the existing MAC protocols andselected ODMAC as the state-of-the-art MAC scheme for EH-WSNs. Our analytical comparison shows the efficiency of thelatter, as well as its ability to adapt to the ambient energythat can be harvested. Additionally, the analysis suggests thatthermal energy harvesting is indeed able to support dailymeasurements, given the measured power budgets. The fullimplementation, evaluation and optimization of ODMAC in asensor node testbed constitute our current and future work.

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