WISEMEN: WhIte SpacE for smart MEteriNg Siva Subramani, Zhong Fan, Sedat Gormus, Parag Kulkarni, Mahesh Sooriyabandara, Woon Hau Chin

Abstract-In this paper we present a case for Smart Metering to be a prime application of TV White Spaces (TVWS) and investigate network architecture to enable smart metering com­munication in TVWS that ensures access reliability, primary user protection (if one exists) and efficient use of spectrum. We consider the communication access network connecting the smart meters to the concentrator as a wireless mesh network and propose using Radio Environment Maps (REMs) in conjunction with a geo-Iocation database to manage the channel being used by the concentrator for communicating with the smart metering nodes. We also examine through emulation based experiments, the effect of primary user arrival interval on the packet delivery ratio of the smart meter nodes and the time it takes for the network to migrate to a vacant channel subsequent to detecting primary activity on the operating channel. Our findings suggest that it is feasible to realise smart metering communications in the TVWS and that the proposed mechanism shows good adaptive behaviour.

I. INT RODUCTION

Smart Grid is a new paradigm that covers modernization of

generation, transmission and distribution of power grids. It is

receiving a lot of attention recently as an answer to the carbon

footprint reduction and to support the ecological green drive.

The smart grid can be visualized as the fusion of two networks:

the electrical generation/transmission/distribution network, and

the modem data communications network. One of the key

components of the Smart Grid is the Advanced Metering

Infrastructure (AMI!) which is expected to enable transport of

metering data from the smart meter at the consumer premises

to the utility provider and control information from the utility

provider in the reverse direction. A smart meter is an advanced

meter that provides the necessary real-time management of

power flows for energy consumption efficiency. Smart meters

and AMI could be considered as a cog in the complex wheel

of smart grid.

Several communication technologies, both wired and wire­

less, are being considered for realizing smart metering com­

munications. Solution approaches using the ISMIUNII bands

(902-928 MHz, 2400-2483 MHz, and 5725-5875 MHz) are

being considered for the low-bandwidth AMI networks as

devices operating in these bands are widely available. In this

paper we investigate an alternative approach - using WhIte

SpacE for smart MEteriNg (WISEMEN). Considering that

government regulators are keen to make use of TV white

space for wireless broadband and essential applications in

households, realising smart metering communications in this

spectrum is an excellent opportunity. If TVWS is mandated

and standardized for smart meter/grid communications, the

NAN/AMI (see Fig. 1) could be established reliably in the

dedicated spectrum. As of writing of this paper, government

regulators have not yet dedicated a part of the TVWS spectrum for the aforementioned application. Therefore, the discussion

in this paper focuses on the 'opportunistic use' of the TVWS

spectrum for enabling smart metering communications.

Employing opportunistic access in TVWS bands for smart

meter communications is challenging. Firstly, there is a need to

maintain up to date information about the vacant and occupied

channels. Secondly, there is a need to setup a network in the

vacant channel. Finally, there is a need for mechanisms that

will enable network reconfiguration - dynamically migrating

the whole network to a new channel upon detection of activity

from a primary user on the operating channel.

To address these issues we propose setting up a mesh

network in the TVWS. This network comprises of smart meter

nodes sending data to a concentrator which in tum forwards

this to the utility provider's backend system over a wide area

link. We propose using Radio Environment Maps (REMs)

in conjunction with a geo-location database to manage the

channel being used by the concentrator for communicating

with the smart metering nodes. The REM keeps itself up to

date based on sensing information provided by the nodes in

the network.

We have implemented our proposal as modifications to the

open source Contiki [1] operating system running on IEEE

802.15.4 hardware [2]. In our previous work [3], we studied

the network discovery latency and effect of concentrator

failures on delivery probability and recovery latency. In this

paper, we go a step further and examine the effect of primary

user arrival interval on the packet delivery ratio (PDR) of

the smart meter nodes and the average time it takes for all

the nodes to discover a concentrator. We make the following

contributions in this paper: We provide an outline of the

architectural design enabling smart meter networks in TVWS and study the different performance aspects of the proposed

solution through emulation based experiments.

The remainder of this paper is organised as follows. Section

II provides some background information. Section III outlines the network design considerations following which the REM

The authors are with Telecommunication Research Laboratory, based spectrum access mechanism for realising smart meter Toshiba Research Europe Limited, Bristol BSI 4ND, UK Email: {Siva.Subramani, Zhong.Fan, Sedat.Gormus, Parag.Kulkami, Ma- communications is detailed in section IV. Section V presents hesh, Woonhau.Chin}@toshiba-trel.com quantitative results from an evaluation of the proposed mecha-

This work has been partially supported by the European Union FP7 nism through emulation based experiments. Finally, the paper FARAMIR project.

1 In this paper, by AMI we refer to the Neighbourhood Area Network (NAN) concludes in section VI discussing potential directions for

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II. BACKGROUND AND RELATED RESEARCH

A smart meter is an advanced meter (an electric meter or

a non-electric meter such as a gas and water meter) that

measures energy consumption in much more detail than a

conventional meter. Smart meters are expected to provide

accurate and up-to-date readings automatically at requested

time intervals to the utility company, electricity distribution

network, or to the wider smart grid for monitoring and billing

purposes [4].

The range of communications technologies under consid­

eration are diverse, with an eclectic mix of public and pri­

vate, wired and wireless, standard and proprietary networking

products. In a NAN that typically connects the smart meters

at the customer premises to the concentrator, there may be a

need for the network to provide wider coverage. Currently,

WiFi and cellular are some of the example technologies

being considered for this segment. With the latest spectrum

regulatory developments, the TV white space (460-862 MHz

in the UK) is also being considered. E.g. Bahl et al. have

demonstrated WhiteFi in [5], an implementation of a Wi­

Fi like protocol operating in the TV white space which is

capable of detecting incumbents as well as presents methods

for detecting access points. A recent trial conducted by Google

described in [6] is another attempt to use TV white space for

broadband applications.

TV white spaces are the frequency bands that would be

freed up (see Fig. 2) as a result of the Digital Switchover

(DSO) when the television broadcast switches from analogue

to digital mode of broadcasting. Government regulators are

considering making these channels licence-exempt and avail­

able for wireless broadband use and essential applications in

households [7]. The UHF white space spectrum provides good

signal propagation and penetration characteristics, and requires

less power to carry the signal. It has clear technical advantages

over the use of the unlicensed 900 MHz band that can result

in lower deployment costs. TVWS is already being considered

for smart meter applications. E.g. the work in [9] considers the use of white spaces for AMI assuming an IEEE 802.22

regional area network as the underlying architecture. However,

the focus of this work is on the 'data fusion' and resilience

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Fig. 2. TV white space (470 - 862 MHz) in the UK [81

aspects of combining spectrum sensing data. Unlike this, the

objective of this paper is to show how to establish and maintain

an AMI mesh network in the TVWS.

III. NET W ORK DESIGN CONSIDERATIONS

Assuming that the spectrum is available, the next question

that immediately springs to mind is how to establish a network

to operate in this spectrum. One of the possible approaches

could be to deploy a mesh network for interconnecting the

smart meters to a concentrator in the NAN. Smart meter nodes

within radio range of the concentrator could directly connect

to it. Other smart meter nodes which are not within radio

range of the concentrator may connect over a multi-hop link

going through other smart meter nodes which are closer to the

concentrator. The smart meter nodes would send the data to

the concentrator which would forward it via a wide area link

to the utility providers backend system.

The NAN must cover a reasonably large area, e.g. one

concentrator in each neighbourhood covering a few hundred

smart meters. In terms of traffic, the smart metering network

is characterised by its low bit rate and delay tolerant traffic.

There would be a periodic update of smart meter readings to

the concentrator e.g. every 15 minutes, an hour or even less

frequent depending on the needs of the utility provider. On

the other hand, occasionally there may also be mission-critical

data that requires timely and reliable delivery, e.g. on-demand

data of distribution network management applications such as

overload or outage detection. Mobility is not an issue as the

meter nodes themselves are fixed to the customer premises.

However, there could be changes in the radio environment as

a result of which the routing path may vary. Smart meter nodes

may appear/disappear dynamically due to nodellink failures.

The status of the channels in the TVWS may change between

occupied and free depending on when a primary user may be

transmitting. Taking this into account, it is imperative that any

network deployed in the TVWS should have self-organising

characteristics.

Different technology options are available to build such a

network. The use of commodity IEEE 802.11 hardware seems

like a natural choice because of the level of maturity that

this technology has achieved and the ready availability of

commercial off the shelf hardware. However, WiFi hardware

tends to be relatively expensive (in comparison to IEEE

802.15.4 hardware) and power hungry. In such a case, low cost

and low power IEEE 802.15.4 technology could pave the wave for building these mesh networks. Moreover, there is ready

To utility provider Geolocation database

I

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Fig. 3. Architecture and REM functions

availability of IEEE 802.15.4 hardware (e.g. the TELOSB

motes [2]) and open source software stacks (e.g. the TinyOS

[10] and the Contiki [1] operating systems) which can facilitate

rapid prototyping and deployment of these mesh networks.

The network elements in such a network are resource con­

strained (have limited processing power, memory and energy)

while the links connecting these are lossy. Such networks are

called low power and lossy networks (LLNs). Routing for

Low power and Lossy networks (RPL) [11] is a mechanism

for enabling connectivity in LLNs and is currently in the

process of being standardised by the Internet Engineering Task Force (IETF) Routing Over Low power and Lossy networks

(ROLL) [12] working group. RPL facilitates establishing and

maintaining bi-directional routes between the sensor nodes

and the sink (smart meters and concentrator respectively in

the context of this work). However, it does not support

the self-organising functionalities necessary to deal with the

dynamics described above. The approach adopted in this paper

is to augment the RPL specification with such self-organising

functionalities (without affecting compatibility with RPL) so

as to realise a practical solution.

There exist studies on realising a self-organising hierarchi­

cal network combining cognitive radio access technologies

with the distributed mesh structure [l3][14]. However, the

requirements of such networks are different from the mesh networks necessary to realize smart metering communications.

The former employ power hungry always ON IEEE 802.11

or other more sophisticated radios in order to support high

bandwidth applications unlike the latter which are likely to

employ low duty cycled IEEE 802.15.4 radios (constrained

platform compared to the former) to support low bandwidth

communications. Thus, solutions developed for the former

cannot be readily used in the case of the latter. This motivates

the need for a mesh networking solution that is self-organising,

and is simple and practical to implement given the resource

constrained nature of the devices.

IV. REM BASED SMART METER SPECTRUM ACCESS

In the NAN scenario described above, the concentrators and

the smart meters distributed in their vicinty form a wireless

mesh network. All the nodes are assumed to have access to a

Radio Environment Map (REM) and a geolocation database. REM is envisioned as an integrated database that characterizes

the radio environment consisting of multi-domain information

such as spectrum availability profile, geographical features,

regulations, relevant policies, radio equipment profile and past

experiences, etc. [15][16]. The REM information could be

exploited to enable various cognitive functionalities such as

situation awareness and dynamic spectrum access. Each node in the network can provide local sensing information to the

REM. The same could be achieved through a dedicated sensor

network in the physical proximity of the node. The usefulness

of the REM depends on how accurate is the information

contained in it which in tum depends on the updates from

the nodes in the network regarding their observations of the

radio environment at their location.

In the earlier definitions of REM, there was a significant

focus on storing fine grained measurements and use of calcula­tion based reasoning [15][17]. Storing detailed spectrum usage

information in radio environment maps leads to potentially

high overheads. Therefore, later approaches [16] [18] have

instead relied on a statistical characterisation, storing only

key information in a compact form that could still be used

in optimizing the collaborative sensing process. The effect

of imperfect radio environment map information has been

analysed in [19] and it has been found that not all the nodes

need to conduct sophisticated spectrum sensing. Collaborative

sensing and periodic updates may be sufficient for a practical

realisation.

In Figure 3, the REM is shown to be connected to a

geolocation database if it is available (as an optional entity2).

The combination of REM and geolocation database assist in

the initial configuration of the mesh network and protecting

primary users by providing useful information for network re­

configuration.

A. Initialization: Formation of the mesh network

Figure 4 shows the initialisation procedure. When a concen­

trator switches ON, it first contacts the REM and notifies the

REM of its geographical location information. Based on this

information, the REM assigns a channel to the concentrator

which is not in use by the neighbouring concentrators. The

concentrator then tunes its radio to this channel and starts

advertising its presence.

When a smart meter powers ON, it starts the scanning pro­

cess until it hears an advertisement indicating the availability

of a concentrator. It could hear such an advertisement directly

from the concentrator if it is within direct radio range of

the concentrator. If the node is not within direct radio range

of the concentrator, it may hear the advertisement forwarded

by another node (over a multi-hop link) which is already

associated with the concentrator. The smart meter node may

hear a number of advertisements originating either from the

same concentrator or from different concentrators as it scans

through each of the channels. The smart meter will choose

the best available concentrator depending on suitable criteria

and associate with it. Simple criteria for the choice of a

concentrator could be how far away the concentrator is or

how busy the concentrator is e.g. number of smart meters

2In the latest developments in regulations, it is stated that cooperating sens­ing methods alone is insufficient and the provision of geolocation information is mandatory to ensure reliability [20] [21].

Concentrator ON:

New meter ON:

Operational Phase:

Network reconfiguratlon:

( Concentrator J l Smart meter J LocatiOn information

Frequency cnannel

Frequency Chamel N�oour discovery 1+-------------+ Scanning -------------

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Inform PU anival on detedior (acnange in radio scene)

(or change in radio scene)

Instruction to change frequency chamel

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frequency channel

Registration

Fig. 4. Procedure in Initialization and cluster formation

that the concentrator is already serving. Once associated, the

smart meter node would continue to send meter readings to

the concentrator periodically and keep scanning so as to look out for any activity from a primary user.

Even though the nodes in the network are static, dynam­

ics could arise due to channel switching resulting from the

detection of primary user activity on the current operational

channel, a degraded/broken link due to a node failure or

radio environmental effects such as fading/shadowing etc. The

mesh network should re-configure itself on the fly amidst such

changes so as to continue its normal operation. If a smart meter

node experiences outage, it may affect the connectivity of other

smart meter nodes using it as a part of the multi-hop link

to the concentrator. In such scenarios, the nodes with failed

connectivity to the concentrator will re-scan and associate with

other concentrators that may be available in the vicinity. In the

ensuing discussion we primarily focus on the scenario where

primary user detection leads to a network wide reconfiguration.

B. Primary User Detection and Network Re-configuration

The current operational channel could become unavailable

due to the unexpected arrival of the incumbent or primary

user (PU). As mentioned earlier, nodes keep scanning looking

for activity from the primary user. If any node detects a

PU activity, it immediately notifies the concentrator which in

tum notifies the REM about this. Since the REM oversees

the spectrum bands that are being used in the neighboring

concentrators, it is able to advise the concentrator to switch

to a vacant channel. The concentrator would then propagate

this switch over information (control message) throughout its

tree so that smart meter nodes can follow the concentrator.

The time it takes to achieve this depends on the maximum

number of hops in the network, e.g. in a network with 4 hops

and a control message frequency of 250ms, it takes about 1 second (4*250ms) to propagate this information. Nodes that

fail to receive this would eventually lose connectivity, initiate

the scanning process and associate with the best concentrator

available in the vicinity. Thus, the REM ensures that the

spectrum switching is seamless. The short duration during

which the switch over occurs, data that is transmitted may

be delayed/lost. Forwarding nodes could buffer the packets until a new link is established which could delay the arrival

of data at the destination. For the packets already scheduled

for transmission, if the maximum number of re-transmissions

are reached, the packets would be eventually dropped. There

may not be much that can be done to avoid this. However, it is

envisaged that this may be tolerable in the context of metering

data. As will be shown later in this paper, the switch over time

is quite small (on the order of a few seconds) and the effect

that this has on the packet delivery ratio is also not significant.

V. PERFORMANCE EVALUATION

The proposal described in this paper encompasses connec­

tivity enabling mechanisms and self-organising functionalities

to deal with the dynamics resulting from PU activity detection

or variations in the radio environment. As mentioned earlier in

this paper, RPL [11] is a mechanism for enabling connectivity

in LLNs and is currently in the process of being standardised by the IETE RPL facilitates establishing and maintaining

bi-directional routes between the nodes in a tree and the

sink (concentrator in the case of smart metering networks).

However, it does not support the self-organising functionalities

necessary to deal with the dynamics described in this paper.

To study the performance aspects, the RPL implementation

in the Contiki [1] operating system was augmented with the

dynamic channel adaptation functionality described above and

emulation based experiments were carried out. Contiki is a

portable, multi-tasking operating system geared for resource

constrained environments (e.g. low power networked embed­

ded systems) with a typical execution code footprint of 200

bytes. It is open source and employs the fl,lP lightweight stack

which is widely used both commercially and for the purpose

of research. In addition to providing support for running on

target hardware, Con tiki also provides an emulator called

Cooja which facilitates development, testing and debugging

of the code before running it on the target platform, thereby

simplifying the software development process. IEEE 802.15.4

compliant TelosB [2] devices were used as the hardware

platform in this study. These devices use the CC2420 radio

which operates in the 2.4GHz band. This radio uses one of

the 16 channels (channel 11 to channel 26) for its operation.

In this paper, the PU arrival in TVWS was emulated in the

aforementioned band.

A single concentrator network with 50 meter nodes is con­

sidered in order to evaluate the performance of the proposed

protocol extensions. The smart meters are uniformly placed

in a 200x200 m2 area around the concentrator node. Most of

the meter nodes reach the concentrator node over multi-hop

links where the maximum hop distance is 4 hops from the

concentrator. When a smart meter is switched ON, it initiates

scanning operation starting from a randomly selected channel.

The channel scan period is set to 250 ms for each node. If we

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Fig. 5. ElTect of PU arrival interval on network re-configuration latency

assume there are 16 channels, it takes a maximum period of

4 seconds for each one hop node to discover a concentrator.

Each smart meter node periodically reports its meter reading

using a 70 byte UDP data packet. The main objective of

the experiments described herein is to show the impact of

primary user arrival interval on the system performance. The

key questions that are of interest in this context are:

• What is the effect of PU arrival interval on the network

configuration latency?

• What is the effect of PU arrival interval on the packet

delivery ratio of the smart meter nodes?

Given this, the following scenarios were considered in this

study.

A. Impact of the primary user arrival interval on the network

configuration latency

In this scenario, the concentrator starts on a particular

channel. Smart meter nodes that are switched ON will initiate

the concentrator discovery process and associate with the

concentrator. As mentioned earlier, if PU activity is detected

on this channel, the concentrator will have to switch from

the current channel to a vacant channel. In this case, the

smart meter nodes connected to the concentrator will have

to be informed about the channel change and the nodes that

are in the process of associating with the concentrator will

have to initiate re-discovery process. The main objective of

this scenario is to evaluate the impact of the primary user

arrival interval on the concentrator discovery latency. In other

words, how long does it take (on average) for all the smart

meter nodes to associate with the concentrator. Towards the

same goal, experiments were run until all the smart metering

nodes in the network were connected to the concentrator.

Each experiment was repeated ten times with different initial

random seeds and the results presented here are an average

over these.

Fig. 5 shows the impact of the primary user arrival interval

on the network configuration time. As evident from this figure, we observe an interesting trend wherein the discovery time

is at its maximum for a primary user arrival interval of 4

seconds. Recall from section V that the time required for a

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Primary User Arrival Interval (s)

Fig. 6. Effect of PU arrival interval on PDR

18

full scan is 4 seconds. In this case, a full channel scan period

is the same as the primary user arrival interval. Therefore,

the scanning period of a node may occasionally overlap with

the switching time of the primary user. When this happens,

the node may have to re-scan the whole frequency band until

the concentrator is discovered. For larger values of PU arrival

interval, it can be seen that the the network configuration time

is steady (on average around 15 seconds). This is attributed

to the control message propagated by the concentrator which

aids the smart meter nodes to discover it on the new channel.

Hence, having a concentrator signal the switch over event (on

detection of a PU) to the smart meter nodes is beneficial for

speeding up the network configuration process.

B. Impact of the primary user arrival interval on the PDR of

a smart metering network

In this scenario, we study the effect of PU arrival interval

on the PDR of the network. The more frequently the meter

readings are sent, the more pronounced will be the impact

on the PDR. We experimented with different meter reading

intervals (3, 6 and 93 seconds respectively). These values

result in different levels of network utilisation. Experiments

were carried out until 100 meter readings were received at

the concentrator. Similar to that in the previous scenario,

each experiment was repeated ten times with different initial

random seeds and the results presented here are an average

over these.

The trend in Fig. 6 shows that when the primary user arrival

is more frequent, the concentrator has to frequently switch

from the occupied to the vacant channel which introduces a

large overhead. This negatively impacts the PDR performance

of the network. Furthermore, as described above, the impact

is even more pronounced in the case of sending smart meter

readings more frequently. This is because more packets will

be lost during the switch over period due to unavailability

of access to the concentrator. Although, results here show

that a PDR of 99.99% can be achieved for less frequent PU arrival intervals, it should be noted that the actual performance

30ur experiments study the impact in more aggressive scenarios. In reality however, meter reading intervals may be sent on the order of a few minutes

that can be attained will depend on the specific network

configuration. For example, the time required to propagate the

control message (information about the new channel that the

concentrator is moving to) prior to a switch over will depend

on the maximum number of hops in the network. As the

number of hops increase the time required for propagating

the control message in the network may increase. It should

be noted that this control information is piggybacked on top

of a 'beacon' like control message that the concentrator sends

out regularly. This message is forwarded by nodes already a

part of the tree (those that are associated with the concentrator)

and is used by new smart meter nodes to detect a concentrator

and potentially associate with it. Therefore, if the frequency

of the control message is increased in the interests of speeding

up the switch over process, this will increase the control

traffic overhead thereby negatively impacting overall network

performance.

VI. CONCLUSION

In this paper we have presented a wireless mesh networking

solution in TV white spaces for smart metering communi­

cations. We have discussed advantages of using TVWS for

smart metering NANs and associated network architectures.

More specifically, the mesh network makes use of the reliable

WS spectrum information provided by the Radio Environ­

ment Map and geolocation database. Technical issues such as

network initialization, self-organization and re-configuration

upon primary arrival have been discussed in detail. Emulation

experiments based on the modified RPL implementation in

the Contiki operating system have demonstrated the good performance in terms of network reconfiguration latency and

packet delivery ratio during channel switch-over upon primary

detection. For future work, we are interested in the system

performance under more realistic primary activity patterns as

well as the scalability of the proposed scheme.

ACKNOW LEDGMENT

This work has been partially supported by the European

Union FP7 FARAMIR project.

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