Low-Power Wide Area Network Technologies for Internet-of-Things: A Comparative Review

IKPEHAI, Augustine, ADEBISI, B., RABIE, K.M., ANOH, K., ANDE, R.E., HAMMOUDEH, M., GACANIN, H. and MBANASO, U.M.

Available from Sheffield Hallam University Research Archive (SHURA) at:

http://shura.shu.ac.uk/23929/

This document is the author deposited version. You are advised to consult the publisher's version if you wish to cite from it.

Published version

IKPEHAI, Augustine, ADEBISI, B., RABIE, K.M., ANOH, K., ANDE, R.E., HAMMOUDEH, M., GACANIN, H. and MBANASO, U.M. (2018). Low-Power Wide Area Network Technologies for Internet-of-Things: A Comparative Review. IEEE Internet of Things Journal, p. 1.

Copyright and re-use policy

See http://shura.shu.ac.uk/information.html

Sheffield Hallam University Research Archivehttp://shura.shu.ac.uk

Low-Power Wide Area Network Technologies forInternet-of-Things: A Comparative Review

Augustine Ikpehai, Member, IEEE, Bamidele Adebisi, Senior Member, Khaled M. Rabie, Member, IEEE,Kelvin Anoh, Member, IEEE, Ruth E. Ande, Graduate Student Member, IEEE, Mohammad Hammoudeh,

Member, IEEE, Haris Gacanin, Senior Member, IEEE and Uche M. Mbanaso, Member, IEEE

Abstract—The rapid growth of Internet-of-Things (IoT)in the current decade has led to the development of amultitude of new access technologies targeted at low-power,wide area networks (LP-WANs). However, this has also createdanother challenge pertaining to technology selection. Thispaper reviews the performance of LP-WAN technologies forIoT, including design choices and their implications. Weconsider Sigfox, LoRaWAN, WavIoT, random phase multipleaccess (RPMA), narrow band IoT (NB-IoT) as well as LTE-Mand assess their performance in terms of signal propagation,coverage and energy conservation. The comparative analysespresented in this paper are based on available data sheetsand simulation results. A sensitivity analysis is also conductedto evaluate network performance in response to variations insystem design parameters. Results show that each of RPMA,NB-IoT and LTE-M incurs at least 9 dB additional path lossrelative to Sigfox and LoRaWAN. This study further revealsthat with a 10% improvement in receiver sensitivity, NB-IoT882 MHz and LoRaWAN can increase coverage by up to398% and 142% respectively, without adverse effects on theenergy requirements. Finally, extreme weather conditions cansignificantly reduce the active network life of LP-WANs. Inparticular, the results indicate that operating an IoT devicein a temperature of -20

◦C can shorten its life by about half;

53% (WavIoT, LoRaWAN, Sigfox, NB-IoT, RPMA) and 48%in LTE-M compared with environmental temperature of 40

◦C.

Index Terms—Access technologies, low-power wide area net-work (LP-WAN), Internet-of-things (IoT), LoRaWAN, Sigfox,sensitivity analysis, energy conservation, link budget, WavIoT,random phase multiple access (RPMA), LTE-M, narrow bandIoT (NB-IoT).

I. INTRODUCTION

B y 2025, up to 75 billion devices would be connectedin Internet-of-things (IoT), with potential economic

impact of around $11.1 trillion a year [1], [2]. The keyunderpinning of IoT is the large number of interconnecteddevices that exchange information and enable services.

A. Ikpehai, B. Adebisi, K.M Rabie, K. Anoh, R. E. Andeand M. Hammoudeh are with the Faculty of Science &Engineering, Manchester Metropolitan University, Manchester, M15GD, UK. (e-mails: a.ikpehai@mmu.ac.uk; b.adebisi@mmu.ac.uk;k.rabie@mmu.ac.uk; k.anoh@mmu.ac.uk; ruth.e.ande@stu.mmu.ac.uk;m.hammoudeh@mmu.ac.uk).Haris Gacanin is a research director with Nokia, Copernicuslaan 50, 2018Antwerp, Belgium. (e-mail: haris.gacanin@nokia-bell-labs.com)Uche M. Mbanaso is with Centre for Cyberspace Studies,Nasarawa University, Keffi, Nasarawa State, Nigeria. (e-mail:uche.magnus@mbanaso.org)

This work was supported in part by “CityVerve: IoTs and SmartCities Demonstrator” project funded by Innovate UK under Grant102561 and in part by “Triangulum” (part of H2020 Smart Cities andCommunities programme) funded by the European Commission underGrant 646578-Triangulum-H2020-2014-2015/H2020-SCC-2014.

Although IoT connectivity will be dominated by short-range technologies for many years, the work [3] predictsthat by 2025, 25% of wireless industrial IoT connectionswill be provided with LP-WAN technologies includingLoRa1, Sigfox, LTE-M as well as NB-IoT. This signifiesthe continued importance of LP-WAN as the IoT landscapeevolves.

Machine-to-machine (M2M) communication in IoT rep-resents a very large market, growing rapidly at compoundannual growth rate of over 20% [5]. Nevertheless, the mar-ket is fragmented and awash with many access technologiesand vertical solutions that sometimes do not interoperate.The result is that despite the potentials of IoT, organisationsand end users are faced with overwhelming choices ofaccess technologies. Therefore, it becomes challenging todecide where or how to begin their IoT road map as partof their wider digital transformation journey. Insufficientcomparative studies of LPWAN technologies has beenidentified as one of the major barriers to potential IoTusers [6]. This study does not involve detailed physical ormedia access control (MAC) layer specifications, the resultsnevertheless provide indicative performance of the LP-WANtechnologies set-up under the same operating conditions.

Until now, most of the studies independently evaluatedLP-WAN technologies in different operating environments.The main reason is that many of the leading technologies arebased on proprietary protocols whose detailed specificationsare not freely available in the public domain. For example,LoRaWAN and Sigfox are the top two LP-WAN technolo-gies in terms of installed base. The underlying chirp spreadspectrum (CSS) modulation technique in the physical layerof LoRa is proprietary and owned by Semtech Corporation.On the other hand, Sigfox deploys and operates the net-work but freely provides the protocol specification to chipsmanufacturers. Thus, LoRa is a closed chipset but opennetwork; (even private networks are possible) and Sigfoxis a closed network but open chipset. The implication isthat in the former, Semtech controls the production, supportand price of LoRa chipsets, while in the latter, Sigfoxcontrols the provision, access and price of the networkresources. Other technologies are different variants of eitherproprietary physical or MAC layer. Hence, no mainstreamLP-WAN protocol stack is currently fully open.

However, given the scope of IoT, it is important forsolution providers, system designers and users to have awide perspective of LP-WAN options without being locked-

1In this paper, LoRa denotes the physical layer modulation only, whileLoRaWAN refers to the wide area network protocol suite that adopts LoRaat physical layer and LoRa MAC at media access control sub-layer [4].

2

in. Motivated by that gap, the objective of this paper is toassess the performance of the widely available LP-WANtechnology specifications. The system parameters used aremostly drawn from the datasheets of the respective devicemanufacturers.

The contribution of this work is two-fold. First, weidentify an approach to optimise the LP-WAN by exploitingthe link budget and design parameters without increasing theenergy requirements of the system. The second contribu-tion resides in identifying the multi-variate dependence ofenergy consumption on field, application and technology-specific characteristics, each of which can be exploited todifferent extents. These results provide useful insights whichare extremely valuable when deciding the trade-off betweennetwork performance, design complexity and cost.

II. RELATED WORK

LP-WAN technologies are increasingly being deployedas last mile connectivity to compliment traditional technolo-gies and replace them in many use-cases. However, majorityof existing studies that compare the LP-WAN technologiesare either literature reviews [7]–[11] or brief experimentswith two or three technologies and are often focused ona single aspect of the systems. The studies neither includerigorous analysis of multiple performance metrics nor inves-tigate how the technologies perform relative to each other indifferent environments, using identical system parameters.In some evaluation cases, the technologies were tested inone environment and are therefore limited in scope.

In many of the studies where practical systems weredesigned, comparisons were not made with competing LP-WAN solutions. In fact, in some of the experiments, singletechnology was deployed for particular use-case which alsolimits the assessment outcomes to type-specific scenarios[12], [13]. In some other works, the comparisons wererestricted to physical and associated MAC layers onlywhile some evaluation studies focused on either cellular-only or non-cellular LP-WAN technologies [5], [14], [15].However, as some of the LP-WAN technologies are stillat their infancy, many practical issues are being resolved.Thus, a cross-cutting comparison will not only identifysuitability for specific applications but also ascertain amongthe LP-WAN specifications, the extent of design diversityin relation to the network performance.

LoRaWAN is currently one of the most deployed LP-WAN access networks for IoT [16] and also one of thewidely reported LP-WAN technologies in literature. Signalpropagation is crucial in IoT [17] as it affects networkperformance in terms of coverage, reliability and data rate.Path loss and coverage have been reported in many LP-WAN studies including LoRa [18] and NB-IoT [19] while[20] proposed an indoor channel attenuation model forLoRa, following a series of measurement campaigns. A fewother empirical studies such as [21] compared the perfor-mance of LoRa’s CSS modulation scheme with frequency-shift keying (FSK). The paper found that coverage andnetwork reliability are both affected by the payload size.However, the experiments were limited to the universityenvironment and the results did not provide insights into po-tential behaviours in other environments. More challengingterrains such as dense urban areas and other metropolitan

environments with higher levels of interference may exhibitdifferent performance due to difference in the interferencesources and patterns. This will not only provide additionalinsights but also help to analyse the results in the contextof individual field environment.

For cellular LP-WANs, [22] investigated the capacity andcoverage of LTE-M and NB-IoT in a rural area in Denmark.The paper reported that cellular technologies such as LTE-Msupport maximum coupling loss of about 156 dB, however,in deep indoor applications can experience up to additional30 dB penetration loss. An interesting aspect would be acomparison with non-cellular technologies.

Apart from the various analytical models that have beenreported [23]–[25], other experimental studies have beenconducted to individually evaluate different aspects of LP-WAN including coverage [26], [27], energy consumption[28], capacity [22], [29], [30] and scalability [23], [24],[30]. The main commonality in these and many otherexisting studies is that they mostly focused on one ortwo technologies. Comparing results from different studiesmay not provide a balanced view as each experiment orsimulation model is conducted with a set of assumptions orconditions which varies across studies and authors.

According to [31], until recently, there were very fewstudies involving more than three technologies. Although,[31] compared the performance of GPRS and NB-IoT withSigfox and LoRa, the dearth of publications in this areasuggests that a considerable amount of follow-on work stillneeds to be carried out. The objective of this paper istherefore to contribute in that regard.

Recent reviews of LP-WANS are summarised in TableI according to their areas of focus. Many of them de-scribe single technologies while energy consumption wasnot considered in most. However, given its importance inIoT generally and LP-WANs in particular, it is necessaryto consider the energy performance of existing LP-WANtechnologies as part of a complete system review.

III. LINK BUDGET AND IMPLICATIONS IN LP-WANS

LP-WANs in IoT access networks share similar imple-mentation methodologies with traditional wireless networksbut with some differences in system design. According toShannon-Hartley theorem, the signal-to-noise ratio (SNR)of each bit can be written as [38]

Eb

N=

ε

C

(2

Cε − 1

)=

2 η − 1

η(1)

where C is the capacity (in bits/s), ε is the bandwidth andη is the spectral efficiency (in bits/s/Hz). For a bit durationof (1/C) seconds, if S denotes the average signal powerof each bit and N the noise power in Watts, the averageenergy per bit is Eb= S

C (in J) and the total noise poweris N · ε Watts. The implication of (1) is that, Eb

N varieswith η and the modulation scheme in LP-WAN technologiesmust ensure that η is maximised. For a wireless signal tobe correctly decoded at the receiver, it must meet energydetection threshold. The receiver sensitivity (σ) can beexpressed as [39]

σ =Eb

N+ 10 · log10C + (φ− 174 dBm) (2)

3

Table I: Summary of Related Work

Ref. Specification Focus area Contribution/ Methodology Energy? Limitations/Gaps[7], LoRa & Sigfox, Basic literature review & grouping Little analytic[32] RPMA or into licensed and unlicensed No rigour. No

WavIoT bands. Description of advantages performanceof LP-WANS over legacy comparison

long range technologies. or evaluation.[8] LoRaWAN Description of Yes Only LoRaWAN

LoRaWAN studied. Otherin terms of capacity, cellular and

security, battery life. non-cellularLP-WANs excluded.

[33] LTE-M, NB-IoT, Basic overview Description of each Insufficient depth ofLoRa, Sigfox, & discussion technology and review. NoRPMA. of LP-WAN measurements performance

technologies. of reliability. No comparison[5], [9], Sigfox, LoRa, Very basic comparison or evaluation.[11] NB-IoT, 3G/4G of technologies & their differences.[27] 5G Indoor coverage No Only 1 site &

evaluation based 1 technologyon ray tracing. considered.

[34] 5G, LTE-M, Description of various ComparativeEC-GSM cellular , LP-WANs No performance

enablers & applications. with non-cellularLP-WANs was

not considered.Capacity, scalability, No Battery life and

[16], [30] LoRaWAN Comprehensive densification, use-cases its dependenciesreview or tutorial and reliability. Used were not covered.

simulation to illustrate the LoRaWAN notlimits of LoRaWAN: compared withscalability & reliability. other LP-WANs.Comprehensive review of NB-IoT not

[35] NB-IoT NB-IoT including security, Yes compared withapplications & energy consumption other technologies

[13] Deployment of LoRa No Only LoRato monitor a river. considered. OneMeasurements taken. use-case studied.

[17] Indoor signal No Only LoRapropagation characteristics studied.using RSSI as measure Performanceof performance. relative to

[20] LoRa/LoRaWAN Measurement-based No otherattenuation model: coverage technologiesand reliability for land and sea were

[36] Indoor evaluation: SNR, Yes not reported.packet loss, throughputRSSI, energy consumption.

[37] Experimentation, Effects of interference No Singleevaluation, on scalability. Evaluation technologysystem design & of LoRa in terms of coverage, considered.

[21] implementation. reliability using CSS & NoFSK in terrestrial deployments.

[28] Experimental evaluation of energy Yes Only NB-IoTconsumption of NB-IoT devices. studied

[14] NB-IoT NB-IoT firmware developed & No Performancedeployed on devices. Cloud relative to otherplatform, application server, user other technologiesapplication also deployed. were not reported.

[18], Simulation of LoRa with multiple Yes[23] SF and rates. Distribution of SF

and discrete power settingsLoRa to reduce packet rerror rate (PER). Only LoRa

[29] Detailed analysis of LoRa and No investigated.LoRaWAN, simulation of Performancethroughput & collision rate relative to otherand measurements technologiesof coverage & reliability. not reported.

[19] NB-IoT Simulation or Coverage of NB-IoT No Only NB-IoTanalytical based on in-band investigated.solution for deployment in LTE.

[22] LTE-M, LP-WANs Coverage, reliability, energy Yes Non-cellularconsumption in indoor LP-WANs

NB-IoT and outdoor cases. not covered[24] Sigfox Derivation of expression of No Only Sigfox

packet error rate for UNB investigated.LP-WAN such as Sigfox.

[25] LoRa, NB-IoT Description of MAC of No Energy &Sigfox LoRa, NB-IoT & Sigfox. its dependencies

Comparison of PER. were excluded.

4

Table II: Link Budget of LP-WAN Technologies used in the simulation [4], [31], [47]–[52]

Technology NB-Fi (WavIoT) LoRaWAN U-NB(Sigfox) RPMA (Ingenu) LTE-M NB-IoT868MHz 868MHz 868MHz 2.4GHz 1.8-2.7GHz 700-2100 MHz

Downlink DL DL DL DL DL(2.6GHz) DL(882MHz,1840MHz)Tx Power, dBm 30 21 24 21 40 35Tx cable loss,dB -3 -3 -6 -3 -3 -3Bandwidth (kHz) 0.1 125 0.1/0.6 1000 1400 180Tx antenna gain, dBi 0 9 9 9 10 16σ, dBm -147 -137 -129 -133 -129 -141Rx env. noise 0 0 0 0 0 0Rx antenna gain 0 0 0 8 0 0Total (link budget) 174 164 156 168 176 189Obstacle loss (fixed), dB 20 20 20 30 30 30Obstacle loss (variable), dB 35.8 28.1 30.7 29.8 27.1 29.4

The noise figure (φ) is typically around 2 to 6 dB for LP-WAN radio front-end [39], [40].

In many access networks such as Sigfox and LoRaWAN,each message sent by the end node can be received bymultiple gateways. According to [41]–[43], the potentialthroughput available per coverage unit can be expressed as

R(λ, θ) = λ · log2(1 + θ)Pr(SNR ≥ θ), (3)

where λ is the gateway density, Pr(.) is the probabilityoperator, θ is the threshold SNR and R is the potentialthroughput in bps/Hz/km2. However, (3) assumes fixeddata rate and does not provide rate adaptation such thatthe bit rate of a gateway is adjusted upward or downwardaccording to the SNR instead of outage when the receiverSNR < θ. On the other hand, the average area spectralefficiency (ASE) also in bps/Hz/km2 considers SNR ofindividual gateway and avoids outage at low SNR byreducing the data rate instead, this is more amenable toIoT scenarios. The ASE can be expressed as [44]

E (λ)=λ · E [log2 (1 + SNR)], SNR ≥ θ. (4)

Equation (4) supports bit rate adaptation according to theSNR instead of outage. According to [41]–[44], ASE de-pends on the number of gateway per km2 and path loss. Thevalue of σ can also be reduced to extend the transmissionrange, however, the performance penalty is reduced bit ratewhich is acceptable in many IoT deployment scenarios.This systematic trade-off underpins the long range operationof LP-WAN technologies. The value of σ in LP-WANs istypically lower than -128 dBm (-129 dBm to -155 dBm)and ranges from -90 dBm to -110 dBm [45] in traditionalwireless systems such as Wi-Fi [46]. The implication isthat in terms of absolute signal power in Watts, LP-WANreceivers can detect wireless signals that are 1000 timesweaker than Wi-Fi and other technologies with -100 dBmsensitivity. LP-WAN technologies exploits this capability inthe various specifications.

Table II presents the technical specifications of widely-available LP-WAN technologies from datasheets [4], [47],[48], measured obstacle losses in European urban envi-ronments [48] as well as other parameters reported inliterature [31], [49]–[52]. It is generally desirable thatthe main lobe of the transmitted radiation pattern reachesthe receiver to ensure that maximum signal strength andcoverage are delivered. The field condition can be worsenedby obstruction losses which will further reduce the linkbudget. The fixed obstacle losses in Table II are the reported

Table III: Additional Simulation Parameters

Parameter ValueNumber of gateways 1Baseline payload 12 bytesRadio electronics energy,Eelec 50 nJ/bitOn-Air time Sigfox-EU:6.24s

LoRa (varies with SF)Background noise, dBm -130 dBmBattery model E91-AA alkaline battery

average values obtained from measurements in urban areasin Europe through 20 cm concrete walls [48].

The parameters in Tables II and III are used to simulatethe transmission from gateway to end device based on thescenario described in the next section. Sigfox and LoRaare two leading LP-WAN technologies. However, Sigfoxsupports a maximum payload of 12 bytes while LoRa cansupport up to 242 bytes [36], [53], [54]. In order to comparethe technologies, we adopt Sigfox payload as the baseline inTable III. Energy consumed by the radio electronic circuitin low-power devices such as wireless sensors has beenreported to be about 50 nJ/bit [55], [56]. The choice ofAlkaline battery is informed by the fact that they are low-cost, widely available and are commonly used in low-powerapplications such as wireless sensor networks. In Europe,at 100 kbps, it takes about 2.08 s [57] to send a 12-byteSigfox frame and since the endpoints do not synchronisewith Sigfox base station, each message is sent three timesto improve chances of detection [5], [58].

IV. NETWORK MODEL AND METHODOLOGY

This paper considers an access network in which thegateway antenna is at 25 m height on a tower and the IoTend point comprises an antenna located at 8 m (based onthe average height of 2-storey building in UK [21], [59]).This set-up applies to many IoT and smart city scenariosincluding monitoring of multi-storey building packing spaceavailability in smart cities, monitoring of structural healthof buildings and bridges, detection of noise map in urbanareas, smart grid, industrial IoT, etc. The downlink isconsidered and we discuss the network performance indifferent terrains including open/rural areas, suburban, smalland large cities. In each case, the indicative performance ofthe network is analysed following simulation of each accesstechnology in different environments. In this study, EU-868LoRaWAN/Sigfox, WavIoT, 2.4 GHz RPMA-Ingenu, 2.6GHz LTE-M, NB-IoT (882 MHz/1840MHz) specificationsand other parameters outlined in Table II are employed in

5

the simulation using MATLAB. The 3GPP recommends in-band deployment for LTE-M using existing LTE installedbase. In this paper, we adopt the 2.6 GHz for LTE-M as it iswidely used in Europe and beyond in existing LTE systems.

Although NB-IoT supports three modes of deployment,namely; standalone, in-band LTE and guard band in LTE, itcan also coexists with GSM, LTE and UMTS [60]. We adopt882 MHz and 1840 MHz for NB-IoT which are equivalentto bands 5 and 3 downlink frequencies respectively, in 3GPPNB-IoT (release 13) [49]. These choices are informed bythe fact that many mobile operators already own blocks offrequency in 1800 MHz band and 800 MHz-900 MHz usedfor various services such as GSM and LTE. This clearlyprovides an upgrade path to NB-IoT for such operatorsthrough frequency re-farming.

In IoT access network design and implementation, thethree most critical questions are:

i) what is the maximum distance the link can coverwithout compromising quality-of-service (QoS) orSNR,

ii) what field parameters can be explored to reducecost of implementation and

iii) what endogenous variables can be tweaked toconserve energy [61] and optimise network per-formance.

To address these questions, the methodology adopted issummarised as follows.

1) Use of system-specific attributes of WavIoT, Sigfox,LoRaWAN, RPMA and LTE-M to generate propa-gation characteristics including Fresnel clearance andpath loss.

2) Application of equal transmit power across the tech-nologies, followed by estimation of the correspondingcoverage at each power level in rural and urbanenvironments.

3) For each technology, the path loss information previ-ously calculated is extracted and used to generate thereceived signal strength indicator (RSSI) as well asSNR at different distances in the field. SNR is bench-marked against 20 dB widely used in conventionalwireless systems.

4) As coverage is more challenging in urban areas, asensitivity analysis is conducted for urban deploymentscenario with a view to exploiting system parametersfor network performance optimisation.

5) Investigation of the key factors affecting energy con-sumption in end devices.

6) For specific LP-WAN system configurations, estimatethe battery life of the radio transceiver based on 1%maximum duty cycle allowed in Europe.

While there are no simple or direct answers to the questionsposed, the following sections illustrate these issues andprovides insights to different aspects of the questions withsome rationales.

V. SIGNAL PROPAGATION

A. Fresnel Clearance

Ideally, radio waves should travel in a straight line fromthe source to destination. An obstruction that is close to

or infringing the LoS causes attenuation. For the givenscenario, the Fresnel clearance is illustrated in Fig. 1.

The Fresnel zone radius at a point P along the LoS isgenerally defined as [62]

Fzr =

√n · cd1d2f ·D

= 17 .32

√d1d2f ·D

(5)

where n = 1 denotes the first Fresnel zone, c is the speed oflight, f is the frequency in GHz and D= d1+d2 is the totaldistance in km. In reality, at least 60% Fresnel clearance isrequired.

By applying (5), Fig. 2 presents the minimum clearancerequirements for 868 MHz, 2.4 GHz and 2.6 GHz LP-WANtechnologies as the obstacle is moved to different pointsalong the transmission path in a 50 km network. It can beseen that the required Fresnel clearance increases as theobstacle moves towards the midpoint such that the largestclearance is required if the obstacle is positioned at themiddle. Fig. 2 further shows that when the obstacle is movedfrom 1km to 5 km then 25 km, with LTE-M, the minimumclearance increases from 6.38 m to 13.67 m then 22.79m, respectively. In the Sigfox/LoRaWAN networks withthe obstacles at the same positions, the minimum Fresnelclearance are respectively 11.04 m, 23.66 m and 39.44 m.The key observation here is that the closer the obstructionto the gateway transmitter, the more impact it has on theaccess network.

B. Path Loss

Path loss estimation is a crucial part of planning anddesign of the access networks. In wireless communication,every time the distance is doubled, only one-fourth of thesignal power is received and this has some far-reachingimplications for connectivity in IoT as it degrades the SNRat the end receiver. The path loss discussed in this paperrelates only to outdoor applications. The Free Space PathLoss (FSPL) model is based on clear LoS and does not takeinto account terrestrial objects such as hills, trees, buildings,etc. In ideal case, the path loss experienced by signals canbe expressed as

FSPL(dB) = 20 · log10l + 20 · log10f − 32 .44 (6)

where l is the distance (in km) and f is the frequency (inHz). However, the assumption of free space between thetransmitter and receiver in FSPL is invalid in many real lifedeployments, as RF paths are typically laden with additionalfactors which further degrade the path loss. Such factorsinclude [63] cable losses, antenna gain, LoS condition,receiver sensitivity, etc. Particularly in IoT, a wireless signalwill typically encounter different obstacles on its path andeach has an additive effect on the total path loss. Commonexamples of obstacles and their attenuation factors aresummarised in Table IV.

According to Table IV, if Sigfox or LoRaWAN signalwith transmit power of 14 dBm encounters a non-solidmetal door on its path, the signal power is reduced to 8dBm (approximately 25% of its original value) when itexits the door. In urban areas with high vehicular densityand high-rise buildings, signal can suffer from reflection

6

Fresnel zone

Rx

Tx

ht

hg

d1 d2

Fresnel radius

obstacle

P

Figure 1: LoS condition and Fresnel clearance

0 5 10 15 20 25 30 35 40 45 50

Distance of obstacle from Tx (km)

5

10

15

20

25

30

35

40

Fre

snel

cle

aran

ce (

m)

868MHz (LoRa/Sigfox Europe)

2.4GHz (RPMA))

2.6GHz (LTE-M)

Figure 2: Effects of obstacle position on Fresnel clearance

Table IV: Examples of materials and signal attenuation at 2.4 GHz[63], [64]

Material/object Attenuation FrequencyDry wall 3dB 2.4GHzGlass wall with metal frame 6dB 2.4GHzOffice window 3dB 2.4GHzMetal door (non-solid) 6dB 2.4GHzWood 3dB 2.4GHzSolid concrete (e.g. wall, bridge) 8-15dB 2.4GHz/1.3GHzAluminum siding 20.4dB 815MHzConcrete floor 10dB 1.3GHz6-inch diameter metal pole 3dB 1.3GHz

from metallic surfaces, diffraction or even scattering offthe lampposts and moving vehicles which contribute tothe overall losses. If multiple copies of the signal arrivethe receiver, they can destructively interfere and severelydegrade reception. The path loss can be expressed as

σ = Pt +Gainnet − PL (7)

where σ is the receiver sensitivity, PL is total the pathloss in dB and Gainnet is the net gain. The Gnet includesall gains resulting from transmit and receive antenna aswell as all losses related to the radio and antenna hardwareincluding filters, cables, attenuators, antennas and obstaclelosses, etc. The σ in (7) is equivalent to the minimumsignal power that will be detected by the receiver. The

IoT implementation engineer must therefore ensure that thereceived power is often greater than σ.

1) LP-WAN and Signal Propagation in Different Ter-rains: From (7), it is clear that adequate knowledge of theterrain is required in the network design. Some models de-rived from empirical studies have been reported to providemore realistic path loss. In particular, the Hata-Okumuramodel, which is based on various Okumura correctionfunctions is widely used for predicting path loss in radiofrequency (RF) planning in the wireless communicationindustry. The key strength of this model is that it caters fordifferent field scenarios such as large cities, small suburbanand rural environments [65]–[68] which has influenced itswide acceptance. In its general form, the model can beexpressed as

PLHata =69.55 + 26.16 · log10f − 13.82 · log10hg−a(ht) + [44.9− 6.55 · log10hg] log10d− ct

(8)where f is the frequency (in MHz), hg is the gatewayantenna height (in m) and d is the distance (in km) fromtransmitting to receiving antenna. The terrain parametera(ht) is a function of IoT terminal height, defined for urbanareas as

a(ht)(urban) = 3 .20 [log10(11 .75ht)]2 − 4 .97 (9)

for suburban, open and rural areas, a(ht) is defined as

a(ht)(sub−rural) = [1.1 · log10f − 0.7)]ht−[1.56 · log10f − 0.8]

(10)

where ht is the height of the remote terminal (IoT endpoint)antenna above the ground. Also, the parameter ct is thecorrection factor defined as

ct =

4.78 · [log10f ]2 − 18.33 · log10f + 40.94 rural/open

2[log10

(f28

)]2+ 5.4 suburban

0 urban(11)

For the various access technologies, the path loss in differentenvironments is presented in Figs. 3 and 4.

7

0 5 10 15 20 25 30 35 40 45 5080

100

120

140

160rural

0 5 10 15 20 25 30 35 40 45 50

Distance (km)

100

120

140

160

180

Pat

h L

oss

(d

B)

suburban

868MHz Europe (LoRa/Sigfox)

2.4GHz (RPMA)

2.6GHz (LTE-M)

Figure 3: Path loss of access technologies in rural and suburbanenvironments

0 5 10 15 20 25 30 35 40 45 50100

120

140

160

180

200small city

0 5 10 15 20 25 30 35 40 45 50

Distance (km)

100

120

140

160

180

200

Pat

h L

oss

(d

B)

large city

868 MHz (LoRa/Sigfox Europe)

2.4 GHz (RPMA))

2.6 GHz (LTE-M)

Figure 4: Path loss of access technologies in urban environments

By applying (8)-(11) using the parameters in Tables IIand III, Figs. 3 and 4 respectively, illustrates the path loss inurban and rural environments for the scenario presented inFig. 1. These results generally agree with the literature, forinstance Fig. 3a closely aligns with the coupling loss of 156dB reported in [22] for LTE-M. It can be seen in Fig. 3 thatat a distance of 3 km between the transmitter and receiver,RPMA and LTE-M each incurs additional path loss of about9 dB and 11 dB in rural and large cities respectively, relativeto Sigfox/LoRaWAN. At 10 km, the additional path lossexperienced by RPMA and LTE-M are 8.2 dB and 9.2 dB inrural areas as well as 12 dB and 13 dB respectively in largecities (compared with Sigfox/LoRaWAN). These results arewithin the range of those in [6], [27] which reported thatLP-WAN could achieve up to 20 dB improvement overcellular systems. It is also noted that RPMA and LTE-M are more susceptible to path loss than LoRaWAN andSigfox. The amount of loss also depends on the natureof the obstacle (Table III), the results in this subsectionhowever show that as the frequency increases, the abilityto overcome obstacles drastically reduces. The 868 MHztechnologies require significantly taller masts or towers.

If that is considered against the fact that attenuation ismore pronounced in the > 2 GHz region and many ISMradio equipment (e.g. Wi-Fi, ZigBee) also share the 2.4GHz band, then the system designers and implementationengineers need to consider the delicate balance betweennetwork reliability, cost and efficiency in the choice of radiotechnology. Although more bandwidth is available in 2.4GHz and 2.6 GHz compared to 868 MHz, however Figs. 3and 4 show that at every distance, the 2.x GHz technologies(RPMA, LTE-M) incur at least 9 dB additional path losswhich suggests that they are less able to overcome theeffects of obstacles.

2) Non LoS (NLOS) Propagation : For IoT applicationsin urban environments lined with high rise buildings and tallstreet furniture, NLOS links arise when the gateways andIoT end points are located on two parallel or intersectingstreets. With a rectangular street grid, signals experiencereflection off the walls as well as diffraction at the buildingcorners [69] and other vertical structures such as lamp postsand bus stop signage, located near the street junction orparallel street. In such cases, the arriving signals undergomultiple reflections from the walls on the main and sidestreets as well as scattering around the vertical objects. The1-turn path loss, relative to free space can be expressed as[70]

PL1−Turn = PLLOS .cos2ϕ

S2.x1x2x1 + x2

(12)

where x1, x2 are the distances from the intersection togateway and end device respectively, S is the scatteringparameter and ϕ is the viewing angle from the gateway toend device which depends on the heights of the antennas.The PLLOS term in (12) is the ITU-LOS path loss modeldefined as

PLLOS =

10δdB/10(

γ2

8π.hg.ht

)2 (ddc

)2.5d ≤ dc

10δdB/10(

γ2

8π.hg.ht

)2 (ddc

)4d > dc

(13)

Where δ is the environmental parameter, d is the totaldistance x1+x2 and dc is the cross-over distance. In general,PLLOS will vary across different environments, dependingon building heights, antenna height and the amount ofwaveguide provided by the street canyon. By employing avirtual source at the centre of the intersection and capturinglocal street variables such as main street width, side streetwidth as well as distance from gateway antenna to the mainstreet wall, it has been reported that the NLOS path loss canbe written as [71], [72]

PLNLOS =

10.log10

(1δ

(√2πwt.xt

. 4πx1x2

γ

))2d ≤ dc

10.log10

(1δ

(√2πwt.xt

.4πx1x

22

γ.dc

))2d > dc

(14)where wt, xt are the main street width and distance fromgateway to the wall respectively. While (13) includes thewaveguide and 1-turn corner-turning effects of the path,(14) captures local street characteristics in addition. Wehave chosen wt to cater for shared surface street (used bypedestrians and vehicles in UK, minimum 4.8m [73]) and

8

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Distance from Rx to center of intersection (m)

60

80

100

120

140

160

180

200

220

240

Pat

h l

oss

(d

B)

LoRa/Sigfox/WavIoT

RPMA(Ingenu)

LTE-M

(a) Gateway height 8m, end device height 0.5m

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Distance from Rx to center of intersection (m)

60

80

100

120

140

160

180

200

220

240

Pat

h l

oss

(d

B)

LoRa/Sigfox/WavIoT

RPMA(Ingenu)

LTE-M

(b) Gateway height 8m, end device height 2m

Figure 5: Non LOS path loss in urban environment with S = f−0.024 [69], δ = 7, wt = 7, xt = 2

assume a regular shaped junction such that main and sidestreets are of equal widths.

Fig. 5 illustrates the effects of dc and street char-acteristics on NLOS path loss in urban street canyonsfor low and medium altitude IoT applications. The pathloss significantly increases when d > dc. In other words,reflection dominates the path loss when the receiving enddevice is close to the street junction, up to the cross overdistance dc. This is because the rays from the main streetbend and arrive on the side street at an incidence angleclose to 90◦ which results in very little or no refraction.Thereafter, the signals are reflected multiple times as theypropagate further down the side street. Hence, for enddevices located far from the junction, (beyond dc), the pathloss is dominated by diffraction. By increasing the heightof the end device from 0.5 m to 2 m, Fig. 5 shows that thepath loss immediately after dc in LoRa (146 m), RPMA(403 m) and LTE-M (436 m) can be reduced by 77.24 dB,86.07 dB and 86.78 dB respectively.

VI. NETWORK PERFORMANCE ANALYSIS

In this section, we compare performance of the variousLP-WAN technologies using the system parameters in eachspecification according to its data sheet and the scenario inthe previous section. A sensitivity analysis is also conductedfor each specification in urban area. The outcomes provideuseful insights for attaining the trade-off between networkoptimisation and design complexity on one hand and coston the other. We also examine the energy consumption andits dependencies in different use-cases. The battery life andenergy consumption discussed in this subsection are withrespect to the radio transceiver and includes transmit andreceive consumption only. Other device components such asmicro-controller unit (MCU), wait windows, sleep currents,and other latent consumption in the system are not included.

A. Coverage

One of the key performance metrics in wireless systemsis the communication range. Given the scale of IoT in termsof applications, one of the challenges is how to ensure

that the selected access technology is capable of providingadequate coverage for the area of interest. While coverageis relatively less constrained in rural and open areas, itis more challenging in large urban environments such asmetropolitan areas with heavy presence of high-rise modernbuildings, road side furniture such as street lights (lampposts) and other man-made obstacles that reduce signalstrength. By substituting (8)-(11) into (7), the maximumrange can be expressed as

R = 10(Pt+Gainnet−σ−y1+y2+kt)

bh (15)

where y1 = 69.55 + 26.16 · log10f , y2 = 13.82 · log10hg ,kt=a(ht) + ct and bh = 44.9− 6.55 · log10hg .

Fig. 6 illustrates the maximum range achievable in ru-ral and suburban areas using the main access technologyspecifications outlined in Table II. In particular, this figureshows that the coverage of each technology is dependent onthe transmit power. In practice, coverage will vary amongsites depending on the field characteristics. For example,the presence of multiple obstacles such as trees or wallsalong the propagation path will also add to the attenuationof signal power. It is seen in Fig. 6 that in rural areas,using equal transmit power in the specifications considered,NB-IoT offers a significantly wider coverage (57.79 kmat 40 dBm) than LTE-M (8.57 km at 40 dBm) in thecellular domain while LoRaWAN offers the longest range(29.67 km) among the non-cellular LP-WAN technologies.Overall, NB-IoT provides the widest coverage out of thesix specifications considered. Although, Table II indicatesthat LTE-M and RPMA provide higher link budget thanLoRaWAN, the low obstacle loss in LoRaWAN means thatgiven the same transmit power and operating condition, itis more resilient to obstructions which improves the signalpower reaching the end device from the gateway. On theother hand, even with lower transmit power of NB-IoT thanLTE-M, the former relies on its better sensitivity and higherantenna gain to significantly extend the network coverage.

Similarly, the range in urban environment is illustratedin Fig. 7. The figure shows coverage of 2.89 km (NB-IoT),2.68 km (LoRa), 1.74 km(WavIoT) in large urban areas,

9

0 5 10 15 20 25 30 35 40

Transmit Power (dBm)

0

10

20

30

40

50

60

Max

imu

m r

ang

e (k

m)

WaveIoT

LoRa

Sigfox

RPMA(Ingenu)

LTE-M

NB-IoT 1840MHz

(a) Range in open and rural areas

0 5 10 15 20 25 30 35 40

Transmit Power (dBm)

0

5

10

15

Max

imu

m r

ang

e (k

m)

WaveIoT

LoRa

Sigfox

RPMA(Ingenu)

LTE-M

NB-IoT 1840MHz

(b) Suburban areas

Figure 6: Maximum range in rural and suburban areas

0 5 10 15 20 25 30 35 40

Transmit Power (dBm)

0

1

2

3

4

5

6

Max

imu

m r

ang

e (k

m)

WaveIoT

LoRa

Sigfox

RPMA(Ingenu)

LTE-M

NB-IoT 1840MHz

(a) Range in small cities

0 5 10 15 20 25 30 35 40

Transmit Power (dBm)

0

0.5

1

1.5

2

2.5

3

Max

imu

m r

ang

e (k

m)

WaveIoT

LoRa

Sigfox

RPMA(Ingenu)

LTE-M

NB-IoT 1840MHz

(b) Range in large cities and metropolises

Figure 7: Maximum range of access technologies in urban environments

meaning that NB-IoT end device can receive signal fartheraway from the gateway than LTE-M while LoRa receivesthe farthest signal among the non-cellular technologies.Thus, the sub-GHz technologies are particularly beneficialin rural areas where masts are less likely to be spread out.They also have some advantages in cities, because the sub-GHz technologies have lower penetration losses across brickwalls, vegetation, concrete, metals, etc, which is suitable forunderground applications such as standpipe monitoring. Aswe move from small to large cities, the performance ofRPMA further diverges from Sigfox. This is mainly dueto their lower resilience to obstacle losses in large urbanareas. Fig. 7 shows that RPMA and LTE-M are poor attravelling over long distances, which requires the towersto be closer together to deliver reliable coverage. Hence,2.x GHz technologies may be less attractive in some ruraldeployments and deep indoor applications. Given the widerange of frequency and different modes of deployment sup-ported by NB-IoT, it can be used in > GHz band or sub-GHzto cater for IoT connectivity in different terrains. In [32],the field experiments using LoRa deployment to cover the

city of Padova in Italy reported a network coverage of about1.2 km. This closely aligns with our result for small cities inFig. 7a (1.4 km) based on LoRaWAN system specificationwith gateway transmit power of 21dBm employed in thiswork. Figs. 6 and 7 show that the 868MHz technologies forinstance do not exhibit the same coverage performance evenwhen operated at the same frequency and equal transmitpower. This is mainly due to the differences in the systemsdesign and configurations.

For each technology type, the equivalent isotropicallyradiated power (EIRP) and SNR are also calculated. TheEIRP is the output power radiated from the tip of thegateway antenna. Using the PL results in Fig. 3, the EIRPand nominal SNR for suburban environment at differentlink distances are summarised in Table V (for the downlinkonly).

In many wireless systems such as Wi-Fi, the target SNRis usually 20 dB or higher to ensure reliable communication[63]. However, as seen in Table V, at link distance of 20 km,Sigfox and LoRaWAN yield SNR of 9.7 dB while RPMAoffered 9.4 dB. These low SNR values are indications thatthe connectivity between IoT end point and gateway may be

10

-10 -8 -6 -4 -2 0 2 4 6 8 10

Percentage change in parameter (%)

-60

-40

-20

0

20

40

60

80

100

120

140

Per

cen

tag

e ch

ang

e in

ran

ge

(%)

Antenna gains

Transmit power

Receiver sensitivity

Tx antenna height

(a) LoRaWAN urban area

-10 -8 -6 -4 -2 0 2 4 6 8 10

Percentage change in parameter (%)

-60

-40

-20

0

20

40

60

80

100

120

140

Per

cen

tag

e ch

ang

e in

ran

ge

(%)

Antenna gains

Transmit power

Receiver sensitivity

Tx antenna height

(b) Sigfox urban area

Figure 8: Maximum range of LoRaWAN and Sigfox in urban environments

Table V: RSSI and SNR for suburban environments at differentdistances for various access technologies

Spec. WAVIoT LoRaWAN Sigfox RPMA LTE-MEIRP 27 27 27 27 47PL10 136.5 136.5 136.5 144.9 145.5PL20 147.3 147.3 147.3 155.6 156.3RSSI10 -109.5 -109.5 -109.5 -117.9 -98.5RSSI20 -120.3 -120.3 -120.3 -120.6 -109.3SNR10 20.5 20.5 20.5 12.1 31.5SNR20 9.7 9.7 9.7 9.4 20.7

impaired with high frame error rate which will ultimatelyresult in sub-optimal link performance. In order to provideremedies for this condition, the following options can beexplored

• Improve receiver sensitivity• Increase the transmit power• Use higher gain antenna• Where possible, remove obstacles or other sources of

losses• Increase the antenna height• Increase gateway density by deploying more base

stations which will reduce the distance from gatewayto IoT end point.

Some of these factors are investigated as part of the sensi-tivity analysis in the next section.

B. Sensitivity Analysis and Network Optimisation

We observed from Fig. 7 that coverage limitation ismore imminent in urban areas, therefore the objective ofthis subsection is to investigate ways to optimise networkperformance by conducting a sensitivity analysis. Equations(11) and (15) as well as their dependence on field param-eters suggest that some variables can be tuned to optimiseperformance of the communication network. As the currentLP-WAN specifications employ different values of systemparameters, percentage changes are employed instead of ab-solute values. In each case, a variable is changed by a fixedpercentage and the resultant improvement or degradation inthe network coverage is estimated. Thus, the transmit power,antenna gain, receiver sensitivity, height of gateway antennaare varied within ±10% in succession.

Figs. 8 and 9, respectively illustrates the coverage gainin large cities and metropolitan areas. In each case, theoutcome is compared among four access technologies andit can be seen that the network responds to the changesin different degrees. In Fig. 8, LoRaWAN and Sigfoxexhibit similar performance and trend until 3% changein system parameter. Beyond that point, LoRaWAN out-performs Sigfox and this is more pronounced from 8-10% where LoRaWAN provides more coverage gain. LP-WAN transceivers are engineered to be very sensitive. Asa result, the end points can detect signals that are smallerthan transmit power by many order of magnitude. Fig. 8however shows that by increasing the receiver sensitivityby 10%, LoRaWAN and Sigfox can extend coverage by141.7% and 129.57%, respectively, meaning a comparativegain of 12.13% in favour of LoRaWAN. Furthermore, Fig.9 illustrates the additional coverage gain when similarparameters are varied in RPMA and LTE-M. It is observedthat a 10 % improvement in receiver sensitivity can increasecoverage by 135.56% in RPMA and 19% in LTE-M. It isfurther noted that increase in transmit power alone yieldsabout 17% coverage improvement in the best case (Fig. 9a).

Similarly in Fig. 10, it is observed that with a 10%improvement in NB-IoT 882 MHz receiver sensitivity, cov-erage can increase by about 398% while a similar changein transmit power and antenna gain yields 130% and 122%respectively. In the case of NB-IoT 1840 MHz, a 10%improvement in sensitivity yields about 191% coverage gainwhile a similar change in transmit power and antenna gainenhance coverage by about 34% and 30% respectively.Based on the specifications and parameters employed inthis paper, we can see in Figs. 8-10 that among the sys-tem parameters employed, receiver sensitivity and transmitpower are two of the key parameters for optimising LP-WAN coverage. This approach affords the opportunity toidentify the most sensitive parameters. With such knowl-edge, system designers and engineers can explore furtherways of tweaking the identified parameters in a manner thatis cost-effective without compromising network reliability,power requirement or other key performance indicators ofthe access networks.

11

-10 -8 -6 -4 -2 0 2 4 6 8 10

Percentage change in parameter (%)

-60

-40

-20

0

20

40

60

80

100

120

140

Per

cen

tag

e ch

ang

e in

ran

ge

(%)

Antenna gains

Transmit power

Receiver sensitivity

Tx antenna height

(a) RPMA (2.4GHz ISM)

-10 -8 -6 -4 -2 0 2 4 6 8 10

Percentage change in parameter (%)

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

Per

cen

tag

e ch

ang

e in

ran

ge

(%)

Antenna gains

Transmit power

Receiver sensitivity

Tx antenna height

(b) LTE-M (2.6GHz)

Figure 9: Coverage optimisation with 2.4GHz and 2.6GHz LTE in urban environments

-10 -8 -6 -4 -2 0 2 4 6 8 10

Percentage change in parameter (%)

-50

0

50

100

150

200

250

300

350

400

Per

cen

tag

e ch

ang

e in

ran

ge

(%)

Antenna gains

Transmit power

Receiver sensitivity

Tx antenna height

(a) NB-IoT 882 MHz

-10 -8 -6 -4 -2 0 2 4 6 8 10

Percentage change in parameter (%)

-100

-50

0

50

100

150

200

Per

cen

tag

e ch

ang

e in

ran

ge

(%)

Antenna gains

Transmit power

Receiver sensitivity

Tx antenna height

(b) NB-IoT 1840 MHz

Figure 10: Range optimisation with NB-IoT at 882 MHz and 1840 MHz, bands 5 and 3 downlink frequency respectively, 3GPP NB-IoT (rel. 13)[49]

While increase in antenna gain, transmit power andantenna height can improve the received signal level, theyalso increase the chances of higher interference. Thereforethe pros and cons of each optimisation approach needs tobe carefully considered.

C. Transmission Delay

The transmission delay of a message of length | Ψ |bitscan be determined from Shanon-Hartley theorem as

τ(ε, σ, | Ψ |) = | Ψ |ε· log2

(1 + ε−1.

βσ

φN0

)(16)

where σ(dBm) = Pt − α is the receiver sensitivity (Pt isthe transmit power, α is the coupling loss), β is a correctionfactor to account for difference between Shanon limit andrealistic systems and N0 is the thermal noise power spectraldensity (PSD) which is about -174 dBm/Hz. The minimumtime required to transmit message A is achieved when ε −→∞ . This is supported by the fact that larger bandwidthcauses messages to be transmitted faster such that [74]

τ(∞, σ, | Ψ |) = ln2. | Ψ |(βσ

φN0

)−1

(17)

Equations (16) and (17) further imply that given a fixedreceiver sensitivity in each LP-WAN specification, the trans-mission delay depends on the data rate and message length.Sending 50 byte payload in LoRaWAN in Europe in somecases can take approximately 2.8s using the minimum datarate (SF12) and 0.154 s using the maximum data rate (SF7).This means that the end devices have to wait 4 min 37 safter each transmission in the former and 15.25 s in thelatter. However, Fig. 11 indicates that if the payload sizeis reduced by 20%, the time-on-air (ToA) can decrease by328 ms and 61.44 ms for SF12 and SF7, respectively.

ToA for LoRaWAN is significantly affected by bothpayload size and SF. However, as seen in Fig. 11, the impactof SF is higher than that of the payload, which implies thatfor a given payload, the ToA increases more rapidly with theSF value. The reason is that SF 7 and SF 12 offer the highestand lowest data rates respectively. Therefore, increasing theSF is equivalent to reducing the network throughput and if

12

5 10 15 20 25 30 35 40 45 50

Payload size (Byte)

0

0.5

1

1.5

2

2.5

3T

ime

on

air

(se

c)

SF7

SF8

SF9

SF10

SF11

SF12

Figure 11: Time on Air of LoRaWAN messages vs payload size withcoding rate 4/5, preamble symbols 8, bandwidth 125kHz, duty cycle0.1%

Transmitter

Electronics Amplifier

k-bit

message

Receiver

Electronics

d

k-bit

message

ó=IL Û G Û @á'ØßØÖ Û G 'ØßØÖ Û G

'Íë:Gá@; 'Ëë:G;

Figure 12: The first order radio energy dissipation model

all other parameters remain unchanged, the penalty for thatis higher ToA which will also impact the end-to-end latencyexperienced by the software (application).

D. Energy Consumption

To investigate the energy consumption of the IoT enddevices, this paper adopts the first order radio energy dissi-pation model introduced in [55] and subsequently appliedin other studies such as [56], [75]. In estimating the batterylife of the transceivers, we adopt the E91-AA alkalinebattery model and apply its operational characteristics [76].According to [55], the transmitter consumes energy tooperate the radio electronics and the power amplifier whilethe receiver consumes energy to run the radio electronicsas shown in Fig. 12.

The energy required to transmit a k-bit message fromthe IoT end device to the gateway over a distance d can bedefined as

ETx(k, d) = ETxelec(k) + ETxamp(k, d) (18)

where ETxelec is the electrical energy consumed by the elec-tronic circuit of the transmitter radio and depends on signalprocessing techniques such as modulation, spreading andcoding, while ETxamp is the communication-related energyconsumed by the amplifier which depends on the environ-mental factors including the distance from transmitter toreceiver. At the receiver, since the radio only needs energyto operate the electronics to detect the signal, the energyconsumed is

ERx(k) = ERxelec(k) = k · Eelec (19)

Both the free space and multipath-fading channels areconsidered in the energy model, depending on the distance

0 5 10 15 20 25 30 35 40 45 50

Transmission distance (km)

10

20

30

40

50

60

70

80

Tra

nsm

it e

ner

gy

per

mes

sag

e (

J)

WaveIoT

LoRa

Sigfox

RPMA(Ingenu)

NB-IoT 837MHz

NB-IoT 1747MHz

LTE-M

(a) Comparing transmit energy for WavIoT, LoRa, Sigfox, RPMA, LTE-Mand NB-IoT

0 5 10 15 20 25 30 35 40 45 50

Transmission distance (km)

10

10.5

11

11.5

12

12.5

13

13.5

14

Tra

nsm

it e

ner

gy

per

mes

sag

e (

J)

WaveIoT

LoRa

Sigfox

RPMA(Ingenu)

NB-IoT 837MHz

NB-IoT 1747MHz

(b) Enlarged Fig. 11a showing WavIoT, LoRa, Sigfox, RPMA andNB-IoT

Figure 13: Transmit energy consumed per message using gatewayheight of 12 m and end device height of 0.5 m, data rate 100 bps andpayload of 12 bytes

between the gateway and end device. If the transmission dis-tance is less than a certain threshold dc, the free space model(d2 power loss) is used, otherwise the multipath model (d4

power loss) applies [55]. Therefore, power control can beemployed to invert this loss by setting the power amplifiervalue of ETxamp at the transmitter in a way that ensuresthat adequate signal power arrive at the receiver. Thus, thetransmitter energy consumption can be written as

ETx(k, d) =

{k(ETxelec + εFriisd

2) d < dc

k(ETxelec + εTwo−rayd4) d ≥ dc

(20)

where the threshold distance is define as dc = 4π√Lhght

γ,

L ≥ 1 is the system loss factor and γ is the wavelengthof the signal. Applying the parameters in Table II, ifthe gateway is 12 m high and the end device is 0.5 m(low altitude applications such as smart agriculture andsmart cities) with L=1 (assume no system loss), the dc forWavIoT/Sigfox/LoRa, RPMA and LTE-M are 218.18 m,603.26 m and 653.54 m respectively.

13

0 5 10 15 20 25 30 35 40 45 50

Distance (km)

2

4

6

8

10

12

14

16

Est

imat

ed b

atte

ry l

ife

(Yea

rs) WaveIoT

Sigfox

LoRa

RPMA(Ingenu)

NB-IoT 1747MHz

LTE-M

(a) Device battery operating at 40◦C

0 5 10 15 20 25 30 35 40 45 50

Distance (km)

2

4

6

8

10

12

14

16

Est

imat

ed b

atte

ry l

ife

(Yea

rs)

WaveIoT

Sigfox

LoRa

RPMA(Ingenu)

NB-IoT 1747MHz

LTE-M

(b) Device battery operating at 20◦C

0 5 10 15 20 25 30 35 40 45 50

Distance (km)

2

3

4

5

6

7

8

9

10

11

12

Est

imat

ed b

atte

ry l

ife

(Yea

rs)

WaveIoT

Sigfox

LoRa

RPMA(Ingenu)

NB-IoT 1747MHz

LTE-M

(c) Device battery operating at 0◦C

0 5 10 15 20 25 30 35 40 45 50

Distance (km)

1

2

3

4

5

6

7

8

Est

imat

ed b

atte

ry l

ife

(Yea

rs)

WaveIoT

Sigfox

LoRa

RPMA(Ingenu)

NB-IoT 1747MHz

LTE-M

(d) Device battery operating at -20◦C

Figure 14: Estimated battery life in LP-WAN transceiver operating at 1% duty cycle under different weather conditions, using gateway height of12 m and end device height of 0.5 m, data rate 100 bps and payload of 12 bytes.

Fig. 13 compares the transmit energy consumed permessage for the LP-WAN technologies considered in thisstudy. It can be seen that overall, LTE-M consumes thehighest amount of energy for every message sent by theIoT end device, followed by RPMA. For distances below 2km, the technologies consume approximately equal amountof energy. However, beyond 2 km, the performance gapincreases rapidly with distance.

Here we apply a duty cycle of 1% (corresponding to140 messages per day) to the devices to conform with theregulatory requirements in Europe and analyse the energyconsumption of the devices. Fig. 14 presents the estimatedbattery life in the devices when deployed with the LP-WAN technologies and operated at different temperatures.This figure shows clearly that the operating temperature is akey determinant of battery life. In particular, extremely coldtemperature can significantly shorten the active lifespan ofthe end device due to rapid degradation of its battery’s ca-pacity. Nevertheless, based on the configurations employedin this section, LoRa appears to be the most promising tosupport long battery life while LTE-M provides the shortestbattery life across the temperatures investigated. For exam-ple, with a coverage requirement of 25 km, Fig. 14 showsthat at 40

◦C, LoRaWAN, Sigfox, NB-IoT and WavIoT can

each support up to 15.65 years while RPMA and LTE-M

devices are active for 14.9 years and 8.8 years respectively.However, when the temperature drops to -20

◦C, the figure

indicates that the battery life reduces to about 7.32 yearsfor LoRaWAN, Sigfox and WavIoT as well as 7 yearsand 4.2 years for RPMA and LTE-M respectively. Theseresults generally suggest that even with the same systemconfigurations and battery capacity when IoT devices aredeployed, their active lifetimes will vary across regionsaccording to the local temperature and seasonal variations.These results can potentially offer useful insights into theplanning, designing and management of the IoT accessnetworks, especially in use-cases such as haulage/logistics,smart agriculture and many smart city scenarios in whichthe sensors may be unavoidably exposed to extreme weatherconditions.

In Fig. 15, we present the variation of daily transmitenergy consumption with payload size for different cover-age requirements using LoRaWAN. Although the effect ofdistance is generally more pronounced, the figure indicatesthat energy required is significantly affected by message sizeas well. For example, for a coverage distance of 50 km,reducing the message size by 20% can result in transmitenergy savings of about 47µJ per message and this canextend the device life. The availability of SF in LoRaWANprovides a way to further trade-off between system variables

14

0 50 100 150 200 250

Payload (Bytes)

0

50

100

150

200

250T

ran

smit

en

erg

y p

er m

essa

ge

(J)

10 km

20 km

30 km

40 km

50 km

Figure 15: Effects of payload size on daily energy consumption fordifferent coverage requirements in LoRaWAN using gateway height12 m, end device height 0.5 m, duty cycle of 1%, and data rate of 25kbps.

and energy consumption, thereby offering additional degreeof flexibility in IoT access network design.

VII. CONCLUSION

Current IoT access technologies are optimised for dif-ferent end-use applications. This paper showed that eachof RPMA and LTE-M incur at least 9 dB additional pathloss compared with Sigfox and LoRAWAN. That meansthey are more suitable for short range applications. In realdeployments, the alternatives include the use of higher gainantennas or reduction of coverage per gateway both ofwhich impact the deployment cost.

To illustrate the consequences of link budget on systemperformance, parameters were adopted from the LP-WANdatasheets. The results consistently indicated that receiversensitivity is the most significant variable in LP-WANoptimisation, followed by transmit power. For example,it was shown that with a 10% improvement in receiversensitivity, LoRaWAN can increase network coverage byabout 142% and Sigfox by 130% whereas with similarreceiver sensitivity in NB-IoT 882 MHz and LTE-M, thecoverage gains were about 398% and 136% respectively.

Across the technologies, the active device life is affectedby payload size, distance and environmental conditions suchas temperature. In addition to the applied load, it wasobserved that extreme weather conditions can significantlyreduce active device life of IoT objects in the LP-WAN.Compared with environmental temperature of 40

◦C, the

results indicate operating the IoT devices at a temperatureof -20

◦C, can reduce the device life by about half; 53%

(WavIoT, LoRaWAN, Sigfox, RPMA) and 48% in LTE-M. Indoor signal propagation is remarkably different fromoutdoor due to the plethora of objects contributing to theinterference level as well as attenuation from differentobjects some of which are outlined in Table IV. Thus, ourfuture work in this area includes the investigation of theeffects of indoor losses on IoT network performance usingthe LP-WAN technologies discussed in this paper and anyfuture ones available.

NOMENCLATURE

α coupling lossβ correction factorbetween Shanon limit and realistic

systemsδ environmental parameterεFriis dissipation variable, free spaceεTwo−ray dissipation variable, multi-path fadingη receiver sensitivityγ wavelengthλ gateway densityφ noise figureσ receiver sensitivityθ threshold SNRε bandwidthϕ the viewing angle from gateway to end device

antennaΨ message lengtha(ht) terrain parameterC channel capacityct terrain correction factordc cross-over distanceETxamp electrical energy consumed by transmitting radio

amplifierEb energy per bitEelec electrical energy consumed by radio electronic cir-

cuitGnet net gainhg gateway antenna heightN0 thermal noise powerPt transmit powerwt width of main streetxt distance: gateway antenna to wall on main street

REFERENCES

[1] Statistica, “Internet of things (iot) connected devices installed baseworldwide from 2015 to 2025 (in billions),” 2015, accessed on 26May. 2018. [Online]. Available: https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/

[2] M. James, C. Michael, B. Peter, W. Jonathan, D. Richard, B. Jacques,and A. Dan, “Unlocking the potential of the Internet of things,”2015, accessed on 26 May. 2018. [Online]. Available: https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world

[3] Drivesnccontrol, “1 in 4 wireless IIoT connections will use LPWAby 2025,” 2017. [Online]. Available: http://m.drivesncontrols.com/news/fullstory.php/aid/5553/1_in_4_wireless_IIoT_connections_will_use_LPWA_by_2025.html26May.2018)

[4] T. M. WG, “A technical overview of LoRa and LoRaWAN,”2015. [Online]. Available: https://docs.wixstatic.com/ugd/eccc1a_ed71ea1cd969417493c74e4a13c55685.pdf(accessedon29Jan.2018)

[5] K. E. Nolan, W. Guibene, and M. Y. Kelly, “An evaluation of lowpower wide area network technologies for the Internet of things,” inProc. IEEE Int. Wireless Commun. Mob. Comp., 2016, pp. 439–444.

[6] U. Raza, P. Kulkarni, and M. Sooriyabandara, “Low power wide areanetworks: An overview,” IEEE Commun. Survs. Tuts., vol. 19, no. 2,pp. 855–873, 2017.

[7] N. Poursafar, M. E. E. Alahi, and S. Mukhopadhyay, “Long-rangewireless technologies for IoT applications: A review,” in Proc. IEEEIntl. Conf. Sensing Technol., Dec 2017, pp. 1–6.

[8] J. de Carvalho Silva, J. J. P. C. Rodrigues, A. M. Alberti, P. Solic,and A. L. L. Aquino, “LoRaWAN- A low power WAN protocol forInternet of Things: A review and opportunities,” in Proc. IEEE Intl.Multidisc. Conf. Computer and Energy Sci.(SpliTech), July 2017, pp.1–6.

15

[9] K. Mekki, E. Bajic, F. Chaxel, and F. Meyer, “A comparative study ofLPWAN technologies for large-scale IoT deployment,” ScienceDirectICT Express, vol. 12, no. 05, 2018.

[10] M. Aazam, S. Zeadally, and K. A. Harras, “Offloading in fogcomputing for IoT: Review, enabling technologies, and researchopportunities,” ScienceDirect Future Generation Computer Systems,vol. 04, no. 057, 2018.

[11] S. Al-Sarawi, M. Anbar, K. Alieyan, and M. Alzubaidi, “Internet ofThings (IoT) communication protocols: Review,” in Proc. IEEE Intl.Conf. Information Technol., May 2017, pp. 685–690.

[12] K. Zheng, S. Zhao, Z. Yang, X. Xiong, and W. Xiang, “Design andimplementation of LPWA-based air quality monitoring system,” IEEEAccess, vol. 4, pp. 3238–3245, 2016.

[13] W. Guibene, J. Nowack, N. Chalikias, K. Fitzgibbon, M. Kelly, andD. Prendergast, “Evaluation of LPWAN technologies for smart cities:River monitoring use-case,” in Proc. IEEE Wireless Commun. andNetwork. Conf. Workshops (WCNCW), 2017, pp. 1–5.

[14] J. Chen, K. Hu, Q. Wang, Y. Sun, Z. Shi, and S. He, “NarrowbandInternet of things: Implementations and applications,” IEEE Internetof Things Journal, vol. 4, no. 6, pp. 2309–2314, 2017.

[15] W. Guibene, K. E. Nolan, and M. Y. Kelly, “Survey on cleanslate cellular-IoT standard proposals,” in Proc. IEEE Int. Conf.Comput. and Inform. Technol; Ubiquitous Comput. and Commun.;Dependable, Autonomic and Secure Comput; Pervasive Intell. andComput (CIT/IUCC/DASC/PICOM), 2015, pp. 1596–1599.

[16] F. Adelantado, X. Vilajosana, P. Tuset-Peiro, B. Martinez, J. Melia-Segui, and T. Watteyne, “Understanding the limits of LoRaWAN,”IEEE Commun. Mag., vol. 55, no. 9, pp. 34–40, 2017.

[17] L. Gregora, L. Vojtech, and M. Neruda, “Indoor signal propagationof LoRa technology,” in Proc. Intl. Conf. Mechatronics-Mechatronika(ME), 2016, pp. 1–4.

[18] B. Reynders, W. Meert, and S. Pollin, “Power and spreading factorcontrol in low power wide area networks,” in Proc. Intl. Conf.Commun. (ICC), 2017, pp. 1–6.

[19] N. Mangalvedhe, R. Ratasuk, and A. Ghosh, “NB-IoT deploymentstudy for low power wide area cellular IoT,” in Proc. IEEE AnnualIntl. Symp. Pers., Indoor, Mob. Radio Commun., 2016, pp. 1–6.

[20] J. Petajajarvi, K. Mikhaylov, A. Roivainen, T. Hanninen, and M. Pet-tissalo, “On the coverage of LPWANs: range evaluation and channelattenuation model for LoRa technology,” in Proc. IEEE Intl. Conf.ITS Telecommunications, 2015, pp. 55–59.

[21] M. Aref and A. Sikora, “Free space range measurements withsemtech LoRa technology,” in Proc. IEEE Conf. Intel. Data Acqu.and Adv. Comput. Syst: Technol. and Appl. Wireless Syst., 2014, pp.19–23.

[22] M. Lauridsen, I. Z. Kovács, P. Mogensen, M. Sorensen, and S. Holst,“Coverage and capacity analysis of LTE-M and NB-IoT in a ruralarea,” in Proc. IEEE Veh. Tech Conf., 2016, pp. 1–5.

[23] O. Georgiou and U. Raza, “Low power wide area network analysis:Can LoRa scale?” IEEE Wireless Commun. Lett., vol. 6, no. 2, pp.162–165, 2017.

[24] Y. Mo, C. Goursaud, and J.-M. Gorce, “Theoretical analysis of UNB-based IoT networks with path loss and random spectrum access,” inProc. IEEE Int. Symp. Personal, Indoor, and Mobile Radio Commun.,2016, pp. 1–6.

[25] H. Mroue, A. Nasser, S. Hamrioui, B. Parrein, E. Motta-Cruz, andG. Rouyer, “MAC layer-based evaluation of IoT technologies: LoRa,SigFox and NB-IoT,” in Proc. IEEE Middle East and North AfricaCommun. Conf (MENACOMM, 2018, pp. 1–5.

[26] M. D’Aloia, A. Longo, R. Ruggero, S. Stanisci, D. Amendolare,M. Vessia, F. Lomastro, and M. Rizzi, “An innovative LPWA networkscheme to increase system reliability in remote monitoring.”

[27] S. Gangakhedkar, O. Bulakci, and J. Eichinger, “Addressing deepindoor coverage in narrowband-5G,” in Proc. IEEE Veh. Tech. Conf,2017, pp. 1–5.

[28] C. Y. Yeoh, A. bin Man, Q. M. Ashraf, and A. K. Samingan,“Experimental assessment of battery lifetime for commercial off-the-shelf NB-IoT module,” in Proc. IEEE Intl. Conf. Advanced Commun.Techn.,, 2018, pp. 1–1.

[29] A. Augustin, J. Yi, T. Clausen, and W. M. Townsley, “A study oflora: Long range & low power networks for the Internet of things,”Sensors, vol. 16, no. 9, p. 1466, 2016.

[30] K. Mikhaylov, J. Petaejaejaervi, and T. Haenninen, “Analysis ofcapacity and scalability of the LoRa low power wide area networktechnology,” in Proc. VDE European Wireless Conf., 2016, pp. 1–6.

[31] M. Lauridsen, H. Nguyen, B. Vejlgaard, I. Z. Kovács, P. Mogensen,and M. Sorensen, “Coverage comparison of GPRS, NB-IoT, lora,and Sigfox in a 7800 km2 area,” in Proc. IEEE Veh. Technol. Conf.,2017, pp. 1–5.

[32] M. Centenaro, L. Vangelista, A. Zanella, and M. Zorzi, “Long-rangecommunications in unlicensed bands: The rising stars in the IoT and

smart city scenarios,” IEEE Wireless Commun., vol. 23, no. 5, pp.60–67, 2016.

[33] H. Wang and A. O. Fapojuwo, “A survey of enabling technologiesof low power and long range machine-to-machine communications,”IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2621–2639, 2017.

[34] G. A. Akpakwu, B. J. Silva, G. P. Hancke, and A. M. Abu-Mahfouz,“A survey on 5G networks for the Internet of things: Communicationtechnologies and challenges,” IEEE Access, vol. 6, pp. 3619–3647,2018.

[35] M. Chen, Y. Miao, Y. Hao, and K. Hwang, “Narrow band Internetof things,” IEEE Access, vol. 5, pp. 20 557–20 577, 2017.

[36] P. Neumann, J. Montavont, and T. Noël, “Indoor deployment of low-power wide area networks (lpwan): A LoRaWAN case study,” inProc. IEEE Conf. Wireless and Mobile Computing, Networking andCommunications (WiMob), 2016, pp. 1–8.

[37] K. Mikhaylov, J. Petäjäjärvi, and J. Janhunen, “On LoRaWANscalability: Empirical evaluation of susceptibility to inter-networkinterference,” in European Conf. Networks and Communications(EuCNC). IEEE, 2017, pp. 1–6.

[38] Mathuranathan, “Channel capacity & shannon theorem -demystified,” 2008, accessed on 07 Aug. 2018. [Online]. Available:https://www.gaussianwaves.com/2008/04/channel-capacity/

[39] T. Myers, “Back to basics: The Shannon-Hartley theorem,” 2016,accessed on 17 Dec. 2017. [Online]. Available: https://www.ingenu.com/2016/07/back-to-basics-the-{S}hannon-{H}artley-theorem/

[40] Semtech, “LoRa modulation basics,” Application Note, 2015.[41] A. AlAmmouri, J. G. Andrews, and F. Baccelli, “SINR and through-

put of dense cellular networks with stretched exponential path loss,”IEEE Trans. Wireless Commun., vol. 17, no. 2, pp. 1147–1160, 2018.

[42] X. Zhang and J. G. Andrews, “Downlink cellular network analysiswith multi-slope path loss models,” IEEE Trans. Commun., vol. 63,no. 5, pp. 1881–1894, 2015.

[43] M. Di Renzo, W. Lu, and P. Guan, “The intensity matching approach:A tractable stochastic geometry approximation to system-level anal-ysis of cellular networks,” IEEE Trans. Wireless Commun., vol. 15,no. 9, pp. 5963–5983, 2016.

[44] A. Al Ammouri, J. G. Andrews, and F. Baccelli, “A unifiedasymptotic analysis of area spectral efficiency in ultradense cellularnetworks,” IEEE Trans. Inform. Theory, 2018.

[45] K.-H. Phung, H. Tran, Q. Nguyen, T. T. Huong, and T.-L. Nguyen,“Analysis and assessment of LoRaWAN,” in Proc. IEEE Intl Conf.Recent Adv. Sig. Process., Telecomm. & Comput., 2018, pp. 241–246.

[46] Link-Labs, “A comprehensive look at low power, wide areanetworks for Internet of things engineers and decision makers,”2016, accessed on 14 Dec. 2017. [Online]. Available: https://www.link-labs.com/symphony

[47] Ingenu, “An educational guide a white paper by ingenu how RPMAworks,” 2015, accessed on 14 Dec. 2017. [Online]. Available:http://www.ingenu.com

[48] WavIOT, “Comparison of LPWAN technologies,” 2017, accessed on23 Dec. 2017. [Online]. Available: https://waviot.com/wp.pdf

[49] J.Schlienz and D. Raddino, “Narrowband Internetof Things whitepaper, Munich, Germany, Rohde &Schwarz,” 2016, accessed on 03 Aug. 2018. [On-line]. Available: https://cdn.rohde-schwarz.com/pws/dl_downloads/dl_application/application_notes/1ma266/1MA266_0e_NB_IoT.pdf

[50] A. Ratilainen, “NB-IoT presentation for IETF LPWAN,” 2016, ac-cessed on 03 Aug. 2018. [Online]. Available: https://datatracker.ietf.org/meeting/97/materials/slides-97-lpwan-30-nb-iot-presentation-00

[51] R. Ratasuk, J. Tan, N. Mangalvedhe, M. H. Ng, and A. Ghosh,“Analysis of NB-IoT deployment in LTE guard-band,” in Proc. IEEEVeh. Technol. Conf. (VTC Spring), 2017, pp. 1–5.

[52] S.-M. Oh and J. Shin, “An efficient small data transmission schemein the 3GPP NB-IoT system,” IEEE Commun. Letters, vol. 21, no. 3,pp. 660–663, 2017.

[53] D. Hernandez, G. Peralta, L. Manero, R. Gomez, J. Bilbao, andC. Zubia, “Energy and coverage study of LPWAN schemes forindustry 4.0,” in Proc. IEEE Intl. Workshop of Electron., Con-trol, Measurement, Signals and their Application to Mechatronics(ECMSM), 2017, pp. 1–6.

[54] G. G. Ribeiro, L. F. de Lima, L. Oliveira, J. J. Rodrigues, C. N.Marins, and G. A. Marcondes, “An outdoor localization system basedon Sigfox,” in Proc. IEEE Conf. Veh. Technol., 2018, pp. 1–5.

[55] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, “Anapplication-specific protocol architecture for wireless microsensornetworks,” IEEE Trans. wireless commun., vol. 1, no. 4, pp. 660–670, 2002.

[56] Y. Zhuang, J. Pan, and G. Wu, “Energy-optimal grid-based clus-tering in wireless microsensor networks,” in Proc. IEEE Intl. Conf.Distributed Computing Systems Workshops, 2009, pp. 96–102.

16

[57] Sigfox, “Sigfox technical overview,” 2017, accessed on 7 Mar.2018. [Online]. Available: https://www.element14.com/community/docs/DOC-87914/l/sigfox-technical-overview

[58] B. Vejlgaard, M. Lauridsen, H. Nguyen, I. Z. Kovács, P. Mogensen,and M. Sorensen, “Interference impact on coverage and capacity forlow power wide area IoT networks,” in Proc. IEEE Wireless Commun.and Networking Conf. (WCNC), 2017, pp. 1–6.

[59] Randallthorp, “West Carbourne building heights,” 2014, accessed on10 Jun. 2018. [Online]. Available: https://www.scambs.gov.uk/sites/default/files/documents/85S-77J%20BuildingHeights.pdf

[60] A. D. Zayas and P. Merino, “The 3GPP NB-IoT system architecturefor the Internet of Things,” in Proc IEEE Intl. Conf. Commun.Workshops (ICC Workshops), 2017, pp. 277–282.

[61] A. Ikpehai, B. Adebisi, and A. Kelvin, “Effects of traffic character-istics on energy consumption of iot end devices in smart city,” inProc. IEEE Global Infor. Infra. and Networking Symp.

[62] H. Mazar, “LOS radio links, clearance above tall buildings,” inConvention of Electrical and Electronics Engineers in Israel, Pro-ceedings. IEEE, 1991, pp. 145–148.

[63] Cisco, “Radio frequency fundamentals,” 2014, accessed on 2 Jan.2018. [Online]. Available: https://www.cisco.com/c/en/us/td/docs/solutions/Enterprise

[64] T. S. Rappaport et al., Wireless communications: principles andpractice. prentice hall PTR New Jersey, 1996, vol. 2.

[65] V. Abhayawardhana, I. Wassell, D. Crosby, M. Sellars, andM. Brown, “Comparison of empirical propagation path loss modelsfor fixed wireless access systems,” in Proc. IEEE Veh. Tech. Conf,vol. 1, 2005, pp. 73–77.

[66] W. Debus, “RF path loss & transmission distance calculations,”2006. [Online]. Available: https://pdfs.semanticscholar.org/f126/eb84e340cf74d63ab26782c9ad30cd111114.pdf(accessedon16Jan.2018)

[67] M. Hata, “Empirical formula for propagation loss in land mobileradio services,” IEEE Trans. on Veh. Tech, vol. 29, no. 3, pp. 317–

325, 1980.[68] Y. Okumura, “Field strength and its variability in VHF and UHF

land-mobile radio-services,” Review of the Electrical Commun. Lab.,vol. 16, 1968.

[69] J. S. Lu, H. L. Bertoni, K. A. Remley, W. F. Young, and J. Ladbury,“Site-specific models of the received power for radio communicationin urban street canyons,” IEEE Trans. Antenn. and Propagation,vol. 62, no. 4, pp. 2192–2200, 2014.

[70] J. Lee, M.-D. Kim, H. K. Chung, and J. Kim, “NLOS path lossmodel for low-height antenna links in high-rise urban street gridenvironments,” Intl. Journal of Antenn. and Propagat., vol. 2015,2015.

[71] T. Mangel, O. Klemp, and H. Hartenstein, “A validated 5.9 GHznon-line-of-sight path-loss and fading model for inter-vehicle com-munication,” in Proc. IEEE Conf. Telecommunications (ITST), 2011,pp. 75–80.

[72] H. M. El-Sallabi, “Fast path loss prediction by using virtual sourcetechnique for urban microcells,” in Proc. IEEE Veh. Technol. Conf.Proceedings, vol. 3, 2000, pp. 2183–2187.

[73] D. Cottingham, “How wide are roads?” 2018, accessed on19 Aug. 2018. [Online]. Available: https://mocktheorytest.com/resources/how-wide-are-roads/

[74] W. Yang, M. Wang, J. Zhang, J. Zou, M. Hua, T. Xia, and X. You,“Narrowband wireless access for low-power massive Internet ofThings: A bandwidth perspective,” IEEE Wireless Commun., vol. 24,no. 3, pp. 138–145, 2017.

[75] Y. Zhuang, J. Pan, and L. Cai, “Minimizing energy consumption withprobabilistic distance models in wireless sensor networks,” in Proc.IEEE INFOCOM, 2010, pp. 1–9.

[76] J. K. Young, “A practical guide to battery technologiesfor wireless sensor networking,” 2008, accessed on 18 Jun.2018. [Online]. Available: https://www.sensorsmag.com/components/a-practical-guide-to-battery-technologies-for-wireless-sensor-networking