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Department of Electronics and Communications Engineering Internet of Things (IoT): Wide-area IoT protocols* *Long-range IoT radio access technologies (RATs) Department of Electronics and Communications Engineering Tampere University of Technology, Tampere, Finland Vitaly Petrov: [email protected]

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Department of Electronics and Communications Engineering

Internet of Things (IoT): Wide-area IoT protocols*

*Long-range IoT radio access technologies (RATs)

Department of Electronics and Communications Engineering

Tampere University of Technology, Tampere, Finland

Vitaly Petrov: [email protected]

Department of Electronics and Communications Engineering

People-centric IoT applications and services

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Features of long-range IoT radio access technologies (RATs)

q Massive amount of connected devices (we currently

talk about ~0.01-10 devices per square meter) q Specific traffic characteristics q Stringent requirements of the end devices

§  Energy consumption / battery lifetime §  Communication range §  Simplicity of the solution / low cost

q Therefore, we end up designing dedicated RATs for IoT, preferably, long-range.

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Historical insights..

q Originally, there was no common understanding that

dedicated technologies are needed. q Approach 1: Let machines talk “conventional” Wi-Fi q Approach 2: Let machines talk “conventional” LTE

q Below, we discuss, why this idea is not nice at all☺

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Approach 1 motivation (Conventional Wi-Fi for IoT)

App App App App

IoT optimized fixed/wireless connectivity

Interoperability, connectivity, access control, service

discovery, privacy

q Mitigate technology fragmentation by reusing existing deployments

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Problem statement

q Wi-Fi – de-facto technology for WLANs q Expected to minimize time-to-market for various MTC applications q Assessing their performance is important

*N. Hunn, “Using 802.11 for M2M”, http://www.m2mforum.com/2006/eng/images/stories/hunnezurio.pdf

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Network architecture

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q  Application challenges ―  Small burst transmissions ―  Large number of devices ―  Tight battery budget

q  Technology challenges ―  Contention-based access ―  High signaling overhead

Challenges

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Assessment methodology

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Measurement of PER 1.  MTC aware use case 2.  State-of-the-art technology 3.  Saturated queues 4.  Two signaling schemes

Open-source driver: ath9k ―  Manual rate control ―  No retries (genuine statistics)

IEEE 802.11 test bench

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PER measurements

q Realistic levels of PER estimated

RTS/CTS scheme

Basic scheme

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Optimistic scenario: ―  Full-buffer traffic ―  Single link ―  Fixed data rate ―  Maximum power

Goodput prediction

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Goodput estimation

Semi-analytical model verified

*G. Martorell, F. Riera-Palou, and G. Femenias, ”Closed-Loop Adaptive IEEE 802.11n with PHY/MAC Cross-Layer Constraints”, MACOM 2011

Basic scheme

RTS/CTS scheme

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Goodput accuracy

Acceptable accuracy level

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Reference MTC device

A typical MTC device Traffic rate 100 Kbps Message size ~20 bytes

*N. Himayat, S. Talwar, K. Johnsson, S. Andreev, O. Galinina, A. Turlikov, ”Proposed IEEE 802.16p Performance Requirements for Network Entry by Large Number of Devices”, IEEE 802.16 Broadband Wireless Access Working Group C80216p-10-0006, 2010.

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Deployment requirements

q Wi-Fi coverage area ~50 meters q ~200 MTC devices per cluster

*A. Maeder, P. Rost, D. Staehle, ”The Challenge of M2M Communications for the Cellular Radio Access Network”, 11th Wurzburg Workshop on IP EuroView2011, 2011

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Conventional system

Conventional system works bad

q Scales poorly q Too much overhead

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Aggregation techniques

*defined by IEEE 802.11-2007

*defined by IEEE 802.11n-2009

q  MAC layer aggregation

q  PHY layer aggregation

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Aggregation results (asymptotic)

q Potential for some improvement

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Numerical results (optimistic)

q Scalable MTC support is challenging

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Approach 1. Major conclusions

q  Main results ―  Conventional IEEE 802.11-2007 scales poorly for MTC ―  IEEE 802.11n-2009 aggregation looks promising, but still

non-sufficient

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•  Aggregated traffic (M2M applications) •  Traffic analysis (numerous devices)

•  Performance metrics analysis (closed-form approximation)

•  Throughput •  Mean delay •  Energy efficiency

•  Target: •  Performance evaluation

•  Challenges •  Overload protection •  Energy efficiency •  Small data transmission

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Approach 2. Use “conventional” LTE for IoT. System topology

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Approach 2. Initial network entry

Msg1: Preamble

Msg2: Random access response (RAR)

Msg3: Layer 3 message

Msg4: Contention resolution identity

Msg1: Preamble retransmission

Collision

Ramping failureRamping failure

UE eNodeB

Traffic arrival

Del

ayRAR Rx

+ proces-

sing

Scheduling opportunity

Scheduling grant (SG)

Data transmission

Scheduling request

UE eNodeB

Del

ay

Scheduling opportunity

Per

iodi

city

Traffic arrival

SG Rx +

proces-sing

Response window + backoff window

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LTE RACH model

Specialty: •  dependant queues •  ~30000 sources •  54 preambles

Metrics (closed-form approximation) • throughput • energy expenditure • energy efficiency

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Approach 2 numerical results (1)

q Delay is too high for all the schemes...

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

Prob

abili

ty

Delay, ms

COBALTPRACHPUCCH

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Approach 2 numerical results (2)

q Energy efficiency is too low for all the schemes...

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

1000 5000 10000Indi

vidu

al p

ower

con

sum

ptio

n, m

W

Number of MTC devices

PUCCHPRACHCOBALTTheory

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Approach 2. Major conclusions

q  Main results ―  Conventional LTE scales poorly for MTC ―  LTE-Advanced with certain channel access

enhancements scales better, but still not sufficient

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Therefore, novel IoT-specific radio access technologies are proposed

q Due to the abovementioned limitations of

“conventional” RATs for IoT, novel IoT-specific radio access technologies were proposed

q These are, including but not limited to:

§  SIGFOX §  LoRaWAN §  IEEE 802.11ah (branded as Wi-Fi HaLow) §  NB-IoT (roughly, IoT version of LTE)

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SIGFOX

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LoRaWAN

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Wi-Fi HaLow (IEEE 802.11ah)

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Narrow band IoT (NB-IoT)

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Motivation to go lower in carrier and bandwidth

q Why do we move from 2.4GHz to 900MHz?

q Why do we move from 5MHz channel bandwidth in LTE to 180kHz channel bandwidth in NB-IoT?

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Motivation to go lower in carrier and bandwidth

q Why do we move from 2.4GHz to 900MHz?

Answer: lower path loss -> greater comm. range

q Why do we move from 5MHz channel bandwidth in LTE to 180kHz channel bandwidth in NB-IoT?

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Motivation to go lower in carrier and bandwidth

q Why do we move from ~2GHz to ~900MHz?

Answer: lower path loss -> greater comm. range

q Why do we move from 5MHz channel bandwidth in LTE to 180kHz channel bandwidth in NB-IoT?

Answer: higher Tx power spectral density -> greater comm. range

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Long-range IoT technologies comparison

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Long-range IoT RATs vs short-range IoT RATs

q Long-range strengths:

§  More devices connected to a single access point -> economical gains

§  Greater communication range -> more applications/services supported

§  ... q Short-range strengths:

§  Ability to control/modify internal parameters and the topology -> Greater level of flexibility

§  Shorter communication range -> higher security §  ...