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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: vitaly.petrov@tut.fi

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    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

    22

    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 failure Ramping failure

    UE eNodeB

    Traffic arrival

    D el

    ay RAR Rx

    + proces-

    sing

    Scheduling opportunity

    Scheduling grant (SG)

    Data transmission

    Scheduling request

    UE eNodeB

    D el

    ay

    Scheduling opportunity

    P er

    io di

    ci ty

    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

    Pr ob

    ab ili

    ty

    Delay, ms

    COBALT PRACH PUCCH

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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 10000I nd

    iv id

    ua l p

    ow er

    c on

    su m

    pt io

    n, m

    W

    Number of MTC devices

    PUCCHPRACHCOBALT Theory

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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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