lpwan london meetup: solving urban challenges

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Large Scale LPWAN Solving Urban Challenges Mo Haghighi Research Scientist Intel [email protected]

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Page 1: LPWAN London Meetup: Solving Urban Challenges

Large Scale LPWANSolving Urban Challenges

Mo HaghighiResearch [email protected]

Page 2: LPWAN London Meetup: Solving Urban Challenges

LoRa

LoRaWAN™ is a Low Power Wide AreaNetwork (LPWAN) specification intended forwireless battery operated Things in regional,national or global network. LoRaWAN targetkey requirements of internet of things suchas secure bi-directional communication,mobility and localization services.

Page 3: LPWAN London Meetup: Solving Urban Challenges

LoRa

The modulation is based on spread-spectrum techniques and a variation of chirp spreadspectrum (CSS) with integrated forward error correction (FEC). LoRa significantly improvesthe receiver sensitivity and as with other spread-spectrum modulation techniques uses theentire channel bandwidth to broadcast a signal, making it robust to channel noise andinsensitive to frequency offsets caused from the use of low cost crystals.

Page 4: LPWAN London Meetup: Solving Urban Challenges

LoRaA powerful feature of LoRa is the ability todemodulate several “orthogonal” orsimultaneous signals at the samefrequency, assuming they have differentchirp rates. LoRa chirp rates are called“spreading factors,” with higher spreadingfactors denoting slower chirps.

Page 5: LPWAN London Meetup: Solving Urban Challenges

LoRaorthogonality is detecting multiple datastreams in the same channel and at the sametime. This is a feature of a coded channel, andit offers a solution for getting back goodspectral efficiency for wider band systems.Because coded signals are spread across alarger swath of spectrum, those signals takeup more frequency real estate. Narrowbandsignals, however, can pack quite a bit of trafficinto that same bandwidth. If there are multiplecoded streams simultaneously on the air, youbuy back some (though usually not all) of thespectral efficiency you give up with coding.

Page 6: LPWAN London Meetup: Solving Urban Challenges

LoRaWAN allows for varying speeds: the spreading factor (SF). Devices with a low spreading factor have higher data rates and use less airtime, but on shorter range. Despite slowing down

transmission rate, a higher spread factor will ensure the packets are received over a greater distance.

Page 7: LPWAN London Meetup: Solving Urban Challenges

Mode BW

(KHz)

CR SF Transmission

Time(ms)

Sensitivity

(dB)

1 125 4/5 12 4245 -134

2 250 4/5 12 2193 -131

3 125 4/5 10 1208 -129

4 500 4/5 12 1167 -128

5 250 4/5 10 674 -126

6 500 4/5 11 715 -125.5

7 250 4/5 9 428 -123

8 500 4/5 9 284 -120

9 500 4/5 8 220 -117

10 500 4/5 7 186 -114

sending/receiving a 100-byte packet

Page 8: LPWAN London Meetup: Solving Urban Challenges

LoRa

Adaptive Data Rate is a method where the actual data rate is adjusted to ensure reliablepacket delivery, optimal network performance, and scale for capacity. For example, nodesclose to the gateway will use a higher data rate (shorter time on air) and a lower output power.Only nodes that are at the very edge of the link budget will use the lowest data rate andhighest output power. The ADR method can accommodate changes in the networkinfrastructure and support varying path loss. To maximize both battery life of end- devices andoverall network capacity, the LoRa network infrastructure manages the data rate and RFoutput for each end-device individually by implementing ADR.

ADR

Page 9: LPWAN London Meetup: Solving Urban Challenges

ICRILoRa

Receiverat

114mheight

coveringthe

entirepark

coveringupto

15kmradius

Page 10: LPWAN London Meetup: Solving Urban Challenges

WaterQuality•pH•Oxygen•Turbidity•Conductivity•ORP•Height

Micro-Climate• Temperature

• Humidity

• SolarIrradiance• Airpressure

Page 11: LPWAN London Meetup: Solving Urban Challenges

1km

700m

200m

20m

600m

350m

Mapping Radio Coverage

Page 12: LPWAN London Meetup: Solving Urban Challenges

LoRaBOXEdge Analytics

LoRaBox has been developed to collect radio signals such as BLE and LoRa beacons from the sensor devices in the field, process their sensory readings, and forward the aggregated data to cloud services. It can also act as a multi-functional gateway for testing LoRa network.

“Edge processing and decentralized data aggregation is one of the top requirements for modern LPWAN IoT”

Page 13: LPWAN London Meetup: Solving Urban Challenges

End-to-end Intel technology

Enable IoTRest APIs

for live data

Local LPWAN between sensors andLoRaBox for real-time edge processing

LoRaBox

Page 14: LPWAN London Meetup: Solving Urban Challenges

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1 6 11 16 21 26

2 7 12 17 22 27

3 8 13 18 23 28

4 9 14 19 24 29

5 10 15 20 25 30

RSSI SNR

-113 -11.7

-113 -11.1

-113 -10.2

NS

-108 -11.8

RSSI SNR

-109 -9.0

-114 -11.5

-113 -11.8

-103 -11.9

-98 -11.9

RSSI SNR

-113 -12

NS

-102 -1.8

-112 -1.5

-109 -1.5

RSSI SNR

NS

-100 -2.3

-101 -2.0

-105 -11.8

-108 -11.2

RSSI SNR

-113 -11.1

-112 -11.5

NS

NS

-115 -11.2

CH 5

SF 12

BW 125KHz

CR 4/5

Tx Power 14

20

Mapping Radio Coverage

Page 15: LPWAN London Meetup: Solving Urban Challenges

LoRaWANLoRaWAN is a server-side implementation ofa multiple access protocol designed tominimize collisions with a large number ofendpoints. It requires a server application torun the MAC functions over a networkconnection.

LoRaWAN network architecture is typicallylaid out in a star-of-stars topology in whichgateways are a transparent bridge relayingmessages between end-devices and acentral network server in the backend.

Page 16: LPWAN London Meetup: Solving Urban Challenges

LoRaWAN vs.P2P+LoRaWAN

Page 17: LPWAN London Meetup: Solving Urban Challenges

P2P/LoRaWAN

NetworkWideProgramming

• Re-programmingthenetwork

• Pushingconfigurations

• Opportunisticdatacollection

Page 18: LPWAN London Meetup: Solving Urban Challenges

P2P/LoRaWAN

AdaptiveDataRate/Radio

• DataratevsRFoutput

• LinkBudgetanalysis

• MunisingPacketloss

• MaximizeCapacity

Page 19: LPWAN London Meetup: Solving Urban Challenges

Network Optimization ApplyingvariousalgorithmictechniquesonanetworkofLoRa devicesformeasuringandimproving

thefollowingdynamicsbyconductingalike-for-likecomparisonbetweenthedatareceivedviaLoRa

andWi-Fi.

• Latency

• Throughput

• Energy

• Datarate

• SignalStrength

• PacketErrorRate

Page 20: LPWAN London Meetup: Solving Urban Challenges

Network Optimization Ø GameTheory.Allowingnodestochoosetheirradioparametersindependentlyformaximum

efficiency

Ø MatchingTheory.Enablingnodesmaintainacertainleveloffunctionalityaccordingtoother

nodes

Ø Utility-based.Formingcompetitivescenariosforimprovingtheperformanceofthenodesandthe

receivers.

ü SpreadingFactor

ü CodingRate

ü Channel

ü Bandwidth

Page 21: LPWAN London Meetup: Solving Urban Challenges

Thank You!