From Rateless to Distanceless: Enabling Sparse Sensor Network Deployment in Large Areas Wan DU, Zhenjiang LI, Jansen Christian LIANDO, and Mo LI School of Computer Engineering, Nanyang Technological University (NTU), Singapore

Sensor network deployments LUSTER [L. Selavo et al., SenSys’07] GreenOrbs [Y. Liu et al., INFOCOM’11, TPDS’12] Trio [P. Dutta et al., IPSN’06] Golden Gate Bridge [S. Kim et al., SenSys’ 06, IPSN’07]

Environmental monitoring normally requires sparse sampling in space.

Sparse environment monitoring Soil organic matter [S. Ayoubi et al., Biomass and Remote Sensing of Biomass 2011].

Sparse environment monitoring Agriculture [D. G. Hadjimitsis et al., Remote Sensing of Environment - Integrated Approaches 2013].

Sparse environment monitoring Application Requirement Spatial Correlation Temperature [C. Guestrin et al., ICML’05, A. Krause et al., IPSN’06, JMLR’08].

Sparse environment monitoring W01 W05 W04 W02 W08 W06 W09 W03 W10 W07 1 km W11 W12 2.5km 3km Application Requirement Spatial Correlation Wind distribution [W. Du et al., IPSN’14, TOSN’14].

Sparse environment monitoring W01 W05 W04 W02 W08 W06 W09 W03 W10 W07 1 km W11 W12 2.5km 3km W01 W05 W04 W02 W08 W06 W09 W03 W10 W07 1 km W11 W12 2.5km 3km Application Requirement Spatial Correlation Wind distribution [W. Du et al., IPSN’14, TOSN’14].

Sparse environment monitoring W01 W05 W04 W02 W08 W06 W09 W03 W10 W07 1 km W11 W12 2.5km 3km Dense sensor networks. Extra relaying nodes may not be able to add. Cost and maintenance. Regulation restrictions.

Sparse environment monitoring W01 W05 W04 W02 W08 W06 W09 W03 W10 W07 1 km W11 W12 2.5km 3km Cellular communication module. Cost ($4550/12 stations/year). No coverage in some wild fields. WiMAX or WiFi with directional antenna. Power consumption (around 200mW). Installation on floating platforms.

Sparse environment monitoring W01 W05 W04 W02 W08 W06 W09 W03 W10 W07 1 km W11 W12 2.5km 3km Low-power wireless sensor networks without adding extra relaying nodes?

Long-range wireless sensors TinyNode [H. Dubois-Ferrière et al., IPSN’ 06] – EPFL. Semtech XE1205 Radio. Up to 1.8km at 1.2kb/s. 868 or 915 MHz. Fleck-3 [P. Sikka et al., IPSN’ 07] – CSIRO. Nordic nRF905 Up to 1.3km at 100kb/s [1] Dubois-Ferrire et. al., IPSN 2006

In-field test Packet Reception Rate Reservoir

Open field, Urban road and Lake In-field test 60% 20% Packet Reception Rate Byte Reception Rate Open field, Urban road and Lake

In-field test Packet Reception Rate Byte Reception Rate Reservoir

Sparse sensor network Enable long-distance link communication. Fully exploit the sparse network diversity.

Using the correct bits Forward Error Correction (FEC) coding. Fixed correction capacity. Accurate channel estimation. Src Rec1 Special for sparsely deployed sensor network? But not for all networks. Data 00101 Codeword 10100101 Received 10?001?? Data 00101

Using the correct bits Forward Error Correction (FEC) coding. Fixed correction capacity. Accurate channel estimation. Automatic Repeat-reQuest (ARQ). Packet combining [H. Dubois-Ferrière et al., Sensys’ 05]. Block retransmission [R. K. Ganti et al., Sensys’ 06]. Passively adapt to channel after transmissions. Special for sparsely deployed sensor network? But not for all networks. Src Rec1 X1 X2 X3 X1 X2 X3 X1 X2 X3 X1 X3

Rateless codes Erasure channel. Additive white Gaussian noise (AWGN). Luby Transform (LT) code [M. Luby, FOCS’02] and Raptor code [A. Shokrollahi, TON’06]. Additive white Gaussian noise (AWGN).  Strider [A. Gudipati et al., SIGCOMM’11] and Spinal code [J. Perry et al., SIGCOMM’12]. Transmitting an unlimited encoded stream to achieve the proper data rate.

Rateless codes Erasure channel. Additive white Gaussian noise (AWGN). Luby Transform (LT) code [M. Luby, FOCS’02] and Raptor code [A. Shokrollahi, TON’06]. Additive white Gaussian noise (AWGN).  Strider [A. Gudipati et al., SIGCOMM’11] and Spinal code [J. Perry et al., SIGCOMM’12]. Transmitting an unlimited encoded stream to achieve the proper data rate.

LT code Original Blocks X1 X2 X3 X4 Special for sparsely deployed sensor network? But not for all networks.

LT code Original Blocks Encoded Blocks Robust Soliton X1 Y1 X2 Y2 X3

LT code Original Blocks Encoded Blocks Received Blocks Robust Soliton X1 Y1 Y1 X2 Y2 Y2 X3 Y3 Y3 X4 Y4 Y4 Y5 Y5 Y6 Y6 Y7 Robust Soliton

LT code Original Blocks Encoded Blocks Received Blocks Robust Soliton X1 Y1 Y1 X2 Y2 X3 Y3 Y3 X4 Y4 Y4 Y5 Y6 Y6 Y7 Robust Soliton

LT code Original Blocks Encoded Blocks Received Blocks Recovered Data X1 Y1 Y1 X1 X2 Y2 X2 X3 Y3 Y3 X3 X4 Y4 Y4 X4 Y5 Special for sparsely deployed sensor network? But not for all networks. Y6 Y6 Y7 Robust Soliton Gaussian Elimination

From rateless to distanceless Automatically achieve the best data rate. Transmitter Receiver X4 X3 X2 X1 X4 X3 X2 X1 Y3 Y2 Y1 Y5 Y4 Y3 Y2 Y1 ACK

From rateless to distanceless Automatically achieve the best data rate. Release the distance constraints. Transmitter Receiver X4 X3 X2 X1 X4 X3 X2 X1 Y3 Y2 Y1 Y7 Y6 Y5 Y4 Y3 Y2 Y1 ACK Insensitive to distance.

From distanceless link to distanceless network Receiver1 X4 X3 X2 X1 Transmitter Y4 Y3 Y2 Y1 X4 X3 X2 X1 Y4 Y3 Y3 Y2 Y2 Y1 Y1 Y4 Receiver2 Y4 Y3 Y2 Y1

From distanceless link to distanceless network Transmitter X4 X3 X2 X1 Transmitter Y8 Y7 Y6 Y5 Y4 Y3 Y2 Y1 Insensitive to transmitters. Receiver2 Y8 Y7 Y6 Y5 Y4 Y1 X4 X3 X2 X1

Distanceless Transmission (DLTs) Distanceless in duty-cycled mode Distanceless network Distanceless Link

LT code on motes Number of blocks 4 Blocks 8 Blocks 16 Blocks Overhead Robust Soliton + BP 5.1 18.5 28.3

LT code on motes Number of blocks 4 Blocks 8 Blocks 16 Blocks Overhead Robust Soliton + BP 5.1 18.5 28.3 Robust Soliton + GE 3.0 6.0 10.9

LT code on motes Number of blocks 4 Blocks 8 Blocks 16 Blocks Overhead Robust Soliton + BP 5.1 18.5 28.3 Robust Soliton + GE 3.0 6.0 10.9 SYNAPSE + GE 1.8 2.0

LT code on motes Number of blocks 4 Blocks 8 Blocks 16 Blocks Overhead Robust Soliton + BP 5.1 18.5 28.3 Robust Soliton + GE 3.0 6.0 10.9 SYNAPSE + GE 1.8 2.0 Best seed + GE 0.76 0.97 1.5

LT code on motes Number of blocks 4 Blocks 8 Blocks 16 Blocks Overhead Robust Soliton + BP 5.1 18.5 28.3 Robust Soliton + GE 3.0 6.0 10.9 SYNAPSE + GE 1.8 2.0 Best seed + GE 0.76 0.97 1.5 Decoding time (ms) GE 0.9 2.4 10.1

Parallel receiving and decoding Transceiver Receiving (R) Microcontroller Decoding (D) Transceiver R R R R Microcontroller D D D D SPI Reading

Accumulative Gaussian elimination Triangularization Back Substitution New Blocks New Blocks

Decoding time < 0.4ms

From distanceless link to distanceless network Dynamic block size? Receiver1 ETX=1 X4 X3 X2 X1 Transmitter Y4 Y3 Y2 Y1 X4 X3 X2 X1 ACK Y4 Y3 Y3 Y2 Y2 Y1 Y1 ETX=1 Receiver2 X4 X3 X2 X1 Y8 Y7 Y6 Y5 Y4 Y3 Y2 Y1

Distanceless networking Expected Distanceless Transmission Time (EDTT). Number of original blocks Block reception rate Coding efficiency

Distanceless networking Sink EDTT=10ms Receiver1 EDTT=11ms X4 X3 X2 X1 Transmitter Y4 Y3 Y2 Y1 EDTT=16ms X4 X3 X2 X1 Y4 Y3 Y3 Y2 Y2 Y1 Y1 EDTT=18ms Receiver2 Receiver2 X4 X3 X2 X1 Y8 Y7 Y6 Y5 Y4 Y3 Y2 Y1

Distanceless in duty-cycled mode Receiver1 Transmitter Data Packet Data Packet Receiver2 Data Packet Data Packet Data Packet

Distanceless in duty-cycled mode Receiver1 Transmitter Receiver2 Data Packet Data Packet Data Packet Data Packet Data Packet

Distanceless in duty-cycled mode Rateless preamble in low duty-cycled mode. Receiver1 Transmitter Y1 Y2 Y3 Y4 Y5 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 Receiver2 Y11 Y12 Y13 Y14 Y15 Y16 Y17 Y18 Y19 Y20 Y21 Y22 Y23 Y24 Y25

Distanceless in duty-cycled mode Rateless preamble in low duty-cycled mode. Receiver1 Transmitter Y6 Y7 Y8 Y9 Y10 Y6 Y7 Y8 Y9 Y10 Receiver2 Y11 Y12 Y13 Y14 Y15 Y16 Y17 Y18 Y19 Y20 Y6 Y7 Y8 Y9 Y10 Y21 Y22 Y23 Y24 Y25

Distanceless in duty-cycled mode Rateless preamble in low duty-cycled mode. Receiver1 Transmitter Receiver2 Y11 Y12 Y13 Y14 Y15 Y11 Y12 Y13 Y14 Y15 Y16 Y17 Y18 Y19 Y20 Y6 ACK Y10 Y21 Y22 Y23 Y24 Y25 Y11 Y12 Y13 Y14 Y15 X4 X3 X2 X1

System Implementation

System Implementation Application Packets Network Packets MAC Bits PHY

System Implementation PHY MAC Network Application Parallel receiving and decoding Routing Forwarder checking Logical link control Decoding Encoding Encoded blocks Bits Data /ACK ACK Packets

Wind measurement deployment 1 km W11 W12 2.5km 3km [W. Du et al., IPSN’14, TOSN’14]

Data Logger Battery TinyNode

A single 1.0-km link (W01->W06)

A single 1.0-km link (W01->W06)

A single 1.0-km link (W01->W06) 2.3X

Wind data collection network Traffic load. 1 packet/min. 64 byte/packet. Benchmark approaches . CTP + BoX-MAC [D. Moss et al., TP Standford’08]. ORW (Opportunistic Routing in Wireless sensor networks) [O. Landsiedel et al., IPSN’12]. ORW + Seda [R. K. Ganti et al., Sensys’06].

Data yield

Latency

Energy consumption

Overhead

Orthogonal to the hardware platforms. Conclusions Distanceless - A networking paradigm for sparse wireless sensor networks. In-field deployment for wind distribution measurement over an urban reservoir. Orthogonal to the hardware platforms.

Thank you!

TinyNode-based deployment SensorScope [G. Barrenetxea et al., SenSys'08, IPSN’08], 16 TinyNode in 500m*500m PermaDAQ [J. Beutel et al., IPSN'09] X-Sense [J. Beutel et al., DATE‘11]

Overhead and decoding time Rateless code on motes Rateless Deluge [IPSN’08], SYNAPSE [SECON’08], AdapCode [INFOCOM’08], SYNAPSE++ [TMC’10], ReXOR [TMC’11], ECD [ICNP’11], MT-Deluge [DCOSS’11] Packet-level coding Per-hop transmission Do not adapt to channel Overhead and decoding time

Challenges Rateless link transmissions on motes Coordinating the sender and receiver Rateless codes on source-constrained motes Tradeoff between decoding efficiency and decoding time Harnessing network diversity Proper metric to evaluate byte-level links Optimize the performance in low duty cycled networks