Maximizing Network Lifetime of WirelessHART Networks under Graph - - PowerPoint PPT Presentation

Maximizing Network Lifetime of WirelessHART Networks under Graph Routing

Chengjie Wu, Dolvara Gunatilaka, Abusayeed Saifullah*, Mo Sha^, Paras Tiwari, Chenyang Lu, Yixin Chen Cyber-Physical Systems Lab, Washington University in St. Louis Missouri University of Science & Technology * Binghamton University ^

Wireless for Process Automa1on

2

Emerson

• 5.9+ billion hours
• perating

experience

• 26,200+ wireless

field networks $944.92 million by 2020

[Market and Market]

Courtesy: Emerson Process Management

Offshore Onshore

Killer App of IoT!

sensor data

Sensor Actuator

control command

Industrial Wireless Challenges

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Ø Reliability Ø Real-time Ø Control performance Ø Energy efficiency: need long battery life in harsh environments! Controller

WirelessHART

Ø Industrial reliability

q Multi-channel TDMA MAC q Over IEEE 802.15.4 PHY q Redundant routes

Ø Centralized network manager

q collects topology information q generates routes and

transmission schedule

q disseminates to field devices q re-computes routes when

topology changes

4

Industrial wireless standard for process monitoring and control

Graph Rou1ng

Ø Handle link and node failures through path diversity Ø Graph route of a flow

q a primary path q a backup path for each node on the primary path

Ø Transmissions per hop

q Two transmissions on the primary link – dedicated TDMA slots q One transmission on the backup link – shared CSMA/CA slot 5

backup path primary path u v

d

x y z w s

1, 2 3, 4 5, 6 3 4 5 7 5 7 8

Energy Cost of Reliability

Ø Graph routing improves reliability at cost of energy Ø Measurement: +57% reliability at 1.7× energy compared to single-path source routing [EWSN'15]

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Graph routing: 88% Source routing: 31%

Challenges

Ø Maximize network lifetime under graph routing

q Industry demands multi-year battery life q Efficient routing in response to wireless dynamics

Ø Unique challenges for WirelessHART networks

q Centralized multi-path graph routing q Transmissions in dedicated and shared slots

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Contribu1ons

Ø Problem: network lifetime maximization under graph routing

q Network lifetime = time till first node runs out of battery q NP hard

Ø Three approaches

q Optimal integer programming q Linear relaxation of the integer programming q Efficient greedy heuristic

Ø Implementation on a WirelessHART testbed

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Analyzing Power Consump1on

Ø Model based on WirelessHART standard Ø 1-2 transmissions on primary path Ø 3rd transmission on back path

q Small probability, but receiver must turn on and listen.

Ø Load: power consumption / battery capacity

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backup path primary path u v

d

x y z w s

1, 2 3, 4 5, 6 3 4 5 7 5 7 8

Ø Objective: max min node lifetime à min max load Ø Graph route as constraints

q An incoming primary link à an outgoing primary link q An incoming primary link à an outgoing backup link q An incoming backup link à an outgoing backup link

Ø Optimal solution Ø High computational cost à cannot scale to large networks

backup path primary path u v

d

x y z w s

1, 2 3, 4 5, 6 3 4 5 7 5 7 8

Integer Programming

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Linear Programming Relaxa1on

• 1. Relax binary decision variables to real numbers
• 2. Linear Programming à real number solutions
• 3. Round real numbers to integer solutions based on threshold
• 4. Incrementally find the largest threshold with valid routes

Ø Implemented in GNU Linear Programming Kit (GLPK) Ø Near optimal solution with affordable computational cost.

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Greedy Heuris1cs

Ø Compute routes for flows in the rate monotonic order Ø For each flow: find the graph route with minimum load

q Load per node = power consumption / battery capacity q Incrementally add nodes with the smallest load to primary path

and update neighbors’ load

q Then select backup path with minimum load

Ø Iterate until no further improvement Ø Polynomial complexity

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s b

d

f e c

Evalua1on

Ø Implemented on a WirelessHART testbed (69 TelosB motes)

q WirelessHART stack (multi-channel TDMA + routing) q Network manager (scheduler + routing)

Ø Simulations based on testbed topology

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WUSTL wireless sensor-actuator network testbed

Compare to Op1mal (Small Network)

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GH & LP within 80% of optimal

• Lifetime normalized to
• ptimal solution from

Integer Programming

• 10 nodes, 20 links
• SP: Shortest Path
• RRC [Han 2011]
• GH: Greedy Heuristic
• LP: Linear Programming

Network Life1me (Testbed Topology)

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LP and GH lead to longer network lifetime

Execu1on Time (Testbed Topology)

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GH needs less time than LP

Conclusion

Ø Industrial wireless networks is a killer app for IoT

q Driven by industrial standards such as WirelessHART q Deployments rolling out world wide

Ø Graph routing enhances reliability at high energy cost à energy efficiency is critical! Ø Three approaches to maximize network lifetime

q Integer Programming: optimal q Linear Programming Relaxation: faster q Greedy Heuristic: fastest solution for run-time adaptation

Ø Implemented with WirelessHART on testbed

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Reading

Ø C. Wu, D. Gunatilaka, A. Saifullah, M. Sha, P .B. Tiwari, C. Lu and

• Y. Chen, Maximizing Network

Lifetime of WirelessHART Networks under Graph Routing, IEEE International Conference on Internet-of-Things Design and Implementation (IoTDI'16), April 2016. 18