RAP:A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks

RAP:A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks

C. Lu, B.M. Blum, T.F. Abdelzaher, J.A.

Stankovic, and T. He

Adapted Chenyang Lu’s slides

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Design Requirements

Minimize end-to-end deadline miss ratio

Support distributed micro-sensing High-level service API

Large scale, high density Scalability is key

Extreme resource constraints Minimal overheads

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Location-based Communication

ID-based From ID to ID What is the reading of

sensor 125.111.1.5? Rely on (unreliable)

individual sensors

Location-based From location to location What is the virus density in

south terminal of airport? Individual sensors NOT

important Local coordination:

Sensors in interested area aggregate data

Sensor-base comm: Send aggregated result to base station

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RAP: Real-time locAtion-based Protocols

Velocity Monotonic SchedulingPrioritized MAC

Query/Event Service

Coordination Service

Location-Addressed Protocol

Sensing/Control Application

Query/Event Service APIs

Geographic Forwarding

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Query/Event API

RAP provides the following query/event service APIs.

query { attribute_list, area, timing_constraints, querier_loc }

register_event { event, area, query }

Assume that the locations of the base stations are fixed.

Velocity Monotonic Scheduling

Prioritized MAC

Query/Event Service




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Example

register_event {virusFound(0,0,100,100), // area to post eventquery { // query to be triggered

virus.count, // attributearea=(x-1,y-1,x+1,y+1), // query areaperiod=1.5, deadline=5, // timing infobase=(100,100) // base station location

}} Registers a virus_count query for a virus_found event. If any viruses are found in a rectangular area (0,0,100,100),

return the average density of the viruses of the 2*2 square area centered at the event location (Xevent,Yevent)

Peirod: 1.5 sec. End-to-end deadline: 5 sec


Prioritized MAC

Query/Event Service




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Local state Scalability – Routing decisions are local Dense network Efficient greedy forwarding works well Dense network #hop proportional to distance Location-based comm. No location directory service


Prioritized MAC

Query/Event Service




A C

Closest to C

E

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s d

Background – GF

GF always chooses the node that is closest to the destination in FS.

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Deadline & Distance Aware

FCFS scheduling does not work well for real-time communication

Deadline-aware The shorter the deadline, the higher

the packet priority Distance-aware

The longer the distance, the higher the packet priority

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Velocity

Timing constraint: deadline Location constraint: distance to destination Requested Velocity

Embody both constraints Reflect local urgency

Velocity Monotonic Scheduling (VMS):

Priority = Requested Velocity


Prioritized MAC

Query/Event Service




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Example

dis = 60 m; D = 2 sV = 30 m/sLOW Priority

dis = 90 m; D = 2 sV = 45 m/sHIGH Priority

A

B

D

C

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Static VMS Fixed velocity on each hop V = dis(x0,y0,xd,yd)/D

Source location: (x0,y0) Destination location: (xd,yd) End-to-end deadline: D

Dynamic VMS Adapt velocity at intermediate node based on progress Vi = dis(xi,yi,xd,yd)/Si

Velocity at node: Vi Location of node i: (xi,yi) Slack: Si = D – elapsed time

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Priority Queue Single Queue

Ordered by priority If queue is full, higher priority incoming packets overwrite lower priority Implement a priority queue: Overhead is (log n) where n is the number of packets in the queue

Multiple QueuePriority corresponds to a range of requested velocities. A packet is first mapped to a priority, and then inserted into the FIFO queue based on its priorityPackets that miss their deadlines are useless -> Actively drop packets that have missed their deadlines to avoid wasting bandwidth

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Prioritized MAC

Collision Avoidance (CA) Channel idle wait for DIFS = BASE_DIFSPRI Packets with a higher priority (corresponding to a smaller PRIORITY value) on

average choose a smaller waiting period. Contention

Collision (No CTS or No ACK)CW = CW*(2+(PRI-1)/MAXPRI) MAXPRI is the maximum value of priority (corresponding to the lowest priority).

The backoff counter of a node with a pending lower priority packet increases faster than a node with a pending packet with a higher priority.

Similar to 802.11’s EDCF

Idle

TimeBASE_DIFSPRI

ContentionExponential Backoff

TransmissionAvoidance

CW

AcquireChannel


Prioritized MAC

Query/Event Service




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Simulation in GloMoSim: Biometric Sensing

100 nodes on 136X136 m2

Periodic query count on 31 nodes; detail on 15 nodesBase Station

Hot Regions(sources)

FAR

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Workload Network (roughly approximate MICA mote)

Communication range: 30.5 m Packet size: 32B (count), 160 B (detail) Bandwidth: 200 kbps (> MICA)

Protocols Routing: DSR (Dynamic Source Routing), GF

(Geographic Forwarding) Scheduling: FIFO, DS (Deadline-based), SVM,

DVM MAC: 802.11, extended 802.11 with prioritization

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Flow of Packets

DSR – Flow of Packets

GF – Flow of Packets

Base station

Base station

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Deadline Miss RatioOverall

GlomoSim simulation (deadline: detail: 5 s, count: 10 s)

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Deadline Miss Ratio: FAR hot region

GlomoSim simulation (deadline: detail: 5 s, count: 10 s)

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Distance Fairness

SVM provides “fairer” service to remote sensors Critical for scalability of sensor networks!

0.00

0.10

0.200.30

0.40

0.50

0.60

0.700.80

0.90

1.00

0 50 100 150 200

distance from base station (m)

mis

s ra

tio FIFO

SVM

DS

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Conclusion

Velocity Monotonic Scheduling Reduce end-to-end deadline miss ratioFair service to remote sensors

Event/query service API’sHigh-level abstraction for distributed microsensing

Location-based protocol stack ScalableSmall protocol overhead

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Discussions

VMS Best-effort No guarantee What if there’s a void? GF does not work Is velocity the right trade-off between distance and time? How about ETX or other link quality metrics? DVM is worse than SVM?

What if network is congested? Just-in-Time Scheduling

Location of the base station is fixed

Just-in-Time Scheduling for Real-Time Sensor Data Dissemination

K. Liu, N. Abu-Ghazaleh, KD Kang

PerCom 2006

Motivation RAP (a real-time MAC protocol) prioritizes

packets but not delayed High contention due to bursty traffic can result in

increasing transmission & queuing delay What if all packets have the highest priority?

MAC level solutions cannot consider queuing delay at routing layer that can significantly impact E2E delay under overload

Role of routing in the success of real-time data dissemination is not sufficiently examined

Geographic forwarding is used in RAP and SPEED JiTS considers shortest path routing in addition to GF

Key Contributions

Just-in-Time Scheduling Delay packets at every hop for a duration

of time which is a function of the number of hops to the sink and deadline

Use a full estimate of the delay including the queuing delay at the network layer

Not specialized MAC Just use 802.11 Compare to VMS of RAP

JiTS algorithms Basic:

Static (JiTS-S) E2E deadline is fixed at source Let X = source EETD = distance * ETD (Estimated Transmission

delay) where ETD = time difference between receiving an ACK and packet transmission

Dynamic (JiTS-D) Use ”remaining slack time = deadline – elapsed

time” instead of E2E deadline EETD = remaining distance * ETD

Performance evaluation in ns-2

Performance evaluation in ns-2

Delayed, Just-in-Time, packet delivery is better than immediate forwarding!

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Questions?

Documents

RAP:A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks