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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

2. 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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3. Location-based Communication
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
ID-based
From ID to ID
What is the reading of sensor ?
Rely on (unreliable) individual sensors
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4. RAP: Real-time locAtion-based Protocols
Sensing/Control
Application
Query/Event Service APIs
Query/Event Service
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
Velocity Monotonic Scheduling
Prioritized MAC
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5. Query/Event API RAP provides the following query/event service APIs.
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
Velocity Monotonic Scheduling
Prioritized MAC
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.
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6. Example register_event {
Query/Event Service
Example
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
Velocity Monotonic Scheduling
Prioritized MAC
register_event {
virusFound(0,0,100,100), // area to post event
query { // query to be triggered
virus.count, // attribute
area=(x-1,y-1,x+1,y+1), // query area
period=1.5, deadline=5, // timing info
base=(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
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7. Geographic Forwarding
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Query/Event Service
Geographic Forwarding
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
Velocity Monotonic Scheduling
Closest to C
Prioritized MAC A C E
What if there is holes/obstacles?
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
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8. Background – GF
GF always chooses the node that is closest to the destination in FS. s d
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9. 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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10. Velocity Monotonic Scheduling
Query/Event Service
Velocity
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
Velocity Monotonic Scheduling
Prioritized MAC
Timing constraint: deadline
Location constraint: distance to destination
Requested Velocity
Embody both constraints
Reflect local urgency
Velocity Monotonic Scheduling (VMS):
Priority = Requested Velocity
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11. Example D A C B dis = 90 m; D = 2 s V = 45 m/s HIGH Priority
LOW Priority
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12. Velocity Monotonic Scheduling
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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13. Priority Queue Single Queue Multiple 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 Queue
Priority 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 priority
Packets that miss their deadlines are useless -> Actively drop packets that have missed their deadlines to avoid wasting bandwidth
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14. Velocity Monotonic Scheduling
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Query/Event Service
Prioritized MAC
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
Velocity Monotonic Scheduling
Prioritized MAC
Collision Avoidance (CA)
Channel idle  wait for DIFS = BASE_DIFSPRI
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 ’s EDCF
Acquire
Channel
Idle
Time
BASE_DIFSPRI CW
Avoidance
Contention
Exponential Backoff
Transmission
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15. Simulation in GloMoSim: Biometric Sensing
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Simulation in GloMoSim: Biometric Sensing
100 nodes on 136X136 m2
Periodic query count on 31 nodes; detail on 15 nodes
Base
Station
Hot Regions
(sources)
6-7 hop maximum
FAR
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16. Workload Communication range: 30.5 m
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: , extended with prioritization
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17. Flow of Packets Base station Base station GF – Flow of Packets
DSR – Flow of Packets
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18. Deadline Miss Ratio Overall
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Deadline Miss Ratio Overall
GlomoSim simulation (deadline: detail: 5 s, count: 10 s)
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19. Deadline Miss Ratio: FAR hot region
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Deadline Miss Ratio: FAR hot region
GlomoSim simulation (deadline: detail: 5 s, count: 10 s)
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20. Distance Fairness SVM provides “fairer” service to remote sensors
Critical for scalability of sensor networks!
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21. Conclusion Velocity Monotonic Scheduling
Reduce end-to-end deadline miss ratio
Fair service to remote sensors
Event/query service API’s
High-level abstraction for distributed microsensing
Location-based protocol stack
Scalable
Small protocol overhead
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22. Discussions VMS What if network is congested?
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
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23. Just-in-Time Scheduling for Real-Time Sensor Data Dissemination
K. Liu, N. Abu-Ghazaleh, KD Kang
PerCom 2006

24. 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

25. 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
Compare to VMS of RAP

26. JiTS algorithms Basic: Static (JiTS-S) Dynamic (JiTS-D)
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

27. Performance evaluation in ns-2

28. Performance evaluation in ns-2
Delayed, Just-in-Time, packet delivery is better than immediate forwarding!

29. Questions?
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