rap:a real-time communication architecture for large-scale wireless sensor networks
DESCRIPTION
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. Design Requirements. Minimize end-to-end deadline miss ratio Support distributed micro-sensing - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
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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
Velocity Monotonic Scheduling
Prioritized MAC
Query/Event Service
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
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Geographic Forwarding
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
Velocity Monotonic Scheduling
Prioritized MAC
Query/Event Service
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
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
Velocity Monotonic Scheduling
Prioritized MAC
Query/Event Service
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
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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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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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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
Velocity Monotonic Scheduling
Prioritized MAC
Query/Event Service
Coordination Service
Location-Addressed Protocol
Geographic Forwarding
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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
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Just-in-Time Scheduling for Real-Time Sensor Data Dissemination
K. Liu, N. Abu-Ghazaleh, KD Kang
PerCom 2006
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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
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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
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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
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Performance evaluation in ns-2
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Performance evaluation in ns-2
Delayed, Just-in-Time, packet delivery is better than immediate forwarding!
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Questions?