MobiQuery:
A Spatiotemporal Query Service for
Mobile Users in Sensor Networks
Chenyang Lu, Guoliang Xing, Octav Chipara
Chien-Liang Fok, and Sangeeta Bhattacharya
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Outline
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Motivation
Design
Analysis
Simulations
Demo
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Motivation
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Supporting query from users is one of the
most important function of sensor networks
Existing solutions
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Fixed query areas, fixed user: directed diffusion,
TinyDB
Fixed areas, mobile user: TTDD, SEAD
Query from mobile users in mission-critical
applications has not been addressed
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Mobile users and moving query areas
Stringent real-time requirement
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Mission-Critical Applications
•Coordinate fireman efforts to put out wildfires
•Search and rescue missions
•Robotic motion planning in hazardous environments
Query fresh data from surrounding sensors periodically
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Problem Formulation
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Example: “Update a temperature map within 100m
every 2s. Data can be at most 1s old.”
Spatial constraint
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Query area: range of 100m
 all and only the sensors within the query area should
respond to the query
Query area moves with the user
Temporal constraints
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Query period: 2s
 results must be delivered before end of current period
Data freshness: 1s
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Challenges
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Low duty cycle
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Scarce resources
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Mica2: lifetime of 450 days  1% duty cycle
High wakeup delay
Require low storage cost, comm. overhead and network
contention
Trivial solution does not work!
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User issues a query at the beginning of each query period
 1% duty cycle: active for 150ms in every 15s
 Wakeup delay: 0~ 14.85s
Many nodes cannot be woken up and respond
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MobiQuery Approach
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Motion prediction
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Prefetching
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Send prefetch msgs to future pickup points at the right time
Query dissemination
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Calculate future pickup points where the user expects a query
result, based on a user motion profile
Forwarn sleeping nodes and create a routing tree
Data collection
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Sleeping nodes wake up at scheduled time and send data to user
via the tree
Uninvolved nodes
Collector node
Active nodes
Forewarned nodes
Results
Routing tree
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Assumptions
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Network runs a power management protocol
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Maintain a backbone of active nodes
Bound the comm. delay between any two nodes
within the order of a duty cycle
Examples: CCP, SPAN, GAF
MobiQuery can work without backbones with
slight modification
Every node knows its location
Nodes have synchronized clocks
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Generation of Motion Profiles
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Motion prediction
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Motion planning
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Predict future path based on movement history
Motion profile available after actual movement
Robots plan their paths in advance based on map
Motion profile available before actual movement
Advance time of a motion profile
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How early a motion profile available before the actual
movement: positive for motion planning, negative for
motion prediction
Affect the performance of prefetching
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Prefetching
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Greedy prefetching
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Just-in-time prefetching
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Send a prefetch msg to future pickup points ASAP
Many routing trees set up simultaneously
Send a prefetch msg to a future pickup point at the right
time
Only a few trees being set up simultaneously
Advantages of JIT prefectching
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Reduce the network contention
Reduce storage cost
Reduce the cost caused by user motion changes
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Query Dissemination
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The node receiving a prefetch msg distributes
the query to all nodes in query area
A tree is set up during query dissemination
Sleeping nodes are restricted to be leaves
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Wake up when user arrives
Resume sleeping after collecting & sending data
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Data Collection
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Must finish within Tfresh due to data freshness
constraint
Parent nodes wait for results from children to enable
data aggregation
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May miss query deadline due to child failures
Solution based on timeouts
 Each node sends results received so far when timeout
 Leaf nodes send results at Tfresh before query deadline
 Nodes closer to the root have later timeouts
Query results always meet deadline due to the
timeouts, possibly with incomplete results
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Prefetch Forwarding Time
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When (K-1)th collector node to forward a
prefetch msg to Kth pickup point
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Must ensure the query deadline K*Tp to be met
Delay the forwarding to reduce storage time of
query states in the Kth query area
Time costs between the prefetch msg is sent
and Kth query deadline
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Msg travels between two pickup points Ttravel
Sets up the tree in a query area Ttree
Collects data Tcollect
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Prefetch Forwarding Time
Illustration
Ttravel + Ttree + Tcollect
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Prefetch Forwarding Time
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Kth query deadline will be met if forward before
K*Tp – (Ttravel + Ttree + Tcollect)
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Timing analysis
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Ttravel < Tp since the msg must travel faster than the user
Tcollect < Tfresh due to data freshness requirement
Ttree = wakeup delay + broadcast delay from root to furthest
node in a query area
 Assume broadcast delay = data collection delay
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Justified by the similarity between tree setup and data collection
Ttree < sleep period Tsleep + Tfresh
Hence Kth query deadline will be met if forward before
(K-1)*Tp – Tsleep – 2Tfresh
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Warmup Interval
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When the user changes its path
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it may be too late to wake up all the nodes in first
several query areas on the new path
Prefetch forwarding time has passed
Temporally suffer from poor performance
Prefetch msg is forwarded asap to catch up
Resume just-in-time prefetching at some
collector node when
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Current time < prefetching forwarding time
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Warmup Interval
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Suppose a new motion profile received Ta seconds
before the motion change
Suppose warmup interval lasts K query periods
Time to take prefetch msg to reach kth pickup point is
Tprf = vusr*(K*Tp+Ta)/vprf
-- (a)
Query deadlines are not met during warmup
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Time to take user to reach Kth pickup point < Tprf+ tree setup
time + data collection time 
K*Tp+Ta<Tprf+Ttree+Tfresh
– (b)
Solving K from (a) and (b): K ≈Tsleep + 2Tfresh + Ta
How early a new motion profile is available before
actual motion change is important to the performance
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Storage Cost
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Storage cost during T seconds proportional to
 States of a query * max num of routing trees being set up
concurrently within T
 Analyze num of routing trees only
Greedy prefetching
 Intuitively depends on the speeds of msg and user
 Proportional to T * (1-vusr/vprf)
 Proportional to duration of query
Just-in-time prefetching
 Only depend on query parameters
 Roughly proportional to (Tsleep+2*Tfresh)/Tp
 Independent from duration of query
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Network Contention
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Greedy prefetching causes high network
contention
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Set up as many routing trees as possible at the
same time
Worse when adjacent query areas overlap
Just-in-time prefetching causes much lower
network contention
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Just-in-time prefetching delays the setup
processes of adjacent trees
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Simulation Results
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Metrics
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Data fidelity: ratio of the num of nodes that
contribute to a query result to the total num of
nodes in a query area
Success ratio: ratio of the num of queries that
meet deadlines and have data fidelity above a
threshold, to the total num of queries
The threshold of data fidelity set to 95%
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Performance under Accurate
Motion Profile
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No-prefetching (NP): user issues a query at the
beginning of each query period
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Dynamic Behavior
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Greedy prefetching has high jitter due to network congestion
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Impropriate for mission-critical applications
Warmup
interval
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Performance under Imperfect
Motion Prediction
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Effect of advance time of a motion profile
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Warmup proportional to Tsleep + Ta, consistent to the analysis
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Effect of Motion Changes and
Location Errors
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Effect of Motion Changes and
Location Errors
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Motion changes have little effect when
advance time is positive
Performance drops with num of motion
changes when advance time is negative
Error in user position
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Lead to inaccurate motion prediction
Over 85% of queries succeed even when the user
changes his motion pattern every 70s and
location error is 10m
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Conclusions
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A sptiotemporal query service
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Meet stringent spatiotemporal constraints through
just-in-time prefetching
Can handle extreme low duty cycles
Can handle imperfect motion prediction schemes
Analysis to practical issues
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Network storage, network contention, warmup
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Critiques
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Simple topology creation scheme
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Dependence on motion profile
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Create a routing tree in each query area
High comm. cost and network contention
Solution: Incremental tree maintenance
Movement pattern may be highly unpredictable (e.g.,
invader & pursuer game)
Solution: Omni-directional prefetching
Only simulation results
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Solution: MQ demo and prototype is working now!
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Results on 18 Mica2 motes
MQ-DTC
MQ-DTM
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