Mobi Query A Spatiotemporal Query Service for Mobile

Mobi. Query: A Spatiotemporal Query Service for Mobile Users in Sensor Networks Chenyang Lu, Guoliang Xing, Octav Chipara Chien-Liang Fok, and Sangeeta Bhattacharya 1

Outline l l l Motivation Design Analysis Simulations Demo 2

Motivation l l Supporting query from users is one of the most important function of sensor networks Existing solutions l l l Fixed query areas, fixed user: directed diffusion, Tiny. DB Fixed areas, mobile user: TTDD, SEAD Query from mobile users in mission-critical applications has not been addressed l l Mobile users and moving query areas Stringent real-time requirement 3

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 4

Problem Formulation l l Example: “Update a temperature map within 100 m every 2 s. Data can be at most 1 s old. ” Spatial constraint l l l Query area: range of 100 m l all and only the sensors within the query area should respond to the query Query area moves with the user Temporal constraints l l Query period: 2 s l results must be delivered before end of current period Data freshness: 1 s 5

Challenges l Low duty cycle l l l Scarce resources l l Mica 2: lifetime of 450 days 1% duty cycle High wakeup delay Require low storage cost, comm. overhead and network contention Trivial solution does not work! l User issues a query at the beginning of each query period l 1% duty cycle: active for 150 ms in every 15 s l Wakeup delay: 0~ 14. 85 s Many nodes cannot be woken up and respond 6

Mobi. Query Approach l Motion prediction l l Prefetching l l Send prefetch msgs to future pickup points at the right time Query dissemination l l 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 l 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 7

Assumptions l Network runs a power management protocol l l l 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 Mobi. Query can work without backbones with slight modification Every node knows its location Nodes have synchronized clocks 8

Generation of Motion Profiles l Motion prediction l l l Motion planning l l l 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 l l How early a motion profile available before the actual movement: positive for motion planning, negative for motion prediction Affect the performance of prefetching 9

Prefetching l Greedy prefetching l l l Just-in-time prefetching l l l 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 l l l Reduce the network contention Reduce storage cost Reduce the cost caused by user motion changes 10

Query Dissemination l l l 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 l l Wake up when user arrives Resume sleeping after collecting & sending data 11

Data Collection l l Must finish within Tfresh due to data freshness constraint Parent nodes wait for results from children to enable data aggregation l l l May miss query deadline due to child failures Solution based on timeouts l Each node sends results received so far when timeout l Leaf nodes send results at Tfresh before query deadline l Nodes closer to the root have later timeouts Query results always meet deadline due to the timeouts, possibly with incomplete results 12

Prefetch Forwarding Time l When (K-1)th collector node to forward a prefetch msg to Kth pickup point l l l 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 l l l Msg travels between two pickup points Ttravel Sets up the tree in a query area Ttree Collects data Tcollect 13

Prefetch Forwarding Time Illustration Ttravel + Ttree + Tcollect 14

Prefetch Forwarding Time l Kth query deadline will be met if forward before K*Tp – (Ttravel + Ttree + Tcollect) l Timing analysis l l l 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 l Assume broadcast delay = data collection delay § l l 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 – 2 Tfresh 15

Warmup Interval l When the user changes its path l l l 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 l Current time < prefetching forwarding time 16

Warmup Interval l l 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 l l l 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 + 2 Tfresh + Ta How early a new motion profile is available before actual motion change is important to the performance 17

Storage Cost l l l Storage cost during T seconds proportional to l States of a query * max num of routing trees being set up concurrently within T l Analyze num of routing trees only Greedy prefetching l Intuitively depends on the speeds of msg and user l Proportional to T * (1 -vusr/vprf) l Proportional to duration of query Just-in-time prefetching l Only depend on query parameters l Roughly proportional to (Tsleep+2*Tfresh)/Tp l Independent from duration of query 18

Network Contention l Greedy prefetching causes high network contention l l l 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 l Just-in-time prefetching delays the setup processes of adjacent trees 19

Simulation Results l Metrics l l l 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% 20

Performance under Accurate Motion Profile l No-prefetching (NP): user issues a query at the beginning of each query period 21

Dynamic Behavior l Greedy prefetching has high jitter due to network congestion l Impropriate for mission-critical applications Warmup interval 22

Performance under Imperfect Motion Prediction l Effect of advance time of a motion profile l Warmup proportional to Tsleep + Ta, consistent to the analysis 23

Effect of Motion Changes and Location Errors 24

Effect of Motion Changes and Location Errors l l l 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 l l Lead to inaccurate motion prediction Over 85% of queries succeed even when the user changes his motion pattern every 70 s and location error is 10 m 25

Conclusions l A sptiotemporal query service l l 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 l Network storage, network contention, warmup 26

Critiques l Simple topology creation scheme l l Dependence on motion profile l l l 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 l Solution: MQ demo and prototype is working now! 27

Results on 18 Mica 2 motes MQ-DTC MQ-DTM 28