1. Department of Computer Science University of Massachusetts, Amherst TSAR*: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs Peter Desnoyers, Deepak Ganesan, and Prashant Shenoy University of Massachusetts, Amherst (*T iered S torage AR chitecture)

2. UNIVERSITY OF MASSACHUSETTS, AMHERST Why do we need archival storage? Applications need historical sensor information. Why? Trigger events: Traffic monitoring - crash Surveillance - break-in Environmental monitoring - natural disaster lead to requests for past information. This requires archival storage.

3. UNIVERSITY OF MASSACHUSETTS, AMHERST Limited by lack of sufficient, energy-efficient storage and of communication and computation resources on current sensor platforms. Optimized for continuous queries. High energy cost if used for archival - data must be transmitted to central data store. Existing storage and indexing approaches ◊Streaming query systems  TinyDB (Madden 2005), etc.  Data storage and indexing is performed outside of network. ◊In-network storage and indexing  DCS, GHT (Ratnasamy 2002)  Dimensions (Ganesan 2003)  Directed Diffusion (Intangonwiwat 2000)

4. UNIVERSITY OF MASSACHUSETTS, AMHERST Technology Trends Radio J/byte Flash J/byte Max Flash size Mica2304.50.5MB MicaZ3.44.50.5MB Telos3.411MB UMass NAND 0.01>1GB 1000x 100x New flash technologies enable large storage systems on small energy- constrained sensors.

5. UNIVERSITY OF MASSACHUSETTS, AMHERST Hierarchical Storage and Indexing Hierarchical deployments are being used to provide scaling: James Reserve (CENS) Higher powered micro-servers are deployed alongside resource constrained sensor nodes. Key challenge: Exploit proxy resources to perform intelligent search across data on resource- constrained nodes. Sensors Proxies Application

6. UNIVERSITY OF MASSACHUSETTS, AMHERST Key Ideas in TSAR ◊Exploit storage trends for archival.  Use cheap, low-power, high capacity flash memory in preference to communication. ◊Index at proxies and store at sensors.  Exploit proxy resources to conserve sensor resources and improve system performance. ◊Extract key searchable attributes.  Distill sensor data into concise attributes such as ranges of time or value that may be used for location and retrieval but require less energy to transmit.

7. UNIVERSITY OF MASSACHUSETTS, AMHERST TSAR Architecture 1. Interval Skip Graph-based index between proxies. Exploit proxy resources to locate data stored on sensors in response to queries. 2. Summarization process Extracts identifying information: e.g. time period during which events were detected, range of event values, etc.  3. Local sensor data archive Stores detailed sensor information: e.g. images, events. Sensor node archive

8. UNIVERSITY OF MASSACHUSETTS, AMHERST TSAR Architecture 1. Interval Skip Graph-based index between proxies. Exploit proxy resources to locate data stored on sensors in response to queries. 3. Local sensor data archive Stores detailed sensor information, e.g. images, events. 2. Summarization process Extracts identifying information: e.g. time period during which events were detected, range of event values, etc.  Summarization function

9. UNIVERSITY OF MASSACHUSETTS, AMHERST TSAR Architecture 2. Summarization process Extracts identifying information: e.g. time period during which events were detected, range of event values, etc. 3. Local sensor data archive Stores detailed sensor information, e.g. images, events.  Distributed index 1. Interval Skip Graph-based index between proxies Exploit proxy resources to locate data stored on sensors in response to queries.

10. UNIVERSITY OF MASSACHUSETTS, AMHERST  Example - Camera Sensing storage Cyclops camera summarize image Sensor archives information and transmits summary to proxy. Sensor node Summary handle  Birds(t 1,t 2 )=1

11. UNIVERSITY OF MASSACHUSETTS, AMHERST Example - Indexing Index Network of proxies Summary and location information are stored and indexed at proxy. proxy   Birds(t 1,t 2 )=1 Birds t1,t2 1

12. UNIVERSITY OF MASSACHUSETTS, AMHERST Example - Querying and Retrieval Birds in interval (t1,t2)? proxy  Cyclops camera summarize   Cyclops camera summarize  Query is sent to any proxy. Birds t1,t2 1

13. UNIVERSITY OF MASSACHUSETTS, AMHERST Example - Querying and Retrieval Birds in interval (t1,t2)? proxy  Cyclops camera summarize   Cyclops camera summarize  Index is used to locate sensors holding matching records. Birds t1,t2 1

14. UNIVERSITY OF MASSACHUSETTS, AMHERST Record is retrieved from storage and returned to application. Example - Querying and Retrieval proxy  Cyclops camera summarize   Cyclops camera  Birds t1,t2 1

15. UNIVERSITY OF MASSACHUSETTS, AMHERST Outline of Talk ◊Introduction and Motivation ◊Architecture ◊Example ◊Design  Skip Graph  Interval Search  Interval and Sparse Interval Skip Graph ◊Experimental Results ◊Related Work ◊Conclusion and Future Directions

16. UNIVERSITY OF MASSACHUSETTS, AMHERST The index should: support range queries over time or value, be fully distributed among proxies, and Support interval keys indicating a range in time or value. Goals of Index Structure insert(| |) Distributed index (| |) ?

17. UNIVERSITY OF MASSACHUSETTS, AMHERST What is a Skip Graph? 23569121819 Single key and associated pointers Distributed extension of Skip Lists (Pugh ‘90): Probabilistically balanced - no global rebalancing needed. Ordered by key - provides efficient range queries. Fully distributed - data is indexed in place. (Aspnes & Shah, 2003, Harvey et al. 2003) Log(N) search and insert No single root - load balancing, robustness Properties:

18. UNIVERSITY OF MASSACHUSETTS, AMHERST Interval search Given intervals [low,high] and query X: 1 - order by low 2 - find first interval with high <= X 3 - search until low > X 012345678910 0 3 5 8 6 8 9 4 2 2 3 1 5 Query: x=4

19. UNIVERSITY OF MASSACHUSETTS, AMHERST Interval search Given intervals [low,high] and query X: 1 - order by low 2 - find first interval with high <= X 3 - search until low > X 012345678910 03 58 6 89 42 23 15 Query: x=4

20. UNIVERSITY OF MASSACHUSETTS, AMHERST Interval search Given intervals [low,high] and query X: 1 - order by low 2 - find first interval with high <= X 3 - search until low > X 012345678910 03 5 8 6 89 42 23 15 Query: x=4

21. UNIVERSITY OF MASSACHUSETTS, AMHERST Simple Interval Skip Graph 0-30-11-52-45-86-108-99-12 Derived from Interval Tree, Cormen et al. 1990 3355810 12 Method: Index two increasing values: low, max Search on either as needed. Interval keys:YES logN search:YES logN update:NO - (worst case O(N))

22. UNIVERSITY OF MASSACHUSETTS, AMHERST Sparse Interval Skip Graph Goal: efficient update of max(high) values in Interval Skip Graph. Approach: take advantage of ratio of proxies (M) to data items (N) Solution: eliminate redundant links and corresponding updates. Before: complete search tree rooted at each data item. After: retain M trees, one rooted at each proxy, keeping robustness and load balancing properties. M proxies N data items - - - - - - - - - - Worst-case complexity: Search:O(logM) Update:O(M)

23. UNIVERSITY OF MASSACHUSETTS, AMHERST Adaptive Summarization updates queries How accurately should the summary information represent the original data? Detailed summaries = more summaries, precise index Precise index = fewer wasted queries

24. UNIVERSITY OF MASSACHUSETTS, AMHERST Adaptive Summarization updates queries How accurately should the summary information represent the original data? Approximate summaries = fewer summaries, imprecise index imprecise index = more wasted queries ??

25. UNIVERSITY OF MASSACHUSETTS, AMHERST  = summarization (summaries / data) r = EWMA( wasted queries / data ) Target range: r 0 Decrease  if: r > r 0  Increase  if: r < r 0  Adaptive Summarization updates queries Goal: balance update and query cost. Approach: adaptation.

26. UNIVERSITY OF MASSACHUSETTS, AMHERST Prototype and Experiments ◊ Software: Em* (proxy), TinyOS (sensor) ◊ Hardware:Stargate Mica2 mote ◊ Network:802.11 ad-hoc, multihop BMAC 11% ◊ Data:James Reserve [CENS] dataset 30s temperature readings 34 days For physical experiments, data stream was stored on sensor node and replayed.

27. UNIVERSITY OF MASSACHUSETTS, AMHERST Index performance Queries Sensor data How does the index performance scale with the number of proxies and size of dataset? Tested in:Em* emulation Tasks: insert, query Variables: number of proxies (1-48) size of dataset Metric:proxy-to-proxy messages Interval skip graph index

28. UNIVERSITY OF MASSACHUSETTS, AMHERST Index results Sparse skip graph provides >2x decrease in message traffic for small numbers of proxies. Sparse skip graph shows virtually flat message cost for larger index sizes.

29. UNIVERSITY OF MASSACHUSETTS, AMHERST Tested on:4 Stargate proxies 12 Mica2 sensors in tree configuration Task: query Variables: size of dataset Metric:query latency (ms) Query performance data queries What is the query performance on real hardware and real data? 4-proxy network 3-level multi-hop sensor field

30. UNIVERSITY OF MASSACHUSETTS, AMHERST Validates the approach of using proxy resources to minimize the number of expensive sensor operations. Query results Sensor link latency dominates Proxy link delay is negligible The sensor communication consists only of a query and a response - the minimal communication needed to retrieve the data.

31. UNIVERSITY OF MASSACHUSETTS, AMHERST Summary algorithm adapts to data and query dynamics. Tested in:Em*, EMTOSSIM emulation Task: data and queries Variables: query/data ratio Metric:summarization factor  Query/data = 0.2 Query/data =0.03 Query/data = 0.1 Adaptive Summarization Varied query rate Summary rate adapts How well does the adaptation mechanism track changes in conditions? 1/1/

32. UNIVERSITY OF MASSACHUSETTS, AMHERST Related Work ◊In-network Storage:  DCS (Ratnasamy 2002)  Dimensions (Ganesan 2003)  … ◊In-network Indexing:  GHT (Ratnasamy 2002)  DIFS (Greenstein 2003)  DIM (Li 2003)  … ◊Hierarchical Sensor Systems:  Tenet (CENS, USC) ◊Sensor Flash File Systems:  ELF (Dai 2004)  Matchbox (Hill et al. 2000)

33. UNIVERSITY OF MASSACHUSETTS, AMHERST Conclusions and Future Work ◊Proposed novel Interval Skip Graph-based index structure and adaptive summarization mechanism for multi-tier sensor archival storage. ◊Implemented these ideas in the TSAR system. ◊Demonstrated index scalability, query performance, and adaptation of summarization factor, both in emulation and running on real hardware. Future Work ◊Investigate other index structures. ◊Alternate interval- and non-interval-based summary mechanisms. For more information: http://presto.cs.umass.edu