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Joint work with Svilen Mihaylov, Marie Jacob, Mengmeng Liu, Sudipto Guha, Boon Thau Loo DMSN 2008 August 24, 2008 Zachary G. Ives University of Pennsylvania.

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Presentation on theme: "Joint work with Svilen Mihaylov, Marie Jacob, Mengmeng Liu, Sudipto Guha, Boon Thau Loo DMSN 2008 August 24, 2008 Zachary G. Ives University of Pennsylvania."— Presentation transcript:

1 joint work with Svilen Mihaylov, Marie Jacob, Mengmeng Liu, Sudipto Guha, Boon Thau Loo DMSN 2008 August 24, 2008 Zachary G. Ives University of Pennsylvania Funded by NSF IIS , CNS , and DARPA

2 Sensor Networks – Today Many of today’s sensor apps focus on passive monitoring:  Zoology, biology, oceanography, building security, etc.  Many cool apps like ZebraNet (Princeton), CarTel (MIT), intelligent parking lots (USC, others) Has driven research throughout the software stack, e.g.:  Overcoming hardware limitations  Ad hoc networking  Distributed declarative aggregation queries over data  Approximation … Where is sensor network technology heading next?

3 Sensor Networks – Tomorrow Will join a larger ecosystem: connected to databases, streaming Web sources, and the Internet as a whole!  Sensors monitoring physical environment  “Soft sensors” monitoring logical state of devices, network, nodes  Displays and actuators that “follow” and guide / interact with the user High-level programming that abstracts the heterogeneity of this environment, detects events, defines high-level views Applications: interactive environments that integrate, correlate  “Smart buildings” that help occupants / visitors  Hospital: Finding a patient  Home: Reminders to take medications (or feed the fish)  Campus: Where to find an available computer terminal  Cross-layer, cross-system monitoring (e.g., security)

4 A Smart Environment – Penn “CIStem” Our 5 buildings extended with soft & real sensors:  Servers, routers, robots, etc.  Soft: load, users, applications, reservations  Hard: Machine temperature; AC status; power status; position  Workstations  Soft: Machine active; screen saver active  Hard: Light on in lab; audio at keyboard  Conference rooms, classrooms  Soft: Reservations according to calendar  Hard: Movement in room Displays:  PDAs, monitors in halls, LEDs, iPhones, etc.

5 Some Example Use Cases Query: “Direct me to a free workstation with MS Word” Lights & motion in lab; machine available; apps installed; lab near user Query: “How many machines can we keep in this lab – given cooling constraints – and how many requests can they handle?” Run a machine under load; measure throughput; measure temperature Query: “Where are the GRASP mobile robots located?” On-board positioning; nearby ceiling-mounted sensors; complex interpolation function Event: reminders trigger and appear wherever you are! Your calendar; your RFID and position; nearest display Event: gracefully shut down machines near air conditioning outage or fire alarm Air cond. cooling zone, alarm state, machine state, lookup of IP address from location A mix of sensors, DBs, etc.; lots of correlation (join)

6 To build these rich, interactive applications that combine many types of data, we need a new architecture:  Capabilities for integrating stream data:  Queries across different device types, network types  Views and abstraction layers  Cleaning and conflict resolution  Distributed optimization and query processing  Sensing of logical state within devices:  Network monitoring (at different layers, e.g., link, transport)  App, node state (e.g., server jobs, console in-use state)  The ability to “subscribe” displays or apps to views Our target devices may include many non-mote devices

7 Our “Vision Statement” Take a declarative query, posed over a view over any kind of data, sourced by any device…  Partition and distribute the query across a variety of networks and systems…  and feed its output to anyone “subscribing” to it…  in a way that maximizes performance and reliability, while minimizing use of precious resources!

8 The Key Research Challenges The “right” primitives for stream information integration, based on, but going beyond traditional data integration  Views, joins, aggregation, etc. but also…  User defined functions  Regions, neighborhoods, and paths (transitive closure) Highly distributed stream query processing capabilities  Distributing queries across a heterogeneous network  Impact of wireless, multi-hop networks on query processing – failures, changes in topology, etc.

9 Some of the Challenges in Distributed Query Processing  Highly distributed, dynamic networks make computation, coordination, optimization hard!  Join gets “horizontally partitioned” very heavily – how do we execute, optimize, adapt to changes? handle mobility? etc.  How do we support windowed computation over regions, paths, etc.? (windowed transitive closure queries)  How do we determine how much work to place at different nodes in a heterogeneous environment? Performance vs. reliability vs. battery…  How do we do decentralized optimization of queries? (One answer: adaptive techniques!)

10 Conclusions – Much Remains to Do!  The sensor world is going to be heterogeneous!  Many network types, device types  Soft sensors, physical sensors, routers, applications, …  All sorts of data formats, including video  Can we build a unified infrastructure for managing heterogeneity, consistency, and data acquisition / integration?  Can we make it perform?


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