Managing Heterogeneity in Highly Distributed Stream, Cloud, and Sensor Systems
With the advent of low-cost wireless sensing devices, it is predicted that the world will quickly move to one in which many environments are instrumented for reasons of security, scientific monitoring, environmental control, entertainment, etc. There are many fundamental questions about how to develop applications in this emerging sensor network world. Perhaps the most important are how to support rich, complex applications that may have confidentiality requirements, heterogeneous types of sensors, different connectivity levels, and timing constraints. The Aspen (Abstract Sensor Programming Environment) project focuses on the challenges in developing a programming environment and runtime system for this style of environment.
We are investigating a number of complementary topics and ideas:
- Complex analysis in a cluster/cloud setting: Many sensor and stream data items need complex analysis. Building upon ideas from MapReduce and from our ORCHESTRA distributed query engine, we are developing new techniques for supporting cluster computation with incremental updates over recursive operations (e.g., PageRank, optimization, ...).
- Distributed coordination and control: Many complex computations need to be continuously rebalanced, redistributed, and replanned based on monitored activity -- this is a form of adaptive processing. We are developing new declarative techniques to address these problems.
- New programming model: group-based programming: We are building upon a declarative style of programming to develop a new language, group-based programming, for complex sensor applications. The goal is to combine compositional, database-style declarative computation with constraints on timing, security, distribution, and actuation in a seamless way. This work is funded by NSF CNS-0721541.
- Security and privacy: We have studied how sensor network application security is affected by node-levelcompromise. We are developing further language constructs for specifying encryption levels and other properties for data along certain channels.
- Runtime monitoring and checking: We seek to develop techniques for monitoring performance and triggering events in response to constraint violations. This work is funded by NSF CNS-0721541.
- Home health care and hospital applications: We hope to develop a number of applications useful in home hospice and hospital care, which monitor patients, and also connect patients with the care they need. This work is funded by NSF CNS-0721541.
- Declarative information integration and query optimization: The core programming model is based on database query languages. We are developing techniques for supporting schema mappings over streams,distributed in-network join computation, and recursive queries for regions. Importantly, we are developing techniques for performing distributed, decentralized optimization of such computations. This work is funded by NSF IIS-0713267.
- Stream algorithms: In a distributed setting, many nodes have limited resources and must use approximate algorithms to make decisions and capture synopses of system activity. This work is funded by NSF IIS-0713267.
- Interfacing to Java code: Many real control systems require Java, C, or other procedural code for sophisticatd sensor data processing or decision-making. This work is funded by Lockheed Martin.
- Declarative monitoring and re-optimization: We seek to build a declarative infrastructure for monitoring distributed query execution status, plus adaptive re-optimization, using declarative techniques. This work is funded by Lockheed Martin.
The ASPEN "smart building" demonstration, SmartCIS, won Honorable Mention for Best Demonstration at ACM SIGMOD 2009.
- Under preparation: Minimally Incremental Query Reoptimization based on Environmental Changes.
- Under submission: REX: Recursive, Delta-Based Data-Centric Computation
- Svilen R. Mihaylov, Marie Jacob, Zachary G. Ives, Sudipto Guha. Dynamic Join Optimization in Multi-Hop Wireless Sensor Networks. VLDB 2010 and Proc. VLDB Endowment 3(1).
- Mengmeng Liu, Nicholas E. Taylor, Wenchao Zhou, Zachary G. Ives, Boon Thau Loo. Maintaining Recursive Views of Regions and Connectivity in Networks. To appear, IEEE Transactions on Knowledge and Data Engineering, Special issue on Best Papers of ICDE.
- Mengmeng Liu, Svilen R. Mihaylov, Zhuowei Bao, Marie Jacob, Zachary G. Ives, Boon Thau Loo, Sudipto Guha.SmartCIS: Integrating Digital and Physical Environments. Demonstration description. SIGMOD 2009.
- Mengmeng Liu, Nicholas E. Taylor, Wenchao Zhou, Zachary Ives, and Boon Thau Loo. Recursive Computation of Regions and Connectivity in Networks. ICDE 2009.
- Svilen R. Mihaylov, Marie Jacob, Zachary G. Ives, Sudipto Guha. A Substrate for In-Network Sensor Data Integration. Workshop on Data Management for Sensor Networks (DMSN), Auckland, New Zealand, 2008.
- Sebastian Fischmeister, Insup Lee, Robert Trausmuth. Hardware Acceleration for Verifiable, Adaptive Real-Time Communication. IEEE International Conference on Emerging Technologies and Factory Automation (TFA), Hamburg, Germany, September 2008.
- Madhukar Anand, Eric Cronin, Micah Sherr, Matt Blaze, Zachary Ives, Insup Lee. Sensor Network Security: More Interesting Than You Think, Usenix Workshop on Hot Topics in Security, July 2006.
- Madhukar Anand, Zachary Ives, Insup Lee. Quantifying Eavesdropping Vulnerability in Sensor Networks, VLDB Workshop on Data Management for Sensor Networks, August 2005.
- Prof. Boon Thau Loo
- Svilen Mihaylov
- Mengmeng Liu
- Prof. Matt Blaze
- Marie Jacob
- Prof. Lyle Ungar
- Prof. Dean Foster (Wharton)
- Aaron Evans
- Tak Man Ma
- Ted Paulakis
- Madhukar Anand
- Micah Sherr
- Eric Cronin
Funded in part by a seed grant from ISTAR, the Penn Institute for Strategic Threat Analysis and Response; NSF IIS-0713267; NSF CNS-0721541; a research grant from Lockheed Martin; and a research grant from Google.