Cyber Physical Computing

Department of Computer Science

The University of Illinois at Urbana Champaign

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Computing Research Infrastructure:

An Experimental Facility for Applied Sensor Networks Research Networks

Funded by NSF

PI: Tarek Abdelzaher

Distributed sensor and actuator networks offer a new frontier for computer science research that may well cause the next society-transforming leap since the introduction of personal computers and the formation of the Internet. This frontier lies at the intersection of the logical and physical realms, where computing systems become more seamlessly integrated with the environment and less external to the physical world. Elements of the computing system are subjected not only to requirements of logical correctness but also to physical constraints of time, space and natural resources such as energy. We henceforth call such systems cyber-physical computing systems. This project developed an experimental research facility for cyber-physical computing, housed outdoors on research terrain owned by the University of Illinois at Urbana Champaign. Its goal is to promote research related to outdoors remote sensor network deployment, such as solar energy management, storage, data mining, reliability, debugging, and communication efficiency.

 

The testbed was used by several UIUC faculty in different fields including sensor networks (Tarek Abdelzaher, CS), data mining (Jiawei Han, CS), security/reliability (Carl Gunter, CS), networking (Robin Kravets, CS), distributed systems (Indy Gupta, CS), and ornithology (Mike Ward, Natural Resources and Environmental Science). It is currently being used as an edge testbed for Future Internet Architecture (FIA) research.

 

The Testbed

 

Ten solar-powered nodes equipped with cameras and microphones connected to a low-end laptop for in-situ data processing. A mirror of the testbed exists in the lab for pre-deployment experiments.

 

Outdoors

 

 

Indoors

 

 

 

Accomplishments

 

Solar Energy Management: The testbed led to advances in solar energy management in sensor networks. The main insight that makes solar energy management different from management of battery-operated devices lies in the need to balance energy supply and demand in the former case, as opposed to merely save energy in the latter. For example, when solar energy is plentiful, it is expedient to increase energy consumption, or else the battery will charge to capacity preventing further energy harvesting. Hence, network activities can be tiered with lower tiers providing mere survival functionality of minimum energy requirements and higher tiers offer additional functionality that can be activated when energy supply permits. The testbed was instrumental in exploring different ways that optional functionality can be developed to take advantage of excess energy, when available, to enhance reliability of data storage or delivery.    

 

Dong Kun Noh, Lili Wang, Yang Yong, Hieu Le and Tarek Abdelzaher, 'Minimum Variance Energy Allocation for a Solar-Powered Sensor System,' International Conference on Distributed Computing in Sensor Systems (DCOSS) Marina Del Rey, CA, June 2009

 

Yong Yang, Lili Wang, Dong Kun Noh, Hieu Khac Le, and Tarek Abdelzaher, 'SolarStore: Enhancing Data Reliability in Solar-powered Storage-centric Sensor Networks,' Mobisys, Krakow, Poland, June 2009.

 

Yong Yang, Lu Su, Yan Gao, Tarek Abdelzaher, 'SolarCode: Utilizing Erasure Codes for Reliable Data Delivery in Solar-powered Wireless Sensor Networks,' IEEE Infocom  (miniconference), San Diego, CA, March 2010.

 

Storage management: The primary responsibilities of a sensor network are often considered to be sensing and communication. Remotely deployed sensor networks, however, may not always be connected to the external world (e.g., the Internet). Thus, another important function becomes one of data storage. This testbed served as an important platform for experimenting with network-storage-related research ideas. Sensor data needs to be stored until opportunities arise for retrieving it. Stored data may be lost due to failures, energy depletion, or disk capacity depletion. There may be trade-offs among the different failure modes. For example, when data generation is unbalanced, re-distributing content to balance disk consumption will reduce the chances of local storage overflow on any one node, but will increase energy consumption and hence increase the changes of energy-depletion-induced losses. Similarly, data replication would reduce failure-induced losses, but would consume more energy and storage, hence increasing the chances of loss due to energy consumption or capacity overflow. A goal of the storage system is therefore to maximize the odds of data survival in the presence of failures, capacity overflow, and energy depletion, and in the face of uncertainty regarding the amount of future solar energy available, the amount of data to be generated, and the possible failures. The testbed developed in this project served as an invaluable vehicle for comparing different solutions to the above problem and demonstrating which of these work best in a realistic setting.    

 

Lili Wang, Yong Yang, Dong Kun Noh, Hieu K. Le, Jie Liu, Tarek F. Abdelzaher, and Michael Ward, 'AdaptSens: An Adaptive Data Collection and Storage Service for Solar-Powered Sensor Networks,' IEEE Real-time Systems Symposium, Washington, DC, December 2009.

 

Liqian Luo, Qing Cao, Chengdu Huang, Lili Wang, Tarek Abdelzaher, Michael Ward, 'Design, Implementation and Evaluation of EnviroMic: A Storage-Centric Audio Sensor Network,' ACM Transactions on Sensor Networks, Vol. 5, No. 3, May 2009

 

System troubleshooting: One of the primary practical considerations in remote deployment of sensor networks is remote node maintenance and troubleshooting. When a node becomes silent, the cause of silence is often not clear. Hence, the urgency of intervention is hard to estimate. For example, if the node runs out of energy, no intervention is needed. The node will resume once more solar energy is harvested. If the node simply becomes disconnected from the network but remains functional, a repair may be needed, but it is not urgent. The node typically has enough storage capacity to continue collecting data that can be retrieved later when connectivity is restored. On the other hand, if the node suffers an electrical failure due to flooding, then urgent intervention is advisable as it is likely that other nodes in its vicinity are in danger of flooding and failure as well. The testbed helped test new monitoring hardware and data mining software that allowed failures of silent nodes to be classified for purposes of assessing urgency of intervention. These solutions have the potential to significantly reduce remote sensor network maintenance cost by property distinguishing issues that require (expensive) emergency intervention, such as sending an emergency team to the remote field, from issues that can wait until they are taken care of by (cheaper) periodic maintenance.

 

Mohammad Maifi Hasan Khan, Hieu K. Le, Michael LeMay, Parya Moinzadeh, Lili Wang, Yong Yang, Dong K. Noh, Tarek Abdelzaher, Carl A. Gunter, Jiawei Han, Xin Jin, 'Diagnostic Powertracing for Sensor Node Failure Analysis,' IPSN, Stockholm, Sweden, April, 2010.

 

Data Mining: Research on data mining, in general, builds on the existence of sufficient data sets that drive development and testing of the different mining algorithms. This testbed offered an interesting collection of sensor data sets that helped advance data mining research. New algorithms were developed for pattern matching, classification, and semi-supervised learning.  

 

Hyung Sul Kim, Sangkyum Kim, Tim Weninger, Jiawei Han, and Tarek Abdelzaher, 'NDPMine: Efficiently Mining Discriminative Numerical Features for Pattern-Based Classification', Proc. European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), Barcelona, Spain, September 2010.

 

Lu Su, Yong Yang, Bolin Ding, Jing Gao, Tarek F. Abdelzaher, and Jiawei Han, 'Hierarchical Aggregate Classification with Limited Supervision for Data Reduction in Wireless Sensor Networks,' ACM Sensys, Seattle, WA, November 2011.

 

Hyungsul Kim, David Sheridan, Sungjin Im, Shobha Vasudevan, Tarek Abdelzaher, and Jiawei Han, 'Signature Pattern Covering via Local Greedy Algorithm and Pattern Shrink', Proc. 2011 IEEE Int. Conf. on Data Mining (ICDM'11), Vancouver,Canada, Dec. 2011.

 

Efficient Communication: The testbed offered opportunities for evaluating practical MAC-layer, multicast, congestion control, and routing protocols in a realistic environment involving a remote sensor network deployment. Different protocols were evaluated in the outdoors setting. Some were evaluated in simulations with parameters inspired by and calibrated using data obtained from the testbed. Of particular interest was the development of cyber-physical communication protocols that utilizes knowledge of the physical world (such as properties of sensed and communicated data) to significantly improve decisions related to communication efficiency (such as which data to drop when congestion occurs).

 

Lu Su, Bolin Ding, Yong Yang, Tarek Abdelzaher, Guohong Cao, 'oCast: Optimal Multicast Routing Protocol for Wireless Sensor Networks,' The 17th IEEE International Conference on Network Protocols (ICNP), Princeton, NJ, October 2009.

 

Hossein Ahmadi, Tarek Abdelzaher, Indranil Gupta, 'Congestion Control for Spatio-temporal Data in Cyber-physical Systems,' International Conference on Cyber-physical Systems (ICCPS), Stockholm, Sweden, April, 2010.

 

Hossein Ahmadi, Tarek Abdelzaher, Robin Kravets, 'Adaptive Multi-metric Routing in Distressed Mobile Sensing Networks,' IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (IEEE SUTC), Newport Beach, CA, June, 2010.