• Shayan Mehrazarin (MS, graduated in 2016, Software Integration Engineer at Panasonic Avionics Corporation)
• Setu Taase (BS, graduated in 2015, Software Engineer at Booz Allen Hamilton)
• Shawn Her Many Horses (BS, graduated in 2015, Software Engineer at ZipRecruiter)
• Basil Alhakami (MS, graduated in 2015, Software Developer at Sona Enterprises)
• Payman Khani (MS, graduated in 2015, IT Coordinator and Part Time Faculty at California State University, Dominguez Hills)
• Yan Ma (MS, graduated in 2014, Senior Staff Software Engineer at Blackhawk Network)
• Ryan Hausen (BS, graduated in 2013, now PhD student at UC Santa Cruz)
• FNU Nilofar (MS, graduated in 2013, Software Engineer)
• Xinyu Xue (MS, graduated in 2013, Software Engineer at TAFCO CORPORATION)
• Xiang Hou (MS, graduated in 2013, Software Engineer at Hikvision)
• Lucas Burson (BS, graduated in 2012, Software Engineer at NetApp)
• Zane Sumpter (BS, graduated in 2012, Software Engineer at NetApp)
• Masaaki Takahashi (MS, graduated in 2011, GPA 4.0/4.0, Wichita State University)

The research objective of this project is to create a framework to effectively preserve data generated in sensor network applications that operate in challenging environments. These applications include visual and acoustic sensor networks, ocean seismic or underwater sensor networks, and volcanic and glacial monitoring. In such challenging environments, the data uploading opportunities would be unpredictable and rare, making the network connectivity to the base station inherently intermittent and storing data inside the network necessary. In particular, this project 1) Invents a series of energy- and storage-efficient data preservation algorithms to adaptively overcome all the key causes of data loss, including energy depletion, storage depletion, hardware failure of sensor nodes, and overall storage overflow in the entire network. The proposed data preservation techniques include distributing, redistributing, replicating, and aggregating the sensed data inside the network; 2) Takes a unified storage-energy optimization approach, in which storage space and battery energy, the two most stringent resources in sensor networks, are viewed as two sub-components of the same unified resource in the sensor network. The joint allocation of storage and energy is optimized for data preservation by exploiting their synergies via aforesaid data preservation techniques.

Publications:

1. DAO-R: Integrating Data Aggregation and Offloading in Sensor Networks Via Data Replication, Basil Alhakami, Bin Tang, Jianchao Han, and Mohsen Beheshti, International Journal of Sensor Networks, Vol.29, No.2, 2019.
2. DAO²: Overcoming Overall Storage Overflow in Intermittently Connected Sensor Networks, Bin Tang, Proceedings of the IEEE International Conference on Computer Communications (Infocom 2018).
3. Data Preservation in Base Station-less Sensor Networks: A Game Theoretic Approach, Yutian Chen and Bin Tang, Proceedings of the 6th EAI International Conference on Game Theory for Networks (GameNets 2016).
4. A MacBook Based Earthquake Early Warning System, Shayan Mehrazarin, Bin Tang, Ken Leyba, Jianchao Han, and Mohsen Beheshti Proceedings of the IEEE INFOCOM 2016, Demo Session.
5. Seismic Data Collection with Shakebox and Analysis Using MapReduce, Bin Tang, Jianchao Han, Mohsen Beheshti, Garrett Poppe, Liv Nguekap, and Rashid Siddiqui, Journal of Computer Communications, vol. 3, 94-101, 2015
6. DAO-R: Integrating Data Aggregation and Offloading in Sensor Networks Via Data Replication, Basil Alhakami, Bin Tang, Jianchao Han, and Mohsen Beheshti, Proceedings of the IEEE Global Communications Conference (GLOBECOM 2015).
7. Data Preservation in Data-Intensive Sensor Networks With Spatial Correlation, Nathaniel Crary, Bin Tang, and Setu Taase, Proceedings of the ACM International Workshop on Mobile Big Data (MobiData 2015) in conjunction with Mobihoc 2015.
8. Seismic Data Collection with Shakebox and Analysis Using MapReduce, Bin Tang, Jianchao Han, Mohsen Beheshti, Garrett Poppe, Liv Nguekap, and Rashid Siddiqui, Proceedings of the 2015 Conference on New Advances in Big Data (NABD 2015).
9. Maximizing Data Preservation Time in Linear Sensor Networks, Ryan Hausen, Bin Tang, Samuel Sambasivam, Simon Lin, Proceedings of the IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS'14), Poster Session.
10. Achieving Data K-Availability in Intermittently Connected Sensor Networks, Bin Tang, Neeraj Jaggi, and Masaaki Takahashi, Proceedings of the International Conference on Computer Communications and Networks (ICCCN'14).
11. A Generalized Data Preservation Problem in Sensor Networks - A Network Flow Perspective, Bin Tang, Rajiv Bagai, FNU Nilofar, and Mehmet Bayram Yildirim, Proceedings of the 8th International Workshop on Wireless Sensor, Actuator and Robot Networks (WiSARN), in conjunction with ADHOCNOW 2014.
12. Energy-Efficient Data Redistribution in Sensor Networks, Bin Tang, Neeraj Jaggi, Haijie Wu, and Rohini Kurkal, ACM Transactions on Sensor Networks, Volume 9, Issue 2, Number 11, Pages pages 1-11, 2013
13. Data Preservation in Intermittently Connected Sensor Networks with Data Priority, Xinyu Xu, Xiang Hou, Bin Tang, and Rajiv Bagai, Proceedings of the IEEE International Conference on Sensing, Communication, and Networking (SECON'13).
14. Maximizing Data Preservation in Intermittently Connected Sensor Networks, Xiang Hou, Zane Sumpter, Lucas Burson, Xinyu Xue, Bin Tang, Proceedings of the IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS'12).
15. Energy-Efficient Data Preservation in Intermittently Connected Sensor Networks, Masaaki Takahashi, Bin Tang, and Neeraj Jaggi, Proceedings of the International Workshop on Wireless Sensor, Actuator and Robot Networks (WiSARN), in conjunction with IEEE INFOCOM 2011.
16. Energy-Efficient Data Redistribution in Sensor Networks, Bin Tang, Neeraj Jaggi, Haijie Wu, and Rohini Kurkal, Proceedings of the IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS'10), San Francisco, California, November 2010.

This work was supported by the National Science Foundation under Grant No. 1419952.