| dc.identifier.uri | http://hdl.handle.net/11401/77472 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | With the drastic growth of attention in crowed sensing and wireless based social network applications, it is in desperate need to establish a comprehensive infrastructure that can efficiently sense the data, then accurately matches and delivers the gathered information to the various parties of interests in a timely manner. On the other end of the picture, the huge amount of user data needs to be reliably stored with easy and fast access at anytime and from anywhere. These inter-connected challenging problems form a complete information service framework of my thesis. In this thesis, we first introduce a set of adaptive sampling schemes based on improved compressive sensing technique for efficient information sensing and data gathering. Then in the second, we provide a storage efficient and traffic light-weighted fast content based information matching overlay for proper data dissemination and future processing. At last, we propose a space cost-effective and fast cloud based storage system using data deduplication and coding techniques for fast and reliable data storage. The proposed components work seamlessly towards a highly efficient and reliable framework that outperforms most peer systems for the various emerging applications. | |
| dcterms.available | 2017-09-20T16:52:46Z | |
| dcterms.contributor | Wang, Xin | en_US |
| dcterms.contributor | Doboli, Alex | en_US |
| dcterms.contributor | Robertazzi, Thomas | en_US |
| dcterms.contributor | Das, Samir. | en_US |
| dcterms.creator | Li, Ying | |
| dcterms.dateAccepted | 2017-09-20T16:52:46Z | |
| dcterms.dateSubmitted | 2017-09-20T16:52:46Z | |
| dcterms.description | Department of Electrical Engineering. | en_US |
| dcterms.extent | 69 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/77472 | |
| dcterms.issued | 2015-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:52:46Z (GMT). No. of bitstreams: 1
Li_grad.sunysb_0771E_12671.pdf: 724174 bytes, checksum: d33fd9a67c89914a5f9194f3e1bf11c8 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | cloud storage, information matching, sensor networks, smart sensing | |
| dcterms.subject | Electrical engineering | |
| dcterms.title | From Sensor Networks to the Cloud: Smart System for Data Sensing, Matching and Storage | |
| dcterms.type | Dissertation | |
