The Wireless Sensor Networks (WSNs) have emerged as a new paradigm for collecting and processing data from physical environments, such as wild life sanctuaries, large warehouses, and battlefields. Users can access sensor data by issuing queries over the network, e.g., to find what are the 10 highest temperature values in the network. Typically, a WSN operates by constructing a logical topology, such as a spanning tree, built on top of the physical topology of the network. The constructed logical topology is then used to disseminate queries in the network, and also to process and return the results of such queries back to the user. A major challenge in this context is prolonging the network's lifetime that mainly depends on the energy cost of data communication via wireless radios, which is known to be very expensive as compared to the cost of data processing within the network.
In this research, we investigate some of the core problems that deal with the different aspects of in-network query processing in WSNs. In that context, we propose an efficient filtering based algorithm for the top-k query processing in WSNs. Through a systematic study of the top-k query processing in WSNs we propose several solutions in this thesis, which are applicable not only to the top-k queries, but also to in-network query processing problems in general. Specifically, we consider broadcasting and convergecasting, which are two basic operations that are required by many in-network query processing solutions.
Scheduling broadcasting and convergecasting is another problem that is important for energy efficiency in WSNs. Failure of communication links, which are common in WSNs, is yet another important issue that needs to be addressed.
In this research, we take a holistic approach to deal with the above problems while processing the top-k queries in WSNs. To this end, the thesis makes several contributions. In particular, our proposed solutions include new logical topologies, scheduling algorithms, and an overall sophisticated communication framework, which allows to process the top-k queries efficiently and with increased reliability. Extensive simulation studies reveal that
our solutions are not only energy efficient, saving up to 50% of the energy cost as compared to the current state-of-the-art solutions, but they are also robust to link failures.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/1333 |
| Date | 11 1900 |
| Creators | Malhotra, Baljeet Singh |
| Contributors | Nikolaidis, Ioanis (Computing Science, University of Alberta), Nascimento, Mario (Computing Science, University of Alberta), Harms, Janelle (Computing Science, University of Alberta), Rafiei, Davood (Computing Science, University of Alberta), Musilek, Petr (Electrical and Computer Engineering, University of Alberta), Labrinidis, Alexandros (Computer Science, University of Pittsburgh) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | 1018361 bytes, application/pdf |
| Relation | Baljeet Malhotra, Mario A. Nascimento, and Ioanis Nikolaidis. Better tree - better fruits: Using dominating set trees for max queries. Proc. of the 5th VLDB Workshop on Data Management for Sensor Networks (DMSN'08), pages 1-7, 2008., Baljeet Malhotra, Ioanis Nikolaidis, and Mario A. Nascimento. WISH-RIBS: Broadcast Scheduling and Opportunistic Failure Recovery in Wireless Networks. Proc. of the 8th Conf. on Communications Networks and Services Research (CNSR), pages 108-115, 2010., Baljeet Malhotra, Mario A. Nascimento, and Ioanis Nikolaidis. Exact Top-K Queries in Wireless Sensor Networks, IEEE Trans. on Knowledge and Data Engineering (accepted), 2010. |
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