Title: A Sensor Network Data Aggregation Technique
Department of Computer Science
Abstract: This is a Computer Science M.Sc Thesis on Existing sensor network data aggregation techniques assume that the nodes are preprogrammed and send data to a central sink for offline querying and analysis. This approach faces two major drawbacks. First, the system behavior is preprogrammed and cannot be modified on the fly. Second, the increased energy wastage due to the communication overhead will result in decreasing the overall system lifetime. Thus, energy conservation is of prime consideration in sensor network protocols in order to maximize the network’s operational lifetime. In this paper, we give an energy efficient approach to query processing by implementing new optimization techniques applied to in-network aggregation. We first discuss earlier approaches in sensors data management and highlight their disadvantages. We then present our approach and evaluate it through several simulations to prove its efficiency, competence and effectiveness.
Project Intro: One example of monitoring applications includes organizing vehicle traffic in a large city [1]. Unfortunately, sensor data is subject to several sources of errors resulting from power limitations, wireless communication, latency, throughput, and various environmental effects. Such errors may seriously impact the answer to any query posed in the network. Current production of motes are roughly 2cm x 4cm x 1cm and are equipped with a radio, a processor, memory, a small package of AA batteries, and a collection of sensors. Additionally, new nodes are not passive devices [2], they are capable of sharing, computing and combining sensor readings; therefore, they are becoming tiny computers with different functionalities. Smart-sensor devices have been developed to an extent that it is now feasible to deploy large, distributed networks of such nodes [3, 4, 5, and 6] and extracting the data from the network is an essential step for the applications to work.
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Index Keywords : Sensor Networks, Data Base, Data Fusion, Aggregation, Indexing, Energy Efficiency
