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Power Optimization of Sensor Networks for Landslide Monitoring

Sensor Networks are one of the main systems of monitoring and collecting data from harsh and unattended environments. So that Sensor Networks can provide timely critical warnings, such systems need to be capable of aggregating and analyzing data with minimum delay. When Sensor Networks are exposed to harsh environments the probability of their sensor nodes failing is high. The research deals with challenges in the time involved for data aggregation in hierarchical networks like balanced tree structure and progressive tree structure. Both a progressive tree structure and a balanced tree structure are constructed with a set number of Sensor Nodes (SN) and Intermediate nodes (INs). In a balanced tree structure, SNs are divided equally among the INs. A progressive tree structure is constructed with the first two INs having the same amount of SNs attached, after that, each IN will have one additional attached SN than the previous IN. By carefully adjust the WSN tree structure, one can reduce the energy consumed by the whole distributed network. The concept backing the hypothesis is that as the number of computations and transmissions is reduced, the energy consumed by the processing and the radio system will also reduce, which in turn reduces the overall energy consumed by the WSN. Simulation tools include MATLAB and NS2. Implementation in MicaZ motes using NesC.

Balanced Sensor Network
Progressive Sensor Network
IN failure in Balanced Sensor network


Team Members
Leader Of the Team : P. Venkat Rangan
Faculty : Maneesha V. Ramesh
Student: Sreedevi A. G.

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