THE USE OF MOBILE AGENTS FOR DATA COLLECTION IN DYNAMIC SENSOR NETWORK ENVIRONMENTS
DOI:
https://doi.org/10.18372/2310-5461.69.20943Keywords:
sensor networks, mobile agent, sensor energy consumption, sensor parameter drift, drift correction algorithm, Kalman filterAbstract
The problem of c measurement results gathering and optimizing traffic in sensor networks with dense coverage of the territory is considered. The solution to the problem is based on data decorrelation, i.e., the elimination of their redundancy. An analysis deterministic and stochastic methods using mobile agents is carried out. To eliminate the limitations of the deterministic approach, it is proposed to apply the method of controlled directed diffusion, based on the theory of controlled Markov processes. An improved network control method has been developed, which combines the deterministic and stochastic methods. In the control process using mobile agents, the optimal transmission rate is maintained with restrictions on the energy consumption of sensors. A generalized method for minimizing the volume of the bundle of routes by the criteria of delivery speed with restrictions on energy consumption has been developed.
A method for detecting errors and correcting sensor measurement results in the wireless sensor networks has been developed, based on the assumption of spatiotemporal correlation of measurement results in neighboring sensors and the absence of mutual correlation of drift processes of technical parameters. The mathematical basis of the method is the Kalman non-search filtering. Analytical expressions for calculating the predicted future measurement results have been obtained. Vector forecast data are used in the Kalman non-search filter to estimate the actual values of the measured quantities. A modified measurement result correction scheme is proposed, in which the parameter drift is analyzed simultaneously in a dense neighborhood of the nearest sensors. This solution is associated with additional computational costs, however, due to parallelization, a faster reduction in the number of drift errors is obtained, and the efficiency of the sensor parameter drift correction algorithm for the network as a whole significantly increases.
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