No abstract

Localization is fundamental for many wireless sensor network applications. Localizing ground-based fixed nodes through an airborne mobile anchor node is particularly useful for sensors deployed from the air, yet its dynamics are not well-understood. In this work-in-progress paper, we propose a new formulation for probabilistic localization using gradient descent, and compare it with a deterministic multilateration algorithm. We perform simulations to evaluate the localization accuracy using designated airborne mobile anchor's position. We also study the impact in both favorable and poor geometrical position of the mobile anchor node during localization. Results to date show that probabilistic gradient descent algorithm outperforms deterministic multilateration in all scenarios, with localization error reduced by up to 75%.

Tracking movement of mobile nodes has received significant scientific and commercial interest, but long term tracking of resource-constrained mobile nodes remains challenging due to the high energy consumption of satellite receivers. Cooperative position tracking has been proposed for energy efficiency, however, all the cooperative schemes use opportunistic cooperation and optimize for either energy or accuracy. Considering the existence of a reasonably stable group of mobile nodes like animals, birds, and mobile assets, we propose a cluster-based cooperative tracking algorithm, where cluster head centrally coordinates resource usage among cluster members. Variants of this strategy include the use of a cooperative Kalman filter with and without inertial sensor inputs to estimate nodes' positions. We use the Boid flocking algorithm to generate group position movements in 3D and perform various experiments to compare the energy and position accuracy tradeoff of our proposed scheme with individual-based tracking and existing cooperative schemes. We perform the experiments for fixed periodic GPS sampling and dynamic GPS sampling triggered by node position error uncertainty tolerance limit. Experiments results show that in periodic sampling scheme cooperative tracking yields more than one-quarter reduction in energy consumption and more than one-third improvement in position accuracy over individual-based tracking, however, results for dynamic sampling scheme are comparable with existing cooperative scheme.