Maximizing the accuracy of sound based tracking via a low-cost network of reconfigurable embedded nodes

Subramanian, Varun; Umbarkar, Anurag; Doboli, Alex

doi:10.1109/ahs.2011.5963961

Cited by 2 publications

(5 citation statements)

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“…Trajectory prediction algorithms help in reducing errors by minimizing data loss and delays of data communications through optimized selection of data communication paths (DCPs). In addition to our previous method [28], this paper presents a new algorithm for predicting the trajectory of energy sources (sinks). The two algorithms differ depending on their assumptions on the trajectory characteristics.…”

Section: B Mobile Energy Source/sink Trajectory Predictionmentioning

confidence: 99%

“…The first algorithm is for trajectories that pass stochastically through a set of bounded regions. This algorithm was also presented in [28]. The second method assumes trajectories with both quasi-static parts and parts in bounded regions.…”

Section: B Mobile Energy Source/sink Trajectory Predictionmentioning

confidence: 99%

“…As explained in [28], a bounded trajectory refers to a mobile energy source's physical trajectory that is located inside a region defined by minimum and maximum gradients. The region is called trajectory approximating region (TAR).…”

Section: ) Stochastically Bounded Trajectoriesmentioning

confidence: 99%

“…8(b). Probability p n depends of the unknown length l trajectory of the trajectory inside the sub-region containing the node, the area Area sub−region of the sub-region, and the number N alt traj of alternative trajectories that are inside the sub-region and pass through the node [28] p n ∝ l trajectory Area sub−region N alt traj ≈ T x(t) 2 + y(t) 2 dt Area sub−region N alt traj (19) or…”

Section: ) Stochastically Bounded Trajectoriesmentioning

confidence: 99%

“…The trajectories are presented in [28], and cover different trajectory directions, velocity values, angle ranges, and rates of change of velocity and angle. The data communication paths (DCPs) were defined such that there are three unique path configurations for each of the four target points.…”

Section: A Trajectory Prediction and Optimizationmentioning

confidence: 99%

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Linear Programming-Based Optimization for Robust Data Modeling in a Distributed Sensing Platform

Umbarkar

Subramanian

Doboli

2014

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Creating accurate data models describing the dynamics of physical phenomena in time and space is important in optimized control and decision making. Models highlight various trends and patterns. However, producing accurate models is challenging as different errors are introduced by sampling platforms with limited resources, e.g., insufficient sampling rates, data loss due to buffer overwriting, reduced communication bandwidth, and long communication delays. Furthermore, the dynamics of the environment, like mobile energy sources and sinks, might further increase errors as resources must be shared between the sampling and communication activities. This paper presents a procedure to systematically construct robust data models using samples acquired through a grid network of embedded sensing devices with limited resources, like bandwidth and buffer memory. Models are in the form of ordinary differential equations. The procedure constructs local data models by lumping state variables. Local models are then collected centrally to produce global models. The proposed modeling scheme uses a linear programming formulation to compute the lumping level at each node, and the parameters of the networked sensing platform, i.e., best data communication paths and bandwidths. Two algorithms are described to predict the trajectories of mobile energy sources/sinks as predictions can further reduce data loss and delays during communication. The computed parameters and trajectory predictions are used to configure the local decision making routines of the networked sampling nodes. Even though the procedure can be used to model a broader set of phenomena, experiments discuss the effectiveness of the method for thermal modeling of ULTRASPARC Niagara T1 architecture. Experiments show that the presented method reduces the overall error between 58.29% and 76.91% with an average of 68.87%, and communication delay between −11.49% and 57.62% with an average of 21.85%.

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Section: B Mobile Energy Source/sink Trajectory Predictionmentioning

confidence: 99%

Section: B Mobile Energy Source/sink Trajectory Predictionmentioning

confidence: 99%

Section: ) Stochastically Bounded Trajectoriesmentioning

confidence: 99%

Section: ) Stochastically Bounded Trajectoriesmentioning

confidence: 99%

Section: A Trajectory Prediction and Optimizationmentioning

confidence: 99%

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