2,664 research outputs found
SimpleTrack:Adaptive Trajectory Compression with Deterministic Projection Matrix for Mobile Sensor Networks
Some mobile sensor network applications require the sensor nodes to transfer
their trajectories to a data sink. This paper proposes an adaptive trajectory
(lossy) compression algorithm based on compressive sensing. The algorithm has
two innovative elements. First, we propose a method to compute a deterministic
projection matrix from a learnt dictionary. Second, we propose a method for the
mobile nodes to adaptively predict the number of projections needed based on
the speed of the mobile nodes. Extensive evaluation of the proposed algorithm
using 6 datasets shows that our proposed algorithm can achieve sub-metre
accuracy. In addition, our method of computing projection matrices outperforms
two existing methods. Finally, comparison of our algorithm against a
state-of-the-art trajectory compression algorithm show that our algorithm can
reduce the error by 10-60 cm for the same compression ratio
Data and resource management in wireless networks via data compression, GPS-free dissemination, and learning
“This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting the data of interest. As most of the low-cost wireless sensors won’t be equipped with a GPS module, the virtual coordinates are used to estimate the locations. The proposed work utilizes the anchor-based virtual coordinate system and DV-Hop (Distance vector of hops to anchors) to estimate the relative location of nodes to anchors. Also, it uses circle and hyperbola constraints to encode the position of interest (POI) and any user-defined trajectory into a data request message which allows only the sensors in the POI and routing trajectory to collect and route. It also provides location anonymity by avoiding using and transmitting GPS location information. This has been extended also for heterogeneous WSNs and refined the encoding algorithm by replacing the circle constraints with the ellipse constraints. Last, it proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. It reduces the computation time of encoding geospatial trajectory by more than 90% and preserves the location anonymity for the local edge servers”--Abstract, page iv
Collectively Simplifying Trajectories in a Database: A Query Accuracy Driven Approach
Increasing and massive volumes of trajectory data are being accumulated that
may serve a variety of applications, such as mining popular routes or
identifying ridesharing candidates. As storing and querying massive trajectory
data is costly, trajectory simplification techniques have been introduced that
intuitively aim to reduce the sizes of trajectories, thus reducing storage and
speeding up querying, while preserving as much information as possible.
Existing techniques rely mainly on hand-crafted error measures when deciding
which point to drop when simplifying a trajectory. While the hope may be that
such simplification affects the subsequent usability of the data only
minimally, the usability of the simplified data remains largely unexplored.
Instead of using error measures that indirectly may to some extent yield
simplified trajectories with high usability, we adopt a direct approach to
simplification and present the first study of query accuracy driven trajectory
simplification, where the direct objective is to achieve a simplified
trajectory database that preserves the query accuracy of the original database
as much as possible. Specifically, we propose a multi-agent reinforcement
learning based solution with two agents working cooperatively to collectively
simplify trajectories in a database while optimizing query usability. Extensive
experiments on four real-world trajectory datasets show that the solution is
capable of consistently outperforming baseline solutions over various query
types and dynamics.Comment: This paper has been accepted by ICDE 202
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