Reporting Flock Patterns

Benkert, Marc; Gudmundsson, Joachim; Hübner, Florian; Wolle, Thomas

doi:10.1007/11841036_59

Cited by 53 publications

(70 citation statements)

References 15 publications

(12 reference statements)

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“…So instead we generated short videos. 1 We use two types of data sets to evaluate our method: a synthetic data set generated using a slightly modified version of the NetLogo Flocking model [25,26], and a real-world data set consisting of deer, elk, and cattle, tracked in the Starkey project [19].…”

Section: Discussionmentioning

confidence: 99%

Trajectory Grouping Structure

Buchin

Kreveld³

et al. 2013

Lecture Notes in Computer Science

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Abstract.The collective motion of a set of moving entities like people, birds, or other animals, is characterized by groups arising, merging, splitting, and ending. Given the trajectories of these entities, we define and model a structure that captures all of such changes using the Reeb graph, a concept from topology. The trajectory grouping structure has three natural parameters that allow more global views of the data in group size, group duration, and entity inter-distance. We prove complexity bounds on the maximum number of maximal groups that can be present, and give algorithms to compute the grouping structure efficiently. We also study how the trajectory grouping structure can be made robust, that is, how brief interruptions of groups can be disregarded in the global structure, adding a notion of persistence to the structure. Furthermore, we showcase the results of experiments using data generated by the NetLogo flocking model and from the Starkey project. The Starkey data describe the movement of elk, deer, and cattle. Although there is no ground truth for the grouping structure in this data, the experiments show that the trajectory grouping structure is plausible and has the desired effects when changing the essential parameters. Our research provides the first complete study of trajectory group evolvement, including combinatorial, algorithmic, and experimental results.

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Section: Discussionmentioning

confidence: 99%

Trajectory Grouping Structure

Buchin

Kreveld³

et al. 2013

Lecture Notes in Computer Science

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“…Longer term objectives are to be able to compute decentrally meaningful movement patterns, such as flocks [52,53], convoys [54] or leadership [55]. These works employ trajectory-based data but can be adapted to work with data from cordon-structured networks.…”

Section: Discussionmentioning

confidence: 99%

Decentralized Monitoring of Moving Objects in a Transportation Network Augmented with Checkpoints

Both

Duckham

Laube

et al. 2012

The Computer Journal

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This paper examines efficient and decentralized monitoring of objects moving in a transportation network. Previous work in moving object monitoring has focused primarily on centralized information systems, like moving object databases and geographic information systems. In contrast, in this paper monitoring is in-network, requiring no centralized control and allowing for substantial spatial constraints to the movement of information. The transportation network is assumed to be augmented with fixed checkpoints that can detect passing mobile objects. This assumption is motivated by many practical applications, from traffic management in vehicle ad hoc networks to habitat monitoring by tracking animal movements. In this context, this paper proposes and evaluates a family of efficient decentralized algorithms for capturing, storing and querying the movements of objects. The algorithms differ in the restrictions they make on the communication and sensing constraints to the mobile nodes and the fixed checkpoints. The performance of the algorithms is evaluated and compared with respect to their scalability (in terms of communication and space complexity), and their latency (the time between when a movement event occurs, and when all interested nodes are updated with records about that event). The conclusions identify three key principles for efficient decentralized monitoring of objects moving past checkpoints: structuring computation around neighboring checkpoints; taking advantage of mobility diffusion and separating the generation and querying of movement information.

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“…Benkert et al [13] modified the original definition of a flock to be a set of entities moving close together during a time interval. The applied data mining approach bases on projection of 2D trajectories into multidimensional space and query operations on quadtrees.…”

Section: Mining Movement Patternsmentioning

confidence: 99%

“…Adding a proximity constraint, [13] defined an (n, k, p)-flock as any set M of n mobile sensor nodes, where for every moment in a time period of k consecutive time steps, there exists some disk of pattern radius p that contains every sensor node in M . More formally and in the context of our definition of mWSN above, an (n, k, p)-flock is a set M ⊆ A such that for every time instant t ∈ {t i , ..., t j } ⊆ T , with j − i + 1 ≥ k, there exists a circle of radius p that spatially contains l(m, t) for all m ∈ M .…”

Section: Decentralized Detection Of Flocksmentioning

confidence: 99%

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Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

Laube

Duckham

Wolle

2008

Geographic Information Science

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Abstract. Movement patterns, like flocking and converging, leading and following, are examples of high-level process knowledge derived from lowlevel trajectory data. Conventional techniques for the detection of movement patterns rely on centralized "omniscient" computing systems that have global access to the trajectories of mobile entities. However, in decentralized spatial information processing systems, exemplified by wireless sensor networks, individual processing units may only have access to local information about other individuals in their immediate spatial vicinity. Where the individuals in such decentralized systems are mobile, there is a need to be able to detect movement patterns using collaboration between individuals, each of which possess only partial knowledge of the global system state. This paper presents an algorithm for decentralized detection of the movement pattern flock, with applications to mobile wireless sensor networks. The algorithm's reliability is evaluated through testing on simulated trajectories emerging from unconstrained random movement and correlated random walk.

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Reporting Flock Patterns

Cited by 53 publications

References 15 publications

Trajectory Grouping Structure

Trajectory Grouping Structure

Decentralized Monitoring of Moving Objects in a Transportation Network Augmented with Checkpoints

Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

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