Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines

Laube, Patrick; Kreveld, Marc van; Imfeld, Stephan

doi:10.1007/3-540-26772-7_16

Cited by 121 publications

(118 citation statements)

References 19 publications

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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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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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“…Laube et al proposed a geographic data mining approach to detect generic aggregation patterns such as flocking behaviour and convergence in geo-spatial lifeline data [6]. One of the approaches similar to their system is to use and find similar sub-trajectories instead of the whole.…”

Section: Related Workmentioning

confidence: 99%

Classification of Multi-Dimensional Trajectories Using Textual Approximation

Xuan¹,

Huang²,

Kawagoe³

2014

IJCCE

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Abstract-Classification and searching time series in the multidimensional space is a big challenge. There has been much research work on this field for many years. TAX (Textual ApproXimation) is a method for searching time series data such as stock or electrocardiogram data. The main idea behind TAX is extract a set of temporal-terms from time series data to approximate using document retrieval methods. The main problem of applying TAX to multidimensional data, such as moving object trajectory data is that how we extract temporal-terms from multidimensional data. In this paper, we propose a novel method to obtain temporal-terms by decomposing original multidimensional data into smaller segmentations, deploying a clustering method to group those segmentations into groups, and assigning a temporal-term for each group. Our research focuses on two-dimensional moving object, representing trajectories and on classification of a large set of the moving object trajectories. The experimental results confirm that our proposed method is effective in multi-multidimensional data classification.

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“…In 2004, Laube et al [12] defined a collection of spatiotemporal patterns based on direction of movement and location, e.g. flock, leadership, convergence and encounter, and they gave algorithms to compute them efficiently.…”

Section: Mining Movement Patternsmentioning

confidence: 99%

“…Laube et al [12] use the term "flocking" to describe a collective movement pattern expressed by a set of moving entities. In our context, the entities refer to the moving sensor nodes of a mWSN.…”

Section: Decentralized Detection Of Flocksmentioning

confidence: 99%

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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Finding REMO — Detecting Relative Motion Patterns in Geospatial Lifelines

Cited by 121 publications

References 19 publications

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

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

Classification of Multi-Dimensional Trajectories Using Textual Approximation

Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

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