Reporting Leaders and Followers among Trajectories of Moving Point Objects

Andersson, Mattias; Gudmundsson, Joachim; Laube, Patrick; Wolle, Thomas

doi:10.1007/s10707-007-0037-9

Cited by 91 publications

(93 citation statements)

References 52 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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“…Methods for Comparison REMO. REMO [2] targets finding leaders among a group of moving objects for k consecutive timestamps. To compare it with our method, we set the size of the potential followers to 1.…”

Section: Methodsmentioning

confidence: 99%

“…A limited number of methods have been proposed to detect following relationships in movement data [11], [12], [2], [6]. In the REMO framework [11], [12], [2] and the chasing pattern proposed in [6], a leader should appears in the front region of the follower(s) or move in the same direction.…”

Section: A Related Workmentioning

confidence: 99%

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Mining Following Relationships in Movement Data

Crofoot

2013

2013 IEEE 13th International Conference on Data Mining

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Abstract-Movement data have been widely collected from GPS and sensors, allowing us to analyze how moving objects interact in terms of space and time and to learn about the relationships that exist among the objects. In this paper, we investigate an interesting relationship that has not been adequately studied so far: the following relationship. Intuitively, a follower has similar trajectories as its leader but always arrives at a location with some time lag. The challenges in mining the following relationship are: (1) the following time lag is usually unknown and varying; (2) the trajectories of the follower and leader are not identical; and (3) the relationship is subtle and only occurs in a short period of time. In this paper, we propose a simple but practical method that addresses all these challenges. It requires only two intuitive parameters and is able to mine following time intervals between two trajectories in linear time. We conduct comprehensive experiments on both synthetic and real datasets to demonstrate the effectiveness of our method.

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“…They are the spatiotemporal "trace" left behind by the behavior of moving entities [2]. Examples of movement patterns include flocking as in a "mob" of sheep [3], leading and following found in group dynamics [4,5], or converging and diverging of pedestrians in crowding scenarios [6,7]. Figure 1 illustrates the movement pattern of a prototypical "flock".…”

Section: Introductionmentioning

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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Reporting Leaders and Followers among Trajectories of Moving Point Objects

Cited by 91 publications

References 52 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

Mining Following Relationships in Movement Data

Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

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