Decentralized Movement Pattern Detection amongst Mobile Geosensor Nodes

Abstract

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.

Deferred decentralized movement pattern mining for geosensor networks

“…Benkert et al (2008) presented several efficient centralized algorithms for approximating the flock detection problem for short flock contiguities (small k). Similarly, Laube et al (2008) presented a decentralized algorithm based on a local extrapolation heuristic, and an analysis of the inevitable errors of omission and/or commission associated with any heuristic. In both cases the results of the presented algorithms depend very much on the characteristics of the input point sets and there is no single best solution on offer.…”

“…In contrast to the decentralized flock detector FLAGS presented in Laube et al (2008), the DDIG algorithm discussed here requires localized sensor nodes. Constant localization has obvious advantages but comes at a potentially high price in terms of battery life, weight, and hardware costs.…”

“…Although GPS provides one possible positioning technology, many other positioning technologies are available and more suited to low-power, low-cost geosensor networks (such as WiFi triangulation, RF or ultrasound range-and direction-finding, Zhao and Guibas 2004). Whereas localization may be part of the initialization of a static network, in mobile wireless sensor networks (mWSN, Laube et al 2008), a node's position constantly changes and needs to be constantly updated. Previous work on the decentralized detection of flocking patterns did not imply position knowledge , this paper by contrast explicitly aims at exploiting the advantage of sensor nodes knowing their coordinate position.…”

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

“…Decentralized algorithms are an active area of research in spatial computing and spatial information science (e.g. [1][2][3][4]), in part because they are well suited to use with new technologies like wireless sensor networks and vehicle ad hoc networks (VANETs). Using a decentralized algorithm enables queries about spatiotemporal events to be satisfied partly or wholly in the network, without the need to communicate and collate information about object movements within a single, centralized information system.…”

“…if count(*) from tmp ≥ n and (select time from g i > select time from last.j ) then 18: insert into j values (i d, time 2 , f ids 2 ) 19: send (link, j , n, l) to cordon with ID (select c from g i ) 20: if count(*) from j ≥ l then 21: return j as record of group movement Algorithm 5. In this algorithm, each cordon stores groups of at least n fish passing in the same direction within the time period t. This group table also includes the time fish passed the cordon and the ID of the cordon they were swimming toward.…”