Abstract
This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes’ coordinate positions. Experimental investigations confirm that our algorithm is efficient, with overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks.
Highlights
Our geographic world is highly dynamic, and monitoring change over space is of considerable interest in many scientific communities
The limited granularity of geosensor networks imposes restrictions on the capability to infer information about surface networks and, so, events occurring on a surface network
An important question in this study is : what events can occur on a dynamic scalar field monitored by a geosensor network? In short, what changes in state are salient for surface networks?
Summary
Our geographic world is highly dynamic, and monitoring change over space is of considerable interest in many scientific communities. The limited granularity of geosensor networks imposes restrictions on the capability to infer information about surface networks and, so, events occurring on a surface network Based on these limitations, recent research has yielded decentralized algorithms that are capable of identifying critical points and edges in a static field monitored by a geosensor network [5,6,7]. To date, this work has not addressed the problem of efficiently and accurately identifying events occurring on monitored surface networks Building on this previous research, this paper: (1) provides a rigorous definition of the fundamental events that can occur on surface networks; and (2) develops and tests a decentralized and coordinate-free algorithm to capture those events efficiently. The formal model of a geosensor network and the extended definitions of critical points are defined, leading to the design of a decentralized algorithm for identifying events at critical points in a dynamic scalar field (Section 4).
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