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Journal ArticleDOI

Pattern-based event detection in sensor networks

TLDR
This paper proposes a pattern-based event detection approach and integrates the approach into an in-network sensor query processing framework, and defines the general patterns as well as five types of basic patterns for event specification.
Abstract
Many applications of wireless sensor networks monitor the physical world and report events of interest. To facilitate event detection in these applications, in this paper we propose a pattern-based event detection approach and integrate the approach into an in-network sensor query processing framework. Different from existing threshold-based event detection, we abstract events into patterns in sensory data and convert the problem of event detection into a pattern matching problem. We focus on applying single-node temporal patterns, and define the general patterns as well as five types of basic patterns for event specification. Considering the limited storage on sensor nodes, we design an on-node cache manager to maintain the historical data required for pattern matching and develop event-driven processing techniques for queries in our framework. We have conducted experiments using patterns for events that are extracted from real-world datasets. The results demonstrate the effectiveness and efficiency of our approach.

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Citations
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Journal ArticleDOI

A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things

TL;DR: A survey of the requirements and solutions and challenges in the area of information abstraction and an efficient workflow to extract meaningful information from raw sensor data based on the current state-of-the-art in this area are provided.
Journal ArticleDOI

Integration of Markov random field with Markov chain for efficient event detection using wireless sensor network

TL;DR: The proposed event detection scheme with WSN adopts a hierarchical structure to efficiently integrate the spatial and temporal correlation of sensor data to effectively increase the detection accuracy and reduce communication cost, in comparison with the existing schemes.
Journal ArticleDOI

A model for integrating heterogeneous sensory data in IoT systems

TL;DR: A model for integrating the heterogeneous sensory data generated in a IoT system based on Hidden Markov Process is proposed and the distributed algorithm for constructing such a model is presented.
Proceedings ArticleDOI

Abnormal-Node Detection Based on Spatio-Temporal and Multivariate-Attribute Correlation in Wireless Sensor Networks

TL;DR: A new framework for detecting abnormal nodes in clustered heterogeneous WSNs makes use of observed spatiotemporal and multivariate-attribute sensor correlations, while considering the background knowledge of the monitored environment and captures the correlation and discovers abnormal nodes efficiently.
Proceedings ArticleDOI

Spatio-temporal Event Detection: A Hierarchy Based Approach for Wireless Sensor Network

TL;DR: This paper introduces an event detection scheme, which adopts a hierarchical architecture to efficiently integrate the spatial and temporal correlation of the sensor data and a fusion algorithm considering both the weight of the sensors and spatial information is used in Markov random field to properly fuse the decisions of the Sensor nodes.
References
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Book

Introduction to Modern Information Retrieval

TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Proceedings ArticleDOI

Directed diffusion: a scalable and robust communication paradigm for sensor networks

TL;DR: This paper explores and evaluates the use of directed diffusion for a simple remote-surveillance sensor network and its implications for sensing, communication and computation.
Journal ArticleDOI

TinyDB: an acquisitional query processing system for sensor networks

TL;DR: This work evaluates issues in the context of TinyDB, a distributed query processor for smart sensor devices, and shows how acquisitional techniques can provide significant reductions in power consumption on the authors' sensor devices.
Book ChapterDOI

Model-driven data acquisition in sensor networks

TL;DR: This paper enrichs interactive sensor querying with statistical modeling techniques, and demonstrates that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy.
Proceedings ArticleDOI

An online algorithm for segmenting time series

TL;DR: This paper undertake the first extensive review and empirical comparison of all proposed techniques for mining time-series data with fatal flaws and introduces a novel algorithm that is empirically show to be superior to all others in the literature.
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