Escalation: Complex Event Detection in Wireless Sensor Networks

Zoumboulakis, Michael; Roussos, George

doi:10.1007/978-3-540-75696-5_17

Cited by 41 publications

(27 citation statements)

References 19 publications

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“…Then, Parsimonious Covering Theory (PCT) [Reggia and Peng 1987] is used to derive abstractions from SAX patterns to analyse sensor data. [Zoumboulakis and Roussos 2007] detect patterns to describe complex events with reasonable accuracy by reducing dimensionality through converting time-series sensor data into SAX representations.…”

Section: :45mentioning

confidence: 99%

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

2018

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Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data is usually multi-variant streams that are heterogeneous, sporadic, multi-modal and spatio-temporal. IoT data can be disseminated with di erent granularities and have diverse structures, types and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised which hinders their scalability. The primary objective of this paper is to provide a holistic overview of the state-of-the-art on indexing, discovery and ranking of IoT data. The paper aims to pave the way for researchers to design, develop, implement and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services.

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Section: :45mentioning

confidence: 99%

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

2018

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“…The problem with such simple detectors is that they are prone to false alarms because they assume that only fires and nothing else may produce smoke. Generally speaking, the existing WSN-based fire detection techniques are either threshold-based [10,16,17,18] or pattern-matching based [6,9,12,21]. Threshold based techniques define a threshold value for their sensor readings and when the sensor value is larger or smaller than the pre-defined threshold value, an alarm is generated.…”

Section: Analysis Of Fire Detection Techniquesmentioning

confidence: 99%

Fire Data Analysis and Feature Reduction Using Computational Intelligence Methods

Bahrepour

Zwaag

Meratnia

et al. 2010

Advances in Intelligent Decision Technologies

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Fire is basically the fast oxidation of a substance that produces gases and chemical productions. These chemical productions can be read by sensors to yield an insight about type and place of the fire. However, as fires may occur in indoor or outdoor areas, the type of gases and therefore sensor readings become different. Recently, wireless sensor networks (WSNs) have been used for environmental monitoring and real-time event detection because of their low implementation costs and their capability of distributed sensing and processing. In this paper, the authors investigate spatial analysis of data for indoor and outdoor fires using data-mining approaches for WSN-based fire detection purposes. This paper also delves into correlated data features in fire data sets and investigates the most contributing features for fire detection applications.

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“…Then, m stops monitoring the clause that contains x * (line 3) and examines other clauses (line 4). If it finds a clause whose atoms are all false (line 5) the master starts monitoring ) and concludes that F = true (lines [13][14].…”

Section: Algorithmmentioning

confidence: 99%

“…Existing solutions are for simple event descriptions, such as attribute-based events [7], threshold-based predicates [8]- [12], or pattern-matching events [13]. Detection of non-parametric complex events was proposed [14]; it requires data collection and data processing at centralized sites to learn the environment and detect unusual events. Our work instead aims to monitor parametric events described by generic Boolean expressions.…”

Section: Introductionmentioning

confidence: 99%

DEAMON: Energy-efficient Sensor Monitoring

Shin

Tsang

Kotz

et al. 2009

2009 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks

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Abstract-In people-centric opportunistic sensing, people offer their mobile nodes (such as smart phones) as platforms for collecting sensor data. A sensing application distributes sensing 'tasks,' which specify what sensor data to collect and under what conditions to report the data back to the application. To perform a task, mobile nodes may use on-board sensors, a body-area network of personal sensors, or sensors from neighboring nodes that volunteer to contribute their sensing resources. In all three cases, continuous sensor monitoring can drain a node's battery.We propose DEAMON (Distributed Energy-Aware MONitoring), an energy-efficient distributed algorithm for long-term sensor monitoring. Our approach assumes only that mobile nodes are tasked to report sensor data under conditions specified by a Boolean expression, and that a network of nearby sensor nodes contribute to monitoring subsets of the task's sensors. Our algorithm to select sensor nodes and to monitor the sensing condition conserves energy of all nodes by limiting sensing and communication operations. We evaluate DEAMON with a stochastic analysis and with simulation results, and show that it should significantly reduce energy consumption.

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Escalation: Complex Event Detection in Wireless Sensor Networks

Cited by 41 publications

References 19 publications

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Fire Data Analysis and Feature Reduction Using Computational Intelligence Methods

DEAMON: Energy-efficient Sensor Monitoring

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