Proximity mining: finding proximity using sensor data history

Takada,; Kurihara,; Hirotsu,; Sugawara, Toshiharu

doi:10.1109/mcsa.2003.1240774

Cited by 17 publications

(9 citation statements)

References 10 publications

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“…Examples of target applications are planning for the intelligent behaviours in sensornetwork and ubiquitous-computing systems with many devices, such as sensors, effectors and robots (Figure 1), where agents reside in these devices to control them (Takada et al, 2003). Examples of application scenarios are described in .…”

Section: Planning Architecturementioning

confidence: 99%

Effective Planning for Conflicting Situations for Ubiquitous Sensor Network Environments

Sugawara¹,

Kurihara²,

Hirotsu³

et al. 2010

Autonomous Agents

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Section: Planning Architecturementioning

confidence: 99%

Effective Planning for Conflicting Situations for Ubiquitous Sensor Network Environments

Sugawara¹,

Kurihara²,

Hirotsu³

et al. 2010

Autonomous Agents

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“…We call this concept of two nodes experiencing similarities in the behavior of the wireless medium as wireless congruity. Although the concept of congruity is not previously explored, we note that prior work in [7] uses similar concepts in a very different problem domain.…”

Section: Concept Of Congruitymentioning

confidence: 99%

Towards Secure Localization Using Wireless "Congruity'

Mishra

Rayanchu

Shukla

et al. 2007

Eighth IEEE Workshop on Mobile Computing Systems and Applications

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Traditional methods for localization in wireless networks rely on the correlation of the received signal strength with physical distance. It is also well known, that these mechanisms fail in an adversarial setting due to the lack of robustness of the signal strength property to malicious intent. In this paper, we present a property of the wireless medium, which we call 'wireless congruity', that captures the relative similarities in wireless media characteristics (such as packet receptions, idle channel time, etc.) as observed by two receivers that are in physical proximity of each other. We show that wireless congruity holds promise for secure localization by presenting an initial yet encouraging set of results obtained through extensive experimentation in a rich indoor wireless environment. I. INTRODUCTIONWith the growth of m-Commerce applications, systems that provide location information for a mobile user are becoming popular. Such localization systems can be classified into two: the ones that use dedicated hardware for localization, such as Cricket [1] which uses ultrasound, and the ones that operate through off-the-shelf 802.11 hardware. The systems in the latter category are of particular commercial interest due to their ease of deployment over the widely available 802.11 WLANs and the resultant cost-savings. Thus, a lot of prior research has focused on building accurate indoor localization systems [2], [3], [4] which use the signal strength property of wireless transmissions to assist in location inference. While signal strength is a good indicator of physical distance, its predictability has become an Achilles heel when faced with the issue of validating it in the presence of malicious intent -it is possible for an attacker to 'guess' the signal strength at a location without being there physically.

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“…Trend analysis has been used to cluster time series in many domains such as time series [19,14], bioinformatics [15] and ubiquitous computing [16]. Yoon et al proposed six trend indicators.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Continuous Trend-Based Classification of Streaming Time Series

Kontaki

Papadopoulos

Manolopoulos

2005

Advances in Databases and Information Systems

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Abstract. Trend analysis of time series data is an important research direction. In streaming time series the problem is more challenging, taking into account the fact that new values arrive for the series, probably in very high rates. Therefore, effective and efficient methods are required in order to classify a streaming time series based on its trend. Since new values are continuously arrive for each stream, the classification is performed by means of a sliding window which focuses on the last values of each stream. Each streaming time series is transformed to a vector by means of a Piecewise Linear Approximation (PLA) technique. The PLA vector is a sequence of symbols denoting the trend of the series, and it is constructed incrementally. The PLA is composed of a series of segments representing the trend of the raw data (either UP or DOWN). Efficient in-memory methods are used in order to: 1) determine the class of each streaming time series and 2) determine the streaming time series that comprise a specific trend class. Performance evaluation based on real-life datasets is performed, which shows the efficiency of the proposed approach both with respect to classification time and storage requirements. The proposed method can be used in order to continuously classify a set of streaming time series according to their trends, to monitor the behavior of a set of streams and to monitor the contents of a set of trend classes.

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Proximity mining: finding proximity using sensor data history

Cited by 17 publications

References 10 publications

Effective Planning for Conflicting Situations for Ubiquitous Sensor Network Environments

Effective Planning for Conflicting Situations for Ubiquitous Sensor Network Environments

Towards Secure Localization Using Wireless "Congruity'

Continuous Trend-Based Classification of Streaming Time Series

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