Aggregate Location Monitoring for Wireless Sensor Networks: A Histogram-Based Approach

Chow, Chi-Yin; Mokbel, Mohamed F.; He, Tian

doi:10.1109/mdm.2009.19

Cited by 9 publications

(11 citation statements)

References 32 publications

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“…Several papers have addressed aggregation for temporal and spatio-temporal data including instantaneous temporal aggregation [21], cumulative temporal aggregation [22], [23], [24], histogram-based aggregation [25], and multi-dimensional temporal aggregation [26]. These techniques differ in how a time line is partitioned into time intervals and how an aggregation group is associated with each time instant.…”

Section: Related Workmentioning

confidence: 99%

Aggregating and Disaggregating Flexibility Objects

Šikšnys

Valsomatzis

Hose

et al. 2015

IEEE Trans. Knowl. Data Eng.

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In many scientific and commercial domains we encounter flexibility objects, i.e., objects with explicit flexibilities in a time and an amount dimension (e.g., energy or product amount). Applications of flexibility objects require novel and efficient techniques capable of handling large amounts of such objects while preserving flexibility. Hence, this paper formally defines the concept of flexibility objects (flex-objects) and provides a novel and efficient solution for aggregating and disaggregating flex-objects. Out of the broad range of possible applications, this paper will focus on smart grid energy data management and discuss strategies for aggregation and disaggregation of flex-objects while retaining flexibility. This paper further extends these approaches beyond flex-objects originating from energy consumption by additionally considering flex-objects originating from energy production and aiming at energy balancing during aggregation. In more detail, this paper considers the complete life cycle of flex-objects: aggregation, disaggregation, associated requirements, efficient incremental computation, and balance aggregation techniques. Extensive experiments based on real-world data from the energy domain show that the proposed solutions provide good performance while satisfying the strict requirements.

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Section: Related Workmentioning

confidence: 99%

Aggregating and Disaggregating Flexibility Objects

Šikšnys

Valsomatzis

Hose

et al. 2015

IEEE Trans. Knowl. Data Eng.

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“…This work is further extended for aggregate location monitoring in sensor network. The proposed methods can provide high-quality location monitoring services [6].…”

Section: Related Workmentioning

confidence: 99%

Multidimensional Analysis of Atypical Events in Cyber-Physical Data

Tang

Kim

et al. 2012

2012 IEEE 28th International Conference on Data Engineering

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Abstract-A Cyber-Physical System (CPS) integrates physical devices (e.g., sensors, cameras) with cyber (or informational) components to form a situation-integrated analytical system that may respond intelligently to dynamic changes of the real-world situations. CPS claims many promising applications, such as traffic observation, battlefield surveillance and sensor-networkbased monitoring. One important research topic in CPS is about the atypical event analysis, i.e., retrieving the events from large amount of data and analyzing them with spatial, temporal and other multi-dimensional information. Many traditional approaches are not feasible for such analysis since they cannot describe the complex atypical events. In this study, we propose a new model of atypical cluster to effectively represent such events and efficiently retrieve them from massive data. The micro-cluster is designed to summarize an individual event, and the macrocluster is used to integrate the information from multiple events. To facilitate scalable, flexible and online analysis, the concept of significant cluster is defined and a guided clustering algorithm is proposed to retrieve significant clusters in an efficient manner. We conduct experiments on real datasets with the size of more than 50 GB, the results show that the proposed method can provide more accurate information with only 15% to 20% time cost of the baselines.

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“…Keeping accurate count of the number of people in outdoor space with the deployment of a wireless sensor network has been studied in [3]. Chow et al proposed a histogram based approach to answer aggregate location queries, i.e., how many people are located in a given region, by maintaining a histogram according to the reported counts from those "counting sensors".…”

Section: B People Countingmentioning

confidence: 99%

“…Secondly, indoor space usually cannot be modeled by a regular grid data structure (which is employed by the histogram based approach) as the size of a room and shape of rooms may be quite different. The advantage of indoor space is the more restricted movement of people that we intend to take advantage of (while [3] only uses the assumption that people generally don't move too fast).…”

Section: B People Countingmentioning

confidence: 99%

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Tracking the Dynamic Distribution of People in Indoor Space with Noisy Partitioning Sensors

Wang

Huang

2012

2012 IEEE 13th International Conference on Mobile Data Management

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Abstract-The term "indoor" here refers generally to enclosed space partitioned into subspaces with connecting doors or gates. Examples include the inside of office buildings, amusement parks, and indoor shopping malls. In many applications, it is desirable to keep track of the distribution of people within the enclosed space. These applications range from smart house with automatically controlled air-conditioning and lighting, shopping assistance allocation, to business intelligence. Contact sensors are accurate but obstructive. Non-contact sensors such as automated visual recognition and RFID tags can be expensive in calibration or cost in order to obtain accurate readings. An interesting costoptimization problem is to understand how to obtain relatively accurate dynamic distribution of people from inaccurate point sensors using correlations among the point sensor readings. The paper formalizes a framework that uses a flow model and a particle-filter learning algorithm for this optimization problem based on the hypotheses that (1) there is an underlying stochastic "flow model" of people moving within the space, and (2) with the help of this flow model, counting of people can be made more accurate by taking advantage of the continuous, albeit inaccurate, point sensor readings. The main challenge is that the performance of particle filters deteriorates rapidly with the number of doors in the indoor space. We propose a divide and conquer method that uses relatively more accurate sensors at a few strategically chosen locations to achieve overall good accuracy. Experimental results given herein show that the algorithm is effective.

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Aggregate Location Monitoring for Wireless Sensor Networks: A Histogram-Based Approach

Abstract: Abstract

Cited by 9 publications

References 32 publications

Aggregating and Disaggregating Flexibility Objects

Aggregating and Disaggregating Flexibility Objects

Multidimensional Analysis of Atypical Events in Cyber-Physical Data

Tracking the Dynamic Distribution of People in Indoor Space with Noisy Partitioning Sensors

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