Multi-Event Handlers for sensor-driven ubiquitous computing applications

Reilly, Sean

doi:10.1109/percom.2009.4912823

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…harvest a large amount of continuous time-based data, as audio or video, that is generated from several sensor nodes in a distributed and concurrent way [2][3][4]. Specifically, the adopted transmission paradigm in such environments is called event-streaming, which represents the generation and the transmission of data as a continuous stream of events reported by multiple sources [5].…”

Section: Introductionmentioning

confidence: 99%

“…Emerging applications based on wireless sensor networks (WSN) such as remote monitoring of biosignals, multimedia surveillance systems, and person locator services [1] ubiquitously (in this context, the term ubiquitous refers to the capacity to collect information from several places at the same time.) harvest a large amount of continuous time-based data, as audio or video, that is generated from several sensor nodes in a distributed and concurrent way [2][3][4]. Specifically, the adopted transmission paradigm in such environments is called event-streaming, which represents the generation and the transmission of data as a continuous stream of events reported by multiple sources [5].…”

Section: Introductionmentioning

confidence: 99%

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Temporal Alignment Model for Data Streams in Wireless Sensor Networks Based on Causal Dependencies

Cruz

Hernández

2014

International Journal of Distributed Sensor Networks

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New applications based on wireless sensor networks (WSN), such as person-locator services, harvest a large amount of data streams that are simultaneously generated by multiple distributed sources. Specifically, in a WSN this paradigm of data generation/transmission is known as event-streaming. In order to be useful, all the collected data must be aligned so that it can be fused at a later phase. To perform such alignment, the sensors need to agree on common temporal references. Unfortunately, this agreement is difficult to achieve mainly due to the lack of perfectly synchronized physical clocks and the asynchronous nature of the execution. Some solutions tackle the issue of the temporal alignment; however, they demand extra resources to the network deployment since they try to impose global references by using a centralized scheme. In this paper, we propose a temporal alignment model for data streams that identifies temporal relationships and which does not require the use of synchronized clocks, global references, centralized schemes, or additional synchronization signals. The identification of temporal relationships without the use of synchronized clocks is achieved by translating temporal dependencies based on a time-line to causal dependencies among streams. Finally, we show the viability and the effectiveness of the model by simulating it over a sensor network with multihop communication.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Temporal Alignment Model for Data Streams in Wireless Sensor Networks Based on Causal Dependencies

Cruz

Hernández

2014

International Journal of Distributed Sensor Networks

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“…The deployment of a UE implies the exchange of numerous data streams asynchronously generated by multiple distributed sources. Usually, the paradigm of data transmission within a UE is the event-streaming, which represents the generation and the transmission of data as a continuous stream of events reported by multiple sources [1], [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Temporal data alignment and association for event-streaming in ubiquitous environments based on fuzzy-causal dependencies

Cruz

Hernández

2014

2014 International Conference on Electronics, Communications and Computers (CONIELECOMP)

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Advances in sensors as well as wireless networks and ad-hoc networks have allowed new applications based on ubiquitous environments (UE) to be conceived. The deployment of a UE implies the exchange of numerous data streams asynchronously generated by multiple distributed sources. Such data must be fusioned at a certain stage. To perform the data fusion, the involved devices need to agree on some common temporal references of the whole system. To attain such references, the current solutions propose the use of centralized schemes and/or global references. Unfortunately, in a UE it is difficult to get global references mainly due to the asynchronous execution nature of the distributed systems. In this paper, we propose a distributed data alignment and association approach that establishes temporal references among the exchanged data streams, without requiring the use of synchronized clocks or centralized schemes. This is achieved by translating temporal/spatial references based on a time-line and physical locations to fuzzy-causal dependencies among streams. Through the establishment of the fuzzy causal dependencies, we infer a degree of temporal closeness among the data streams, which can be useful to correlate, filter or combine such data at a later processing.

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“…Such systems require the acquisition of a large number of data streams that are simultaneously generated and transmitted by multiple distributed devices. This paradigm of data generation is known as event-streaming, which represents the generation and the transmission of data as a continuous stream of events reported by multiple sources [2][3][4] [5]. For these reasons, in order to be useful to the application, all the collected data needs a preprocessing called data fusion 1 .…”

Section: Introductionmentioning

confidence: 99%

Data Alignment for Data Fusion in Wireless Multimedia Sensor Networks Based on M2M

Cruz¹

2012

KSII TIIS

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Advances in MEMS and CMOS technologies have motivated the development of low cost/power sensors and wireless multimedia sensor networks (WMSN). The WMSNs were created to ubiquitously harvest multimedia content. Such networks have allowed researchers and engineers to glimpse at new Machine-to-Machine (M2M) Systems, such as remote monitoring of biosignals for telemedicine networks. These systems require the acquisition of a large number of data streams that are simultaneously generated by multiple distributed devices. This paradigm of data generation and transmission is known as event-streaming. In order to be useful to the application, the collected data requires a preprocessing called data fusion, which entails the temporal alignment task of multimedia data. A practical way to perform this task is in a centralized manner, assuming that the network nodes only function as collector entities. However, by following this scheme, a considerable amount of redundant information is transmitted to the central entity. To decrease such redundancy, data fusion must be performed in a collaborative way. In this paper, we propose a collaborative data alignment approach for event-streaming. Our approach identifies temporal relationships by translating temporal dependencies based on a timeline to causal dependencies of the media involved.

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Multi-Event Handlers for sensor-driven ubiquitous computing applications

Cited by 3 publications

References 11 publications

Temporal Alignment Model for Data Streams in Wireless Sensor Networks Based on Causal Dependencies

Temporal Alignment Model for Data Streams in Wireless Sensor Networks Based on Causal Dependencies

Temporal data alignment and association for event-streaming in ubiquitous environments based on fuzzy-causal dependencies

Data Alignment for Data Fusion in Wireless Multimedia Sensor Networks Based on M2M

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