Wireless integrated network sensors
Communications of the ACM
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Continuous queries over data streams
ACM SIGMOD Record
Issues in data stream management
ACM SIGMOD Record
TAG: a Tiny AGgregation service for ad-hoc sensor networks
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
TOSSIM: accurate and scalable simulation of entire TinyOS applications
Proceedings of the 1st international conference on Embedded networked sensor systems
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ACM SIGMOD Record
Lightweight detection and classification for wireless sensor networks in realistic environments
Proceedings of the 3rd international conference on Embedded networked sensor systems
RTSTREAM: Real-Time Query Processing for Data Streams
ISORC '06 Proceedings of the Ninth IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
S-ToPSS: semantic Toronto publish/subscribe system
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
In-network aggregation trade-offs for data collection in wireless sensor networks
International Journal of Sensor Networks
Statistical en-route filtering of injected false data in sensor networks
IEEE Journal on Selected Areas in Communications
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Detecting the failure of a data stream is relatively easy when the stream is continually full of data. The transfer of large amounts of data allows for the simple detection of interference, whether accidental or malicious. However, during interference, data transmission can become irregular, rather than smooth. When the traffic is intermittent, it is harder to detect when failure has occurred and may lead to an application at the receiving end requesting retransmission or disconnecting. Request retransmission places additional load on a system and disconnection can lead to unnecessary reversion to a checkpointed database, before reconnecting and reissuing the same request or response. In this paper, we model the traffic in data streams as a set of significant events, with an arrival rate distributed with a Poisson distribution. Once an arrival rate has been determined, over-time, or lost, events can be determined with a greater chance of reliability. This model also allows for the alteration of the rate parameter to reflect changes in the system and provides support for multiple levels of data aggregation. One significant benefit of the Poisson-based model is that transmission events can be deliberately manipulated in time to provide a steganographic channel that confirms sender/receiver identity.