19,684 research outputs found
Integrating sensor streams in pHealth networks
Personal Health (pHealth) sensor networks are generally used to monitor the wellbeing of both athletes and the general public to inform health specialists of future and often serious ailments. The problem facing these domain experts is the scale and quality of data they must search in order to extract meaningful results. By using peer-to-peer sensor architectures and a mechanism for reducing the search space, we can, to some extent, address the scalability issue. However, synchronisation and normalisation of distributed sensor streams remains a problem in many networks. In the case of pHealth sensor networks, it is crucial for experts to align multiple sensor readings before query or data mining activities can take place. This paper presents a system for clustering and synchronising sensor streams in preparation for user queries
StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge
Today, massive amounts of streaming data from smart devices need to be
analyzed automatically to realize the Internet of Things. The Complex Event
Processing (CEP) paradigm promises low-latency pattern detection on event
streams. However, CEP systems need to be extended with Machine Learning (ML)
capabilities such as online training and inference in order to be able to
detect fuzzy patterns (e.g., outliers) and to improve pattern recognition
accuracy during runtime using incremental model training. In this paper, we
propose a distributed CEP system denoted as StreamLearner for ML-enabled
complex event detection. The proposed programming model and data-parallel
system architecture enable a wide range of real-world applications and allow
for dynamically scaling up and out system resources for low-latency,
high-throughput event processing. We show that the DEBS Grand Challenge 2017
case study (i.e., anomaly detection in smart factories) integrates seamlessly
into the StreamLearner API. Our experiments verify scalability and high event
throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner:
Distributed Incremental Machine Learning on Event Streams: Grand Challenge.
In Proceedings of the 11th ACM International Conference on Distributed and
Event-based Systems (DEBS '17), 298-30
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
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