22,793 research outputs found
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
Performance Comparison of the RPL and LOADng Routing Protocols in a Home Automation Scenario
RPL, the routing protocol proposed by IETF for IPv6/6LoWPAN Low Power and
Lossy Networks has significant complexity. Another protocol called LOADng, a
lightweight variant of AODV, emerges as an alternative solution. In this paper,
we compare the performance of the two protocols in a Home Automation scenario
with heterogenous traffic patterns including a mix of multipoint-to-point and
point-to-multipoint routes in realistic dense non-uniform network topologies.
We use Contiki OS and Cooja simulator to evaluate the behavior of the
ContikiRPL implementation and a basic non-optimized implementation of LOADng.
Unlike previous studies, our results show that RPL provides shorter delays,
less control overhead, and requires less memory than LOADng. Nevertheless,
enhancing LOADng with more efficient flooding and a better route storage
algorithm may improve its performance
Discovering human activities from binary data in smart homes
With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual’s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods
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