234,353 research outputs found
Multi-Sensor Event Detection using Shape Histograms
Vehicular sensor data consists of multiple time-series arising from a number
of sensors. Using such multi-sensor data we would like to detect occurrences of
specific events that vehicles encounter, e.g., corresponding to particular
maneuvers that a vehicle makes or conditions that it encounters. Events are
characterized by similar waveform patterns re-appearing within one or more
sensors. Further such patterns can be of variable duration. In this work, we
propose a method for detecting such events in time-series data using a novel
feature descriptor motivated by similar ideas in image processing. We define
the shape histogram: a constant dimension descriptor that nevertheless captures
patterns of variable duration. We demonstrate the efficacy of using shape
histograms as features to detect events in an SVM-based, multi-sensor,
supervised learning scenario, i.e., multiple time-series are used to detect an
event. We present results on real-life vehicular sensor data and show that our
technique performs better than available pattern detection implementations on
our data, and that it can also be used to combine features from multiple
sensors resulting in better accuracy than using any single sensor. Since
previous work on pattern detection in time-series has been in the single series
context, we also present results using our technique on multiple standard
time-series datasets and show that it is the most versatile in terms of how it
ranks compared to other published results
A realization of classification success in multi sensor data fusion
The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously.The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases.MSDF is an evolving technique related to the problem for
combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991)
Principal Component Analysis – A Realization of Classification Success in Multi Sensor Data Fusion
The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously.The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases.MSDF is an evolving technique related to the problem for
combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991)
On Heterogeneous Neighbor Discovery in Wireless Sensor Networks
Neighbor discovery plays a crucial role in the formation of wireless sensor
networks and mobile networks where the power of sensors (or mobile devices) is
constrained. Due to the difficulty of clock synchronization, many asynchronous
protocols based on wake-up scheduling have been developed over the years in
order to enable timely neighbor discovery between neighboring sensors while
saving energy. However, existing protocols are not fine-grained enough to
support all heterogeneous battery duty cycles, which can lead to a more rapid
deterioration of long-term battery health for those without support. Existing
research can be broadly divided into two categories according to their
neighbor-discovery techniques---the quorum based protocols and the co-primality
based protocols.In this paper, we propose two neighbor discovery protocols,
called Hedis and Todis, that optimize the duty cycle granularity of quorum and
co-primality based protocols respectively, by enabling the finest-grained
control of heterogeneous duty cycles. We compare the two optimal protocols via
analytical and simulation results, which show that although the optimal
co-primality based protocol (Todis) is simpler in its design, the optimal
quorum based protocol (Hedis) has a better performance since it has a lower
relative error rate and smaller discovery delay, while still allowing the
sensor nodes to wake up at a more infrequent rate.Comment: Accepted by IEEE INFOCOM 201
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