7,172 research outputs found
A sub-mW IoT-endnode for always-on visual monitoring and smart triggering
This work presents a fully-programmable Internet of Things (IoT) visual
sensing node that targets sub-mW power consumption in always-on monitoring
scenarios. The system features a spatial-contrast binary
pixel imager with focal-plane processing. The sensor, when working at its
lowest power mode ( at 10 fps), provides as output the number of
changed pixels. Based on this information, a dedicated camera interface,
implemented on a low-power FPGA, wakes up an ultra-low-power parallel
processing unit to extract context-aware visual information. We evaluate the
smart sensor on three always-on visual triggering application scenarios.
Triggering accuracy comparable to RGB image sensors is achieved at nominal
lighting conditions, while consuming an average power between and
, depending on context activity. The digital sub-system is extremely
flexible, thanks to a fully-programmable digital signal processing engine, but
still achieves 19x lower power consumption compared to MCU-based cameras with
significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa
Energy Consumption Of Visual Sensor Networks: Impact Of Spatio-Temporal Coverage
Wireless visual sensor networks (VSNs) are expected to play a major role in
future IEEE 802.15.4 personal area networks (PAN) under recently-established
collision-free medium access control (MAC) protocols, such as the IEEE
802.15.4e-2012 MAC. In such environments, the VSN energy consumption is
affected by the number of camera sensors deployed (spatial coverage), as well
as the number of captured video frames out of which each node processes and
transmits data (temporal coverage). In this paper, we explore this aspect for
uniformly-formed VSNs, i.e., networks comprising identical wireless visual
sensor nodes connected to a collection node via a balanced cluster-tree
topology, with each node producing independent identically-distributed
bitstream sizes after processing the video frames captured within each network
activation interval. We derive analytic results for the energy-optimal
spatio-temporal coverage parameters of such VSNs under a-priori known bounds
for the number of frames to process per sensor and the number of nodes to
deploy within each tier of the VSN. Our results are parametric to the
probability density function characterizing the bitstream size produced by each
node and the energy consumption rates of the system of interest. Experimental
results reveal that our analytic results are always within 7% of the energy
consumption measurements for a wide range of settings. In addition, results
obtained via a multimedia subsystem show that the optimal spatio-temporal
settings derived by the proposed framework allow for substantial reduction of
energy consumption in comparison to ad-hoc settings. As such, our analytic
modeling is useful for early-stage studies of possible VSN deployments under
collision-free MAC protocols prior to costly and time-consuming experiments in
the field.Comment: to appear in IEEE Transactions on Circuits and Systems for Video
Technology, 201
Autonomous real-time surveillance system with distributed IP cameras
An autonomous Internet Protocol (IP) camera based object tracking and behaviour identification system, capable of running in real-time on an embedded system with limited memory and processing power is presented in this paper. The main contribution of this work is the integration of processor intensive image processing algorithms on an embedded platform capable of running at real-time for monitoring the behaviour of pedestrians. The Algorithm Based Object Recognition and Tracking (ABORAT) system architecture presented here was developed on an Intel PXA270-based development board clocked at 520 MHz. The platform was connected to a commercial stationary IP-based camera in a remote monitoring station for intelligent image
processing. The system is capable of detecting moving objects and their shadows in a complex environment with varying lighting intensity and moving foliage. Objects
moving close to each other are also detected to extract their trajectories which are then fed into an unsupervised neural network for autonomous classification. The novel intelligent video system presented is also capable of performing simple analytic functions such as tracking and generating alerts when objects enter/leave regions or cross tripwires superimposed on live video by the operator
DTLS Performance in Duty-Cycled Networks
The Datagram Transport Layer Security (DTLS) protocol is the IETF standard
for securing the Internet of Things. The Constrained Application Protocol,
ZigBee IP, and Lightweight Machine-to-Machine (LWM2M) mandate its use for
securing application traffic. There has been much debate in both the
standardization and research communities on the applicability of DTLS to
constrained environments. The main concerns are the communication overhead and
latency of the DTLS handshake, and the memory footprint of a DTLS
implementation. This paper provides a thorough performance evaluation of DTLS
in different duty-cycled networks through real-world experimentation, emulation
and analysis. In particular, we measure the duration of the DTLS handshake when
using three duty cycling link-layer protocols: preamble-sampling, the IEEE
802.15.4 beacon-enabled mode and the IEEE 802.15.4e Time Slotted Channel
Hopping mode. The reported results demonstrate surprisingly poor performance of
DTLS in radio duty-cycled networks. Because a DTLS client and a server exchange
more than 10 signaling packets, the DTLS handshake takes between a handful of
seconds and several tens of seconds, with similar results for different duty
cycling protocols. Moreover, because of their limited memory, typical
constrained nodes can only maintain 3-5 simultaneous DTLS sessions, which
highlights the need for using DTLS parsimoniously.Comment: International Symposium on Personal, Indoor and Mobile Radio
Communications (PIMRC - 2015), IEEE, IEEE, 2015,
http://pimrc2015.eee.hku.hk/index.htm
Energy efficient scheduling for cluster-tree wireless sensor networks with time-bounded data flows: application to IEEE 802.15.4/ZigBee
Cluster scheduling and collision avoidance are crucial issues in large-scale cluster-tree Wireless Sensor Networks
(WSNs). The paper presents a methodology that provides a Time Division Cluster Scheduling (TDCS) mechanism
based on the cyclic extension of RCPS/TC (Resource Constrained Project Scheduling with Temporal Constraints)
problem for a cluster-tree WSN, assuming bounded communication errors. The objective is to meet all end-to-end
deadlines of a predefined set of time-bounded data flows while minimizing the energy consumption of the nodes by
setting the TDCS period as long as possible. Sinceeach cluster is active only once during the period, the end-to-end
delay of a given flow may span over several periods when there are the flows with opposite direction. The scheduling
tool enables system designers to efficiently configure all required parameters of the IEEE 802.15.4/ZigBee beaconenabled
cluster-tree WSNs in the network design time. The performance evaluation of thescheduling tool shows that the
problems with dozens of nodes can be solved while using optimal solvers
Engineering ambient visual sensors
Visual sensors are an indispensable prerequisite for those AmI environments that require a surveillance component. One practical issue concerns maximizing the operational longevity of such sensors as the operational lifetime of an AmI environment itself is dependent on that of its constituent components. In this paper, the intelligent agent paradigm is considered as a basis for managing a camera collective such that the conflicting demands of power usage optimization and system performance are reconciled
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