5,875 research outputs found
Towards a cloud‑based automated surveillance system using wireless technologies
Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.Ministerio de Economía y Competitividad TEC2016-77785-PJunta de Andalucía P12-TIC-130
Sensor node localisation using a stereo camera rig
In this paper, we use stereo vision processing techniques to
detect and localise sensors used for monitoring simulated
environmental events within an experimental sensor network testbed. Our sensor nodes communicate to the camera through patterns emitted by light emitting diodes (LEDs). Ultimately, we envisage the use of very low-cost, low-power,
compact microcontroller-based sensing nodes that employ
LED communication rather than power hungry RF to transmit data that is gathered via existing CCTV infrastructure.
To facilitate our research, we have constructed a controlled
environment where nodes and cameras can be deployed and
potentially hazardous chemical or physical plumes can be
introduced to simulate environmental pollution events in a
controlled manner. In this paper we show how 3D spatial
localisation of sensors becomes a straightforward task when
a stereo camera rig is used rather than a more usual 2D
CCTV camera
Efficient Data Collection in Multimedia Vehicular Sensing Platforms
Vehicles provide an ideal platform for urban sensing applications, as they
can be equipped with all kinds of sensing devices that can continuously monitor
the environment around the travelling vehicle. In this work we are particularly
concerned with the use of vehicles as building blocks of a multimedia mobile
sensor system able to capture camera snapshots of the streets to support
traffic monitoring and urban surveillance tasks. However, cameras are high
data-rate sensors while wireless infrastructures used for vehicular
communications may face performance constraints. Thus, data redundancy
mitigation is of paramount importance in such systems. To address this issue in
this paper we exploit sub-modular optimisation techniques to design efficient
and robust data collection schemes for multimedia vehicular sensor networks. We
also explore an alternative approach for data collection that operates on
longer time scales and relies only on localised decisions rather than
centralised computations. We use network simulations with realistic vehicular
mobility patterns to verify the performance gains of our proposed schemes
compared to a baseline solution that ignores data redundancy. Simulation
results show that our data collection techniques can ensure a more accurate
coverage of the road network while significantly reducing the amount of
transferred data
Scheduling for Multi-Camera Surveillance in LTE Networks
Wireless surveillance in cellular networks has become increasingly important,
while commercial LTE surveillance cameras are also available nowadays.
Nevertheless, most scheduling algorithms in the literature are throughput,
fairness, or profit-based approaches, which are not suitable for wireless
surveillance. In this paper, therefore, we explore the resource allocation
problem for a multi-camera surveillance system in 3GPP Long Term Evolution
(LTE) uplink (UL) networks. We minimize the number of allocated resource blocks
(RBs) while guaranteeing the coverage requirement for surveillance systems in
LTE UL networks. Specifically, we formulate the Camera Set Resource Allocation
Problem (CSRAP) and prove that the problem is NP-Hard. We then propose an
Integer Linear Programming formulation for general cases to find the optimal
solution. Moreover, we present a baseline algorithm and devise an approximation
algorithm to solve the problem. Simulation results based on a real surveillance
map and synthetic datasets manifest that the number of allocated RBs can be
effectively reduced compared to the existing approach for LTE networks.Comment: 9 pages, 10 figure
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