Video Surveillance Applications based on ultra-low power sensors

International audiencePower consumption is an important goal for many applica- tions, expecially when the power can be wasted doing nothing. Video surveillance is one of this application where the camera can be on for long period without "see" nothing. For this reason several power man- agement techniques were carried out in order to reduce the activities of the camera when it is not needed. In this work we focus on surveillance applications performed through Video Surveillance Camera (VSC) that are not permanently active, but need to be properly "woken-up", by speci c ultra Low Power wireless Sensor Nodes (LPSN) able to monitor continuously the area. named. The LPSN are equipped by Piezoelectric "Passive" Infrared (PIR) sensors to detect the movement, thus they have a speci c transmission range (to wirelessly send the "wake-up" messages to the camera sensor device) and a sensing range to detect events of in- terest (i.e. a man that crosses a speci c area). Di erent deployments may highly impact not only in terms of events detectable, but also in terms of the number of VDS that can be woken-up. In this work, we propose a neural/genetic algorithm, that tries to compute the best deployment of the LPSN, based on two weight factors that "prioritize" the rst ob- jective, that is the number of VSC that can be woken-up or the second objective, namely the events detectable. The two objectives can be op- posite and based on the di erent values assigned to the weight factors, di erent deployments can be obtained. The performance evaluation is realized through a simulation tool and we will show the e ectiveness of our approach to reach very e ective deployments in di erent scenarios

Field test of multi-hop image sensing network prototype on a city-wide scale

Open Access funded by Chongqing University of Posts and Telecommuniocations Under a Creative Commons license, https://creativecommons.org/licenses/by-nc-nd/4.0/Wireless multimedia sensor network drastically stretches the horizon of traditional monitoring and surveillance systems, of which most existing research have utilised Zigbee or WiFi as the communication technology. Both technologies use ultra high frequencies (mainly 2.4 GHz) and suffer from relatively short transmission range (i.e. 100 m line-of-sight). The objective of this paper is to assess the feasibility and potential of transmitting image information using RF modules with lower frequencies (e.g. 433 MHz) in order to achieve a larger scale deployment such as a city scenario. Arduino platform is used for its low cost and simplicity. The details of hardware properties are elaborated in the article, followed by an investigation of optimum configurations for the system. Upon an initial range testing outcome of over 2000 m line-of-sight transmission distance, the prototype network has been installed in a real life city plot for further examination of performance. A range of suitable applications has been proposed along with suggestions for future research.Peer reviewe

Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks

Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that transmits alerts when relevant video sequences are detected. Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort. To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.Comment: Presented at SPIE Security + Defence 2016 Proc. SPIE 9997, Target and Background Signatures I

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

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 $128\mathrm{x}64$ binary pixel imager with focal-plane processing. The sensor, when working at its lowest power mode ($10\mu W$ 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 $193\mu W$ and $277\mu W$, 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