12,478 research outputs found
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Near-sensor data analytics is a promising direction for IoT endpoints, as it
minimizes energy spent on communication and reduces network load - but it also
poses security concerns, as valuable data is stored or sent over the network at
various stages of the analytics pipeline. Using encryption to protect sensitive
data at the boundary of the on-chip analytics engine is a way to address data
security issues. To cope with the combined workload of analytics and encryption
in a tight power envelope, we propose Fulmine, a System-on-Chip based on a
tightly-coupled multi-core cluster augmented with specialized blocks for
compute-intensive data processing and encryption functions, supporting software
programmability for regular computing tasks. The Fulmine SoC, fabricated in
65nm technology, consumes less than 20mW on average at 0.8V achieving an
efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to
25MIPS/mW in software. As a strong argument for real-life flexible application
of our platform, we show experimental results for three secure analytics use
cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN
consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with
secured remote recognition in 5.74pJ/op; and seizure detection with encrypted
data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE
Transactions on Circuits and Systems - I: Regular Paper
CMOS Vision Sensors: Embedding Computer Vision at Imaging Front-Ends
CMOS Image Sensors (CIS) are key for imaging technol-ogies. These chips are conceived for capturing opticalscenes focused on their surface, and for delivering elec-trical images, commonly in digital format. CISs may incor-porate intelligence; however, their smartness basicallyconcerns calibration, error correction and other similartasks. The term CVISs (CMOS VIsion Sensors) definesother class of sensor front-ends which are aimed at per-forming vision tasks right at the focal plane. They havebeen running under names such as computational imagesensors, vision sensors and silicon retinas, among others. CVIS and CISs are similar regarding physical imple-mentation. However, while inputs of both CIS and CVISare images captured by photo-sensors placed at thefocal-plane, CVISs primary outputs may not be imagesbut either image features or even decisions based on thespatial-temporal analysis of the scenes. We may hencestate that CVISs are more “intelligent” than CISs as theyfocus on information instead of on raw data. Actually,CVIS architectures capable of extracting and interpretingthe information contained in images, and prompting reac-tion commands thereof, have been explored for years inacademia, and industrial applications are recently ramp-ing up.One of the challenges of CVISs architects is incorporat-ing computer vision concepts into the design flow. Theendeavor is ambitious because imaging and computervision communities are rather disjoint groups talking dif-ferent languages. The Cellular Nonlinear Network Univer-sal Machine (CNNUM) paradigm, proposed by Profs.Chua and Roska, defined an adequate framework forsuch conciliation as it is particularly well suited for hard-ware-software co-design [1]-[4]. This paper overviewsCVISs chips that were conceived and prototyped at IMSEVision Lab over the past twenty years. Some of them fitthe CNNUM paradigm while others are tangential to it. Allthem employ per-pixel mixed-signal processing circuitryto achieve sensor-processing concurrency in the quest offast operation with reduced energy budget.Junta de Andalucía TIC 2012-2338Ministerio de Economía y Competitividad TEC 2015-66878-C3-1-R y TEC 2015-66878-C3-3-
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
Automatic Environmental Sound Recognition: Performance versus Computational Cost
In the context of the Internet of Things (IoT), sound sensing applications
are required to run on embedded platforms where notions of product pricing and
form factor impose hard constraints on the available computing power. Whereas
Automatic Environmental Sound Recognition (AESR) algorithms are most often
developed with limited consideration for computational cost, this article seeks
which AESR algorithm can make the most of a limited amount of computing power
by comparing the sound classification performance em as a function of its
computational cost. Results suggest that Deep Neural Networks yield the best
ratio of sound classification accuracy across a range of computational costs,
while Gaussian Mixture Models offer a reasonable accuracy at a consistently
small cost, and Support Vector Machines stand between both in terms of
compromise between accuracy and computational cost
Over-the-air software updates in the internet of things : an overview of key principles
Due to the fast pace at which IoT is evolving, there is an increasing need to support over-theair software updates for security updates, bug fixes, and software extensions. To this end, multiple over-the-air techniques have been proposed, each covering a specific aspect of the update process, such as (partial) code updates, data dissemination, and security. However, each technique introduces overhead, especially in terms of energy consumption, thereby impacting the operational lifetime of the battery constrained devices. Until now, a comprehensive overview describing the different update steps and quantifying the impact of each step is missing in the scientific literature, making it hard to assess the overall feasibility of an over-the-air update. To remedy this, our article analyzes which parts of an IoT operating system are most updated after device deployment, proposes a step-by-step approach to integrate software updates in IoT solutions, and quantifies the energy cost of each of the involved steps. The results show that besides the obvious dissemination cost, other phases such as security also introduce a significant overhead. For instance, a typical firmware update requires 135.026 mJ, of which the main portions are data dissemination (63.11 percent) and encryption (5.29 percent). However, when modular updates are used instead, the energy cost (e.g., for a MAC update) is reduced to 26.743 mJ (48.69 percent for data dissemination and 26.47 percent for encryption)
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