8,010 research outputs found
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)
Simulation of undular bores evolution with damping
Propagation of undular bores with damping is considered in the framework of perturbed extended Korteweg-de Vries (peKdV) equation. Two types of damping terms for the peKdV equation, namely linear and Chezy frictional terms, which describe the turbulent boundary layers in the fluid flow are considered. Solving the peKdV equation numerically using the method of lines shows that under the influence of damping, the lead-ing solitary wave of the undular bores will split from the nonlinear wavetrain, propagates and behaves like an isolated solitary wave. The amplitude of the leading wave will remain the same for some times before it starts to decay again at a larger time. In general the amplitude of the leading wave and the mean level across the undular bore decreases due to the effect of damping
FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices
Deep neural networks show great potential as solutions to many sensing
application problems, but their excessive resource demand slows down execution
time, pausing a serious impediment to deployment on low-end devices. To address
this challenge, recent literature focused on compressing neural network size to
improve performance. We show that changing neural network size does not
proportionally affect performance attributes of interest, such as execution
time. Rather, extreme run-time nonlinearities exist over the network
configuration space. Hence, we propose a novel framework, called FastDeepIoT,
that uncovers the non-linear relation between neural network structure and
execution time, then exploits that understanding to find network configurations
that significantly improve the trade-off between execution time and accuracy on
mobile and embedded devices. FastDeepIoT makes two key contributions. First,
FastDeepIoT automatically learns an accurate and highly interpretable execution
time model for deep neural networks on the target device. This is done without
prior knowledge of either the hardware specifications or the detailed
implementation of the used deep learning library. Second, FastDeepIoT informs a
compression algorithm how to minimize execution time on the profiled device
without impacting accuracy. We evaluate FastDeepIoT using three different
sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus.
FastDeepIoT further reduces the neural network execution time by to
and energy consumption by to compared with the
state-of-the-art compression algorithms.Comment: Accepted by SenSys '1
Wisent: Robust Downstream Communication and Storage for Computational RFIDs
Computational RFID (CRFID) devices are emerging platforms that can enable
perennial computation and sensing by eliminating the need for batteries.
Although much research has been devoted to improving upstream (CRFID to RFID
reader) communication rates, the opposite direction has so far been neglected,
presumably due to the difficulty of guaranteeing fast and error-free transfer
amidst frequent power interruptions of CRFID. With growing interest in the
market where CRFIDs are forever-embedded in many structures, it is necessary
for this void to be filled. Therefore, we propose Wisent-a robust downstream
communication protocol for CRFIDs that operates on top of the legacy UHF RFID
communication protocol: EPC C1G2. The novelty of Wisent is its ability to
adaptively change the frame length sent by the reader, based on the length
throttling mechanism, to minimize the transfer times at varying channel
conditions. We present an implementation of Wisent for the WISP 5 and an
off-the-shelf RFID reader. Our experiments show that Wisent allows transfer up
to 16 times faster than a baseline, non-adaptive shortest frame case, i.e.
single word length, at sub-meter distance. As a case study, we show how Wisent
enables wireless CRFID reprogramming, demonstrating the world's first
wirelessly reprogrammable (software defined) CRFID.Comment: Accepted for Publication to IEEE INFOCOM 201
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
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