1,766 research outputs found
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy
In this paper we shall consider the problem of deploying attention to subsets
of the video streams for collating the most relevant data and information of
interest related to a given task. We formalize this monitoring problem as a
foraging problem. We propose a probabilistic framework to model observer's
attentive behavior as the behavior of a forager. The forager, moment to moment,
focuses its attention on the most informative stream/camera, detects
interesting objects or activities, or switches to a more profitable stream. The
approach proposed here is suitable to be exploited for multi-stream video
summarization. Meanwhile, it can serve as a preliminary step for more
sophisticated video surveillance, e.g. activity and behavior analysis.
Experimental results achieved on the UCR Videoweb Activities Dataset, a
publicly available dataset, are presented to illustrate the utility of the
proposed technique.Comment: Accepted to IEEE Transactions on Image Processin
A comprehensive survey of multi-view video summarization
[EN] There has been an exponential growth in the amount of visual data on a daily basis acquired from single or multi-view surveillance camera networks. This massive amount of data requires efficient mechanisms such as video summarization to ensure that only significant data are reported and the redundancy is reduced. Multi-view video summarization (MVS) is a less redundant and more concise way of providing information from the video content of all the cameras in the form of either keyframes or video segments. This paper presents an overview of the existing strategies proposed for MVS, including their advantages and drawbacks. Our survey covers the genericsteps in MVS, such as the pre-processing of video data, feature extraction, and post-processing followed by summary generation. We also describe the datasets that are available for the evaluation of MVS. Finally, we examine the major current issues related to MVS and put forward the recommendations for future research(1). (C) 2020 Elsevier Ltd. All rights reserved.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2B5B01070067)Hussain, T.; Muhammad, K.; Ding, W.; Lloret, J.; Baik, SW.; De Albuquerque, VHC. (2021). A comprehensive survey of multi-view video summarization. Pattern Recognition. 109:1-15. https://doi.org/10.1016/j.patcog.2020.10756711510
Cost aware Inference for IoT Devices
Networked embedded devices (IoTs) of limitedCPU, memory and power resources are revo-lutionizing data gathering, remote monitoringand planning in many consumer and businessapplications. Nevertheless, resource limita-tions place a significant burden on their ser-vice life and operation, warranting cost-awaremethods that are capable of distributivelyscreening redundancies in device informationand transmitting informative data. We pro-pose to train a decentralized gated networkthat, given an observed instance at test-time,allows for activation of select devices to trans-mit information to a central node, which thenperforms inference. We analyze our proposedgradient descent algorithm for Gaussian fea-tures and establish convergence guaranteesunder good initialization. We conduct exper-iments on a number of real-world datasetsarising in IoT applications and show that ourmodel results in over 1.5X service life withnegligible accuracy degradation relative to aperformance achievable by a neural network.http://proceedings.mlr.press/v89/zhu19d/zhu19d.pdfPublished versio
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