4,313 research outputs found
Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks
The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks
Identification of Smart Jammers: Learning based Approaches Using Wavelet Representation
Smart jammer nodes can disrupt communication between a transmitter and a
receiver in a wireless network, and they leave traces that are undetectable to
classical jammer identification techniques, hidden in the time-frequency plane.
These traces cannot be effectively identified through the use of the classical
Fourier transform based time-frequency transformation (TFT) techniques with a
fixed resolution. Inspired by the adaptive resolution property provided by the
wavelet transforms, in this paper, we propose a jammer identification
methodology that includes a pre-processing step to obtain a multi-resolution
image, followed by the use of a classifier. Support vector machine (SVM) and
deep convolutional neural network (DCNN) architectures are investigated as
classifiers to automatically extract the features of the transformed signals
and to classify them. Three different jamming attacks are considered, the
barrage jamming that targets the complete transmission bandwidth, the
synchronization signal jamming attack that targets synchronization signals and
the reference signal jamming attack that targets the reference signals in an
LTE downlink transmission scenario. The performance of the proposed approach is
compared with the classical Fourier transform based TFT techniques,
demonstrating the efficacy of the proposed approach in the presence of smart
jammers
Data Management in Industry 4.0: State of the Art and Open Challenges
Information and communication technologies are permeating all aspects of
industrial and manufacturing systems, expediting the generation of large
volumes of industrial data. This article surveys the recent literature on data
management as it applies to networked industrial environments and identifies
several open research challenges for the future. As a first step, we extract
important data properties (volume, variety, traffic, criticality) and identify
the corresponding data enabling technologies of diverse fundamental industrial
use cases, based on practical applications. Secondly, we provide a detailed
outline of recent industrial architectural designs with respect to their data
management philosophy (data presence, data coordination, data computation) and
the extent of their distributiveness. Then, we conduct a holistic survey of the
recent literature from which we derive a taxonomy of the latest advances on
industrial data enabling technologies and data centric services, spanning all
the way from the field level deep in the physical deployments, up to the cloud
and applications level. Finally, motivated by the rich conclusions of this
critical analysis, we identify interesting open challenges for future research.
The concepts presented in this article thematically cover the largest part of
the industrial automation pyramid layers. Our approach is multidisciplinary, as
the selected publications were drawn from two fields; the communications,
networking and computation field as well as the industrial, manufacturing and
automation field. The article can help the readers to deeply understand how
data management is currently applied in networked industrial environments, and
select interesting open research opportunities to pursue
Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification over Encrypted Wi-Fi Traffic
The consumer UAV (unmanned aerial vehicle) market has grown significantly
over the past few years. Despite its huge potential in spurring economic growth
by supporting various applications, the increase of consumer UAVs poses
potential risks to public security and personal privacy. To minimize the risks,
efficiently detecting and identifying invading UAVs is in urgent need for both
invasion detection and forensics purposes. Given the fact that consumer UAVs
are usually used in a civilian environment, existing physical detection methods
(such as radar, vision, and sound) may become ineffective in many scenarios.
Aiming to complement the existing physical detection mechanisms, we propose a
machine learning-based framework for fast UAV identification over encrypted
Wi-Fi traffic. It is motivated by the observation that many consumer UAVs use
Wi-Fi links for control and video streaming. The proposed framework extracts
features derived only from packet size and inter-arrival time of encrypted
Wi-Fi traffic, and can efficiently detect UAVs and identify their operation
modes. In order to reduce the online identification time, our framework adopts
a re-weighted -norm regularization, which considers the number of
samples and computation cost of different features. This framework jointly
optimizes feature selection and prediction performance in a unified objective
function. To tackle the packet inter-arrival time uncertainty when optimizing
the trade-off between the detection accuracy and delay, we utilize Maximum
Likelihood Estimation (MLE) method to estimate the packet inter-arrival time.
We collect a large number of real-world Wi-Fi data traffic of eight types of
consumer UAVs and conduct extensive evaluation on the performance of our
proposed method
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
Security and Privacy Challenges in Cognitive Wireless Sensor Networks
Wireless sensor networks (WSNs) have attracted a lot of interest in the
research community due to their potential applicability in a wide range of
real-world practical applications. However, due to the distributed nature and
their deployments in critical applications without human interventions and
sensitivity and criticality of data communicated, these networks are vulnerable
to numerous security and privacy threats that can adversely affect their
performance. These issues become even more critical in cognitive wireless
sensor networks (CWSNs) in which the sensor nodes have the capabilities of
changing their transmission and reception parameters according to the radio
environment under which they operate in order to achieve reliable and efficient
communication and optimum utilization of the network resources. This chapter
presents a comprehensive discussion on the security and privacy issues in CWSNs
by identifying various security threats in these networks and various defense
mechanisms to counter these vulnerabilities. Various types of attacks on CWSNs
are categorized under different classes based on their natures and targets, and
corresponding to each attack class, appropriate security mechanisms are also
discussed. Some critical research issues on security and privacy in CWSNs are
also identified.Comment: 36 pages, 4 figures, 2 tables. The book chapter is accepted for
publication in 201
The Future is Unlicensed: Coexistence in the Unlicensed Spectrum for 5G
5G has to fulfill the requirements of ultra-dense, scalable, and customizable
networks such as IoT while increasing spectrum and energy efficiency. Given the
diversity of envisaged applications and scenarios, one crucial property for 5G
New Radio (NR) is flexibility: flexible UL/DL allocation, bandwidths, or
scalable transmission time interval, and most importantly operation at
different frequency bands. In particular, 5G should exploit the spectral
opportunities in the unlicensed spectrum for expanding network capacity when
and where needed. However, unlicensed bands pose the challenge of "coexisting
networks", which mostly lack the means of communication for negotiation and
coordination. This deficiency is further exacerbated by the heterogeneity,
massive connectivity, and ubiquity of IoT systems and applications. Therefore,
5G needs to provide mechanisms to coexist and even converge in the unlicensed
bands. In that regard, WiFi, as the most prominent wireless technology in the
unlicensed bands, is both a key enabler for boosting 5G capacity and competitor
of 5G cellular networks for the shared unlicensed spectrum. In this work, we
describe spectrum sharing in 5G and present key coexistence solutions, mostly
in the context of WiFi. We also highlight the role of machine learning which is
envisaged to be critical for reaching coexistence and convergence goals by
providing the necessary intelligence and adaptation mechanisms.Comment: 7 pages, 4 figure
A Blind Zone Alert System based on Intra-vehicular Wireless Sensor Networks
Due to the increasing number of sensors deployed in modern vehicles,
Intra-Vehicular Wireless Sensor Networks (IVWSNs) have recently received a lot
of attention in the automotive industry as they can reduce the amount of wiring
harness inside a vehicle. By removing the wires, car manufacturers can reduce
the weight of a vehicle and improve engine performance, fuel economy, and
reliability. In addition to these direct benefits, an IVWSN is a versatile
platform that can support other vehicular applications as well. An example
application, known as a Side Blind Zone Alert (SBZA) system, which monitors the
blind zone of the vehicle and alerts the driver in a timely manner to prevent
collisions, is discussed in this paper. The performance of the IVWSN-based SBZA
system is evaluated via real experiments conducted on two test vehicles. Our
results show that the proposed system can achieve approximately 95% to 99%
detection rate with less than 15% false alarm rate. Compared to commercial
systems using radars or cameras, the main benefit of the IVWSN-based SBZA is
substantially lower cost.Comment: 9 pages, 8 figures, 1 table, IEEE Transactions on Industrial
Informatics, 201
Reconfigurable Wireless Networks
Driven by the advent of sophisticated and ubiquitous applications, and the
ever-growing need for information, wireless networks are without a doubt
steadily evolving into profoundly more complex and dynamic systems. The user
demands are progressively rampant, while application requirements continue to
expand in both range and diversity. Future wireless networks, therefore, must
be equipped with the ability to handle numerous, albeit challenging
requirements. Network reconfiguration, considered as a prominent network
paradigm, is envisioned to play a key role in leveraging future network
performance and considerably advancing current user experiences. This paper
presents a comprehensive overview of reconfigurable wireless networks and an
in-depth analysis of reconfiguration at all layers of the protocol stack. Such
networks characteristically possess the ability to reconfigure and adapt their
hardware and software components and architectures, thus enabling flexible
delivery of broad services, as well as sustaining robust operation under highly
dynamic conditions. The paper offers a unifying framework for research in
reconfigurable wireless networks. This should provide the reader with a
holistic view of concepts, methods, and strategies in reconfigurable wireless
networks. Focus is given to reconfigurable systems in relatively new and
emerging research areas such as cognitive radio networks, cross-layer
reconfiguration and software-defined networks. In addition, modern networks
have to be intelligent and capable of self-organization. Thus, this paper
discusses the concept of network intelligence as a means to enable
reconfiguration in highly complex and dynamic networks. Finally, the paper is
supported with several examples and case studies showing the tremendous impact
of reconfiguration on wireless networks.Comment: 28 pages, 26 figures; Submitted to the Proceedings of the IEEE (a
special issue on Reconfigurable Systems
Lightweight Hierarchical Model for HWSNET
Heterogeneous wireless sensor networks (HWSNET) are more suitable for real
life applications as compared to the homogeneous counterpart. Security of
HWSNET becomes a very important issue with the rapid development of HWSNET.
Intrusion detection system is one of the major and efficient defensive methods
against attacks in HWSNET. Because of different constraints of sensor networks,
security solutions have to be designed with limited usage of computation and
resources. A particularly devastating attack is the sleep deprivation attack.
Here a malicious node forces legitimate nodes to waste their energy by
resisting the sensor nodes from going into low power sleep mode. The target of
this attack is to maximize the power consumption of the affected node, thereby
decreasing its battery life. Existing works on sleep deprivation attack have
mainly focused on mitigation using MAC based protocols, such as S-MAC (sensor
MAC), T-MAC (timeout MAC), B-MAC (Berkley MAC), G-MAC (gateway MAC). In this
article, a brief review of some of the recent intrusion detection systems in
wireless sensor network environment is presented. Finally, a framework of
cluster based layered countermeasure for Insomnia Detection has been proposed
for heterogeneous wireless sensor network (HWSNET) to efficiently detect sleep
deprivation attack. Simulation results on MATLAB exhibit the effectiveness of
the proposed model.Comment: 14 pages, 7 figures, AIRCC Journa
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