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ردیف | عنوان | نوع |
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1 |
Internet of Things-enabled Passive Contact Tracing in Smart Cities
ردیابی تماس غیرفعال با قابلیت اینترنت اشیا در شهرهای هوشمند-2022 Contact tracing has been proven an essential practice during pandemic outbreaks and is a
critical non-pharmaceutical intervention to reduce mortality rates. While traditional con-
tact tracing approaches are gradually being replaced by peer-to-peer smartphone-based
systems, the new applications tend to ignore the Internet-of-Things (IoT) ecosystem that is
steadily growing in smart city environments. This work presents a contact tracing frame-
work that logs smart space users’ co-existence using IoT devices as reference anchors. The
design is non-intrusive as it relies on passive wireless interactions between each user’s
carried equipment (e.g., smartphone, wearable, proximity card) with an IoT device by uti-
lizing received signal strength indicators (RSSI). The proposed framework can log the iden-
tities for the interacting pair, their estimated distance, and the overlapping time duration.
Also, we propose a machine learning-based infection risk classification method to char-
acterize each interaction that relies on RSSI-based attributes and contact details. Finally,
the proposed contact tracing framework’s performance is evaluated through a real-world
case study of actual wireless interactions between users and IoT devices through Bluetooth
Low Energy advertising. The results demonstrate the system’s capability to accurately cap-
ture contact between mobile users and assess their infection risk provided adequate model
training over time.
© 2021 Elsevier B.V. All rights reserved. keywords: بلوتوث کم انرژی | ردیابی تماس | اینترنت اشیا | طبقه بندی خطر عفونت | Bluetooth Low Energy | Contact Tracing | Internet of Things | Infection Risk Classification |
مقاله انگلیسی |
2 |
Is the Internet of Things a helpful employee? An exploratory study of discourses of Canadian farmers
آیا اینترنت اشیا یک کارمند مفید است؟ بررسی اکتشافی گفتمان های کشاورزان کانادایی-2022 The increasing global population and the growing demand for high-quality products have called
for the modernization of agriculture. “Internet of Things” is one of the technologies that is pre-
dicted to offer many solutions. We conducted a discourse analysis of 19 interviews with farmers in
Ontario, Canada, asking them to describe their experience of working with IoT and related
technologies. One main discourse with two opposing tendencies was identified: farmers recognize
their relationship with IoT and related technology and view technology as a kind of “employee”,
but some tend to emphasize (1) an optimistic view which is discourse of technology is a “Helpful
Employee”; while others tend to emphasize (2) a pessimistic view which is a discourse of tech-
nology is an “Untrustworthy Employee”. We examine these tendencies in the light of the literature
on organizational behavior and identify potential outcomes of these beliefs. The results suggest
that a farmer’s style of approaching technology can be assessed on a similar scale as managers’
view of their employees and provide a framework for further research. keywords: فناوری اینترنت اشیا | کشاورزی | تحلیل گفتمان | سبک استفاده از تکنولوژی | Internet of things technology | Agriculture | Discourse analysis | Style of use of technology |
مقاله انگلیسی |
3 |
PortiK: A computer vision based solution for real-time automatic solid waste characterization – Application to an aluminium stream
PortiK: یک راه حل مبتنی بر بینایی کامپیوتری برای شناسایی خودکار زباله جامد در زمان واقعی - کاربرد در جریان آلومینیوم-2022 In Material Recovery Facilities (MRFs), recyclable municipal solid waste is turned into a precious commodity.
However, effective recycling relies on effective waste sorting, which is still a challenge to sustainable develop-
ment of our society. To help the operations improve and optimise their process, this paper describes PortiK, a
solution for automatic waste analysis. Based on image analysis and object recognition, it allows for continuous,
real-time, non-intrusive measurements of mass composition of waste streams. The end-to-end solution is detailed
with all the steps necessary for the system to operate, from hardware specifications and data collection to su-
pervisory information obtained by deep learning and statistical analysis. The overall system was tested and
validated in an operational environment in a material recovery facility.
PortiK monitored an aluminium can stream to estimate its purity. Aluminium cans were detected with 91.2%
precision and 90.3% recall, respectively, resulting in an underestimation of the number of cans by less than 1%.
Regarding contaminants (i.e. other types of waste), precision and recall were 80.2% and 78.4%, respectively,
giving an 2.2% underestimation. Based on five sample analyses where pieces of waste were counted and weighed
per batch, the detection results were used to estimate purity and its confidence level. The estimation error was
calculated to be within ±7% after 5 minutes of monitoring and ±5% after 8 hours. These results have demon-
strated the feasibility and the relevance of the proposed solution for online quality control of aluminium can
stream. keywords: امکانات بازیابی مواد | شناسایی مواد زائد جامد | یادگیری عمیق | شبکه عصبی عمیق | بینایی کامپیوتر | Material recovery facilities | MRF | Solid waste characterization | Deep-learning | Deep neural network | Computer vision |
مقاله انگلیسی |
4 |
Post-Quantum Blockchain-Based Data Sharing for IoT Service Providers
به اشتراک گذاری داده های مبتنی بر بلاک چین پسا کوانتومی برای ارائه دهندگان خدمات اینترنت اشیا-2022 Quantum technologies have made significant advances and are likely to lead to important security challenges and threats to networks in
the near future. On the other hand, sharing the huge amount of data from the Internet of Things (IoT) in the context of data as a service
could provide new revenue streams for infrastructure providers and service providers. However, post-quantum computing exposes the
entire data sharing ecosystem to a new set of security risks. In this article, we propose a novel blockchain-based system for data sharing in
the post-quantum era. The proposed system facilitates data sharing among multiple organizations while meeting compliance and regulatory
requirements via private blockchain. We implemented the proposed architecture and information flow using three blockchain networks
(namely Hyperledger Fabric, Ethereum, and Quorum) and selected NTRU as our quantum resistant security algorithm (QRSA) to compare
the parallelization performance of Toom-Cook’s and Karatsuba’s computation methods. Experimental results show that parallel computation
has a positive impact when the security level of QRSAs is lowered, and the transaction time savings is almost 50 percent in favor of Quorum. Finally, we outline the main challenges and potential solutions.
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مقاله انگلیسی |
5 |
Post-Quantum Era in V2X Security: Convergence of Orchestration and Parallel Computation
دوران پسا کوانتومی در امنیت V2X: همگرایی ارکستراسیون و محاسبات موازی-2022 Along with the potential emergence of quantum computing, safety and security of new and
complex communication services such as automated driving need to be redefined in the post-quantum era. To ensure reliable, continuous, and
secure operation of these scenarios, quantum-resistant security algorithms (QRSAs) that enable
secure connectivity must be integrated into the
network management and orchestration systems
of mobile networks. This article explores a roadmap study of post-quantum era convergence with
cellular connectivity using the Service & Computation Orchestrator (SCO) framework for enhanced
data security in radio access and backhaul transmission with a particular focus on vehicle-to-everything services. Using NTRU as a QSRA, we
show that the parallelization performance of the
Toom-Cook and Karatsuba computation methods
can vary based on different CPU load conditions
through extensive simulations, and that the SCO
framework can facilitate the selection of the most
efficient computation for a given QRSA. Finally,
we discuss the evaluation results, identify the current standardization efforts, and present possible
directions for the coexistence of post-quantum
and mobile network connectivity through an SCO
framework that leverages parallel computing.
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مقاله انگلیسی |
6 |
A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
بررسی جامع تشخیص حملات سایبری: مجموعه دادهها، روشها، چالش ها و جهتگیریهای تحقیقاتی آینده-2022 Rapid developments in network technologies and the amount and scope of data transferred on networks
are increasing day by day. Depending on this situation, the density and complexity of cyber threats
and attacks are also expanding. The ever-increasing network density makes it difficult for cybersecurity professionals to monitor every movement on the network. More frequent and complex cyberattacks make the detection and identification of anomalies in network events more complex. Machine
learning offers various tools and techniques for automating the detection of cyber attacks and for
rapid prediction and analysis of attack types. This study discusses the approaches to machine learning
methods used to detect attacks. We examined the detection, classification, clustering, and analysis of
anomalies in network traffic. We gave the cyber-security focus, machine learning methods, and data
sets used in each study we examined. We investigated which feature selection or dimension reduction
method was applied to the data sets used in the studies. We presented in detail the types of classification
carried out in these studies, which methods were compared with other methods, the performance
metrics used, and the results obtained in tables. We examined the data sets of network attacks presented
as open access. We suggested a basic taxonomy for cyber attacks. Finally, we discussed the difficulties
encountered in machine learning applications used in network attacks and their solutions
Keywords: Cyber attacks | Machine learning | Deep learning | Geometric deep learning | Cyber security | Adversarial machine learning | Intrusion detection |
مقاله انگلیسی |
7 |
A survey on blockchain, SDN and NFV for the smart-home security
مروری بر بلاک چین، SDN و NFV برای امنیت خانه های هوشمند-2022 Due to millions of loosely coupled devices, the smart-home security is gaining the attention
of industry professionals, attackers, and academic researchers. The smart home is a typical
home where many sensors, actuators, and IoT devices are used to automate home users’ daily
activities. Although a smart home provides comfort, safety, and satisfaction to users, it opens up
multiple challenging security issues when automating and offering intelligent services. Recent
studies have investigated not only blockchain but SDN and NFV to address these challenges.
We present a comprehensive survey on blockchain, SDN, and NFV for smart-home security.
The paper also proposes a new architecture of the smart-home security. First, we describe the
features of the smart home and its current security issues. Next, we outline the characteristics of
blockchain, SDN, and NFV, including their contribution to improving the smart-home security.
While SDN enhances the management and access control of the home network by providing a
programmable controller to home nodes, NFV implements the functions of network appliances
(e.g., network monitoring, firewall) as virtual machines and ensures the high availability of the
network. Blockchain reinforces IoT data’s privacy, integrity, and security and improves the trust
in transactions among untrusted IoT devices. Finally, we discuss open issues and challenges in
the field and propose recommendations towards high-level security for the smart home.
Keywords: Smart homes | IoT | Privacy | Security | Trust | Blockchain | SDN | NFV |
مقاله انگلیسی |
8 |
A Two-layer Fog-Cloud Intrusion Detection Model for IoT Networks
مدل تشخیص نفوذ مه-ابر دو لایه برای شبکه های اینترنت اشیا-2022 The Internet of Things (IoT) and its applications are becoming ubiquitous in our life. However,
the open deployment environment and the limited resources of IoT devices make them vulnerable
to cyber threats. In this paper, we investigate intrusion detection techniques to mitigate attacks
that exploit IoT security vulnerabilities. We propose a machine learning-based two-layer hierarchical intrusion detection mechanism that can effectively detect intrusions in IoT networks
while satisfying the IoT resource constraints. Specifically, the proposed model effectively utilizes
the resources in the fog layer of the IoT network by efficiently deploying multi-layered feedforward neural networks in the fog-cloud infrastructure for detecting network attacks. With a fog
layer into the picture, analysis is dynamically distributed across the fog and cloud layer thus
enabling real-time analytics of traffic data closer to IoT devices and end-users. We have performed
extensive experiments using two publicly available datasets to test the proposed approach. Test
results show that the proposed approach outperforms existing approaches in multiple performance metrics such as accuracy, precision, recall, and F1-score. Moreover, experiments also
justified the proposed model in terms of improved service time, lower delay, and optimal energy
utilization.
keywords: Fog computing | Intrusion detection | IoT network | Machine learning | Security |
مقاله انگلیسی |
9 |
Beacon Non-Transmission attack and its detection in intelligent transportation systems
حمله عدم انتقال Beacon و تشخیص آن در سیستم های حمل و نقل هوشمند-2022 Message dropping by intermediate nodes as well as RF jamming attack have been studied
widely in ad hoc networks. In this paper, we thoroughly investigate a new type of attack
in intelligent transportation systems (ITS) defined as Beacon Non-Transmission (BNT) attack
in which attacker is not an intermediate vehicle, but rather a source vehicle. In BNT attack,
a vehicle suppresses the transmissions of its own periodic beacon packets to get rid of the
automated driving misbehavior detection protocols running in ITS, or to mount a Denial-ofService (DoS) attack to cripple the traffic management functionality of ITS. Considering BNT
attack as a critical security threat to ITS, we propose two novel and lightweight techniques to
detect it. Our first technique bases its detection by assuming a certain distribution of the number
of beacons lost from a vehicle while accounting for loss due to channel-error. However, it fails to
classify shortish BNT attacks wherein amount of denial and channel-error loss are comparable.
Our second technique, suitable for identifying both shortish and longish BNT attacks, considers
beacon loss pattern of a vehicle as a time-series data and employs autocorrelation function (ACF)
to determine the existence of an attack. In order to trade-off detection accuracy for equitable
use of limited computational resources, we propose a random inspection model in which the
detection algorithm is executed at random time instances and for randomly selected set of
vehicles. We have performed extensive simulations to evaluate the performance of proposed
detection algorithms under random inspection and a practical attacker model. The results
obtained corroborate the lightweight nature of both techniques, and the efficacy of ACF based
technique over simple threshold based technique in terms of higher detection accuracy as well
as smaller reaction delay.
keywords: DoS attack | Intrusion detection | Driving anomaly detection | Beacon | Autocorrelation function | Intelligent Transportation Systems |
مقاله انگلیسی |
10 |
Detection of moving objects using thermal imaging sensors for occupancy estimation
تشخیص اجسام متحرک با استفاده از سنسورهای تصویربرداری حرارتی برای تخمین اشغال-2022 Thermal imaging sensors have been increasingly integrated in a wide range of smart building
and Internet of Things systems. Low-resolution thermal imaging sensors are especially suitable
for applications that require non-intrusive monitoring with proper privacy protection. In this
paper, we present an in-depth investigation of a low-resolution thermal imaging sensor (i.e.,
Melexis MLX90640) focusing on algorithm design issues and solutions when detecting moving
objects. This type of sensors are designed to operate with a two-subpage chessboard reading
pattern, which gives rise to blob displacements across two subpages when target objects are
in motion. We have conducted systematic characterization of the sensor and demonstrated
issues through experimental measurements and analysis. We have also proposed a subpage
bilinear interpolation method and an enhanced sensor data preprocessing method for occupancy
estimation with moving objects. The performance of the proposed method is analyzed by
training and testing classification algorithms using two datasets collected with objects of
different moving speeds. Our performance results indicate that the proposed method could be
used for occupancy estimation in various smart building and Internet of Things applications.
keywords: طبقه بندی | حسگر مادون قرمز | اینترنت اشیا | یادگیری ماشین | برآورد اشغال | ساختمان های هوشمند | Classification | Infrared array sensor | Internet of Things | Machine learning | Occupancy estimation | Smart buildings |
مقاله انگلیسی |