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ردیف | عنوان | نوع |
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61 |
A novel machine learning pipeline to detect malicious anomalies for the Internet of Things
پایپ لاین یادگیری ماشینی جدید برای شناسایی ناهنجاری های مخرب برای اینترنت اشیا-2022 Anomaly detection is an imperative problem in the field of the Internet of Things (IoT). The
anomalies are considered as samples that do not follow a normal pattern and significantly differ
from the expected values. There can be numerous reasons an IoT sensor data is anomalous. For
example, it can be due to abnormal events, IoT sensor faults, or malicious manipulation of data
generated from IoT devices. There has been wide-scale research done on anomaly detection
problems in general, i.e., finding the samples in data that differ significantly from the expected
values. However, there has been limited work done to figure out the inherent cause of the
anomalies in IoT sensor data. Accordingly, once an abnormal data sample has been observed,
the challenge of detecting whether the anomaly is due to an abnormal event or IoT sensor data
manipulation by an attacker has not been explored in detail.
In this paper, rather than finding the typical anomalies, we propose a method to detect malicious anomalies. The given paper puts forward an idea of where anomalies in IoT can be categorized into different types. Consequently, rather than finding an anomalous sample point, our method filters only malicious anomalies in the measured IoT data. Initially, we provide an attack model for the IoT sensor data and show how the model can affect the decision-making abilities of IoT-based applications by introducing malicious anomalies. Further, we design a novel Machine Learning (ML) based method to detect these malicious anomalies. Our ML method is inspired by ensemble machine learning and uses threshold and aggregation methods rather than the traditional methods of output aggregation in ensemble learning. The proposed ML architecture is tested using pollutant, telemetry, and vehicular traffic data obtained from the state of California. Simulation results show that our architecture performs with a decent accuracy for various sizes of malicious anomalies. In particular, by setting the parameters of the anomaly detector, the precision, recall, and F-score values of 93%, 94%, and 93% are obtained; i.e., a well-balance between all three metrics. By varying model parameters either precision or recall value can be increased further at the cost of other showing that the model is tunable to meet the application requirement. keywords: IoT | Anomaly detection | Ensemble learning | Predictive analytics |
مقاله انگلیسی |
62 |
A survey of blockchain-based IoT eHealthcare: Applications, research issues, and challenges
بررسی مراقبت های بهداشتی الکترونیک اینترنت اشیاء مبتنی بر بلاک چین: برنامه های کاربردی، مسائل تحقیقاتی و چالش ها-2022 Blockchain (BC) technology has recently emerged as an essential component for different applications, including healthcare and IoT, because of its decentralized ledger, source provenance,
and tamper-proof nature. The Internet of Things (IoT) and BC have enabled health systems to
expand their scalability and maintain consistency on a decentralized platform. As a result, many
researchers have developed BC-enabled IoT eHealth systems and explored the application of
BC technology in diverse fields of eHealthcare. This paper conducts a comprehensive survey
on the emerging applications of BC technology in healthcare. We summarize applications,
research issues, security threats, research challenges, opportunities, and the future scope of BC
technologies in the IoT-enabled healthcare system when BC is adopted to handle the privacy
and storage of current and future medical records. Furthermore, we analyze the state-of-the-art
BC works in the medical area, assessing their benefits-drawbacks, and guiding future researchers
to overcome the limitations of the existing articles.
Keywords: Blockchain | IoT | Healthcare | EHR challenge | Medical area |
مقاله انگلیسی |
63 |
A Survey of Indoor Location Technologies, Techniques and Applications in Industry
بررسی فن آوری ها، تکنیک ها و کاربردهای مکان داخلی در صنعت-2022 The recent academic research surrounding indoor positioning systems (IPS) and indoor location-
based services (ILBS) are reviewed to establish the current state-of-the-art for IPS and ILBS. This
review is focused on the use of IPS / ILBS for cyber-physical systems to support secure and safe
asset management (including people as assets), exploring the potential applications of IPS for
industry as suggested in the literature. Current application areas in industry are presented,
separated into physical item and human traceability applications for context. The literature are
reviewed to identify gaps in the ILBS development for industrial applications, future research
needs to focus a development framework to enable scalable solutions for industry. The key gaps
identified in the literature are: (i) a lack of pathways to extend IPS research into an ILBS, (ii) no
end-to-end ILBS have been developed and (iii) no framework has been reported that outlines the
information pathways from sensor data collection and location information to an established
ILBS. The technologies reviewed are presented in a comparison table (Table 1) intended as a
reference for selecting technologies for future systems based on requirements. The techniques
used to extract location information from each of the technologies identified are also explored
stating current accuracy and aligning the techniques with their suitable technologies.
1. Introduction keywords: موقعیت داخلی | اینترنت اشیا | سیستم های حسگر | خدمات مبتنی بر مکان داخلی | سیستم های فیزیکی سایبری | Indoor location | Internet of things | Sensor systems | Indoor location based services | Cyber physical systems |
مقاله انگلیسی |
64 |
A survey on security in internet of things with a focus on the impact of emerging technologies
بررسی امنیت در اینترنت اشیا با تمرکز بر تاثیر فناوری های نوظهور-2022 Internet of Things (IoT) have opened the door to a world of unlimited possibilities for imple-
mentations in varied sectors in society, but it also has many challenges. One of those challenges is
security and privacy. IoT devices are more susceptible to security threats and attacks. Due to
constraints of the IoT devices such as area, power, memory, etc., there is a lack of security so-
lutions that are compatible with IoT devices and applications, which is leading this world of
securely connected things to the “internet of insecure things.” A promising solution to this
problem is going beyond the standard or classical techniques to implementing the security so-
lutions in the hardware of the IoT device. The integration of emerging technologies in IoT net-
works, such as machine learning, blockchain, fog/edge/cloud computing, and quantum
computing have added more vulnerable points in the network. This paper introduces a
comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how
emerging technologies such as machine learning and blockchain are integrated in IoT, challenges
resulted from this integration, and potential solutions to these challenges. The paper utilizes the
4-layer IoT architecture as a reference to identify security issues with corresponding solutions. keywords: اینترنت اشیا | امنیت | فراگیری ماشین | بلاک چین | تهدیدها | راه حل های امنیتی | IoT | Security | Machine learning | Blockchain | Threats | Security solutions |
مقاله انگلیسی |
65 |
A throughput drop estimation model and its application to joint optimization of transmission power, frequency channel, and channel bonding in IEEE 802:11n WLAN for large-scale IoT environments
یک مدل تخمین افت توان و کاربرد آن برای بهینهسازی مشترک توان انتقال، کانال فرکانس و پیوند کانال در IEEE 802:11n WLAN برای محیطهای اینترنت اشیا در مقیاس بزرگ-2022 The concept of Internet of Things (IoT) has been widely studied in smart home networks, smart
city networks, smart grid systems, autonomous driving systems, and smart healthcare systems.
In IoT, the IEEE 802.11n wireless local-area network (WLAN) is used as a common communication
technology due to its flexibility and low cost. Then, the high performance WLAN is required to
enhance quality of service (QoS) of large-scale IoT applications connecting a number of devices
or sensors allocated in wide areas. WLAN can use the limited number of partially overlapping
channels (POCs) at 2.4 GHz band. The WLAN performance can be degraded by interfered signals
from other WLANs. Then, to optimize the POC assignment by reducing interferences, we have
proposed the throughput drop estimation model for concurrently communicating multiple links
under interferences. Unfortunately, the 40 MHz channel bonding (CB) and the 20 MHz non-CB
are considered separately, while the transmission power is always fixed to the maximum. In this
paper, we study the throughput drop estimation model under coexistence of CB and non-CB while
the transmission power is changed. Then, we present its application to the joint optimization of
assigning the transmission power, the frequency channel, and the channel bonding to enhance
the throughput performance of IEEE 802.11n WLAN. For evaluations, we compare estimated
throughputs by the model with measured ones in various network topologies to verify the model
accuracy. Then, we apply the model to the joint assignment optimization in them, and confirm
the effectiveness through simulations and experiments using the testbed system.
Keywords: Internet of Things | WLAN | Partially overlapping channel | Access point | Transmission power | Channel bonding | Non-channel bonding | Throughput drop |
مقاله انگلیسی |
66 |
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 |
67 |
AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics
AgroLens: یک معماری مزرعه هوشمند کمهزینه و سبز پسند برای پشتیبانی از تشخیص بیماریهای برگ در زمان واقعی-2022 Agriculture is one of the most significant global economic activities responsible for feeding the
world population of 7.75 billion. However, weather conditions and diseases impact production
efficiency, reducing economic activity and the food sovereignty of economies worldwide. Thus,
computational methods can support disease classification based on an image. This classification
requires training Artificial Intelligence (AI) models on high-performance computing resources,
usually far from the user domain. State of the art has proposed the concept of Edge Computing
(EC), which aims to bring computational resources closer to the domain problem to decrease
application latency and improve computational power closer to the client. In addition, EC has
become an enabling technology for Smart Farms, and the literature has appropriated EC to
support these applications. However, predominantly state-of-the-art architectures are dependent
on Internet connectivity and do not allow diverse real-time classification of diseases based on
crop leaf on mobile devices. This paper sheds light on a new architecture, AgroLens, built with
low-cost and green-friendly devices to support a mobile Smart Farm application, operational
even in areas lacking Internet connectivity. Among our main contributions, we highlight the
functional evaluation of AgroLens for AI-based real-time classification of diseases based on leaf
images, achieving high classification performance using a smartphone. Our results indicate that
AgroLens supports the connectivity of thousands of sensors from a smart farm without imposing
computational overhead on edge-compute. The AgroLens architecture opens up opportunities
and research avenues for deployment and evaluation for large-scale Smart Farm applications
with low-cost devices.
keywords: بیماری گیاهی | مزرعه هوشمند | اینترنت اشیا | یادگیری عمیق | سبز پسند| Plant disease | Smart Farm | Internet of Things | Deep learning | Green-friendly |
مقاله انگلیسی |
68 |
Annealing-based Quantum Computing for Combinatorial Optimal Power Flow
محاسبات کوانتومی مبتنی بر بازپخت برای جریان قدرت بهینه ترکیبی-2022 This paper proposes the use of annealing-based
quantum computing for solving combinatorial optimal power
flow problems. Quantum annealers provide a physical com-
puting platform which utilises quantum phase transitions to
solve specific classes of combinatorial problems. These devices
have seen rapid increases in scale and performance, and are
now approaching the point where they could be valuable for
industrial applications. This paper shows how an optimal power
flow problem incorporating linear multiphase network modelling,
discrete sources of energy flexibility, renewable generation place-
ment/sizing and network upgrade decisions can be formulated as
a quadratic unconstrained binary optimisation problem, which
can be solved by quantum annealing. Case studies with these
components integrated with the ieee European Low Voltage
Test Feeder are implemented using D-Wave Systems’ 5,760
qubit Advantage quantum processing unit and hybrid quantum-
classical solver. Index Terms— Distribution Network | D-Wave | Electric Vehicle | Optimal Power Flow | Power System Planning | Quantum Annealing | Quantum Computing | Smart Charging. |
مقاله انگلیسی |
69 |
An IoT-based interoperable architecture for wireless biomonitoring of patients with sensor patches
یک معماری تعاملی مبتنی بر اینترنت اشیا برای نظارت بیسیم بیماران با پچ های حسگر-2022 The alliance between the Internet of Things (IoT) and healthcare has the potential to improve
healthcare assistance at different stages of care through distributed vital sign sensing, paving
the way for domiciliary hospitalization. In this work, we propose an innovative design for
an IoT-based interoperable healthcare system to wirelessly monitor and classify patient status.
To support our research, we identify gaps, and discuss standards, protocols and technologies
based on works that use relevant IoT applications in healthcare. The proposed architecture is
centered on several low-energy unobtrusive sensors attached to the patients’ bodies, as well as
their beds, which encompass data acquisition nodes linked to a smart gateway that aggregates
data. The smart gateway is integrated with an existing hospital information system through the
exchange of Electronic Health Records (EHR), making relevant patient data easily available to
health professionals on systems which are familiar to them. A use case scenario is presented in
order to fulfill functional and non-functional requirements and provide a better understanding
of connection and communication between the distinct entities of the proposed architecture,
which is based on Bluetooth Low Energy (BLE) technology at the data acquisition level, the
Message Queuing Telemetry Transport (MQTT) protocol at the internal level, and on the Fast
Healthcare Interoperability Resources (FHIR) standard at the higher level.
keywords: Internet of Things | Digital healthcare | Wireless patient biomonitoring | System architecture | Interoperability |
مقاله انگلیسی |
70 |
An IoT-enabled intelligent automobile system for smart cities
یک سیستم خودروی هوشمند مجهز به اینترنت اشیا برای شهرهای هوشمند-2022 In our world of advancing technologies, automobiles are one industry where we can see
improved ergonomics and feature progressions. Artificial Intelligence (AI) integrated with
Internet of Things (IoT) is the future of most of the cutting-edge applications developed
for automobile industry to enhance performance and safety. The objective of this research
is to develop a new feature that can enhance the existing technology present in automo-
biles at low-cost. We had previously developed a technology known as Smart Accident
Precognition System (SAPS) which reduces the rate of accidents in automobile and also
enhance the safety of the passengers. Current research advances this technique by inte-
grating Google Assistant with the SAPS. The proposed system integrates several embedded
devices in the automobiles that monitor various aspects such as speed, distance, safety
measures like seatbelt, door locks, airbags, handbrakes etc. The real-time data is stored in
the cloud and the vehicle can adapt to various situations from the previous data collected.
Also, with the Google Assistant user can lock and unlock, start and stop, alert and do var-
ious automated tasks such as low fuel remainder, insurance remainders etc. The proposed
IoT enabled real-time vehicle system can detect accidents and adapt to change according
to various conditions. Further, with RFID keyless entry authentication the vehicle is secure
than ever before. This proposed system is much efficient to the existing systems and will
have a great positive impact in the automobile industry and society.
© 2020 Elsevier B.V. All rights reserved. keywords: هوش مصنوعی | سیستم هوشمند خودرو | اینترنت اشیا | شهرهای هوشمند | سیستم هوشمند | Artificial intelligence | Intelligent automobile system | Internet of Things | Smart Cities | Smart System |
مقاله انگلیسی |