IEEE Consumer Electronics Magazine

Institute of Electrical and Electronics Engineers (IEEE)
Institute of Electrical and Electronics Engineers (IEEE)
ISSN: 21622248, 21622256

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SCImago
Q2
WOS
Q2
Impact factor
3.7
SJR
0.829
CiteScore
10.0
Categories
Computer Science Applications
Electrical and Electronic Engineering
Hardware and Architecture
Human-Computer Interaction
Areas
Computer Science
Engineering
Years of issue
2012, 2014-2025
journal names
IEEE Consumer Electronics Magazine
IEEE CONSUM ELECTR M
Publications
1 798
Citations
14 756
h-index
55
Top-3 citing journals
IEEE Access
IEEE Access (665 citations)
Sensors
Sensors (385 citations)
Top-3 organizations
University of North Texas
University of North Texas (66 publications)
University of Wollongong
University of Wollongong (25 publications)
Top-3 countries
USA (287 publications)
China (182 publications)
India (156 publications)

Most cited in 5 years

Alladi T., Chamola V., Sikdar B., Choo K.R.
IEEE Consumer Electronics Magazine scimago Q2 wos Q2
2020-03-01 citations by CoLab: 240 Abstract  
As consumer Internet of Things (IoT) devices become increasingly pervasive in our society, there is a need to understand the underpinning security risks. Therefore, in this article, we describe the common attacks faced by consumer IoT devices and suggest potential mitigation strategies. We hope that the findings presented in this article will inform the future design of IoT devices.
Li Z., Sharma V., P. Mohanty S.
IEEE Consumer Electronics Magazine scimago Q2 wos Q2
2020-05-01 citations by CoLab: 126 Abstract  
Data have always been a major priority for businesses of all sizes. Businesses tend to enhance their ability in contextualizing data and draw new insights from it as the data itself proliferates with the advancement of technologies. Federated learning acts as a special form of privacy-preserving machine learning technique and can contextualize the data. It is a decentralized training approach for privately collecting and training the data provided by mobile devices, which are located at different geographical locations. Furthermore, users can benefit from obtaining a well-trained machine learning model without sending their privacy-sensitive personal data to the cloud. This article focuses on the most significant challenges associated with the preservation of data privacy via federated learning. Valuable attack mechanisms are discussed, and associated solutions are highlighted to the corresponding attack. Several research aspects along with promising future directions and applications via federated learning are additionally discussed.
Tan L., Yu K., Ming F., Cheng X., Srivastava G.
IEEE Consumer Electronics Magazine scimago Q2 wos Q2
2022-05-01 citations by CoLab: 105 Abstract  
Artificial Intelligence of Things (AIoT) is emerging as the future of Industry 4.0 and will be widely applied in consumer, commercial, and industrial fields. In AIoT, intelligent objects (smart devices), smart gateways, and edge/cloud nodes are subject to a large number of security threats and attacks. However, the traditional network security approaches are not fully suitable for AIoT. To address this issue, this article proposes a HoneyNet approach that includes both threat detection and situational awareness to enhance the security and resilience of AIoT. We first design a HoneyNet based on Docker technology that collects data to detect adversaries and monitor their attack behaviors. The collected data are then converted into images and used as samples to train a deep learning model. Finally, the trained model is deployed in AIoT to perform threat detection and provide situational awareness. To validate our scheme, we conduct HoneyNet deployment and model training on the SiteWhere AIoT platform and construct a simulation environment on this platform for threat detection and situational awareness. The experimental results demonstrate the feasibility and effectiveness of our solution.
Yu K., Tan L., Shang X., Huang J., Srivastava G., Chatterjee P.
IEEE Consumer Electronics Magazine scimago Q2 wos Q2
2021-03-01 citations by CoLab: 103 Abstract  
COVID-19 is a major global public health challenge and difficult to control in a short time completely. To prevent the COVID-19 epidemic from continuing to worsen, global scientific research institutions have actively carried out studies on COVID-19, thereby effectively improving the prevention, monitoring, tracking, control, and treatment of the epidemic. However, the COVID-19 electronic medical records (CEMRs) among hospitals worldwide are managed independently. With privacy consideration, CEMRs cannot be made public or shared, which is not conducive to in-depth and extensive research on COVID-19 by medical research institutions. In addition, even if new research results are developed, the disclosure and sharing process is slow. To address this issue, we propose a blockchain-based medical research support platform, which can provide efficient and privacy-preserving data sharing against COVID-19. First, hospitals and medical research institutions are treated as nodes on the alliance chain, so consensus and data sharing among the nodes is achieved. Then, COVID-19 patients, doctors, and researchers need to be authenticated in various institutes. Moreover, doctors and researchers need to be registered with the Fabric certificate authority. The CEMRs for COVID-19 patients uses the blockchain's pseudonym mechanism to protect privacy. After that, doctors upload CEMRs on the alliance chain, and researchers can obtain CEMRs from the alliance chain for research. Finally, the research results will be published on the blockchain for doctors to use. The experimental results show that the read and write performance and security performance on the alliance chain meet the requirements, which can promote the wide application of scientific research results against COVID-19.
Chaudhry A.U., Yanikomeroglu H.
IEEE Consumer Electronics Magazine scimago Q2 wos Q2
2021-11-01 citations by CoLab: 100 Abstract  
Free space optics (FSO) refers to optical wireless communications in outdoor environments. The aim of this article is to analyze the role that FSO is envisaged to play in the creation of next-generation satellite networks. To begin with, the reader is introduced to the types of FSO links and functionalities of a basic FSO system. Next, a comparison of FSO and radio frequency (RF) technologies for intersatellite links (ISLs) is provided, including a comparison between FSO and RF links when employed between low Earth orbit satellites. After that, the types of FSO or laser ISLs are considered, and the challenges in establishing laser ISLs, the properties of laser ISLs, and the capabilities of terminals for laser ISLs are discussed. Then, the parameters of a satellite constellation are highlighted, and different aspects of SpaceX's upcoming megaconstellation Starlink are explored. In addition, the optical wireless satellite network that is created by utilizing laser ISLs is examined. Finally, a use case is investigated for next-generation optical wireless satellite networks that are envisioned by the mid to late 2020s.
Mohanty S.P., Yanambaka V.P., Kougianos E., Puthal D.
IEEE Consumer Electronics Magazine scimago Q2 wos Q2
2020-03-01 citations by CoLab: 91 Abstract  
This article presents the first-ever blockchain that can simultaneously handle device and data security, which is important for the emerging Internet-of-Everything (IoE). It presents a unique concept of blockchain that integrates hardware security primitives called physical unclonable functions (PUFs) to solve scalability, latency, and energy requirement challenges and is called PUFchain. This article also introduces a new consensus algorithm called “Proof of PUF-Enabled Authentication” (PoP) for deployment in PUFchain. PoP is 1000 times faster than the well-established proof-of-work (PoW) and 5 times faster than proof-of-authentication (PoAh).
Tripathy A.K., Mohapatra A.G., Mohanty S.P., Kougianos E., Joshi A.M., Das G.
IEEE Consumer Electronics Magazine scimago Q2 wos Q2
2020-09-01 citations by CoLab: 90 Abstract  
COVID-19 (Corona Virus Disease 2019) is a pandemic, which has been spreading exponentially around the globe. Many countries adopted stay-at-home or lockdown policies to control its spreading. However, prolonged stay-at-home may cause worse effects like economical crises, unemployment, food scarcity, and mental health problems of individuals. This article presents a smart consumer electronics solution to facilitate safe and gradual opening after stay-at-home restrictions are lifted. An Internet of Medical Things enabled wearable called EasyBand is introduced to limit the growth of new positive cases by autocontact tracing and by encouraging essential social distancing.
Aloqaily M., Bouachir O., Karray F., Ridhawi I.A., Saddik A.E.
IEEE Consumer Electronics Magazine scimago Q2 wos Q2
2023-11-01 citations by CoLab: 88 Abstract  
The advances in Artificial Intelligence (AI) have led to technological advancements in a plethora of domains. Healthcare, education, and smart city services are now enriched with AI capabilities. These technological advancements would not have been realized without the assistance of fast, secure, and fault-tolerant communication media. Traditional processing, communication and storage technologies cannot maintain high levels of scalability and user experience for immersive services. The metaverse is an immersive three-dimensional (3D) virtual world that integrates fantasy and reality into a virtual environment using advanced virtual reality (VR) and augmented reality (AR) devices. Such an environment is still being developed and requires extensive research in order for it to be realized to its highest attainable levels. In this article, we discuss some of the key issues required in order to attain realization of metaverse services. We propose a framework that integrates digital twin (DT) with other advanced technologies such as the sixth generation (6G) communication network, blockchain, and AI, to maintain continuous end-to-end metaverse services. This article also outlines requirements for an integrated, DT-enabled metaverse framework and provides a look ahead into the evolving topic.
Shahidinejad A., Ghobaei-Arani M., Souri A., Shojafar M., Kumari S.
IEEE Consumer Electronics Magazine scimago Q2 wos Q2
2022-03-01 citations by CoLab: 73 Abstract  
Due to the ever-growing use of active Internet devices, the Internet has achieved good popularity at present. The smart devices could connect to the Internet and communicate together that shape the Internet of Things (IoT). Such smart devices are generating data and are connecting to each other through edge-cloud infrastructure. Authentication of the IoT devices plays a critical role in the success of the integration of IoT, edge, and cloud computing technologies. The complexity and attack resistance of the authentication protocols are still the main challenges. Motivated by this, this article introduces a lightweight authentication protocol for IoT devices named Light-Edge using a three-layer scheme, including IoT device layer, trust center at the edge layer, and cloud service providers. The results show the superiority of the proposed protocol against other approaches in terms of attack resistance, communication cost, and time cost.
Wang W., Fida M.H., Lian Z., Yin Z., Pham Q., Gadekallu T.R., Dev K., Su C.
IEEE Consumer Electronics Magazine scimago Q2 wos Q2
2021-09-30 citations by CoLab: 72 Abstract  
Although AI-empowered schemes bring some sound solutions to stimulate more reasonable energy distribution schemes between charging stations (CSs) and a charging stationproviders (CSP), frequent data sharing between them is possible to incur many security and privacy breaches. To solve these problems, federated learning (FL) is an ideal solution thatonly requires CSs to upload local models instead of detailed data. Although the CSs electricity consumption needs not to beexposed to the server directly, FL-based schemes still have been excavated several security threats such as information exploiting attacks, data poisoning attacks, model poisoning attacks, andfree-riding attacks. Hence, in this paper, both the effectiveness of energy management and the potential risks of FL for electricvehicle infrastructures (EVIs) are considered, we propose alightweight authentication FL-based energy demand predictionfor EVIs with premium-penalty mechanism. Security analysis andperformance evaluation prove that our proposed framework cangenerate an accurate electricity demand prediction framework to defend multiple FL attacks for EVIs.
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MR Device-Based Remote Medical Support System With Object Recognition
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Publishing countries

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USA, 287, 15.96%
China, 182, 10.12%
India, 156, 8.68%
United Kingdom, 67, 3.73%
Australia, 63, 3.5%
Canada, 55, 3.06%
Republic of Korea, 49, 2.73%
Japan, 49, 2.73%
Italy, 42, 2.34%
Saudi Arabia, 35, 1.95%
Spain, 28, 1.56%
Ireland, 25, 1.39%
UAE, 23, 1.28%
Czech Republic, 23, 1.28%
Germany, 21, 1.17%
Singapore, 21, 1.17%
France, 19, 1.06%
Pakistan, 15, 0.83%
Serbia, 14, 0.78%
Finland, 14, 0.78%
Greece, 13, 0.72%
Brazil, 11, 0.61%
Iraq, 9, 0.5%
Netherlands, 9, 0.5%
Russia, 8, 0.44%
New Zealand, 8, 0.44%
Sweden, 8, 0.44%
Egypt, 7, 0.39%
South Africa, 7, 0.39%
Croatia, 6, 0.33%
Portugal, 5, 0.28%
Malaysia, 5, 0.28%
Mexico, 5, 0.28%
Norway, 5, 0.28%
Turkey, 5, 0.28%
Austria, 4, 0.22%
Vietnam, 4, 0.22%
Denmark, 4, 0.22%
Iran, 4, 0.22%
Qatar, 4, 0.22%
Switzerland, 4, 0.22%
Estonia, 3, 0.17%
Bangladesh, 3, 0.17%
Belgium, 3, 0.17%
Thailand, 3, 0.17%
Uruguay, 3, 0.17%
Ukraine, 2, 0.11%
Jordan, 2, 0.11%
Cyprus, 2, 0.11%
Colombia, 2, 0.11%
Lebanon, 2, 0.11%
Luxembourg, 2, 0.11%
Romania, 2, 0.11%
Tanzania, 2, 0.11%
Algeria, 1, 0.06%
Argentina, 1, 0.06%
Barbados, 1, 0.06%
Hong Kong, 1, 0.06%
Israel, 1, 0.06%
Indonesia, 1, 0.06%
Kuwait, 1, 0.06%
Morocco, 1, 0.06%
Mozambique, 1, 0.06%
Palestine, 1, 0.06%
Poland, 1, 0.06%
Uzbekistan, 1, 0.06%
Philippines, 1, 0.06%
Montenegro, 1, 0.06%
Chile, 1, 0.06%
Sri Lanka, 1, 0.06%
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USA, 140, 17.14%
China, 102, 12.48%
India, 85, 10.4%
United Kingdom, 40, 4.9%
Canada, 33, 4.04%
Republic of Korea, 31, 3.79%
Japan, 29, 3.55%
Saudi Arabia, 28, 3.43%
Australia, 24, 2.94%
Italy, 24, 2.94%
UAE, 21, 2.57%
Czech Republic, 21, 2.57%
Spain, 17, 2.08%
Singapore, 12, 1.47%
Pakistan, 11, 1.35%
Serbia, 11, 1.35%
Greece, 8, 0.98%
Finland, 8, 0.98%
France, 7, 0.86%
Egypt, 6, 0.73%
Ireland, 6, 0.73%
Croatia, 6, 0.73%
Germany, 5, 0.61%
Brazil, 4, 0.49%
Norway, 4, 0.49%
Turkey, 4, 0.49%
Sweden, 4, 0.49%
South Africa, 4, 0.49%
Estonia, 3, 0.37%
Vietnam, 3, 0.37%
Iraq, 3, 0.37%
Qatar, 3, 0.37%
Mexico, 3, 0.37%
Thailand, 3, 0.37%
Uruguay, 3, 0.37%
Russia, 2, 0.24%
Austria, 2, 0.24%
Bangladesh, 2, 0.24%
Denmark, 2, 0.24%
Iran, 2, 0.24%
Luxembourg, 2, 0.24%
New Zealand, 2, 0.24%
Tanzania, 2, 0.24%
Portugal, 1, 0.12%
Algeria, 1, 0.12%
Barbados, 1, 0.12%
Hong Kong, 1, 0.12%
Jordan, 1, 0.12%
Cyprus, 1, 0.12%
Kuwait, 1, 0.12%
Lebanon, 1, 0.12%
Malaysia, 1, 0.12%
Mozambique, 1, 0.12%
Romania, 1, 0.12%
Uzbekistan, 1, 0.12%
Montenegro, 1, 0.12%
Switzerland, 1, 0.12%
Sri Lanka, 1, 0.12%
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