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IoT, Volume 4, Issue 2 (June 2023) – 7 articles

Cover Story (view full-size image): Fog computing transfers the advantages and power of the cloud to end users. The fog-based IoT platform model involves three layers, i.e., IoT devices, fog nodes, and the cloud, whose performances were analyzed for individual subsystems and the overall system. A resource optimization problem was developed and solved to determine the optimal service rates at individual fog nodes under some constraint conditions. Further, numerical evaluations for the performance and the optimization problem are provided. View this paper
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19 pages, 2522 KiB  
Article
Performance Modeling and Optimization for a Fog-Based IoT Platform
by Shensheng Tang
IoT 2023, 4(2), 183-201; https://doi.org/10.3390/iot4020010 - 2 Jun 2023
Cited by 2 | Viewed by 2098
Abstract
A fog-based IoT platform model involving three layers, i.e., IoT devices, fog nodes, and the cloud, was proposed using an open Jackson network with feedback. The system performance was analyzed for individual subsystems, and the overall system was based on different input parameters. [...] Read more.
A fog-based IoT platform model involving three layers, i.e., IoT devices, fog nodes, and the cloud, was proposed using an open Jackson network with feedback. The system performance was analyzed for individual subsystems, and the overall system was based on different input parameters. Interesting performance metrics were derived from analytical results. A resource optimization problem was developed and solved to determine the optimal service rates at individual fog nodes under some constraint conditions. Numerical evaluations for the performance and the optimization problem are provided for further understanding of the analysis. The modeling and analysis, as well as the optimization design method, are expected to provide a useful reference for the design and evaluation of fog computing systems. Full article
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33 pages, 508 KiB  
Review
IoT Health Devices: Exploring Security Risks in the Connected Landscape
by Abasi-amefon Obot Affia, Hilary Finch, Woosub Jung, Issah Abubakari Samori, Lucas Potter and Xavier-Lewis Palmer
IoT 2023, 4(2), 150-182; https://doi.org/10.3390/iot4020009 - 25 May 2023
Cited by 9 | Viewed by 6718
Abstract
The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in [...] Read more.
The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of attacks are possible. To understand the risks in this new landscape, it is important to understand the architecture of IoTHDs, operations, and the social dynamics that may govern their interactions. This paper aims to document and create a map regarding IoTHDs, lay the groundwork for better understanding security risks in emerging IoTHD modalities through a multi-layer approach, and suggest means for improved governance and interaction. We also discuss technological innovations expected to set the stage for novel exploits leading into the middle and latter parts of the 21st century. Full article
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19 pages, 639 KiB  
Article
Efficient Non-DHT-Based RC-Based Architecture for Fog Computing in Healthcare 4.0
by Indranil Roy, Reshmi Mitra, Nick Rahimi and Bidyut Gupta
IoT 2023, 4(2), 131-149; https://doi.org/10.3390/iot4020008 - 10 May 2023
Viewed by 1945
Abstract
Cloud-computing capabilities have revolutionized the remote processing of exploding volumes of healthcare data. However, cloud-based analytics capabilities are saddled with a lack of context-awareness and unnecessary access latency issues as data are processed and stored in remote servers. The emerging network infrastructure tier [...] Read more.
Cloud-computing capabilities have revolutionized the remote processing of exploding volumes of healthcare data. However, cloud-based analytics capabilities are saddled with a lack of context-awareness and unnecessary access latency issues as data are processed and stored in remote servers. The emerging network infrastructure tier of fog computing can reduce expensive latency by bringing storage, processing, and networking closer to sensor nodes. Due to the growing variety of medical data and service types, there is a crucial need for efficient and secure architecture for sensor-based health-monitoring devices connected to fog nodes. In this paper, we present publish/subscribe and interest/resource-based non-DHT-based peer-to-peer (P2P) RC-based architecture for resource discovery. The publish/subscribe communication model provides a scalable way to handle large volumes of data and messages in real time, while allowing fine-grained access control to messages, thus enabling heightened security. Our two − level overlay network consists of (1) a transit ring containing group-heads representing a particular resource type, and (2) a completely connected group of peers. Our theoretical analysis shows that our search latency is independent of the number of peers. Additionally, the complexity of the intra-group data-lookup protocol is constant, and the complexity of the inter-group data lookup is O(n), where n is the total number of resource types present in the network. Overall, it therefore allows the system to handle large data throughput in a flexible, cost-effective, and secure way for medical IoT systems. Full article
(This article belongs to the Special Issue Cloud and Edge Computing Systems for IoT)
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19 pages, 1088 KiB  
Article
Secure Adaptive Context-Aware ABE for Smart Environments
by Saad Inshi, Rasel Chowdhury, Hakima Ould-Slimane and Chamseddine Talhi
IoT 2023, 4(2), 112-130; https://doi.org/10.3390/iot4020007 - 20 Apr 2023
Cited by 1 | Viewed by 2094
Abstract
Predicting context-aware activities using machine-learning techniques is evolving to become more readily available as a major driver of the growth of IoT applications to match the needs of the future smart autonomous environments. However, with today’s increasing security risks in the emerging cloud [...] Read more.
Predicting context-aware activities using machine-learning techniques is evolving to become more readily available as a major driver of the growth of IoT applications to match the needs of the future smart autonomous environments. However, with today’s increasing security risks in the emerging cloud technologies, which share massive data capabilities and impose regulation requirements on privacy, as well as the emergence of new multiuser, multiprofile, and multidevice technologies, there is a growing need for new approaches to address the new challenges of autonomous context awareness and its fine-grained security-enforcement models. The solutions proposed in this work aim to extend our previous LCA-ABE work to provide an intelligent, dynamic creation of context-aware policies, which has been achieved through deploying smart-learning techniques. It also provides data consent, automated access control, and secure end-to-end communications by leveraging attribute-based encryption (ABE). Moreover, our policy-driven orchestration model is able to achieve an efficient, real-time enforcement of authentication and authorization (AA) as well as federation services between users, service providers, and connected devices by aggregating, modelling, and reasoning context information and then updating consent accordingly in autonomous ways. Furthermore, our framework ensures that the accuracy of our algorithms is above 90% and their precision is around 85%, which is considerably high compared to the other reviewed approaches. Finally, the solution fulfills the newly imposed privacy regulations and leverages the full power of IoT smart environments. Full article
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17 pages, 2112 KiB  
Article
A DDoS Attack Detection Method Using Conditional Entropy Based on SDN Traffic
by Qiwen Tian and Sumiko Miyata
IoT 2023, 4(2), 95-111; https://doi.org/10.3390/iot4020006 - 12 Apr 2023
Cited by 3 | Viewed by 2461
Abstract
To detect each network attack in an SDN environment, an attack detection method is proposed based on an analysis of the features of the attack and the change in entropy of each parameter. Entropy is a parameter used in information theory to express [...] Read more.
To detect each network attack in an SDN environment, an attack detection method is proposed based on an analysis of the features of the attack and the change in entropy of each parameter. Entropy is a parameter used in information theory to express a certain degree of order. However, with the increasing complexity of networks and the diversity of attack types, existing studies use a single entropy, which does not discriminate correctly between attacks and normal traffic and may lead to false positives. In this paper, we propose new state determination standards that use the normal distribution characteristics of the entropy value at the time which an attack did not occur, subdivide the normal and abnormal range represented by the entropy value, improving the accuracy of attack determination. Furthermore, we show the effectiveness of the proposed method by numerical analysis. Full article
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17 pages, 1909 KiB  
Article
Evaluating Consumer Behavior, Decision Making, Risks, and Challenges for Buying an IoT Product
by Majid Nasirinejad and Srinivas Sampalli
IoT 2023, 4(2), 78-94; https://doi.org/10.3390/iot4020005 - 25 Mar 2023
Cited by 1 | Viewed by 3826
Abstract
Home appliance manufacturers have been adding Wi-Fi modules and sensors to devices to make them ‘smart’ since the early 2010s. However, consumers are still largely unaware of what kind of sensors are used in these devices. In fact, they usually do not even [...] Read more.
Home appliance manufacturers have been adding Wi-Fi modules and sensors to devices to make them ‘smart’ since the early 2010s. However, consumers are still largely unaware of what kind of sensors are used in these devices. In fact, they usually do not even realize that smart devices require an interaction of hardware and software since the smart device software is not immediately apparent. In this paper, we explore how providing additional information on these misunderstood smart device features (such as lists of sensors, software updates, and warranties) can influence consumers’ purchase decisions. We analyze how additional information on software update warranty (SUW) and the type of sensors in smart devices (which draw attention to potential financial and privacy risks) mediates consumer purchase behavior. We also examine how other moderators, such as brand trust and product price, affect consumers’ purchase decisions when considering which smart product option to buy. In the first qualitative user study, we conducted interviews with 20 study participants, and the results show that providing additional information about software updates and lists of sensors had a significant impact on consumer purchase preference. In our second quantitative study, we surveyed 323 participants to determine consumers’ willingness to pay for a SUW. From this, we saw that users were more willing to pay for Lifetime SUW on a smart TV than to pay for a 5-year SUW. These results provide important information to smart device manufacturers and designers on elements that improve trust in their brand, thus increasing the likelihood that consumers will purchase their smart devices. Furthermore, addressing the general consumer smart device knowledge gap by providing this relevant information could lead to a significant increase in consumer adoption of smart products overall, which would benefit the industry as a whole. Full article
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21 pages, 5359 KiB  
Article
Convolutional Neural Network-Based Low-Powered Wearable Smart Device for Gait Abnormality Detection
by Sanjeev Shakya, Attaphongse Taparugssanagorn and Chaklam Silpasuwanchai
IoT 2023, 4(2), 57-77; https://doi.org/10.3390/iot4020004 - 23 Mar 2023
Viewed by 2756
Abstract
Gait analysis is a powerful technique that detects and identifies foot disorders and walking irregularities, including pronation, supination, and unstable foot movements. Early detection can help prevent injuries, correct walking posture, and avoid the need for surgery or cortisone injections. Traditional gait analysis [...] Read more.
Gait analysis is a powerful technique that detects and identifies foot disorders and walking irregularities, including pronation, supination, and unstable foot movements. Early detection can help prevent injuries, correct walking posture, and avoid the need for surgery or cortisone injections. Traditional gait analysis methods are expensive and only available in laboratory settings, but new wearable technologies such as AI and IoT-based devices, smart shoes, and insoles have the potential to make gait analysis more accessible, especially for people who cannot easily access specialized facilities. This research proposes a novel approach using IoT, edge computing, and tiny machine learning (TinyML) to predict gait patterns using a microcontroller-based device worn on a shoe. The device uses an inertial measurement unit (IMU) sensor and a TinyML model on an advanced RISC machines (ARM) chip to classify and predict abnormal gait patterns, providing a more accessible, cost-effective, and portable way to conduct gait analysis. Full article
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