A systematic review of IoT in healthcare: Applications, techniques, and trends

Decentralized paradigm in the field of cybersecurity and machine learning (ML) for the emerging Internet of Things (IoT) has gained a lot of attention from the government, academia, and industries in recent years. Federated cybersecurity (FC) is regarded as a revolutionary concept to make the IoT safer and more efficient in the future. This emerging concept has the potential of detecting security threats, taking countermeasures, and limiting the spreading of threats over the IoT network system efficiently. An objective of cybersecurity is achieved by forming the federation of the learned and shared model on top of various participants. Federated learning (FL), which is regarded as a privacy-aware ML model, is particularly useful to secure the vulnerable IoT environment. In this article, we start with background and comparison of centralized learning, distributed on-site learning, and FL, which is then followed by a survey of the application of FL to cybersecurity for IoT. This survey primarily focuses on the security aspect but it also discusses several approaches that address the performance issues (e.g., accuracy, latency, resource constraint, and others) associated with FL, which may impact the security and overall performance of the IoT. To anticipate the future evolution of this new paradigm, we discuss the main ongoing research efforts, challenges, and research trends in this area. With this article, readers can have a more thorough understanding of FL for cybersecurity as well as cybersecurity for FL, different security attacks, and countermeasures.

Recent Advances on Federated Learning for Cybersecurity and Cybersecurity for Federated Learning for Internet of Things

Breast cancer, the most common cancer in women, is receiving increasing attention. The lack of high-quality medical resources, especially highly skilled doctors, in remote areas makes the diagnosis of breast cancer inefficient and causes great harm to women. The emergence of remote e-health has improved the situation to a certain extent, but its capabilities are still hampered by technical limitations, which manifest in two main aspects. First, due to network bandwidth limitations, it is difficult to guarantee the real-time transmission of breast cancer pathology images between remote areas and cities. Second, the highly skilled breast cancer doctors at large city hospitals are not guaranteed to be available for online diagnosis at all times. To overcome these limitations, this article proposes a deep-learning-empowered breast cancer auxiliary diagnosis scheme for remote e-health supported by 5G technology and beyond (5GB remote e-health). In this scheme, breast pathology images are first received from major hospitals via 5G, and a deep learning model based on the Inception-v3 network is subjected to transfer learning to obtain a diagnostic model. This diagnostic model is then employed on edge servers for auxiliary diagnosis at remote area hospitals. A theoretical analysis and experimental results show that this solution not only overcomes the two problems mentioned above but also improves the diagnostic accuracy for breast cancer in remote areas to 98.19 percent.

Deep-Learning-Empowered Breast Cancer Auxiliary Diagnosis for 5GB Remote E-Health

Internet of Things (IoT) is an integration of the Sensor, Embedded, Computing, and Communication technologies. The purpose of the IoT is to provide seamless services to anything, anytime at any place. IoT technologies play a crucial role everywhere, which brings the fourth revolution of disruptive technologies after the internet and Information and Communication Technology (ICT). The Research & Development community has predicted that the impact of IoT will be more than the internet and ICT on society, which improves the well-being of society and industries. Addressing the predominant system-level design aspects like energy efficiency, robustness, scalability, interoperability, and security issues result in the use of a potential IoT system. This paper presents the current state of art of the functional pillars of IoT and its emerging applications to motivate academicians and researches to develop real-time, energy-efficient, scalable, reliable, and secure IoT applications. This paper summarizes the architecture of IoT, with the contemporary status of IoT architectures. Highlights of the IoT system-level issues to develop more advanced real-time IoT applications have been discussed. Millions of devices exchange information using different communication standards, and interoperability between them is a significant issue. This paper provides the current status of the communication standards and application layer protocols used in IoT with the detailed analysis. The computing paradigms like Cloud, Cloudlet, Fog, and Edge computing facilitate IoT with various services like data offloading, resource and device management, etc. In this paper, an exhaustive analysis of Edge Computing in IoT with different edge computing architectures and existing status are deliberated. The widespread adoption of IoT in society has resulted in privacy and security issues. This paper emphasizes on analyzing the security challenges, privacy and security threats, conventional mitigation techniques, and further scope for IoT security. The features like fewer memory footprints, scheduling, real-time task execution, fewer interrupt, and thread switching latency of Real-Time Operating Systems (RTOS) enables the development of time critical IoT applications. Also, this review offers the analysis of the RTOS’s suitable for IoT with the current status and networking stack. Finally, open research issues in IoT system development are discussed.

/pdf/an-empirical-study-on-system-level-aspects-of-internet-of-3rbpbswc2d.pdf

An Empirical Study on System Level Aspects of Internet of Things (IoT)

Fog computing is an emerging technology to address computing and networking bottlenecks in large scale deployment of IoT applications. It is a promising complementary computing paradigm to cloud computing where computational, networking, storage and acceleration elements are deployed at the edge and network layers in a multi-tier, distributed and possibly cooperative manner. These elements may be virtualized computing functions placed at edge devices or network elements on demand, realizing the “computing everywhere” concept. To put the current research in perspective, this paper provides an inclusive taxonomy for architectural, algorithmic and technologic aspects of fog computing. The computing paradigms and their architectural distinctions, including cloud, edge, mobile edge and fog computing are subsequently reviewed. Practical deployment of fog computing includes a number of different aspects such as system design, application design, software implementation, security, computing resource management and networking. A comprehensive survey of all these aspects from the architectural point of view is covered. Current reference architectures and major application-specific architectures describing their salient features and distinctions in the context of fog computing are explored. Base architectures for application, software, security, computing resource management and networking are presented and are evaluated using a proposed maturity model.

/pdf/fog-computing-a-comprehensive-architectural-survey-2sxms5zo7j.pdf

Fog Computing: A Comprehensive Architectural Survey

Internet of Things (IoT) is being adopted widely impacting every sphere around us. IoT enabled health-care is making the way ahead as it enables remote monitoring of health status seamlessly. The health-care industry historically has generated a large volume of data. With the induction of IoT, this volume has gone up drastically. Therefore, the health data is required to be stored properly to make sense of the data. In this paper, we propose cloudlet assisted IoT enabled e-Health framework. This e-Health framework aims to ease real-time data access using cloudlets. In this framework, we propose and implement a health-care data management scheme to store the enormous health data and process the queries for retrieval of the health data by end-users. We have used NoSQL based model to store health data. Performance of the proposed model has been studied in terms of data transmission time, energy consumption, query response time and data packet loss. Finally, by comparing the evaluation results of our proposed model with performances of the traditional cloud storage-based e-Health system, we justified that our proposed model is performing far better than existing cloud-based e-Health solutions.

Secure Data Management in Cloudlet Assisted IoT Enabled e-Health Framework in Smart City

This is the peer reviewed version of the following article: Sengupta, S, Garcia, J, Masip‐Bruin, X. Essentiality of managing the resource information in the coordinated fog‐to‐cloud paradigm. Int J Commun Syst. 2019, which has been published in final form at https://doi.org/10.1002/dac.4286. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

/pdf/essentiality-of-managing-the-resource-information-in-the-3ly4lvoec0.pdf

Essentiality of managing the resource information in the coordinated fog‐to‐cloud paradigm

The rapid development of Smart Cities is aided by the convergence of information and communication technologies (ICT). Data is a key component of Smart City applications as well as a serious worry. Data is the critical factor that drives the whole development life-cycle in most Smart City use-cases, according to an exhaustive examination of several Smart City use-cases. Mishandling data, on the other hand, can have severe repercussions for programs that get incorrect data and users whose privacy may be compromised. As a result, we believe that an integrated ICT solution in Smart Cities is key to achieve the highest levels of scalability, data integrity, and secrecy within and across Smart Cities. As a result, this paper discusses a variety of modern technologies for Smart Cities and proposes our integrated architecture, which connects Blockchain technologies with modern data analytic techniques (e.g., Federated Learning) and Edge/Fog computing to address the current data privacy issues in Smart Cities. Finally, we discuss and present our proposed architectural framework in detail, taking into account an online marketing campaign and an e-Health application use-cases.

/pdf/toward-blockchain-based-fog-and-edge-computing-for-privacy-2s6h6sqp.pdf

Toward blockchain-based fog and edge computing for privacy-preserving smart cities

Modern cities are equipped with various information and communications technology (ICT) resources including Internet of Things (IoT) devices, computing platforms, and data storage media. Data are one of the most valuable ICT resources in smart cities. Data management strategies play a vital role in managing the requirements of user and business models in a city. Using the benefits of data makes an agile, creative, and smart city via the widespread use of appropriate city services. Centralized and distributed-to-centralized data management (D2CDM) architectures are recommended to organize the large-scale produced city data in smart city networks, including physical data resources (e.g., sensor data) and nonphysical data sources (e.g., city consumer personal databases). In this article, we explain two different ICT technology management solutions for smart city networks in which the ICT resources in a city can be managed and delivered: centralized and distributed-to-centralized. We also describe two main strategies for data management in smart cities, and the advantages of D2C-DM are discussed based on two ongoing case studies. We also mention there is much room for future works and development of this study, such as developing software services in smart cities based on edge-to-cloud orchestration and enhancing the effectiveness of machine learning (ML) and artificial intelligence (AI) techniques through multilevel ICT architecture.

Distributed-to-Centralized Data Management: A New Sense of Large-Scale ICT Management of Smart City IoT Networks

: An early prediction of students' academic performance helps to identify at-risk students and enables management to take corrective actions to prevent them from going astray. Most of the research works in this field have used supervised machine learning approaches to their crafted datasets having numerous attributes or features. Since these datasets are not publicly available, it is hard to understand and compare the significance of the chosen features and the efficacy of the different machine learning models employed in the classification task. In this work, we analyzed 27 research papers published in the last ten tears (2011-2021) that used machine learning models for predicting students' performance. We identify the most frequently used features in the private datasets, their interrelationships, and abstraction levels. We also explored three popular public datasets and performed statistical analysis like the Chi-square test and Person's correlation on its features. A minimal set of essential features is prepared by fusing the frequent features and the statistically significant features. We propose an algorithm for selecting a minimal set of features from any dataset with a given set of features. We compared the performance of different machine learning models on the three public datasets in two experimental setups-one with the complete feature set and the other with a minimal set of features. Compared to using the complete feature set, it is observed that most supervised models perform nearly identically and, in some cases, even better with the reduced feature set. The proposed method is capable of identifying the most essential feature set from any new dataset for predicting students' performance.

/pdf/towards-finding-a-minimal-set-of-features-for-predicting-1fo81r0l.pdf

Towards Finding a Minimal Set of Features for Predicting Students' Performance Using Educational Data Mining

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