NANOELECTRONICS AND EMERGING TECHNOLOGIES: A PARADIGM SHIFT IN ELECTRONICS

Flexible electronics and sensors are a key enabling element for the realization of wearables and geometry adaptive devices needed to follow current trends such as the Internet of things or Industry 4.0. Within this paper, we present a new and flexible packaging platform by the fabrication of an ultra-thin and highly flexible printed circuit board (PCB).The thermoplastic polymer Parylene, which combines a variety of unique properties such as optical transparency, biostability and biocompatibility according to ISO 10993, thermal stability as well as low permeability for gases and water, was used as the base for the new PCB.Particularly, its mechanical properties, especially the absence of intrinsic stresses, as well as its low Young’s modulus and good bendability are advantageous when using Parylene films as a free-standing substrate for flexible applications. The chemical inertness of Parylene against all common acids, bases and solvents ensures its compatibility with established standard microsystem technologies.For the realization of ultra-thin flexible PCBs, Parylene was used as a substrate as well as for the encapsulation and protection layer, respectively. Furthermore, Parylene was the dielectric between the metallic conductive layers. These redistribution layers (RDL) were deposited and patterned by standard microsystem technologies, such as sputtering, lithography and wet chemical etching. Hence, structure sizes of down to 10 µm were successfully realized. Different metals, such as gold, copper, and aluminum were tested for the RDL. Alternatively, printing technologies such as screen printing and aerosol jet printing were successfully demonstrated for the if conductive silver layerse. The electrical connection between the various RDL was of special interest throughout the study and was realized by etching vertical vias through the Parylene using oxygen plasma. Due to the low total thickness of the flexible Parylene PCB of 20 µm or even less, the obtained vias through the Parylene feature an advantageous low aspect ratio. For the contact formation through the vias, different methods were investigated, e. g. sputtering, printing and electrochemical deposition.Finally, the fabricated flexible Parylene PCB were characterized with respect to their bendability and electrical properties. Doing so, the resistivity and capacitance were measured as well as the frequency response between 20 kHz and 500 MHz. Particularly, for frequencies below 1 MHz the realized flexible Parylene PCBs show a good electrical performance.A third function of Parylene with respect to the realization of an ultra-thin and flexible PCB is its usage as a barrier or capping layer. Doing so, new approaches such as embedding electronic or sensor components in the flexible PCB can be enabled. Other integration technologies such as wire bonding, printing or gluing were investigated with respect to their usability for the new flexible PCB as well. Besides, also the direct fabrication of sensors on the Parylene flexible PCB was successfully demonstrated by the realization of a flexible potentiometric pH sensor.The presented new type of ultra-thin PCB is a versatile platform for the realization of flexible electronics, sensors and devices. Due to its biocompatible properties, the flexible Parylene PCB can be used for medical applications but also for the integration of MEMS, chemical sensors and optical components, respectively. Doing so, it enables the realization of smart systems for wearable applications and their integration into light weight constructions. Within this paper, the concept of this new approach, the respected fabrication and integration technologies as well as possible applications are presented in detail. Figure 1

Internet of Things (IoT) ecosystems are becoming increasingly ubiquitous and heterogeneous, adding extra layers of complexity to secure communication and resource allocation. IoT computing resources are often located at the network edge and distributed across many heterogeneous sensors, actuators, and controller devices. This makes it challenging to provide the proper security mechanisms to IoT ecosystems in terms of manageability and maintainability. In an IoT ecosystem, computational resources are naturally distributed and shareable among their constituency, which creates an opportunity to distribute heavy tasks to them. However, resource allocation in IoT requires secure and complex communication and coordination mechanisms, which existing ones do not adequately support. In this paper, we present Secure Actor-based Model for IoT Communication (SecIoTComm), a model for representing secure IoT communication. SecIoTComm aims to represent secure IoT communication properties and design and implement novel mechanisms to improve their programmability and performance. SecIoTComm separates the communication and computation concerns, achieving design modularity in building IoT ecosystems. First, this paper presents the syntax and operational semantics of SecIoTComm. Then, we present an IoT framework implementing the key concepts of the model. Finally, we evaluate the developed framework using various performance and scalability metrics.

Recently, extensive research into healthcare services and their technological advancements is being carried out globally. The Internet of Things (IoT) has proven to be a predominantly potential candidate in these advancements. IoT supports infrastructure development in connecting a wide range of medical devices, sensors, and health practitioners in order to ensure the best medical treatment for patients in both cities and remote places. This has enabled the IoT to provide major support in medical application, giving rise to assisted healthcare facilities. The IoT ecosystem has a complicated architecture, with several components interacting and an evolving variety of applications for users. This is a comprehensive and interdependent system that involves real-time data analysis, interconnections of device, data transfers, and analysis. The IoT creates a connected ecosystem developed from several intelligent systems that integrates respondents with machines and allows for statistics decision making. A network of interconnected devices that perceive vital data related to medical conditions of patients in real time is referred to as the Internet of Medical Things (IoMT), a healthcare application of IoT technology. Sensors used for sensing real-time data related to monitoring temperature, blood pressure levels, heartbeat levels, specialized signal converters, and communication interfaces can be added to existing medical facilities to turn them into IoMT devices. IoMT can be used to create wearable devices, home-care and personal-care medical devices, and mobile healthcare apps, along with creating communication with medical specialists in a given area. The IoMT system’s challenges and ecosystem interface requirements are also a matter of concern considering the complexity of the systems. This study offers a comprehensive evaluation of the various domains of use of IoMT, as well as its future scope and challenges. Recent advances in sensor technology, networks, cloud storage and processing, mobility, and big data analytics have made it possible to create affordable smart medical devices and a connected healthcare environment.

ABSTRACTContextSmart home devices have become increasingly popular in modern households, powered by the Internet of Things (IoT) advances. The data generated by smart devices can provide valuable insights into users' behavior and preferences. By analyzing the data, one can understand how people interact with their homes, thus creating a “smart home profile”. To comprehend the complete IoT ecosystem dynamics of an intelligent environment, it is necessary to learn from each IoT device to predict its status in the future time. Nevertheless, dealing with real‐world IoT data structure requires considerable preprocessing tasks and the employment of classifiers that can learn multiple IoT inputs from a single IoT message.ObjectiveAware of these challenges, this paper proposes a novel methodology to process multi‐label IoT data and provide a comprehensive comparison of multi‐label classifiers for forecasting the status of smart devices, considering their efficiency and accuracy.MethodWe propose a data transformation method to preprocess the IoT data to be used by multi‐label classifiers. This method is based on real data structure.ResultsWe evaluate our proposal in two real‐world scenarios and various multi‐label classifiers. The promising findings indicate that efficient classifiers can generate many correct predictions for a comprehensive IoT ecosystem in a small fraction of a second.ConclusionsOur proposed data transformation can fit the context of prediction to smart homes and work with multi‐label classifiers to understand user behavior.

The Internet of Things (IoT) is a constantly growing environment for connecting hardware, software, physical things, and communicating devices altogether or individually over a network with or without human interventions. It collects, computes, and exchanges data together. This quality of IoT makes the platform flexible for the variety of applications that work on this technology to get incredible benefits, including healthcare domains. In the olden days, the medical system does not have any digital file system, clinical data monitoring, and cyber-physical systems. Cyber Physical System (CPS) is the nucleus of IoT. The smart parameters of IoT create an interesting thing on the Internet of Healthcare System (IoHT) and keep the best place in revenue. Apart from smart homes, cities, and smart devices, IoT established a vital position in IoT. The security and the privacy of Healthcare data should be managed properly. Thus, making a new concept into it called, “Blockchain.” This not only marks its step in cryptocurrencies, but it also tights the security and privacy of all types of data, and it supports heterogeneous applications, especially IoT-based applications. This article describes the Blockchain-IoT-based healthcare applications, security of the Internet of Medical Things (IoMT), and the future direction in the healthcare system based on Blockchain.

In today’s competitive landscape, achieving customer-centricity is paramount for the sustainable growth and success of organisations. This research is dedicated to understanding customer preferences in the context of the Internet of things (IoT) and employs a two-part modeling approach tailored to this digital era. In the first phase, we leverage the power of the self-organizing map (SOM) algorithm to segment IoT customers based on their connected device usage patterns. This segmentation approach reveals three distinct customer clusters, with the second cluster demonstrating the highest propensity for IoT device adoption and usage. In the second phase, we introduce a robust decision tree methodology designed to prioritize various factors influencing customer satisfaction in the IoT ecosystem. We employ the classification and regression tree (CART) technique to analyze 17 key questions that assess the significance of factors impacting IoT device purchase decisions. By aligning these factors with the identified IoT customer clusters, we gain profound insights into customer behaviour and preferences in the rapidly evolving world of connected devices. This comprehensive analysis delves into the factors contributing to customer retention in the IoT space, with a strong emphasis on crafting logical marketing strategies, enhancing customer satisfaction, and fostering customer loyalty in the digital realm. Our research methodology involves surveys and questionnaires distributed to 207 IoT users, categorizing them into three distinct IoT customer groups. Leveraging analytical statistical methods, regression analysis, and IoT-specific tools and software, this study rigorously evaluates the factors influencing IoT device purchases. Importantly, this approach not only effectively clusters the IoT customer relationship management (IoT-CRM) dataset but also provides valuable visualisations that are essential for understanding the complex dynamics of the IoT customer landscape. Our findings underscore the critical role of logical marketing strategies, customer satisfaction, and customer loyalty in enhancing customer retention in the IoT era. This research offers a significant contribution to businesses seeking to optimize their IoT-CRM strategies and capitalize on the opportunities presented by the IoT ecosystem.

Analogous to the way humans use the Internet, devices will be the main users in the Internet of Things (IoT) ecosystem. Therefore, device-to-device (D2D) communication is expected to be an intrinsic part of the IoT. Devices will communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multihop manner. The ability to gather relevant information in real time is key to leveraging the value of the IoT as such information will be transformed into intelligence, which will facilitate the creation of an intelligent environment. Ultimately, the quality of the information gathered depends on how smart the devices are. In addition, these communicating devices will operate with different networking standards, may experience intermittent connectivity with each other, and many of them will be resource constrained. These characteristics open up several networking challenges that traditional routing protocols cannot solve. Consequently, devices will require intelligent routing protocols in order to achieve intelligent D2D communication. We present an overview of how intelligent D2D communication can be achieved in the IoT ecosystem. In particular, we focus on how state-of-the-art routing algorithms can achieve intelligent D2D communication in the IoT.

IoT ecosystems generates vast amount of data from interconnected devices, sensors, and networks. This vast amount of data poses significant challenges for traditional way of analysis. Deep learning comes into picture, with its ability to model complex pattern and unstructured data, addressing these challenges and elevating the IoT systems to new levels of intelligence. The integration of deep learning with Internet of Things (IoT) ecosystems provides an advancement in creating the intelligent network of connected devices. Key deep learning architectures like convolutional neural network (CNN) and Recurrent Neural Network (RNN) excel at processing sensor data for visual inspection and environmental monitoring. Looking forward, advancement in improvised learning, explainable AI, and next generation networks like 5G/6G will further enhance the capabilities of intelligent IoT ecosystems. This chapter explains how deep learning architecture facilitates the applications of IoT.

Undeniably, the Internet of Things (IoT) ecosystem continues to evolve at a breakneck pace, exceeding all growth expectations and ubiquity barriers. From sensor to cloud, this giant network keeps breaking technological bounds in several domains, and wireless sensor nodes (motes) are expected to be predominant as the number of IoT devices grows towards the trillions. However, their future in the IoT ecosystem still seems foggy, where several challenges, such as (i) device’s connectivity, (ii) intelligence at the edge, (iii) security and privacy concerns, and (iv) growing energy needs, keep pulling in opposite directions. This prospective paper offers a succinct and forward-looking review of recent trends, challenges, and state-of-the-art solutions of low-end IoT motes, where reconfigurable computing technology plays a key role in tomorrow’s IoT devices.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

The number of devices being connected to the internet is growing rapidly. This trend—referred to as the Internet of Things (IoT)—reflects the gradual transformation of everyday objects into smart devices. These smart devices are capable of collecting data from their surroundings and sharing those data over the internet. As a result, the development of the IoT raises extensive legal questions from a consumer protection perspective. First, the functionality of smart devices challenges consumer autonomy and the average consumer’s ability to make well-informed transactional decisions. Second, concerns remain about consumer choice, as switching between products or brands is difficult due to interoperability limitations. Third, consumer privacy is threatened by the data-driven nature of the IoT. Protecting EU Consumers in Internet of Things Ecosystems explores solutions to these challenges by critically analyzing the interplay between EU consumer law, EU competition law, and EU data privacy law, aiming to balance innovation and consumer protection in IoT ecosystems. At a time when society must question how the benefits of IoT can be harnessed for the greater good—rather than posing a threat to consumers, businesses, and governments—this volume offers valuable insights for academics, policymakers, businesses, and anyone interested in understanding the impact of technology on our daily lives.

A collaboration of US and Swedish researchers presents, for the first time, radio frequency (RF) reliability studies of fully integrated complimentary metal-oxide-semiconductor (CMOS) radio frequency integrated circuits (RFICs), important for designing next generation wireless communication applications, especially those that are based on ultra-thin flexible packages. Mitra et al. have a wide spread of research areas, including such topics as communications radar, hand-held medical scanners and portable weapon scanning technology. It is, however, their research into CMOS RFICs that lead to the present paper. Mastery of CMOS technology, with its small chip size, is thought to be crucial for the continued miniaturisation of circuits, with the world of nanoelectronics providing a potential gateway to the next generation of highly integrated multifunctional devices, circuits and systems. The Radio Frequency Integrated Circuits (RFIC) Lab members at North Dakota State University. (From Left) Arka Biswas, Chitralekha Biswas, Dipankar Mitra (in the back), and Babak Hamidi. RFIC Characterisation Set-up at NDSU. The ‘thinning-down’ of integrated circuits is one step towards mastering CMOS technology, with thinner and more lightweight circuitry providing more efficient and cost-effective solutions for various applications, including wireless communications, wearable and flexible electronics and Internet of Things (IoT) devices. One of the biggest challenges for realisation of flexible and ultra-thin ICs is the effect die-substrate thinning has on the RF performance of the IC. It was this problem that triggered Mitra et al. into producing the RF reliability studies presented in Electronics Letters. Thinning of the die-substrate, the small block of semiconducting material upon which a circuit is built, is one of the main contributors to performance degradation for RFICs, whilst also being the most important for producing thinner and more flexible circuitry. In their work, Mitra et al. use a voltage-controlled oscillator (VCO) to measure the effects of die-substrate thickness on the RF performance of the IC, including frequency range, RF output power variation, phase noise variation and DC parameters. The usual thickness of die-substrate for CMOS circuits as between 250–300 micrometres. Mitra et al. used a 250 µm thickness VCO as a test case, and then thinned down the die-substrate to 50 µm, 35 µm and 25 µm. They found that deviations of frequency and output power are within 1% and 1 dB respectively, with DC parameters also shown to be fairly similar for the different substrate thicknesses. The sensitivity to phase noise when thinning down was found to be significant, which confirms the well-known fact that phase noise sensitivity increases due to leakage. Flexible electronics is changing the way we make and use electronics. Many existing applications, such as wireless communication, wearable electronics, and healthcare monitoring require flexibility to promote more efficient and cost-effective solutions. Development of these flexible electronics is driving progress in the field, which enables futuristic applications such as a network of IoT devices and connected, smart cities. Several initiatives from governments and industry have also contributed to the progress and it is now estimated that the market for flexible electronics will reach $300 billion by 2028, with expected growth from $29 billion in 2017 to over $63 billion in 2023 for printed, flexible and organic electronics alone. The high-performance of CMOS electronics will be critical to this growth in flexible electronics, as a great number of current and future electronics rely on the fast communication and computation they can provide.

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

The Internet of Things (IoT) is a new technology that improves the quality of people's lives by dominating a wide range of applications and service sectors. Fifth-generation (5G) wireless networks will soon be able to link this IoT ecosystem. It has been thoughtfully created to support the IoT industry's explosive development. One of the essential technologies in the 5G architecture, network slicing allows for dividing the physical network into several logical networks (i.e., slices) with various network properties. As a result, network slicing is a crucial driver of IoT in 5G. Through dedicated slices, network slicing can meet the different networking needs of heterogeneous IoT applications. We thoroughly review network slicing's use in IoT realization in this study. We cover network slicing in various IoT application scenarios and the technical problems that can be resolved using network slicing. This paper also discusses open research issues and integration challenges linked to network slicing in the IoT ecosystem.

Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations 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 solutions 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 solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, 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.