In our digitalized modern society where cyber-physical systems and internet-of-things (IoT) devices are increasingly commonplace, it is paramount that we are able to assure the cybersecurity of the systems that we rely on. As a fundamental policy, we join the advocates of multilayered cybersecurity measures, where resilience is built into IoT systems by relying on multiple defensive techniques. While existing legislation such as the General Data Protection Regulation (GDPR) also takes this stance, the technical implementation of these measures is left open. This invites research into the landscape of multilayered defensive measures, and within this problem space, we focus on two defensive measures: obfuscation and diversification. In this study, through a literature review, we situate these measures within the broader IoT cybersecurity landscape and show how they operate with other security measures built on the network and within IoT devices themselves. Our findings highlight that obfuscation and diversification show promise in contributing to a cost-effective robust cybersecurity ecosystem in today’s diverse cyber threat landscape.

With the emerging developments in millimeter-wave/5G technologies, the potential for wireless Internet of things devices to achieve widespread sensing, precise localization, and high data-rate communication systems becomes increasingly viable. The surge in interest surrounding virtual reality (VR) and augmented reality (AR) technologies is attributed to the vast array of applications they enable, ranging from surgical training to motion capture and daily interactions in VR spaces. To further elevate the user experience, and real-time and accurate orientation detection of the user, the authors proposes the utilization of a frequency-modulated continuous-wave (FMCW) radar system coupled with an ultra-low-power, sticker-like millimeter-wave identification (mmID). The mmID features four backscattering elements, multiplexed in amplitude, frequency, and spatial domains. This design utilizes the training of a supervised learning classification convolutional neural network, enabling accurate real-time three-axis orientation detection of the user. The proposed orientation detection system exhibits exceptional performance, achieving a noteworthy accuracy of 90.58% over three axes at a distance of 8 m. This high accuracy underscores the precision of the orientation detection system, particularly tailored for medium-range VR/AR applications. The integration of the FMCW-based mmID system with machine learning proves to be a promising advancement, contributing to the seamless and immersive interaction within virtual and augmented environments.

This early-stage research article intends to explore the regulatory and liability requirements of a not yet fully developed subset of consumer Internet of Things (IoT) objects: the mixed-functions IoT devices. These objects could be wearables or not but could perform an e-health function, such as measuring your heartbeat, as well as consumer functions, such as displaying chat notifications. I argue that these mixed-functions devices will play an important role within smart homes as they will interact with new medical IoT devices to carry on rehabilitation and other medical functions at home. That is why it is important to start mapping down all the regulatory and liability requirements that might interest mixed-functions IoT device developers for them to understand which thread to follow in this regulatory and liability requirements maze.

The emergence of the Internet of Things (IoT) has brought more challenges for designers to fully understand networked objects and develop pleasurable user experiences (UXs). Due to the radical change of products when they are connected, traditional experience design theories may not be applicable in this new context. Based on two well-established UX design theories, this paper presents a survey study that investigated the pleasurability of IoT devices by comparing a representative IoT device (i.e., the smartwatch) and its conventional form (i.e., the wristwatch). An online questionnaire was deployed to gather feedback from parallel wristwatch and smartwatch users. Their experiences using both types of watches were quantitatively and qualitatively compared by data analysis. The results highlighted the differences in UXs between smartwatches and wristwatches in three types of pleasure and five psychological needs. The study revealed design opportunities to improve the pleasurability of smartwatches and provides novel design insights informing the development of pleasurable UXs for future IoT devices.

In this special issue, we have collected eight articles that offer new points for research on information and communications technology (ICT)-based systems. We focused on the intuitive nature of the relationship between new ICT-based systems and contemporary management, forming an integrative unit of analysis instead of focusing solely on new ICT-based systems and leaving contemporary management as a moderating or mediating factor. This special issue promoted interdisciplinary research at the intersection of new ICT-based systems and contemporary management, including cybernetics systems and knowledge management, service managing and the Internet of things, cloud and marketing management, business process re-engineering and management, knowledge management, and strategic business management, among others.

A 2 × 2 dual band MIMO flexible antenna with a low profile and CPW feeding is designed for Internet of Things (IoT) domains, operating in 5G (3.3–3.6/4.8–5.0 GHz), WiMAX (5.25–5.85 GHz) and WLAN (5.15–5.35/5.725–5.825 GHz) bands. Two resonant frequencies are generated by L-shaped branches and a rectangular monopole. The mentioned MIMO antenna comprises four elements arranged vertically. With the addition of an orthogonal branch, significant isolation is attained, which exceeds 21 dB in the entire bands. The four-element MIMO antenna is fabricated and experimented to analyze the performance. According to the measurement, the antenna can operate bandwidth covering 3.156–3.84 GHz (19.55%) and 4.638–6.348 GHz (31.13%). Furthermore, the provided four-element MIMO antenna possesses a number of advantageous properties, such as ECC, DG, and TARC, indicating its suitability for 5G/WiMAX/WLAN applications. In accordance with the results of bending measurement and human body influence, it is evident that the presented liquid crystal polymer antenna could be an ideal candidate for integrating into wearable 5G/WiMAX/WLAN devices.

Investigations illustrate that the Internet of Things (IoT) can save costs, increase efficiency, improve quality, and provide data-driven preventative maintenance services. Intelligent sensors, dependable connectivity, and complete integration are essential for gathering real-time information. IoT develops home appliances for improved customer satisfaction, personalization, and enhanced big data analytics as a crucial Industry 4.0 enabler. Because the product design process is an important part of controlling manufacturing, there are constant attempts to improve and minimize product design time. Utilizing a hybrid algorithm, this research provides a novel method to schedule design products in production management systems to optimize energy usage and design time (combined particle optimization algorithm and shuffled frog leaping algorithm). The issue with particle optimization algorithms is that they might become stuck in local optimization and take a long time to converge to global optimization. The strength of the combined frog leaping algorithm local searching has been exploited to solve these difficulties. The MATLAB programming tool is used to simulate the suggested technique. The simulation findings were examined from three perspectives: energy usage, manufacturing time, and product design time. According to the findings, the recommended strategy performed better in minimizing energy use and product design time. These findings also suggest that the proposed strategy has a higher degree of convergence when discovering optimal solutions.

With the emergence of Internet of Things (IoT) as a new source of “big” data and value creation, businesses encounter novel opportunities as well as challenges in IoT design. Although recent research argues that digital technology can enable new kinds of development processes that are distinctive from their counterparts in the 20th century, minimal attention has been focused on the IoT design process. In order to contextualize New Product Development (NPD) processes for IoT, this paper comprehensively interrogates existing, and emerging development approaches for products, services, software, and integrated products, and several factors that affect designing IoT. This discussion includes the generic development process, the commonalities and differences of different development approaches, and processes. The paper demonstrates that only a few existing approaches reflect vital characteristics of networked artifacts or the integration of data science within the development model, which is one of the key attributes of IoT design. From these investigations, we propose “The Mobius Strip Model of IoT Development ProcessI,” a conceptual process for IoT design, which is distinctive to others. The continuous loops of the IoT design integrate the attributes and phases of different processes and consist of two different development approaches and strategies. Understanding the particular attributes of the IoT NPD process can help novice and experienced researchers in both feeding and drawing insight from the broader design discourse.

Human gait data can be collected using inertial measurement units (IMUs). An IMU is an electronic device that uses an accelerometer and gyroscope to capture three-axial linear acceleration and three-axial angular velocity. The data so collected are time series in nature. The major challenge associated with these data is the segmentation of signal samples into stride-specific information, that is, individual gait cycles. One empirical approach for stride segmentation is based on timestamps. However, timestamping is a manual technique, and it requires a timing device and a fixed laboratory set-up which usually restricts its applicability outside of the laboratory. In this study, we have proposed an automatic technique for stride segmentation of accelerometry data for three different walking activities. The autocorrelation function (ACF) is utilized for the identification of stride boundaries. Identification and extraction of stride-specific data are done by devising a concept of tuning parameter ( $t_{p}$ ) which is based on minimum standard deviation ( $\sigma$ ). Rigorous experimentation is done on human activities and postural transition and Osaka University – Institute of Scientific and Industrial Research gait inertial sensor datasets. Obtained mean stride duration for level walking, walking upstairs, and walking downstairs is 1.1, 1.19, and 1.02 s with 95% confidence interval [1.08, 1.12], [1.15, 1.22], and [0.97, 1.07], respectively, which is on par with standard findings reported in the literature. Limitations of accelerometry and ACF are also discussed. stride segmentation; human activity recognition; accelerometry; gait parameter estimation; gait cycle; inertial measurement unit; autocorrelation function; wearable sensors; IoT; edge computing; tinyML.