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.

In developing countries, a significant amount of natural gas is used for household water heating, accounting for roughly 50% of total usage. Legacy systems, typified by large water heaters, operate inefficiently by continuously maintaining a large volume of water at a constant temperature, irrespective of demand. With dwindling domestic gas reserves and rising demand, this increases dependence on expensive energy imports.

We introduce a novel Internet of Things (IoT)-inspired solution to understand and predict water usage patterns and only activate the water heater when there’s a predicted demand. This retrofit system is maintenance-free and uses a rechargeable battery powered by a thermoelectric generator (TEG), which capitalizes on the temperature difference between the heater and its environment for electricity. Our study shows a notable 70% reduction in natural gas consumption compared to traditional systems. Our solution offers a sustainable and efficient method for water heating, addressing the challenges of depleting gas reserves and rising energy costs.

Face milling is performed on aluminum alloy A96061-T6 at diverse cutting parameters proposed by the design of experiments. Surface roughness is predicted by examining the effects of cutting parameters (CP), vibrations (Vib), and sound characteristics (SC). Sound characteristics based on surface roughness estimation determine the rarity of the work. In this study, a unique ANN-TLBO hybrid model (Artificial Neural Networks: Teaching Learning Based Algorithm) is created to predict the surface roughness from CP, Vib, and SC. To ascertain their correctness and efficacy in evaluating surface roughness, the performance of these models is evaluated. First off, the CP hybrid model demonstrated an amazing accuracy of 95.1%, demonstrating its capacity to offer trustworthy forecasts of surface roughness values. The Vib hybrid model, in addition, demonstrated a respectable accuracy of 85.4%. Although it was not as accurate as the CP model, it nevertheless showed promise in forecasting surface roughness. The SC-based hybrid model outperformed the other two models in terms of accuracy with a remarkable accuracy of 96.2%, making it the most trustworthy and efficient technique for assessing surface roughness in this investigation. An analysis of error percentages revealed the exceptional performance of SC-based Model-3, exhibiting an average error percentage of 3.77%. This outperformed Vib Model-2 (14.52%) and CP-based Model-1 (4.75%). The SC model is the best option, and given its outstanding accuracy, it may end up becoming the go-to technique for industrial applications needing accurate surface roughness measurement. The SC model’s exceptional performance highlights the importance of optimization strategies in improving the prediction capacities of ANN-based models, leading to significant advancements in the field of surface roughness assessment and related fields. An IoT platform is developed to link the model’s output with other systems. The system created eliminates the need for manual, physical surface roughness measurement and allows for the display of surface roughness data on the cloud and other platforms.

A ‘smart city’ is a buzz term and concept. The ‘smart city’ has mainly been discussed in the scholarly literature on urban planning, architecture, and geography. While the ‘smart city’ has been under-analyzed in international trade law, the term ‘smart city’ is commonly used in Asian trade policies. The Association of Southeast Asian Nations (ASEAN) established the ‘ASEAN Smart Cities Network’ and the ‘smart city’ is now an important market opportunity for exporting smart technologies and services to ASEAN. Against this backdrop, this article addresses how smart cities can be regulated and governed by international trade law. The trade law perspective facilitates a broader understanding of smart city governance, which includes under-explored ‘global’ regulatory dimensions concerning the interaction between local governments and foreign firms. This article selects three relevant trade areas for discussions: (1) Internet of Things in the context of trade in goods and services; (2) international standard-setting activities; and (3) data governance. It further considers what kinds of regulatory issues international smart city projects can add to the current digital trade discourse. Drawing on the smart city literature, the article points out additional problems concerning security and privacy that have not yet been acknowledged in digital trade.

The escalating adoption of wearable technology for health data monitoring has led to the real-time aggregation of personal information. This phenomenon has fuelled heightened apprehensions about data security and privacy, given the storage, processing, and sharing of personal health data by corporations. Regulatory frameworks have been enacted to safeguard individual privacy rights, as exemplified by the General Data Protection Regulation (GDPR). This research paper, by Ms Varda Mone and Ms Fayazullaeva Shakhlo, offers an overview of extant literature on privacy apprehensions concerning wearable devices, conducting an exhaustive review to discern pivotal privacy issues and proffer prospective remedies. Specifically, the paper delineates the ensuing privacy concerns associated with wearables. Predominantly, wearables introduce security vulnerabilities that may facilitate the misappropriation, compromise, or revelation of delicate health data. The copious health information amassed by wearables can be potentially sold or divulged to external parties’ sans user cognisance or consent. Furthermore, the deployment of wearable technology harbours the potential for discriminatory practices against those with disabilities or chronic ailments. Additionally, apprehensions pertaining to privacy and surveillance stem from the capacity of wearable devices to monitor and trace an individual's movements and activities. To conclude, the paper deliberates on plausible measures to address privacy concerns pertaining to wearable devices, encompassing: a) Fortifying the security apparatus of wearable devices, b) Amplifying user autonomy over their health data, and c) Regulating the collection and utilisation of user health data by wearables. The paper asserts that the amelioration of these privacy concerns is indispensable for leveraging wearable technology's potential to enhance human well-being while ensuring the preservation of personal privacy.

Recently, methods and approaches such as Participatory Data Analysis, Data-Enabled Design, and Contextual Inquiry have highlighted how design activities can benefit from behavioral data. This data offers new ways to learn from what people do and how they do it, across time and space. However, behavioral data introduces changes and frictions to design activities and poses several challenges for designers to overcome. In this paper, we conduct two workshops with 18 expert designers, from industry and academia, to understand the nature of these challenges, beyond the technical aspects. We contribute by underlining the challenges and opportunities of incorporating behavioral data into design activities; including a design perspective on data, interacting with participants, and interacting with regulatory bodies. We translate our findings into opportunities for a better alignment between regulatory bodies, designers, and participants. We propose to harness the iterative nature of design activities and embedded it into a process that allows for continuous reflection, reassessment, and review of highly dynamic datasets.

The modularity of components has enhanced the ability to create IoT systems by composing them from off the shelf. However, the breadth of technological choices and capabilities of component devices has made designing these systems harder to select, compose, implement and test, especially for dynamic systems. In this paper, we adopt formal tools from category theory (CT), a branch of mathematics whose central tenet is compositionality, to generate models for IoT systems. More specifically, we introduce a port-graph operad to represent the architectural designs of IoT systems. We use presheaf categories to construct generic IoT schemas to support modularity. Given this information, we briefly describe its relationship to control strategies of dynamical systems that model the interaction of components. Our approach balances genericity and specificity, providing interlinked schematic representations of system architecture and component representation.

We investigate how four internet of things (IoT) companies perceive the large quantity of community-generated content as a significant source of innovation. We study the extent to which these companies are willing to align their internal organisation to cope with the external community dynamics and define beneficial modes of collaboration for all involved stakeholders. Four IoT companies adopting open-source hardware principles were selected as case studies. The data collection was based on 18 interviews highlighting both the perspectives of the companies and their corresponding communities and the opinions of key experts in the domain. In our findings, we illustrate the different manifestations of open business models and the companies’ concrete approaches to working with external stakeholders. It is shown that companies with a business history more clearly claim sovereignty over their strategic decisions in a community-infused model, while, on the other hand, the community-based companies pursue a community-led strategy.

No matter how good a smart device may be, it remains useless outside the context of a digital ecosystem. Internet of Things (IoT) environments are possible as long as services and products can interconnect smoothly and exchange data in real time. Therefore, interoperability ranks high in global policy agendas, with the promise of bringing an end to network effects slanted in favour of ecosystem orchestrators. However, recent regulatory initiatives introducing interoperability obligations risk falling short of their intent or even risk generating unintended consequences in the absence of a coherent approach to standardisation. Against this backdrop, focusing on the UK Open Banking experience, this article makes a proposal for workable interoperability in IoT ecosystems aimed at ensuring market contestability without undermining incentives to innovate.

Blockchain is a well-known prominent technology that has gotten a lot of interest beyond the financial industry, attracting researchers and practitioners from numerous businesses and fields. Specific uses of blockchain in supply chain management (SCM) are addressed in business practice. By combining two perspectives on blockchain in SCM, this study provides comprehensive knowledge in this field using a bibliometric approach. We will explore the worldwide research trend in related topic areas. By collecting data from the Web of Science, we collected 400 articles related to our research topic from 2016 until early 2021. We eliminated research in the form of technical reports, editorials, comments, and consultancy articles to maintain the quality of the data gathering. VOSviewer is used to create visualization maps based on text and bibliographic information. The examination uncovered helpful information, such as annual publishing and citation patterns, the top research topic, the top authors, and the most supporting funding organizations in this field.