Strategies for Implementing Internet of Things Devices in Manufacturing Environments

The Internet of Things (IoT) has been exploited as a threat vector for cyberattacks in manufacturing environments. Manufacturing industry leaders are concerned with cyberattacks because of the associated costs of damages and lost production for their organizations. Grounded in the general systems theory, the purpose of this multiple case study was to explore strategies electrical controls engineers use to implement secure IoT devices in manufacturing environments. The study participants were eight electrical controls engineers working in three separate manufacturing facilities located in the Midwest region of the United States. The data were collected by semistructured interviews and 15 organizational documents. Data were analyzed using methodological triangulation to identify codes and themes. Four themes emerged: (a) a collaborative work environment, (b) employ existing cybersecurity practices, (c) adequate resources must exist to maintain security, and (d) learning and education. One recommendation for controls engineers is to embrace life-long learning, as technology is ever-changing. The implications for positive social change include the potential to improve manufacturing efficiencies and profits, thereby enhancing community support by manufacturing companies and increased wages and benefits for their employees

The Impacts of Privacy Rules on Users' Perception on Internet of Things (IoT) Applications: Focusing on Smart Home Security Service

Department of Management EngineeringAs communication and information technologies advance, the Internet of Things (IoT) has changed the way people live. In particular, as smart home security services have been widely commercialized, it is necessary to examine consumer perception. However, there is little research that explains the general perception of IoT and smart home services. This article will utilize communication privacy management theory and privacy calculus theory to investigate how options to protect privacy affect how users perceive benefits and costs and how those perceptions affect individuals??? intentions to use of smart home service. Scenario-based experiments were conducted, and perceived benefits and costs were treated as formative second-order constructs. The results of PLS analysis in the study showed that smart home options to protect privacy decreased perceived benefits and increased perceived costs. In addition, the perceived benefits and perceived costs significantly affected the intention to use smart home security services. This research contributes to the field of IoT and smart home research and gives practitioners notable guidelines.ope

Methodology for Designing Decision Support Systems for Visualising and Mitigating Supply Chain Cyber Risk from IoT Technologies

This paper proposes a methodology for designing decision support systems for visualising and mitigating the Internet of Things cyber risks. Digital technologies present new cyber risk in the supply chain which are often not visible to companies participating in the supply chains. This study investigates how the Internet of Things cyber risks can be visualised and mitigated in the process of designing business and supply chain strategies. The emerging DSS methodology present new findings on how digital technologies affect business and supply chain systems. Through epistemological analysis, the article derives with a decision support system for visualising supply chain cyber risk from Internet of Things digital technologies. Such methods do not exist at present and this represents the first attempt to devise a decision support system that would enable practitioners to develop a step by step process for visualising, assessing and mitigating the emerging cyber risk from IoT technologies on shared infrastructure in legacy supply chain systems

Tell the Smart House to Mind its Own Business!: Maintaining Privacy and Security in the Era of Smart Devices

Consumers want convenience. That convenience often comes in the form of everyday smart devices that connect to the internet and assist with daily tasks. With the advancement of technology and the “Internet of Things” in recent years, convenience is at our fingertips more than ever before. Not only do consumers want convenience, they want to trust that their product is performing the task that they purchased it for and not exposing them to danger or risk. However, due to the increasing capabilities and capacities of smart devices, consumers are less likely to realize the implications of what they are agreeing to when they purchase and begin using these products. This Note will focus on the risks associated with smart devices, using smart home devices as an illustration. These devices have the ability to collect intimate details about the layout of the home and about those who live within it. The mere collection of this personal data opens consumers up to the risk of having their private information shared with unintended recipients whether the information is being sold to a third party or accessible to a hacker. Thus, to adequately protect consumers, it is imperative that they can fully consent to their data being collected, retained, and potentially distributed. This Note examines the law that is currently in place to protect consumers who use smart devices and argues that a void ultimately leaves consumers vulnerable. Current data privacy protection in the United States centers on the self-regulatory regime of “notice and choice.” This Note highlights how the self-regulatory notice-and-choice model fails to ensure sufficient protection for consumers who use smart devices and discusses the need for greater privacy protection in the era of the emerging Internet of Things. Ultimately, this Note proposes a state-level resolution and calls upon an exemplar state to experiment with privacy protection laws to determine the best way to regulate the Internet of Things

A Storm in an IoT Cup: The Emergence of Cyber-Physical Social Machines

The concept of social machines is increasingly being used to characterise various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimension to the relationship between humans and machines making it difficult to understand their evolution at either the systemic or the conceptual level. This article describes these new socio-technical systems, which we term Cyber-Physical Social Machines, through different exemplars, and considers the associated challenges of security and privacy.Comment: 14 pages, 4 figure