Models for Innovative IoT Ecosystems

Nedeltcheva, Galia Novakova; Shoikova, Elena

doi:10.1145/3175684.3175710

Cited by 9 publications

(4 citation statements)

References 2 publications

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“…We present two business scenarios that differ because of ownership of private data in their IoT environments. Our scenarios are similar to the ones recently described in Nedeltcheva and Shoikova (2017).…”

Section: Privacy Accountabilitysupporting

confidence: 86%

Privacy Accountability and Penalties for IoT Firms

Ciardiello

Liddo

2020

Risk Analysis

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Internet of things (IoT) business partnership are formed by technological partners and traditional manufacturers. IoT sensors and devices capture data from manufacturers' products. Data enforce product/service innovation thanks to data sharing among companies. However, data sharing among firms increases the risk of data breaches. The latter is due to two phenomena: information linkage and privacy interdependency. Data Protection Authorities (DPA) protect data users' rights and fine firms if there is an infringement of privacy laws. DPA sanction the responsible for the infringement of privacy laws. We present two different business scenarios: the first occurs when each firm is a data owner; the second occurs when only the manufacturer is the data owner. For both scenarios, we present two fair penalty schemes that suggest the following: total amount of the fine; and how to share the fine among participants. Penalties critically vary at how innovation networks are structured in IoT industries. Our penalties provide incentives to data sharing since they redistribute firms' responsibility against data breaches. Our penalties may mitigate the risk on the manufacturer if is the unique responsible for data handling.

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Section: Privacy Accountabilitysupporting

confidence: 86%

Privacy Accountability and Penalties for IoT Firms

Ciardiello

Liddo

2020

Risk Analysis

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“…Through the subscription model, Netflix managed to better understand their customer's needs and by satisfying those needs and offering various subscription packages that have different benefits, the company is able to transform from a DVD seller to one of the world's most well-known streaming platform and movie production of all time [9]. A vertical business model is a model where the IoT equipment, gateway and Cloud utilities are all provided and managed by a single organization [8]. A vertical business model is based on the concept of vertical integration strategy, whereby an organization controls its suppliers, dealers and retail locations directly to ensure that its value and supply chain is not affected by the influence of other external factors.…”

Section: Subscription Business Model Figure 1 Subscription Business Model [9] [11]mentioning

confidence: 99%

“…This saves time for the end users as there will not be a need for them to run around for a solution. However, the downside of a vertical business model is that any possible future enhancements or upgrade to the product is fully dependent on the manufacturer itself [8]. Another downside is that problems will arise when new things such as sensors are added to the model or when the business model receives some further change or improvement that may benefit the organization.…”

Section: Vertical Business Modelmentioning

confidence: 99%

A Review on Service Business Model Using IoT

Cheng¹,

Kamsin²,

Zainal³

2021

Atlantis Highlights in Computer Sciences

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The connected devices have become an important element for businesses as it provides significant innovation in business model. This paper discusses on the different business models which contributed to the values of businesses. As for the vertical business model, we have learned that different types of integration can be applied to other companies and manufacturers as a method to access the resources needed for the development of a product. By using a vertical business model, companies are able to control its suppliers and retail location directly without any external influence. As such, it provides greater flexibility and control in the overall business operations.

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“…The adoption of sensors and embedded devices in cloud computing and the Internet of Things (IoT) requires systems with enhanced trust and security within applications [1][2][3]. The majority of high-value/high-profit businesses use and benefit from Internet-based computing, which relies on a large amount of data being collected and made accessible by connecting objects to IoT systems [4,5]. Within the IoT field, there is a rapid expansion in the area of radio frequency identification (RFID), sensors, and communications technologies, and their combined effect is to generate intellectual property (IP) [6], although there is less emphasis on the protection of IP.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

Johnson

Al-Aqrabi

Hill

2020

Sensors

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Internet of Things (IoT) and Cyber-Physical Systems (CPS) have profoundly influenced the way individuals and enterprises interact with the world. Although attacks on IoT devices are becoming more commonplace, security metrics often focus on software, network, and cloud security. For CPS systems employed in IoT applications, the implementation of hardware security is crucial. The identity of electronic circuits measured in terms of device parameters serves as a fingerprint. Estimating the parameters of this fingerprint assists the identification and prevention of Trojan attacks in a CPS. We demonstrate a bio-inspired approach for hardware Trojan detection using unsupervised learning methods. The bio-inspired principles of pattern identification use a Spiking Neural Network (SNN), and glial cells form the basis of this work. When hardware device parameters are in an acceptable range, the design produces a stable firing pattern. When unbalanced, the firing rate reduces to zero, indicating the presence of a Trojan. This network is tunable to accommodate natural variations in device parameters and to avoid false triggering of Trojan alerts. The tolerance is tuned using bio-inspired principles for various security requirements, such as forming high-alert systems for safety-critical missions. The Trojan detection circuit is resilient to a range of faults and attacks, both intentional and unintentional. Also, we devise a design-for-trust architecture by developing a bio-inspired device-locking mechanism. The proposed architecture is implemented on a Xilinx Artix-7 Field Programmable Gate Array (FPGA) device. Results demonstrate the suitability of the proposal for resource-constrained environments with minimal hardware and power dissipation profiles. The design is tested with a wide range of device parameters to demonstrate the effectiveness of Trojan detection. This work serves as a new approach to enable secure CPSs and to employ bio-inspired unsupervised machine intelligence.

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Models for Innovative IoT Ecosystems

Cited by 9 publications

References 2 publications

Privacy Accountability and Penalties for IoT Firms

Privacy Accountability and Penalties for IoT Firms

A Review on Service Business Model Using IoT

Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

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