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1. O-RAN Perspective on Industrial Internet of Things: A SWOT Analysis

   https://doi.org/10.1109/ICIT58465.2023.10143032  

   Rahman, Talha Faizur; Zhang, Minglong; Marojevic, Vuk (June 2023, 2023 IEEE International Conference on Industrial Technology (ICIT))

   Internet of Things (IoT) is becoming increasingly popular due to its ability to connect machines and enable an ecosystem for new applications and use cases. One such use case is industrial loT (1IoT) that refers to the application of loT in industrial settings especially engaging instrumentation and control of sensors and machines with Cloud technologies. Industries are counting on the fifth generation (5G) of mobile communications to provide seamless, ubiquitous and flexible connectivity among machines, people and sensors. The open radio access network (O-RAN) architecture adds additional interfaces and RAN intelligent controllers that can be leveraged to meet the IIoT service requirements. In this paper, we examine the connectivity requirements for IIoT that are dominated by two industrial applications: control and monitoring. We present the strength, weakness, opportunity, and threat (SWOT) analysis of O-RAN for IIoT and provide a use case example which illustrates how O-RAN can support diverse and changing IIoT network services. We conclude that the flexibility of the O-RAN architecture, which supports the latest cellular network standards and services, provides a path forward for next generation IIoT network design, deployment, customization, and maintenance. It offers more control but still lacks products-hardware and software-that are exhaustively tested in production like environments. 

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2. Privacy-Preserving Database Assisted Spectrum Access for Industrial Internet of Things: A Distributed Learning Approach

   https://doi.org/10.1109/TIE.2019.2938491  

   Zhang, Mengyuan; Chen, Jiming; He, Shibo; Yang, Lei; Gong, Xiaowen; Zhang, Junshan (September 2019, IEEE Transactions on Industrial Electronics)

   Industrial Internet of Things (IIoT) has been shown to be of great value to the deployment of smart industrial environment. With the immense growth of IoT devices, dynamic spectrum sharing is introduced, envisaged as a promising solution to the spectrum shortage in IIoT. Meanwhile, cyber-physical safety issue remains to be a great concern for the reliable operation of IIoT system. In this paper, we consider the dynamic spectrum access in IIoT under a Received Signal Strength (RSS) based adversarial localization attack. We employ a practical and effective power perturbation approach to mitigate the localization threat on the IoT devices and cast the privacy-preserving spectrum sharing problem as a stochastic channel selection game. To address the randomness induced by the power perturbation approach, we develop a two-timescale distributed learning algorithm that converges almost surely to the set of correlated equilibria of the game. The numerical results show the convergence of the algorithm and corroborate that the design of two-timescale learning process effectively alleviates the network throughput degradation brought by the power perturbation procedure. 

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3. Detecting consumer IoT devices through the lens of an ISP

   https://doi.org/10.1145/3472305.3472885  

   Saidi, Said Jawad; Mandalari, Anna Maria; Haddadi, Hamed; Dubois, Daniel J.; Choffnes, David; Smaragdakis, Georgios; Feldmann, Anja (January 2021, ANRW '21: Proceedings of the Applied Networking Research Workshop)

   null (Ed.)

   Internet of Things (IoT) devices are becoming increasingly popular and offer a wide range of services and functionality to their users. However, there are significant privacy and security risks associated with these devices. IoT devices can infringe users' privacy by ex-filtrating their private information to third parties, often without their knowledge. In this work we investigate the possibility to identify IoT devices and their location in an Internet Service Provider's network. By analyzing data from a large Internet Service Provider (ISP), we show that it is possible to recognize specific IoT devices, their vendors, and sometimes even their specific model, and to infer their location in the network. This is possible even with sparsely sampled flow data that are often the only datasets readily available at an ISP. We evaluate our proposed methodology to infer IoT devices at subscriber lines of a large ISP. Given ground truth information on IoT devices location and models, we were able to detect more than 77% of the studied IoT devices from sampled flow data in the wild. 

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4. Demo Abstract: A Full-Blown 6TiSCH Network with Partition-based Resource Management for Large-Scale Real-Time Wireless Applications

   https://doi.org/10.1109/RTAS52030.2021.00067  

   Wang, Jiachen; Zhang, Tianyu; Han, Song; Hu, Xiaobo Sharon (May 2021, 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS))

   null (Ed.)

   Industrial Internet of Things (IIoT) systems aim to interconnect a large number of heterogeneous industrial sensing and actuation devices through both wired and wireless communication technologies and further connect them to the Internet to achieve ubiquitous sensing, computing and control services [1]. As a representative IIoT technology, 6TiSCH [2] targets at gluing together the 802.15.4e data link layer (offering industrial performance in terms of timing, reliability and power consumption) and an IP-enabled upper layer stack to achieve both deterministic network performance and seamless integration with Internet services. In recent years, 6TiSCH has been receiving increasing attentions from both industry and academia. We have witnessed its wide deployment in many industrial domains, including advanced manufacturing, industrial process control, smart grids, and healthcare. 

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5. Are Consumers Willing to Pay for Security and Privacy of IoT Devices?

   PARDIS EMAMI-NAENI, JANARTH DHEENADHAYALAN (January 2023, In Proceedings of the 32nd USENIX Security Symposium)

   Internet of Things (IoT) device manufacturers provide little information to consumers about their security and data handling practices. Therefore, IoT consumers cannot make informed purchase choices around security and privacy. While prior research has found that consumers would likely consider security and privacy when purchasing IoT devices, past work lacks empirical evidence as to whether they would actually pay more to purchase devices with enhanced security and privacy. To fill this gap, we conducted a two-phase incentive compatible online study with 180 Prolific participants. We measured the impact of five security and privacy factors (e.g., access control) on participants’ purchase behaviors when presented individually or together on an IoT label. Participants were willing to pay a significant premium for devices with better security and privacy practices. The biggest price differential we found was for de-identified rather than identifiable cloud storage. Mainly due to its usability challenges, the least valuable improvement for participants was to have multi-factor authentication as opposed to passwords. Based on our findings, we provide recommendations on creating more effective IoT security and privacy labeling programs. 

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