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1. A PUF-based Approach for Sustainable Cybersecurity in Smart Agriculture

   https://doi.org/10.1109/OCIT53463.2021.00080  

   Bathalapalli, Venkata K.; Mohanty, Saraju P.; Kougianos, Elias; Yanambaka, Venkata P.; Baniya, Babu K.; Rout, Bibhudutta (December 2021, 2021 19th OITS International Conference on Information Technology (OCIT))

   Padhy, Sudarsan; Oria, Vincent (Ed.)

   The simplicity, low cost, and scalability of Internet of Things (IoT) devices have led researchers to study their applications in a wide range of areas such as Healthcare, Transportation, and Agriculture. IoT devices help farmers to monitor the conditions in a field. These are connected to edge devices for real-time analysis. The edge servers send commands to actuators in the farm directly, without human intervention. At the same time, security vulnerabilities are a big concern, concomitant with the increasing utilization of IoT devices. If the duplication of an IoT device occurs and attackers gain access to the system, then the integrity of the entire ecosystem will be at stake, regardless of the application domain. This paper presents a Physical Unclonable Function (PUF) based hardware security primitive for the authentication of Internet of Agro-Things (IoAT) devices. The proposed security scheme has been prototyped with a testbed evaluation. An arbiter PUF module has been used for the validation of the proposed scheme. The PUF based security primitive is lightweight, scalable, and robust as it mainly depends on inherent manufacturing variations, thereby ensuring no chance for the duplication of IoT devices. 

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2. Stochastic Computing-based Baseband Processing for Resource Constraint IoT Devices

   https://doi.org/10.1007/978-3-031-14627-5_3  

   Kazi Ahmed, Yang Kim (January 2022, INCoS-2022)

   With the recent deployment of 5G network, the ever increasing IoT has got a tremendous boost in its expansion and already has penetrated well into the government, commercial and private sectors. With the countless IoT devices and myriad of applications, many of them are resource constrained and have limited energy budget. These IoT devices demand low-energy technique for their computing and communication tasks to stay active for longer period. The two main baseband processes that dissipate bulk of CPU power from the IoT device are synchronization and Finite Impulse Response (FIR) filtering. In this circumstance, hardware-based baseband processing can take these tasks off of the CPU and may significantly reduce energy consumption. While conventional Binary Radix Computing (BC)-based hardware modules can improve power dissipation, Stochastic Computing (SC)-based hardware will certainly cut down much more both the power as well as silicon space in comparison. With this motivation, we propose novel SC-based hardware designs in regards to synchronization and Finite Impulse Response (FIR) filter for resource constraint IoT devices. Comparative analysis shows that our proposed SC-based design can reduce significantly more power and silicon area compared to the BC as well as other proposed SC designs. 

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3. An Integrated TRNG-PUF Architecture based on Photovoltaic Solar Cells

   https://doi.org/10.1109/MCE.2020.3019762  

   Degada, Amit; Thapliyal, Himanshu (August 2020, IEEE Consumer Electronics Magazine)

   null (Ed.)

   The objective of the article is to present an integrated True Random Number Generator (TRNG) and Physically Unclonable Function (PUF) architecture using Photovoltaic solar cells. We illustrate that the Photovoltaic (PV) solar cell sensor response can be engineered into dynamic (TRNG) and static responses (PUF). The proposed prototype uses the iterative Von Neumann post-processing scheme to produce random bits with 34% better throughput compared to a single Von Neumann operation. The random bit quality was checked by statistical test suites from the National Institute of Science and Technology (NIST) and achieves an average p-value of 0.45 at all variations in light intensity. The PUF response achieves 92.13% reliability and 50.91% uniformity. The integrated TRNG-PUF architecture is beneficial for resource-constrained Cyber-Physical System (CPS). 

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4. Flexible PV-cell Modeling for Energy Harvesting in Wearable IoT Applications

   https://doi.org/https://doi.org/10.1145/3126568  

   Park, Jaehyun; Joshi, Hitesh; Lee, Hyung Gyu; Kiaei, Sayfe; Ogras, Umit Y. (October 2017, ACM transactions on embedded computing systems)

   Wearable devices with sensing, processing and communication capabilities have become feasible with the advances in internet-of-things (IoT) and low power design technologies. Energy harvesting is extremely important for wearable IoT devices due to size and weight limitations of batteries. One of the most widely used energy harvesting sources is photovoltaic cell (PV-cell) owing to its simplicity and high output power. In particular, flexible PV-cells offer great potential for wearable applications. This paper models, for the first time, how bending a PV-cell significantly impacts the harvested energy. Furthermore, we derive an analytical model to quantify the harvested energy as a function of the radius of curvature. We validate the proposed model empirically using a commercial PV-cell under a wide range of bending scenarios, light intensities and elevation angles. Finally, we show that the proposed model can accelerate maximum power point tracking algorithms and increase the harvested energy by up to 25.0%. 

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5. A Permutation Challenge Input Interface for Arbiter PUF Variants Against Machine Learning Attacks

   https://doi.org/10.1109/ISVLSI54635.2022.00094  

   Zhuang, Yu; Li, Gaoxiang; Mursi, Khalid T. (July 2022, 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI))

   Internet of Things (IoT) have broad and deep penetration into our society, and many of them are resource-constrained, calling for lightweight security protocols. Physical unclonable functions (PUFs) leverage physical variations of circuits to produce responses unique for individual devices, and hence are not reproducible even by their manufacturers. Implementable with simplistic circuits and operable with low energy, PUFs are promising candidates as security primitives for resource-constrained IoT devices. Arbiter PUF (APUF) and its variants are lightweight in resource requirements but suffer from vulnerability to machine learning attacks. To defend APUF variants against machine learning attacks, in this paper we investigate a challenge input interface, which incurs low overhead. Analytical and experimental studies were carried out, showing substantial improvement of resistance against machine learning attacks when a PUF is equipped with the interface, rendering interfaced APUF variants promising candidates for security critical applications. 

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