Indian Institute of Information Technology Tiruchirappalli (IIITT) is a higher education academic and research institute located in Tiruchirappalli, Tamil Nadu, India. It is one of the Indian Institutes of Information Technology (IIITs) established under the non-profit Public-Private Partnership and is an funded by the Government of India, Government of Tamil Nadu and the Indian industry partners in the ratio of 50:35:15. Industry partners include Tata Consultancy Services (TCS), Cognizant Technology Solutions (CTS), Infosys, Ramco Systems, ELCOT, and Navitas (Take Solutions). Together with the other IIITs, it has been granted the status of Institute of National importance in 2017.
Physically Unclonable Functions (PUFs) provide an advanced hardware solution for secure authentication and key generation. They utilize the inherent, unpredictable, and unavoidable differences created during semiconductor manufacturing. These unique characteristics, specific to each chip, are harnessed in PUF circuits to produce distinct and unclonable identifiers for devices. By taking advantage of these random variations present in modern semiconductor processes, PUFs strengthen both hardware and software security, offering a dependable and effective way to protect digital systems from potential threats. In this proposed paper, the use of efficient circuit design for Current starved, aging resilient inverter, and linear feedback shift register (LFSR) have resulted in the increase of reliability from 80.06% to 98.06%. The uniqueness, uniformity, and bit-aliasing achieved are of 49.21%, 50.08% and 50.08% , respectively, for a wide temperature range spanning from-40 degrees C to 120 degrees C and reduction of power dissipation to approximately 1.57 times less than the conventional Arbiter PUF. The pre-layout and post-layout Simulation results showed a delay of 1.08 ns and 1.95 ns respectively. The NIST test also revealed that the proposed design had randomness in the response. The proposed designs are also experimentally verified using Xilinx Vivado and implemented on Digilent Basys 3 Artix-7 FPGA, with minimum hardware resources, with a bit generation rate of 95 Mb/sec. Additionally, the proposed design exhibits resilience against machine learning (ML) attacks, with a prediction accuracy of approximately 55% to 79.3% for the PUF responses.
In this study, we investigate the complex interplay between prey and predators under varying human-mediated influences. Theoretical analyses explore the solution's non-negativity and boundedness, existence of feasible equilibria and their stability behaviors, and various bifurcations including saddle-node and Hopf. Using a detailed numerical exploration, we identified distinct population distribution, demonstrating how different factors shape ecological systems in non-trivial ways. The results suggest that human interventions, whether through direct shielding of prey or indirect effects on predator-prey interactions, can significantly disrupt ecological dynamics. Our findings highlight the importance of sustainable strategies that conserve prey populations while supporting predators to maintain ecosystem balance. Furthermore, by adding seasonal changes to some key parameters, we extend our autonomous system to a nonautonomous framework. Simple periodic oscillations, higher periodic oscillations, bursting patterns, and the extinction of predators are observed due to seasonal changes in the parameters.
The Internet of Things (IoT) has witnessed significant growth, driving innovation across a wide range of application domains. Many IoT applications are characterized by their high resource demands and stringent latency requirements. Multi-tier edge computing has emerged, addressing these needs, where the application is scheduled across IoT devices, edge servers, and the cloud. However, ensuring reliable application performance remains a key challenge, particularly in transient IoT device failures caused by electromagnetic interference, battery depletion, hardware failures, or software crashes. In this work, we consider task execution reliability by incorporating failure of the user device, while the previous work primarily focuses on server-side reliability and overlooks user-centric limitations. We also include the user budget constraint while enhancing the task execution reliability by task replication. Additionally, we consider mixed criticality tasks in our application model, reflecting real-world scenarios more accurately, an aspect largely overlooked in existing works. To achieve task execution reliability while ensuring user budget and task latency deadline, we introduce ReMEC, a fuzzy logic-based reliable hybrid task offloading framework that relies on a distributed message queuing strategy to preserve execution state during device failures, and a fixed-point iterative method for optimizing DVFS frequencies to improve energy efficiency without violating task deadlines or compromising reliability. Our comprehensive benchmarking, which rigorously compares ReMEC against two state-of-the-art strategies (RMEAC and FP-TOSM) and three baseline approaches (BR-greedy, LE-greedy, and Random-RR), demonstrates that ReMEC outperforms all of them, achieving average improvements of 26.19% in latency, 31.49% in energy consumption, and 72.16% in application failure rate, thereby demonstrating its practical applicability in real-world IoT scenarios.
Accurate State of Charge (SoC) estimation is critical for reliability, safety, and longevity of lithium-ion batteries, especially in electric vehicles, energy storage systems, and portable electronics. While machine learning models such as extreme gradient boosting (XGBoost) offer promising performance for SoC estimation, their effectiveness is often limited by the quality of input features and the efficiency of hyperparameter tuning. This paper proposes a data-driven framework that integrates systematic feature engineering with a bio-inspired Driving Training-Based Optimization (DTBO) algorithm for hyperparameter tuning of an XGBoost regression model. The proposed approach is validated on two open-source battery datasets, achieving prediction accuracy with an RMSE of 0.0011, MAE of 0.0006, and R² of 0.9999. The results demonstrate that the combined XGBoost-DTBO framework significantly outperforms existing deep learning, hybrid, and physics-informed models, establishing a new benchmark for SoC estimation suitable for real-world battery management systems.
The exponential growth of IoT data demands efficient, secure, and scalable storage solutions on one hand, and efficient data migration and retrieval on the other hand are essential for the systems to be practical and acceptable for different applications. Traditional cloud-based models face latency, security, and high operational costs, while existing bi-directional data storage and retrieval-based IPFS models are not computationally efficient and incur high gas costs due to the necessary blockchain deployment. To overcome the challenges of efficient data migration, we initially developed a two-way data storage and retrieval system, as well as a scalable framework that dynamically monitors and transfers device-generated data to IPFS, records the content identifier (CID) on a blockchain, and enables secure, real-time access via smart contracts. Experimental results demonstrate that the existing work achieved an average data upload time of 117.12 seconds for a file size of 500 MB; our framework achieves a faster upload time of 7.63 seconds, marking a 93.47
