In today's world, everything is connected via the Internet. Smart cities are one application of the Internet of Things (IoT) that is aimed at making city management more efficient and effective. However, IoT devices within a smart city may collect sensitive information. Protecting sensitive information requires maintaining privacy. Existing smart city solutions have been shown not to offer effective privacy protection. We propose a novel continuous method called Differential Privacy-Preserving Smart City (DPSmartCity). When the IoT device produces sensitive data, it applies differential privacy techniques as a privacy-preserving method that uses Laplace distributions or exponential distributions. The controller receives the perturbed data and forwards it to the SDN. SDN controllers eventually send the data to the cloud for further analysis. Accordingly, if the data is not sensitive, it is directly uploaded to the cloud. In this way, DPSmartCity provides a dynamic environment from the point of view of privacy preservation. As a result, adversaries are unable to easily compromise the privacy of the devices. The solution incurs at most 10-18% overhead on IoT devices. Our solution can therefore be used for IoT devices that are capable of handling this overhead.

Abiotic stress tolerance is a critical trait in plant breeding programs aimed at developing climate-resilient crop varieties. The accurate identification and selection of stress-tolerant genotypes require comprehensive evaluation using multiple mathematical indices. However, the manual calculation of these indices from large-scale experimental datasets is time-consuming, error-prone, and computationally demanding. Here, we present PTSIonline (Plant Tolerance and Sensitivity Indices online, http://87.107.144.237), an integrated web-based computational platform designed to streamline the analysis of abiotic stress tolerance indices in crop breeding research. The platform implements 18 widely recognized stress evaluation indices including Tolerance Index (TOL), Mean Productivity (MP), Geometric Mean Productivity (GMP), Harmonic Mean (HM), Stress Susceptibility Index (SSI), Stress Tolerance Index (STI), Yield Index (YI), Yield Stability Index (YSI), Relative Stress Index (RSI), Superiority Index (SI), Abiotic Tolerance Index (ATI), Stress Susceptibility Percentage Index (SSPI), Relative Efficiency Index (REI), Modified Stress Tolerance Indices (K₁STI, K₂STI), Stress Distribution Index (SDI), Drought Index (DI), and Stress Non-Productivity Index (SNPI). PTSIonline features an intuitive user interface that accepts standard experimental data formats and generates comprehensive statistical outputs, visualizations, and genotype rankings within seconds. Comparative analysis demonstrates that PTSIonline provides the most extensive index coverage among available online tools while maintaining computational efficiency suitable for high-throughput phenotyping programs. The platform eliminates computational barriers in stress tolerance research, enabling researchers and plant breeders to rapidly identify superior genotypes from diverse germplasm collections. PTSIonline represents a significant advancement in computational tools for crop improvement under changing environmental conditions.