IoT Based Secure Smart City Architecture Using Blockchain

Standard security protocols are heavy weight in terms of memory foot prints which make all the security protocol unfit for budgeted platforms such as Internet of Things (IoT). The blockchain (BC) is a very efficient architecture to preserve five basic cryptographic primitives, such as authenticity, integrity, confidentiality, availability and non-repudiation. Conventional adoption of blockchain in IoT causes significant energy consumption, delay, and computational overhead which are not suitable for various resource-constrained IoT devices. In our submission we change the basic architecture of blockchain and make it more efficient for IoT application. The article proposes an IoT based smart city architecture which adopted the blockchain technology preserving all the cryptographic security issues. The adoption of blockchain causes very minimal overhead on IoT platform. Comparison of all security parameters with existing literature shows that the architecture is reasonably efficient in terms of security

Field scale spatial wheat yield forecasting system under limited field data availability by integrating crop simulation model with weather forecast and satellite remote sensing

CONTEXT: An accurate crop yield forecast with sufficient lead time is critical for various applications, such as crop management, resources mobilization, agri-commodity trading, crop insurance, etc. Accurate yield forecasting well ahead of harvest at field scale with minimal field input data remains a challenge. OBJECTIVE: This study aimed to develop a novel prototype wheat yield forecasting system by assimilating remote sensing (RS) derived crop parameters and weather forecast into InfoCrop-Wheat crop simulation model (CSM), using minimum field measurements. METHODS: The CSM was calibrated and validated at both research farm and farmers' fields. The crop LAI was retrieved through inversion of the PROSAIL radiative transfer model from Sentinel-2A and Landsat-8 images and validated using in-situ LAI measurements. The CSM was modified to test assimilation of RS derived LAI through “Ensemble Kalman Filtering” (EnKF) and “Forcing” strategies at multiple time-steps. The RS derived LAI was not only used to correct/replace model simulated LAI but other model state variables were also adjusted accordingly. A major challenge of adjusting crop phenology based on RS derived LAI was also attempted. The WRF weather forecast was bias-corrected and incorporated into the modified model-LAI assimilation framework. Generic crop management inputs were specified to the model. Finally, the study demonstrated a workable prototype of a field scale wheat growth and yield forecasting system under limited field data availability. RESULTS AND CONCLUSIONS: The inversion of PROSAIL showed an RMSE of 0.56 m2/m2 in LAI retrievals. Model validation with measured inputs showed normalized error (NE) of 6‐–8% in grain yield. The proposed framework showed only 2%, 5%, 3% and 1% higher NE in simulating days to anthesis, days to physiological maturity, dry matter and grain yield, respectively, than with measured inputs. The “EnkF” outperformed “Forcing” for predicting crop yield as well as phenology and growth of wheat using generic management inputs. The system showed acceptable accuracy in forecasting phenology, dry matter and yield of wheat at field scale when weighted adaptive bias-correction of weather forecast was incorporated with a 15 days lead time. SIGNIFICANCE: The prototype can be scaled-up for wheat and other crops for predicting real-time crop condition and yield losses at farmers' field for a range of applications, notably, crop-insurance, resources allocation, targeted agro-advisories and triggering contingency plans. It offers considerable potential for objective assessment of crops in the marginal and smallholder systems supporting the smart farming paradigm