Dealing with air pollution is one of the major environmental challenges in a smart city environment. Real-time monitoring of pollution data enables the metropolitans to analyze the current traffic situation of the city and take their decisions accordingly. Deployment of the Internet of things (IoT) based sensors has considerably changed the dynamics of predicting air quality. Existing research has used different machine learning tools for pollution prediction; however, comparative analysis of these techniques is often required to have a better understanding of their processing time for multiple datasets. In this paper, we have performed the pollution prediction using four advanced regression techniques and have presented a comparative study to analyze the best model for accurately predicting the air quality with reference to data size and processing time. We have used Apache Spark for conducting experiments and performing pollution estimation using multiple available data sets. Mean Absolute error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of regression models. Furthermore, the processing time of each technique by standalone learning and by fitting the hyperparameter tuning on Apache Spark has also been calculated to find the best-fitted model in terms of processing time and least error rate.

With advances in Fog and edge computing, various problems such as data processing for large Internet of things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for smart grids incur high computation and communication costs, and in recent years there have been efforts to leverage fog computing with smart grids to overcome these limitations. In this paper, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of Paillier crypto-system is used to encrypt consumption data of the smart meter users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the smart meters malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the PPFA aggregation scheme.

The Internet of Things (IoT) is gaining ground as a pervasive presence around us by enabling miniaturized things with computation and communication capabilities to collect, process, analyze, and interpret information. Consequently, trustworthy data act as fuel for applications that rely on the data generated by these things, for critical decision-making processes, data debugging, risk assessment, forensic analysis, and performance tuning. Currently, secure and reliable data communication in IoT is based on public-key cryptosystems such as Elliptic Curve Cryptosystem (ECC). Nevertheless, reliance on the security of de-facto cryptographic primitives is at risk of being broken by the impending quantum computers. Therefore, the transition from classical primitives to quantum-safe primitives is indispensable to ensure the overall security of data en route. In this paper, we investigate applications of one of the post-quantum signatures called Hash-Based Signature (HBS) schemes for the security of IoT devices in the quantum era. We give a succinct overview of the evolution of HBS schemes with emphasis on their construction parameters and associated strengths and weaknesses. Then, we outline the striking features of HBS schemes and their significance for the IoT security in the quantum era. We investigate the optimal selection of HBS in the IoT networks with respect to their performance-constrained requirements, resource-constrained nature, and design optimization objectives. In addition to ongoing standardization efforts, we also highlight current and future research and deployment challenges along with possible solutions. Finally, we outline the essential measures and recommendations that must be adopted by the IoT ecosystem while preparing for the quantum world.

Digital twins (DT) have been proposed to support and enhance manufacturing processes of the industries. The outcome of adopting DT is so encouraging that it is hoped that more than 50% of the large industries will benefit from DT by the end of 2021. Unfortunately, DT lacks a single publicly accepted narrative. In order to help researchers for building a common narrative about DT, we present an elaborated structure of DT, namely, spiral DT-framework. Furthermore, for a secure and reliable management of the DT data, we propose using the blockchain technology rather than cloud or fog. As the classical blockchain suffers from transaction confirmation delays and is vulnerable to the quantum attacks, therefore, we propose a new variant of blockchain, namely twinchain, which is quantum-resilient and offers immediate transaction confirmation. This article also presents a framework for deployment of twinchain for manufacturing of a robot surgical machine.