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We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:M. Kavitha Margret, E. Golden Julie, Y. Harold Robinson, D. Vijayanandh, S. Vimal, Seifidine Kadry, Ahmed M. El-Sherbeeny & Mohammed A. El-Meligy (2021) SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System, IETE Journal of Research, DOI: 10.1080/03772063.2021.1994041Following publication, concerns were raised about the peer review and decision-making processes for this special issue.After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and policy.As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue.The journal has not confirmed if the authors were aware of this compromised peer review process.The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision.We have been informed in our decision-making by our editorial policies and the COPE guidelines.The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

Air pollution demonstrates the appearance of toxins into the air which is blocking human prosperity and the earth. It will portray as potentially the riskiest threats that humanity anytime faced. It makes hurt animals, harvests to thwart these issues in transportation territories need to expect air quality from pollutions utilizing AI systems and IoT. Along these lines, air quality evaluation and assumption has become a huge target for human health factors and also affect internal organs related to respiratory. The accuracy of Air Pollution prediction has been involved with the machine learning techniques and the best accuracy model is identified. The air quality prediction dataset is used for identifying the meteorology air pollution data while the predicted model is involved the decision tree computation for predicting the toxin contents in the region, the Air quality indicator is used to assess the pollution level and monitoring the air quality. The performance analysis shows that the decision tree technique has produced the better results in the performance metrics of Accuracy, precision, recall, and F1-score with the minimized error values while the comparative evaluation of Attribute-enabled classification has identified the best technique for predicting the air quality.

Solar desalination technologies are an alternative and appealing solution for providing potable water to people in remote and coastal areas. This work presents an experimental and theoretical study of roof-type and v-type solar desalination stills with internal modifications. The Taguchi method reduces the number of experiments conducted physically and gives better results while consuming less time. An experimental investigation of a v-type and roof-type solar still incorporating four internal modification parameters like basin liner design, wick materials, water depth and heat storage materials was carried out to optimize freshwater production. The selected parameter levels to improve the freshwater production in v-type and roof type solar stills have been obtained through an experimental study. Experiments were conducted for nine combinations of parameters suggested by the L9orthogonal array. As per the L9orthogonal array, nine combinations of parameters were obtained. The maximum SN ratio of parameter levels was selected to redesign the v-type and roof type basins to achieve a robust design. As per the Taguchi method, the redesigned v-type and roof type solar still designs were developed, giving 125% and 172% more freshwater, respectively compared to conventional v-type and roof type solar stills. When compared to v-type solar still, roof-type still produced more freshwater. Theoretical analysis shows that this roof type produces 45% more freshwater yield than a v-type solar still.

Blockchain is a distributed ledger or data structure. Combined with many other technologies, it uses the internet of things, cloud computing, artificial intelligence, big data, and machine learning. Several industries, especially governments, have employed blockchain technology to overcome a variety of security challenges. Blockchain focuses on double-spending and distributed consensus. However, blockchain networks are inefficient and scalable. Communication overhead occurs due to many replications. This paper proposes an adaptive practical Byzantine fault tolerance algorithm in permission blockchains. This method divides the node into trust nodes and faulty nodes. The nodes with faulty reputations are excluded from voting. Also, the identified trust node has a high reputation in the consensus process. A majority of voting values select the master node. This adaptive PBFT algorithm is excellent for long-term periodicity and increased scalability, and lower overall communication costs. Finally, the performance of adaptive PBFT is compared to other algorithms.

Vehicular fog computing (VFC) plays a vital role in the mobile ad hoc network. In vehicular fog computing, a deep beacon power control (DBPC) protocol is utilised for the sending of the periodical message in the VANET. This algorithm increases the effectiveness in the coverage of the broadcast of safety and security-related information and satisfies the constraints on both the link state and delay. The induction of deep learning model in beacon power control approach aims to overcome the optimisation issue by improving the amount of a fading multiuser interference channel. VANET is one of the ad hoc network real-life applications for communication between near-by equipment such as roadside equipment and vehicles and between vehicles. The proposed technique leads to optimised data transmission in vehicular fog computing. Unnecessary network overhead and also channel congestion can be minimised using this proposed technique. The proposed deep BPC technique is implemented in both Keras and NS2 simulators. Outcomes of both simulations reveal that when deep learning embedded with BPC protocol, the performance increases rapidly.

Mobile ad-hoc network (MANET) plays a significant role in the field of communication. Due to the dynamic movement of nodes, the network infrastructure is frequently changed. All nodes have the capacity to configure themselves and are communicated either directly through some intermediate nodes based on signal strength or through multi-hop routing. However, selection of the intermediate nodes will increase the routing overload in the route discovery procedure. Destination nodes are selected using intermediate nodes for broadcasting data packets with link scalability. The previous works for this problem have limitations such as they are not flexible to deliver the Quality of Service in the network model, and the possibility of packet delivery is less. In this paper, we propose Fault-Tolerant Disjoint Multipath Distance Vector Routing Algorithm (FD-AOMDV) that sprints path discovery phase with a reduced amount of delay. It finds disjoint paths in a way that routing overloads decrease considerably. FD-AOMDV can increase the scalability by reducing the routing overload when the latest route is established. Moreover, owing to the mobility of the node in MANETs, subsequent breakages of a link will cause the active path disconnection and also enlarge the routing overload. The simulation results prove that the proposed work reduces the routing overload, decreases the end-to-end delay, and reduces the packet delivery ratio compared with AOMDV and ZD-AOMDV on Network Simulator 2.