Currently, electric cars are receiving a lot of attention because of their low fuel consumption and pollution-free nature. The efficiency of electric vehicles is constantly being improved via a variety of studies and applications. The battery is a significant portion of an electric vehicle’s overall component count. Because the electric vehicle’s battery acts like the vehicle’s heart, battery performance and efficiency in electric vehicles (EVs) are examined in this research.

In recent times, the utility and privacy are trade-off factors with the performance of one factor tends to sacrifice the other. Therefore, the dataset cannot be published without privacy. It is henceforth crucial to maintain an equilibrium between the utility and privacy of data. In this paper, a novel technique on trade-off between the utility and privacy is developed, where the former is developed with a metaheuristic algorithm and the latter is developed using a cryptographic model. The utility is carried out with the process of clustering, and the privacy model encrypts and decrypts the model. At first, the input datasets are clustered, and after clustering, the privacy of data is maintained. The simulation is conducted on the manufacturing datasets over various existing models. The results show that the proposed model shows improved clustering accuracy and data privacy than the existing models. The evaluation with the proposed model shows a trade-off privacy preservation and utility clustering in smart manufacturing datasets.

Currently, the growth of tannery industries causes a significant volume of waste disposal to the environment due to harmful Cr(VI). Long-time exposure to Cr(VI) imposes serious hazards on all living organisms. Hence, the treatment of tannery waste to remove Cr(VI) is not a choice but mandatory. Therefore, this study focused on the removal of Cr(VI) from the aqueous solutions via a teff (Eragrostis tef) straw based-activated carbon (TSAC) which was derived from locally available agricultural solid waste, teff straw (TS). The prepared TSAC was characterized using BET, FTIR, SEM, and XRD. A central composite approach-based RSM analysis was undertaken for statistical modeling and optimization for maximized Cr(VI) removal with respect to four important factors, namely, initial concentration of Cr(VI), the dosage of TSAC, pH, and adsorption time. Optimized values for maximizing adsorption of Cr(VI) (95 of removal) were acquired to be initial Cr(VI) concentration: 87.57mg/L, TSAC dosage: 2.742g/100mL, pH: 2.2, and contact time:109min. The results from the design of the experiment were also analyzed for the significance of the interaction between the selected process parameters. In addition, the pseudo-second-order kinetic and Langmuir isotherm models were found suitable for describing the adsorption data. The adsorption capacity of Cr(VI) on TSAC was 19.48mg/g. The observed thermodynamic characteristics reveal that Cr(VI) adsorption on TASC is endothermic in nature. From the results, TSAC had shown a potential Cr(VI) efficiency on optimized process conditions that can be exploited effectively as adsorbent for removal of Cr(VI)-contaminated wastes.

In the current-generation wireless systems, there is a huge requirement on integrating big data which can able to predict the market trends of all application systems. Therefore, the proposed method emphasizes on the integration of nanosensors with big data analysis which will be used in healthcare applications. Also, safety precautions are considered when this nanosensor is integrated where depth and reflection of signals are also observed using different time samples. In addition, to analyze the effect of nanosensors, six fundamental scenarios that provide good impact on real-time applications are also deliberated. Moreover, for proving the adeptness of the proposed method, the results are equipped in both online and offline analyses for investigating error measurement, sensitivity, and permeability parameters. Since nanosensors are introduced, the efficiency of the projected technique is increased by implementing media access control (MAC) protocol with recurrent neural network (RNN). Further, after observing the simulation results, it is proved that the proposed method is more effective for an average percentile of 67% when compared to the existing methods.

Cloud storage provides a potential solution replacing physical disk drives in terms of prominent outsourcing services. A threaten from an untrusted server affects the security and integrity of the data. However, the major problem between the data integrity and cost of communication and computation is directly proportional to each other. It is hence necessary to develop a model that provides the trade-off between the data integrity and cost metrics in cloud environment. In this paper, we develop an integrity verification mechanism that enables the utilisation of cryptographic solution with algebraic signature. The model utilises elliptic curve digital signature algorithm (ECDSA) to verify the data outsources. The study further resists the malicious attacks including forgery attacks, replacing attacks and replay attacks. The symmetric encryption guarantees the privacy of the data. The simulation is conducted to test the efficacy of the algorithm in maintaining the data integrity with reduced cost. The performance of the entire model is tested against the existing methods in terms of their communication cost, computation cost, and overhead cost. The results of simulation show that the proposed method obtains reduced computational of 0.25% and communication cost of 0.21% than other public auditing schemes.

The global standards in the field of industrial automation are maintained in industries by completely digitizing their manufacturing process with industry 4.0 standard. Internet of Things (IoT) enables the conservation of cultural heritage with proper assistance on data management on the data collected from the sensors. However, energy efficient conservation is required to monitor the IoT sensors in order to deal with building a better infrastructure. In this paper, we develop a bio-inspired algorithm which can automate the entire furnace monitoring and controlling system in order to eliminate the human intervention involved in the physical process. The algorithm is blended as a web-based remote application for the better control of the tasks involved, energy utilized, and its subsequent log-report maintenance. The entire system employs Wi-Fi communication for data transfer from device to cloud where the stored data including temperature log, forth coming schedule, and process graphic are maintained by the proposed algorithm to predict the machine failure at an earlier stage. The real-time prototype system is supported by a heat treatment process that is completely automated using IoT to monitor and maintain the temperature during the production of metal casting process.