Dickson polynomial-based secure group authentication scheme for Internet of Things.

Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.

Membrane reciprocation and quorum quenching: An innovative combination for fouling control and energy saving in membrane bioreactors.

Membrane bioreactors (MBRs) play a crucial role in wastewater treatment, but they face considerable challenges due to fouling. To tackle this issue, innovative strategies are needed. This study investigated the effectiveness of membrane reciprocation and quorum quenching (QQ) to control fouling in MBRs. The study compared MBRs using membrane reciprocation (30 rpm) and QQ (injecting media containing 100 or 200 mg/L BH4) with conventional MBRs employing different air-scouring intensities. The results demonstrated that combining membrane reciprocation (30 rpm) with QQ (200 mg/L BH4) significantly extended the service time of MBRs, making it approximately six times longer than conventional methods. Moreover, this approach reduced physically reversible resistance. The reduction in signal molecules related to biofouling due to QQ showcased its critical role in controlling biofouling, even under high shear caused by membrane reciprocation. However, the impact of QQ on microbial community structure appeared relatively insignificant when compared to factors such as operation time, aeration intensity, and membrane reciprocation. By combining membrane reciprocation and QQ, the study achieved a remarkable 81 % energy saving compared to extensive aeration (103 s-1 in velocity gradient), in addition to the extended service time. Importantly, this combined antifouling approach did not negatively affect microbial characteristics and wastewater treatment, emphasizing its effectiveness in MBRs. Overall, the findings of this study offer valuable insights for developing synergistic fouling control strategies in MBRs, significantly improving the energy efficiency of the wastewater treatment process.

The relationship between quorum sensing dynamics and biological performances during anaerobic membrane bioreactor treatment.

Anaerobic membrane bioreactors (AnMBRs) enhance carbon neutrality with biomethane recovery from wastewater; however, microbial signaling, which may affect biological performances, was poorly understood. Here, we thus evaluate quorum sensing (QS) dynamics while monitoring acyl-homoserine lactones (AHLs) and autoinducer-2 (AI-2) levels during long-term AnMBR operations after sludge inoculation. Significant organic removal and methane production were achieved with the reactor startup. Signal molecule levels varied with transient organic loading rates, depending on their types. A starving condition may cause an increase in short- and medium-chain AHLs and AI-2. Biopolymers, biosolids, volatile fatty acids, and alkalinity levels had positive correlations with short- and medium-chain AHLs and AI-2, whereas methane production had positive correlations with long-chain AHLs. The principal component analysis of QS signal composition and biological performance data explains their interconnectivity. The findings of this study help to understand that QS signals regulate metabolic pathways in addition to microbial group behaviors.

Layered Antibiofouling Composite Membrane for Quenching Bacterial Signaling.

Bacterial quorum quenching (QQ) media with various structures (e.g., bead, cylinder, hollow cylinder, and sheet), which impart biofouling mitigation in membrane bioreactors (MBRs), have been reported. However, there has been a continuous demand for membranes with QQ capability. Thus, herein, we report a novel double-layered membrane comprising an outer layer containing a QQ bacterium (BH4 strain) on the polysulfone hollow fiber membrane. The double-layered composite membrane significantly inhibits biofilm formation (i.e., the biofilm density decreases by ~58%), biopolymer accumulation (e.g., polysaccharide), and signal molecule concentration (which decreases by ~38%) on the membrane surface. The transmembrane pressure buildup to 50 kPa of the BH4-embedded membrane (17.8 h ± 1.1) is delayed by more than thrice (p < 0.05) of the control with no BH4 in the membrane's outer layer (5.5 h ± 0.8). This finding provides new insight into fabricating antibiofouling membranes with a self-regulating property against biofilm growth.

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems.

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

An Efficient, Ensemble-Based Classification Framework for Big Medical Data.

Fetching useful information from big medical datasets is a complicated task in the big data age. Various classification algorithms are used in the data mining process to analyze information from the big medical dataset. Nevertheless, these classification algorithms are insufficient to handle big medical data. This work proposes an efficient, ensemble-based classification framework for big medical data to deal with this problem. The proposed work involves initially applying the preprocessing technique to remove noise, missing values, and unwanted features from big medical data. The process selects a subset of classifiers from a pool of classifiers. The selected classifiers are combined to form a hybrid system for efficient classification. The methodology further involves incremental learning from data samples, explaining the predicted outputs, and achieving high classification performance. Java is used for simulation, and the Cleveland Heart Disease big dataset and Diabetes big dataset are used for classification. The experimental result shows that the proposed ensemble algorithm provides an efficient classification compared with existing algorithms based on accuracy, precision, F-measure, recall, and execution time.

Isolation and characterization of novel indigenous facultative quorum quenching bacterial strains for ambidextrous biofouling control.

Quorum quenching (QQ), the disruption of microbial communication, has proven to be effective as an innovative anti-biofouling strategy for membrane bioreactors (MBRs). However, QQ bacteria for anaerobic environments have not been extensively analyzed in previous research. This study thus investigated facultative QQ bacterial strains that exhibit potential for use in aerobic and anaerobic MBRs. Two novel QQ strains from the genus Pseudomonas (KS2 and KS10) were isolated from anaerobic digester sludge using signal molecules as the sole carbon source. The two QQ strains exhibited significant signal molecule degradation depending on the oxygen levels and demonstrated endogenous QQ activity, with KS2 producing lactonase and KS10 producing acylase. The QQ strains significantly reduced the formation of the biofilm generated by both Pseudomonas aeruginosa (PAO1) and real sludge. Facultative QQ strains have the potential to offer a more flexible option for effective biofouling control in both aerobic and anaerobic MBRs.