Function as a Service (FaaS) is highly beneficial to smart city infrastructure due to its flexibility, efficiency, and adaptability, specifically for integration in the digital landscape. FaaS has serverless setup, which means that an organization no longer has to worry about specific infrastructure management tasks; the developers can focus on how to deploy and create code efficiently. Since FaaS aligns well with the IoT, it easily integrates with IoT devices, thereby making it possible to perform event-based actions and real-time computations. In our research, we offer an exclusive likelihood-based model of adaptive machine learning for identifying the right place of function. We employ the XGBoost regressor to estimate the execution time for each function and utilize the decision tree regressor to predict network latency. By encompassing factors like network delay, arrival computation, and emphasis on resources, the machine learning model eases the selection process of a placement. In replication, we use Docker containers, focusing on serverless node type, serverless node variety, function location, deadlines, and edge-cloud topology. Thus, the primary objectives are to address deadlines and enhance the use of any resource, and from this, we can see that effective utilization of resources leads to enhanced deadline compliance.
Wastewater is a reservoir of pathogens and hotspots for disseminating antibiotic resistance genes across species. The metagenomic surveillance of wastewater provides insight into the native microbial community, antibiotic-resistance genes (ARGs) and mobile genetic elements. t. The COVID-19 pandemic has caused wider dissemination of ARGs and resistant bacteria in wastewater. Although immensely significant, no research has been performed on the Malaysian wastewater microbial community and ARGs or their correlation with COVID-19 infections. This study utilised a 16S metagenomics approach to characterise the microbial community in Malaysian wastewater during high and low-case phases of the pandemic. Bacteria belonging to Bacteriodales, Bacillales, Actinomycetales and opportunistic pathogens-Arcobacters, Flavobacteria, and Campylobacterales, Neisseriales, were enriched during higher COVID-19 pandemic (active cases). Additionally, copy number profiling of ARGs in water samples showed the prevalence of elements conferring resistance to antibiotics like sulphonamides, cephalosporins, and colistin. The high prevalence of intI1 and other ion-based transporters in samples highlights an extensive risk of horizontal gene transfer to previously susceptible species. Our study emphasises the importance of wastewater surveillance in understanding microbial community dynamics and ARG dissemination, particularly during public health crises like the COVID-19 pandemic.