Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes’ capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node

DEMETER and REPRESSOR OF SILENCING 1 encode 5-methylcytosine DNA glycosylases

Cytosine methylation is an epigenetic mark that promotes gene silencing and plays important roles in development and genome defense against transposons. Methylation patterns are established and maintained by DNA methyltransferases that catalyze transfer of a methyl group from S-adenosyl-l-methionine to cytosine bases in DNA. Erasure of cytosine methylation occurs during development, but the enzymatic basis of active demethylation remains controversial. In Arabidopsis thaliana, DEMETER (DME) activates the maternal expression of two imprinted genes silenced by methylation, and REPRESSOR OF SILENCING 1 (ROS1) is required for release of transcriptional silencing of a hypermethylated transgene. DME and ROS1 encode two closely related DNA glycosylase domain proteins, but it is unknown whether they participate directly in a DNA demethylation process or counteract silencing through an indirect effect on chromatin structure. Here we show that DME and ROS1 catalyze the release of 5-methylcytosine (5-meC) from DNA by a glycosylase/lyase mechanism. Both enzymes also remove thymine, but not uracil, mismatched to guanine. DME and ROS1 show a preference for 5-meC over thymine in the symmetric dinucleotide CpG context, where most plant DNA methylation occurs. Nevertheless, they also have significant activity on both substrates at CpApG and asymmetric sequences, which are additional methylation targets in plant genomes. These findings suggest that a function of ROS1 and DME is to initiate erasure of 5-meC through a base excision repair process and provide strong biochemical evidence for the existence of an active DNA demethylation pathway in plants