In this paper, we propose a new routing protocol for heterogeneous Wireless Body Area Sensor Networks (WBASNs); Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-efficient Multi-hop ProTocol (M-ATTEMPT). A prototype is defined for employing heterogeneous sensors on human body. Direct communication is used for real-time traffic (critical data) or on-demand data while Multi-hop communication is used for normal data delivery. One of the prime challenges in WBASNs is sensing of the heat generated by the implanted sensor nodes. The proposed routing algorithm is thermal-aware which senses the link Hot-spot and routes the data away from these links. Continuous mobility of human body causes disconnection between previous established links. So, mobility support and energy-management is introduced to overcome the problem. Linear Programming (LP) model for maximum information extraction and minimum energy consumption is presented in this study. MATLAB simulations of proposed routing algorithm are performed for lifetime and successful packet delivery in comparison with Multi-hop communication. The results show that the proposed routing algorithm has less energy consumption and more reliable as compared to Multi-hop communication.

The Internet of Things (IoT) is an emerging key technology for future industries and everyday lives of people, where a myriad of battery operated sensors, actuators, and smart objects are connected to the Internet to provide services such as mobile healthcare, intelligent transport system, environmental monitoring, etc. Since energy efficiency is of utmost importance to these battery constrained IoT devices, IoT-related standards and research works have focused on the device energy conserving issues. This paper presents a comprehensive survey on energy conserving issues and solutions in using diverse wireless radio access technologies for IoT connectivity, e.g., the 3rd Generation Partnership Project (3GPP) machine type communications, IEEE 802.11ah, Bluetooth Low Energy (BLE), and Z-Wave. We look into the literature in broad areas of standardization, academic research, and industry development, and structurally summarize the energy conserving solutions based on several technical criteria. We also propose future research directions regarding energy conserving issues in wireless networking-based IoT.

DNA N6-methyladenine (6mA) has subsequently been identified as an important epigenetic modification which plays an important role in various cellular processes. The precise discrimination of N6-methyladenine (6mA) in genomes is required to recognize its biological functions. Although, we have several experimental techniques for the identification of 6mA-sites, in silico prediction has evolved as an alternative approach due to high-cost and labor-intense in experimental techniques. Taking into account, the implementation of an efficient and accurate model for identification of N6-methyladenine is of high priority. Several machine learning and deep learning models have already been developed to classify genome-wide 6mA sites. However, their success in predicting 6mA sites still has room for improvement. Based on this, we proposed a novel deep learning based model for the prediction of DNA N6-methyladenine sites in rice genomes. We built our model based on a special architecture called SpinalNet using DNA 6mA sites in rice genome and obtained an accuracies of 94.31% and 94.77% with an MCCs of 0.88 and 0.89 on two different datasets. The model generalizes well to other genomes as well, validated through cross-species testing. The results validate that the proposed model produces better scores than existing models regarding all evaluation parameters. A user-friendly webserver is made available at http://nsclbio.jbnu.ac.kr/tools/SpineNet6mA/. INDEX TERMS Deep Learning, DNA Sequence, Epigenetics, Neural Networks, SpinalNet Recently, the initiation of experimental approaches using the machine and deep learning methods have overwhelmed numerous complications in recognizing 6mA modifications. The 6mA modification has always been a hot topic in research, and a lot of researchers are using the machine and deep learning algorithms to recognise 6mA sites in the rice

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