The impact of Internet of Things has been revolutionized in all fields of life, but its impact on the healthcare system has been significant due to its cutting edge transition. The role of Internet of Things becomes more dominant when it is supported by the features of mobile computing. The mobile computing extends the functionality of IoT in healthcare environment by bringing a massive support in the form of mobile health (m-health). In this research, a systematic literature review protocol is proposed to study how mobile computing assists IoT applications in healthcare, contributes to the current and future research work of IoT in the healthcare system, brings privacy and security in health IoT devices, and affects the IoT in the healthcare system. Furthermore, the intentions of the paper are to study the impacts of mobile computing on IoT in healthcare environment or smart hospitals in light of our systematic literature review protocol. The proposed study reports the papers that were included based on filtering process by title, abstract, and contents, and a total of 116 primary studies were included to support the proposed research. These papers were then analysed for research questions defined for the proposed study.
Internet of Things (IoT) devices are operating in various domains like healthcare environment, smart cities, smart homes, transportation, and smart grid system. These devices transmit a bulk of data through various sensors, actuators, transceivers, or other wearable devices. Data in the IoT environment is susceptible to many threats, attacks, and risks. Therefore, a robust security mechanism is indispensable to cope with attacks, vulnerabilities, security, and privacy challenges related to IoT. In this research, a systematic literature review has been conducted to analyze the security of IoT devices and to provide the countermeasures in response to security problems and challenges by using mobile computing. A comprehensive and in-depth security analysis of IoT devices has been made in light of mobile computing, which is a novel approach. Mobile computing's technological infrastructures such as smartphones, services, policies, strategies, and applications are employed to tackle and mitigate these potential security threats. In this paper, the security challenges and problems of IoT devices are identified by a systematic literature review. Then, mobile computing has been used to address these challenges by providing potential security measures and solutions. Hardware and software-based solutions furnished by mobile computing towards the IoT security challenges have been elaborated. To the best of our knowledge, this is the first attempt to analyze the security issues and challenges of IoT in light of mobile computing and it will open a gateway towards future research. INDEX TERMS Internet of Things devices, Security, Mobile Computing, Mobile applications, Smartphone. I. Research Questions Keywords What are the security problems and challenges faced by IoT devices in the network? Inclusion Criteria Research papers published in English language were included Primary studies i.e. original research papers were selected Research papers, book chapters or magazines relevant to our main topic were selected Research papers ranges in years from 2011 to 2019 were included for the studies Exclusion Criteria Papers written other than English language are not included Papers did not answer research questions or did not define the topic properly were excluded Gray papers were excluded Elimination of duplicated papers Research papers with less than three pages were removed
Security has become a vital factor for any Internet of things network but it is of paramount importance for Internet of Health Things (IoHT). IoHT also known as Internet of Medical Things (IoMT) is integration of IoT and healthcare environment, where fragile data related to the patients is transmitted from IoT devices to server. During this transmission, if, any eavesdropping or intrusion occurs then it will not only lead to the serious mutilation of entire network but this data will be handled maliciously for wrong doings as well. Therefore, a proper security is indispensable for IoHT based equipments due to exposure to different attacks. Security of IoHT has been the burning issue in last couple of years. In this regard different security models, surveys, frameworks have been presented. In this paper, a proposed Identified Security Attributes (ISA) framework is presented to evaluate the security features of IoHT based device in healthcare environment. The proposed framework uses hybrid MCDM methods such as Analytical Hierarchical Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This framework works in two phase: in first phase the weights of attributes are derived by using AHP method and in second phase security assessment of alternatives is performed based upon security criteria by using TOPSIS method. The outcomes of proposed security assessment framework demonstrate that the reliable and secure alternative among alternatives is selected in IoMT system. This approach can be used as a guideline for future use in IoMT systems or any other IoT based domain. To the best of our knowledge, it is novel approach to address the security assessment of IoT and these MCDM methods have never been used before for assessment and decision making in IoHT system for security.
Single-nucleotide polymorphisms (SNPs) are reported to be associated with many diseases, including autoimmune diseases. In rheumatoid arthritis (RA), about 152 SNPs are reported to account for ~15% of its heritability. These SNPs may result in the alteration of gene expression and may also affect the stability of mRNA, resulting in diseased protein. Therefore, in order to predict the underlying mechanism of these SNPs and identify novel therapeutic sites for the treatment of RA, several bioinformatics tools were used. The damaging effect of 23 non-synonymous SNPs on proteins using different tools suggested four SNPs, including rs2476601 in PTPN22, rs5029941 and rs2230926 in TNFAIP3, and rs34536443 in TYK2, to be the most damaging. In total, 42 of 76 RA-associated intronic SNPs were predicted to create or abolish potential splice sites. Moreover, the analysis of 11 RA-associated UTR SNPs indicated that only one SNP, rs1128334, located in 3′UTR of ETS1, caused functional pattern changes in BRD-BOX. For the identification of novel therapeutics sites to treat RA, extensive gene–gene interaction network interactive pathways were established, with the identification of 13 potential target sites for the development of RA drugs, including three novel target genes. The anticipated effect of these findings on RA pathogenesis may be further validated in both in vivo and in vitro studies.
In current era, the next generation networks like 5th generation (5G) and 6th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.
Previously, we observed increased transcription levels of specific cytochrome P450 monooxygenase (P450) and adenosine triphosphate binding cassette (ABC) transporter genes in human body lice, Pediculus humanus humanus, following exposure to ivermectin using the non-invasive induction assay, which resulted in tolerance. To confirm the roles of these genes in induction and tolerance, the robust genetic model insect Drosophila melanogaster was chosen. Orthologous genes corresponding to the body louse P450 (Cyp9f2, Cyp6g2 and Cyp9h1) and ABC transporter (Mrp1, GC1824 as an ABCB type and CG3327 as an ABCG type) genes were selected for in vivo bioassay. Following a brief treatment with a sublethal dose of ivermectin, the mortality response was significantly slower, indicating the presence of tolerance. Concurrently, the transcription levels of Cyp9f2 and Mrp1 at 3 h and those of Cyp6g2, Cyp9h1, Mrp1, CG1824 and CG3327 at 6 h post-treatment were upregulated, indicating gene induction. In behavioural bioassay using GAL4/UAS-RNA interference transgenic fly lines, increased susceptibility to ivermectin was observed following heat shock in the Cyp9f2 , Cyp6g2 , Cyp9h1 , Mrp1 or CG3327-knockdown flies. Considering that these five genes are orthologous to those which had the largest over-expression level following ivermectin-induced tolerance in the body louse, the current results suggest that they are also associated with ivermectin detoxification in D. melanogaster and that body lice and D. melanogaster are likely to share, in part, similar mechanisms of tolerance to ivermectin.
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