Internet of Things (IoT) is considered as an ecosystem that contains smart objects equipped with sensors, networking and processing technologies integrating and working together to provide an ...environment in which smart services are taken to the end users. The IoT is leading numerous benefits into the human life through the environment wherein smart services are provided to utilize every activity anywhere and anytime. All these facilities and services are conveyed through the diverse applications which are performed in the IoT environment. The most important utilities that are achieved by the IoT applications are monitoring and consequently immediate decision making for efficient management. In this paper, we intend to survey in divers IoT application domains to comprehend the different approaches in IoT applications which have been recently presented based on the Systematic Literature Review (SLR) method. The aim of this paper is to categorize analytically and statistically, and analyze the current research techniques on IoT applications approaches published from 2011 to 2018. A technical taxonomy is presented for the IoT applications approaches according to the content of current studies that are selected with SLR process in this study including health care, environmental monitoring, smart city, commercial, industrial and general aspects in IoT applications. IoT applications are compared with each other according to some technical features such as Quality of Service (QoS), proposed case study and evaluation environments. The achievements and disadvantages of each study is discussed as well as presenting some hints for addressing their weaknesses and highlighting the future research challenges and open issues in IoT applications.
Internet of Things (IoT) is an ever-expanding ecosystem that integrates software, hardware, physical objects, and computing devices to communicate, collect, and exchange data. The IoT provides a ...seamless platform to facilitate interactions between humans and a variety of physical and virtual things, including personalized healthcare domains. Lack of access to medical resources, growth of the elderly population with chronic diseases and their needs for remote monitoring, an increase in medical costs, and the desire for telemedicine in developing countries, make the IoT an interesting subject in healthcare systems. The IoT has a potential to decrease the strain on sanitary systems besides providing tailored health services to improve the quality of life. Therefore, this paper aims to identify, compare systematically, and classify existing investigations taxonomically in the Healthcare IoT (HIoT) systems by reviewing 146 articles between 2015 and 2020. Additionally, we present a comprehensive taxonomy in the HIoT, analyze the articles technically, and classify them into five categories, including sensor-based, resource-based, communication-based, application-based, and security-based approaches. Furthermore, the benefits and limitations of the selected methods, with a comprehensive comparison in terms of evaluation techniques, evaluation tools, and evaluation metrics, are included. Finally, based on the reviewed studies, power management, trust and privacy, fog computing, and resource management as leading open issues; tactile Internet, social networks, big data analytics, SDN/NFV, Internet of nano things, and blockchain as important future trends; and interoperability, real-testbed implementation, scalability, and mobility as challenges are worth more studying and researching in HIoT systems.
Chronic Kidney Disease (CKD) is being typically observed as a health threatening issue, especially in developing countries, where receiving proper treatments are very expensive. Therefore, early ...prediction of CKD that protects the kidney and breaks the gradual progress of CKD has become an important issue for physicians and scientists. Internet of Things (IoT) as a useful paradigm in which, low cost body sensor and smart multimedia medical devices are applied to provide remote monitoring of kidney function, plays an important role, especially where the medical care centers are hardly available for most of people. To gain this objective, in this paper, a diagnostic prediction model for CKD and its severity is proposed that applies IoT multimedia data. Since the influencing features on CKD are enormous and also the volume of the IoT multimedia data is usually very huge, selecting different features based on physicians’ clinical observations and experiences and also previous studies for CKD in different groups of multimedia datasets is carried out to assess the performance measures of CKD prediction and its level determination via different classification techniques. The experimental results reveal that the applied dataset with the proposed selected features produces 97% accuracy, 99% sensitivity and 95% specificity via applying decision tree (J48) classifier in comparison to Support Vector Machine (SVM), Multi-Layer Perception (MLP) and Naïve Bayes classifiers. Also, the proposed feature set can improve the execution time in comparison to other datasets with different features.
Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of ...approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively.
This paper proposes a novel approach that uses a spectral clustering method to cluster patients with e-health IoT devices based on their similarity and distance and connect each cluster to an SDN ...edge node for efficient caching. The proposed MFO-Edge Caching algorithm is considered for selecting the near-optimal data options for caching based on considered criteria and improving QoS. Experimental results demonstrate that the proposed approach outperforms other methods in terms of performance, achieving decrease in average time between data retrieval delays and the cache hit rate of 76%. Emergency and on-demand requests are prioritized for caching response packets, while periodic requests have a lower cache hit ratio of 35%. The approach shows improvement in performance compared to other methods, highlighting the effectiveness of SDN-Edge caching and clustering for optimizing e-health network resources.
The Internet of Things (IoT) signifies to an overall system of interconnected physical Things utilizing existing correspondence conventions. One critical inquiry remains in what manner can make and ...communicate the management of provided services for smart devices by an assortment of protest things that substituted and joined capably. Service composition process permits the interaction between user requirements and smart objects of IoT environment. Leveraging on the service discovery procedure can be influenced on finding the desired services. Consequently, choosing suitable services is the main challenge that covers functionality and required quality to combine several services as the integrated composite service in the IoT. The service composition process has been broadly considered with regards to web suppliers and business processes in the IoT. Currently, the IoT environment identifies the dynamic relationship topics on physical processes that are combined as the enhanced web services heterogeneously. This paper focuses on several service composition approaches that are applied in the IoT environment based on the Systematic Literature Review (SLR) method. The aim of this study is to analytically and statistically categorize and analyze the current research techniques on the service composition in the IoT (published between 2012 and 2017). A technical taxonomy is presented for the service composition approaches according to content of the existing studies that are selected with SLR method in this review with respect to functional and non-functional aspects in service composition approaches. The functional aspect emphasizes on verifying the behavior of service composition approach and the non-functional aspect considers the Quality of Service (QoS) in IoT environment. The approaches are compared with each other according to some technical aspects such as system correctness factors in functional properties approaches, and (QoS) factors, presented algorithms, and existing platforms in non-functional approaches. The advantages and disadvantages of each selected approach discussed as well as providing some hints for solving their weaknesses. A brief contribution to this literature is as follows: (1) Presenting a SLR method for the service composition approaches in IoT, (2) Addressing a discussion of the main challenges, (3) Providing the future research directions and open perspectives.
Typing is a time-consuming task and predictive text is proposed as a solution. Recently, Generative Pre-trained Transformers (GPT) have employed autoregressive deep learning to tackle text ...prediction. However, they face costly retraining, especially for low-resource languages (such as Persian) or domains, and lack controllability. Text augmentation with prompting methods and fine-tuning GPT models on templated data using conditional elements (labels or keywords) aims to address these problems, enhancing controllable text generation in low-resource scenarios. These methods involve discovering and mapping conditional elements to training texts, which is unsuitable for typing assistants. Meanwhile, they do hold inappropriate pre-defined elements for wide use. This paper introduces Conditional GPT-2-Persian (CGPT-2-Persian), which utilizes the initial word of each sentence as the associated conditional element to address practical challenges, extracting labels, and handling in-domain unseen data posed by the mentioned methods. This method outperforms Persian text generation methods in terms of BLEU and ROUGE scores, achieving 87.39% and 14.29%, respectively, after fine-tuning for ten epochs. This study can be efficient for other low-resource languages or domains.
The infectious disease Covid-19 has been causing severe social, economic, and human suffering across the globe since 2019. The countries have utilized different strategies in the last few years to ...combat Covid-19 based on their capabilities, technological infrastructure, and investments. A massive epidemic like this cannot be controlled without an intelligent and automatic health care system. The first reaction to the disease outbreak was lockdown, and researchers focused more on developing methods to diagnose the disease and recognize its behavior. However, as the new lifestyle becomes more normalized, research has shifted to utilizing computer-aided methods to monitor, track, detect, and treat individuals and provide services to citizens. Thus, the Internet of things, based on fog-cloud computing, using artificial intelligence approaches such as machine learning, and deep learning are practical concepts. This article aims to survey computer-based approaches to combat Covid-19 based on prevention, detection, and service provision. Technically and statistically, this article analyzes current methods, categorizes them, presents a technical taxonomy, and explores future and open issues.
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