Healthcare has witnessed a technological advancement in improving the quality of care and speeding the process of diagnosing patients due to its intervention with the internet of medical things. IoT in healthcare (H-IoT) plays a significant role in facilitating the process of diagnosing and detecting diseases. Different IoT-based medical sensors are used to measure biometrics and send them to the cloud for more analysis. However, the sensed data are massive and vary in their criticality level in which some sensed data are more critical (health-related data) than others. Moreover, computing such critical data in the cloud encounters some delay which is not preferable in real-time monitoring applications. Thus, this work proposes an IoT-fog-based framework to classify the streamed data according to their criticality level and compute the critical data in the fog to detect abnormalities with low latency and high response time. Before designing the proposed work, an analysis was conducted to explore the real data collected by IoT-based medical apps. The exploration of the data involved downloading and manually analyzing up-to-date privacy policies of eight IoT-based medical apps that provide details about data collection practices. The study showed that the streamed data in H-IoT include medical sensors data, apps registration data (personal information), device information, and other information related to cookies. The proposed work introduced the design of fog-based data classification and the algorithm for such classification. The implementation and evaluation of the proposed framework is future work.

Internet of things (IoT) has become one of the most prominent technologies that the world has been witnessing nowadays. It provides great solutions to humanity in many significant fields of life. IoT refers to a collection of sensors or object in the universe with the capability of communicating with each other through the internet without human intervention. Currently, there is no standard IoT architecture. As it is in its infancy, IoT is surrounded by numerous security and privacy concerns. Thus, to avoid such concerns that may hinder its deployment, an IoT architecture has to be carefully designed to incorporate security and privacy solutions. In this paper, a systematic literature review was conducted to trace the evolvement of IoT architectures from its initial development in 2008 until 2018. The Comparison among these architectures is based on terms of the architectural stack, covered issues, the technology used and considerations of security and privacy aspects. The findings of the review show that the initial IoT architectures did not provide a comprehensive meaning for IoT that describe its nature, whereas the recent IoT architectures convey a comprehensive meaning of IoT, starting from data collection, followed by data transmission and processing, and ending with data dissemination. Moreover, the findings reveal that IoT architecture has evolved gradually across the years, through improving architecture stack with new solutions to mitigate IoT challenges such as scalability, interoperability, extensibility, management, etc. with lack consideration of security solutions. The findings disclose that none of the discussed IoT architectures considers privacy concerns, which indeed considered as a critical factor of IoT sustainability and success. Therefore, there is an inevitable need to consider security and privacy solutions when designing IoT architecture.

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An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.