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Background: Internet of Things (IoT) technology provides a tremendous and structured solution to tackle service deliverance aspects of healthcare in terms of mobile health and remote patient tracking. In medicine observation applications, IoT and cloud computing serves as an assistant in the health sector and plays an incredibly significant role. Health professionals and technicians have built an excellent platform for people with various illnesses, leveraging principles of wearable technology, wireless channels, and other remote devices for low-cost healthcare monitoring.Objective: This paper proposed the Fog-IoT-assisted multisensor intelligent monitoring model (FIoT-MIMM) for analyzing the patient's physical health condition.Method: The proposed system uses a multisensor device for collecting biometric and medical observing data. The main point is to continually generate emergency alerts on mobile phones from the fog system to users. For the precautionary steps and suggestions for patients' health, a fog layer's temporal information is used.Results: Experimental findings show that the proposed FIoT-MIMM model has less response time and high accuracy in determining a patient's condition than other existing methods. Furthermore, decision making based on real-time healthcare information further improves the utility of the suggested model. [ABSTRACT FROM AUTHOR]

Background: Health monitoring is important for early disease diagnosis and will reduce the discomfort and treatment expenses, which is very relevant in terms of prevention. The early diagnosis and treatment of multiple conditions will improve solutions to the patient's healthcare radically. A concept model for the real-time patient tracking system is the primary goal of the method. The Internet of things (IoT) has made health systems accessible for programs based on the value of patient health.Objective: In this paper, the IoT-based cloud computing for patient health monitoring framework (IoT-CCPHM), has been proposed for effective monitoring of the patients.Method: The emerging connected sensors and IoT devices monitor and test the cardiac speed, oxygen saturation percentage, body temperature, and patient's eye movement. The collected data are used in the cloud database to evaluate the patient's health, and the effects of all measures are stored. The IoT-CCPHM maintains that the medical record is processed in the cloud servers.Results: The experimental results show that patient health monitoring is a reliable way to improve health effectively. [ABSTRACT FROM AUTHOR]

• We prove that a highly scalable data center network (HSDC) is vertex-symmetric. • We investigate the independent spanning trees (IST) problem on HSDC. • We propose an algorithm to construct n ISTs on the n -dimensional HSDC, denoted as H S n. • The algorithm can be parallelized to run in O (n) time using N = 2 n processors. • As a by-product, we obtain the connectivity of H S n to be n. An emerging datacenter network (DCN) with high scalability called HSDC is a server-centric DCN that can help cloud computing in supporting many inherent cloud services. For example, a server-centric DCN can initiate routing for data transmission. This paper investigates the construction of independent spanning trees (ISTs for short), a set of the rooted spanning trees associated with the disjoint-path property, in HSDC. Regarding multiple spanning trees as routing protocol, ISTs have applications in data transmission, e.g., fault-tolerant broadcasting and secure message distribution. We first establish the vertex-symmetry of HSDC. Then, by the structure that n -dimensional HSDC is a compound graph of an n -dimensional hypercube Q n and n -clique K n , we amend the algorithm constructing ISTs for Q n to obtain the algorithm required by HSDC. Unlike most algorithms of recursively constructing tree structures, our algorithm can find every node's parent in each spanning tree directly via an easy computation relied upon only the node address and tree index. Consequently, we can implement the algorithm for constructing n ISTs in O (n N) time, where N = n 2 n is the number of vertices of n -dimensional HSDC; or parallelize the algorithm in O (n) time using N processors. Remarkably, the diameter of the constructed ISTs is about twice the diameter of Q n. [ABSTRACT FROM AUTHOR]