Optimization of Health Care Services with Limited Resources

Health services are an integral part of hospitals or health clinics. Maximum service can get if the availability of resources in the service center is very adequate, but the availability of resources cannot be ascertained that it will always be adequate, and excessive availability of resources can also result in waste. The problem that often occurs is the lack of optimal services provided to patients due to limited available resources. Various obstacles, such as services that are not permitted are repeated, uncertain service distances, and service time are optimal barriers to service. This study aims to solve the problem of optimizing health care services for patients in hospitals using a number of variables in the hospital environment such as available resources, namely doctors, nursing medical personnel, technicians, technical equipment. This study is subject to the aim of minimizing all costs incurred to perform services, namely travel time from places to provide health services to patients, medical staff costs to provide services of the type of service, and so on. These variables are explained in the form of mathematical models that are able to explain existing constraints and minimize costs and time when performing services. The modeling results were tested using Linear Ineraktive Discrete Optimizer (LINDO) programming to determine errors that might occur in the model. The test results provide information that the maximum value of the objective function is 88.00 at the 25th iteration step so that the new model is expected to optimize health services for hospital patients and existing health clinics

Resource allocation for fog computing based on software-defined networks

With the emergence of cloud computing as a processing backbone for internet of thing (IoT), fog computing has been proposed as a solution for delay-sensitive applications. According to fog computing, this is done by placing computing servers near IoT. IoT networks are inherently very dynamic, and their topology and resources may be changed drastically in a short period. So, using the traditional networking paradigm to build their communication backbone, may lower network performance and higher network configuration convergence latency. So, it seems to be more beneficial to employ a software-defined network paradigm to implement their communication network. In software-defined networking (SDN), separating the network’s control and data forwarding plane makes it possible to manage the network in a centralized way. Managing a network using a centralized controller can make it more flexible and agile in response to any possible network topology and state changes. This paper presents a software-defined fog platform to host real-time applications in IoT. The effectiveness of the mechanism has been evaluated by conducting a series of simulations. The results of the simulations show that the proposed mechanism is able to find near to optimal solutions in a very lower execution time compared to the brute force method

An EEG Signal Recognition Algorithm During Epileptic Seizure Based on Distributed Edge Computing

Epilepsy is one kind of brain diseases, and its sudden unpredictability is the main cause of disability and even death. Thus, it is of great significance to identify electroencephalogram (EEG) during the seizure quickly and accurately. With the rise of cloud computing and edge computing, the interface between local detection and cloud recognition is established, which promotes the development of portable EEG detection and diagnosis. Thus, we construct a framework for identifying EEG signals in epileptic seizure based on cloud-edge computing. The EEG signals are obtained in real time locally, and the horizontal viewable model is established at the edge to enhance the internal correlation of the signals. The Takagi-Sugeno-Kang (TSK) fuzzy system is established to analyze the epileptic signals. In the cloud, the fusion of clinical features and signal features is established to establish a deep learning framework. Through local signal acquisition, edge signal processing and cloud signal recognition, the diagnosis of epilepsy is realized, which can provide a new idea for the real-time diagnosis and feedback of EEG during epileptic seizure

Secure Cloud-Edge Deployments, with Trust

Assessing the security level of IoT applications to be deployed to heterogeneous Cloud-Edge infrastructures operated by different providers is a non-trivial task. In this article, we present a methodology that permits to express security requirements for IoT applications, as well as infrastructure security capabilities, in a simple and declarative manner, and to automatically obtain an explainable assessment of the security level of the possible application deployments. The methodology also considers the impact of trust relations among different stakeholders using or managing Cloud-Edge infrastructures. A lifelike example is used to showcase the prototyped implementation of the methodology

BIG, MEDIUM AND LITTLE (BML) SCHEDULING IN FOG ENVIRONMENT

BIG, MEDIUM AND LITTLE (BML) SCHEDULING IN FOG ENVIRONMENTAbstractFog computing has got great attntion due to its importance especially in Internet of Things (IoT) environment where computation at the edge of the network is most desired. Due to the geographical proximity of resources, Fog computing exhibits lower latency compared to cloud; however, inefficient resource allocation in Fog environment can result in higher delays and degraded performance. Hence, efficient resource scheduling in Fog computing is crucial to get true benefits of the cloud like services at the proximity of data generation sources. In this paper, a Big-Medium-Little (BML) scheduling technique is proposed to efficiently allocate Fog and Cloud resources to the incoming IoT jobs. Moreover, cooperative and non-cooperative Fog computing environments are also explored. Additionally, a thorough comparative study of existing scheduling techniques in Fog-cloud environment is also presented. The technique is rigorously evaluated and shows promising results in terms of makespan, energy consumption, latecny and throughput.Keywords: Cloud node, Fog node, Max-Min, Min-Min, Big, Medium, Little, Task, Resource, Cooperative and Non-Cooperative Systems

Sleep Apnea Detection in Fog Based Ambient Assisted Living System

Ambient Assisted Living environments use different sensors and actuators to enable their endusers to live in their preferred environments. Unlike smart homes, where a target audience is usually a family unit, standard Ambient Assisted Living end users are care receivers and care providers. This article describes an approach based on the fog computing paradigm to detect sleep apnea in an Ambient Assisted Living context unobtrusively. The edge nodes process and detect local activities of daily living events and have direct control of the local environment. The fog nodes are used to further process and transmit data. The cloud is used for more complex and anonymous data computation. This research shows that sensors, which are unobtrusive and do not interfere with users' daily routines, can be successfully used for pattern observation

Health Care Equity Through Intelligent Edge Computing and Augmented Reality/Virtual Reality: A Systematic Review

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this research. Intelligent edge computing-aided distribution and collaborative information management is a possible approach for a long-term digital healthcare system. Furthermore, IEC (Intelligent Edge Computing) encourages digital health data to be processed only at the edge, minimizing the amount of information exchanged with central servers/the internet. This significantly increases the privacy of digital health data. Another critical component of a sustainable healthcare system is affordability in digital healthcare. Affordability in digital healthcare is another key component of a sustainable healthcare system. Despite its importance, it has received little attention due to its complexity. In isolated and rural areas where expensive equipment is unavailable, IEC with AR / VR, also known as edge device shadow, can play a significant role in the inexpensive data collection process. Healthcare equity becomes a reality by combining intelligent edge device shadows and edge computing