A Novel IoT-based Framework for Urine Infection Detection and Prediction using Ensemble Bagging Decision Tree Classifier

One of the most common conditions treated in adult primary care medicine is Urinary Tract Infection (UTI), which accounts for a sizeable portion of antibiotic prescriptions. A high degree of diagnostic accuracy is necessary because this issue is so prevalent and important in everyday clinical practice. Particularly in light of the rising prevalence of antibiotic resistance, excessive antibiotic prescriptions should be avoided. To examine the machine learning approach and Internet of Things (IoT) for urinary tract infections, this research proposes an Ensemble Bagging Decision Tree Classifier (EBDTC). In our study, to learn more about UTI, we conducted a study in which we collected the physiological data of 399 patients and preprocessed them using the min-max scalar normalization. Feature extraction using Principle Component Analysis (PCA) and classification using Ensemble Bagging Decision Tree Classifier (EBDTC). The performance outcomes of accuracy (96.25%), precision(96.22%), recall (98.07%), and f-1 measure(97.17%) demonstrate the proposed strategy's significantly improved performance in comparison to other baseline existing techniques

MARGOT: Dynamic IoT Resource Discovery for HADR Environments

Smart City services leverage sophisticated IT architectures whose assets are deployed in dynamic and heterogeneous computing and communication scenarios. Those services are particularly interesting for Humanitarian Assistance and Disaster Relief (HADR) operations in urban environments, which could improve Situation Awareness by exploiting the Smart City IT infrastructure. To this end, an enabling requirement is the discovery of the available Internet-of-Things (IoT) resources, including sensors, actuators, services, and computing resources, based on a variety of criteria, such as geographical location, proximity, type of device, type of capability, coverage, resource availability, and communication topology / quality of network links. To date, no single standard has emerged that has been widely adopted to solve the discovery challenge. Instead, a variety of different standards have been proposed and cities have either adopted one that is convenient or reinvented a new standard just for themselves. Therefore, enabling discovery across different standards and administrative domains is a fundamental requirement to enable HADR operations in Smart Cities. To address these challenges, we developed MARGOT (Multi-domain Asynchronous Gateway Of Things), a comprehensive solution for resource discovery in Smart City environments that implements a distributed and federated architecture and supports a wide range of discovery protocols

Dynamic Offloading Technique for Latency-Sensitive Internet of Things Applications using Fog Computing

Internet of Things (IoT) has evolved as a novel paradigm that provides com-putation power to different entities connected to it. IoT offers services to multiple sectors such as home automation, industrial automation, traffic management, healthcare sector, agriculture industry etc. IoT generally relies on cloud data centers for extended analytics, processing and storage support. The cloud offers highly scalable and robust platform for IoT applications. But latency sensitive IoT applications suffer delay issues as the cloud lies in remote location. Edge/fog computing was introduced to overcome the issues faced by delay-sensitive IoT applications. These platforms lie close to the IoT network, reducing the delay and response time. The fog nodes are usually distributed in nature. The data has to be properly offloaded to available fog nodes using efficient strategies to gain benefit from the integration. Differ-ent offloading schemes are available in the literature to overcome this prob-lem This paper proposes a novel offloading approach by combining two effi-cient metaheuristic algorithms, Honey Badger Algorithm (HBA) and Fla-mingo Search Algorithm (FSA) termed as HB-FS algorithm. The HB-FS is executed in an iterative manner optimizing the objective function in each it-eration. The performance evaluation of the proposed approach is done with different existing metaheuristic algorithms and the evaluations show that the proposed work outperforms the existing algorithms in terms of latency, response time and execution time. The methodology also offers better degree of imbalance with proper load balancing under different conditions

Urinary Tract Infection Analysis using Machine Learning based Classification and ANN- A Study of Prediction

Urinary tract infection is the most frequently diagnosed infection among humans. A urinary tract infection (UTI) affects the areas of urinary system which includes the ureters, bladder, kidneys and urethra. The primary infected area of urinary system involves the lower tract i.e. bladder and urethra. The infection in bladder is painful as well as uncomfortable but if it spreads to kidneys, it can have severe consequences. Women are more susceptible to urinary infection in comparison to men due to their physiology. This paper aims to study and assess the impact and causes of urinary tract infection in human beings and evaluate the machine learning approach for urinary disease forecasting. The paper also proposed machine learning based methodology for the prediction of the urinary infection and estimating the outcomes of the designed procedures over real-time data and validating the same. The paper focuses to get high prediction accuracy of UTI using confusion matrix by Machine Based Classification and ANN technique. Some specific parameters have been selected with the help of Analysis of variance technique. The naive bayes classifier, J48 decision tree algorithm, and Artificial neural network have been used for the prediction of presence of urinary infection. The accuracy achieved by the proposed model is 95.5% approximately

A Decision Framework for Allocation of Constellation-Scale Mission Compute Functionality to Ground and Edge Computing

This paper explores constellation-scale architectural trades, highlights dominant factors, and presents a decision framework for migrating or sharing mission compute functionality between ground and space segments. Over recent decades, sophisticated logic has been developed for scheduling and tasking of space assets, as well as processing and exploitation of satellite data, and this software has been traditionally hosted in ground computing. Current efforts exist to migrate this software to ground cloud-based services. The option and motivation to host some of this logic “at the edge” within the space segment has arisen as space assets are proliferated, are interlinked via transport networks, and are networked with multi-domain assets. Examples include edge-based Battle Management, Command, Control, and Communications (BMC3) being developed by the Space Development Agency and future onboard computing for commercial constellations. Edge computing pushes workload, computation, and storage closer to data sources and onto devices at the edge of the network. Potential benefits of edge computing include increased speed of response, system reliability, robustness to disrupted networks, and data security. Yet, space-based edge nodes have disadvantages including power and mass limitations, constant physical motion, difficulty of physical access, and potential vulnerability to attacks. This paper presents a structured decision framework with justifying rationale to provide insights and begin to address a key question of what mission compute functionality should be allocated to the space-based edge , and under what mission or architectural conditions, versus to conventional ground-based systems. The challenge is to identify the Pareto-dominant trades and impacts to mission success. This framework will not exhaustively address all missions, architectures, and CONOPs, however it is intended to provide generalized guidelines and heuristics to support architectural decision-making. Via effects-based simulation and analysis, a set of hypotheses about ground- and edge-based architectures are evaluated and summarized along with prior research. Results for a set of key metrics and decision drivers show that edge computing for specific functionality is quantitatively valuable, especially for interoperable, multi-domain, collaborative assets

Next Generation of SDN in Cloud-Fog for 5G and Beyond-Enabled Applications: Opportunities and Challenges

In recent years, the number of objects connected to the Internet has significantly increased. Increasing the number of connected devices to Internet is transforming today’s Internet of Things (IoT) into massive IoT of future. It is predicted, in a few years, a high communication and computation capacity will be required to meet demands of massive IoT devices and applications requiring data sharing and processing. 5G and beyond mobile networks are expected to fulfill part of these requirements by providing data rate of up to Terabits per second. It will be a key enabler to support massive IoT and emerging mission critical applications with strict delay constrains. On the other hand, next generation of Software Defined Networking (SDN) with emerging Cloud related technologies (e.g., Fog and Edge computing) can play an important role on supporting and implementing the above-mentioned applications. This paper sets out the potential opportunities and important challenges that must be addressed in considering options for using SDN in hybrid Cloud-Fog systems to support 5G and beyond-enabled applications

Reliable and resilient AI and IoT-based personalised healthcare services: A survey

Recent technological (e.g., IoT, 5G), and economic (e.g., UN 2030 Sustainable Development Goals) developments have transformed the healthcare sector towards more personalized and IoT-based healthcare services. These services are realized through control and monitoring applications that are typically developed using artificial intelligence (AI)/machine learning (ML) based algorithms, that play a significant role to highlight the efficiency of traditional healthcare systems. Current personalized healthcare services are dedicated in a specific environment to support technological personalization (e.g., personalized gadgets/devices). However, they are unable to consider different inter-related health conditions, leading to inappropriate diagnosis and affect sustainability and the long-term health/life of patients. Towards this problem, the state-of-the-art Healthcare 5.0 technology has evolved that supersede previous healthcare technologies. The goal of healthcare 5.0 is to achieve a fully autonomous healthcare service, that takes into account the interdependent effect of different health conditions of a patient. This paper conducts a comprehensive survey on personalized healthcare services. In particular, we first present an overview of key requirements of comprehensive personalized healthcare services (CPHS) in modern healthcare Internet of Things (HIoT), including the definition of personalization and an example use case scenario as a representative for modern HIoT. Second, we explored a fundamental three-layer architecture for IoT-based healthcare systems using both AI and non-AI-based approaches, considering key requirements for CPHS followed by their strengths and weaknesses in the frame of personalized healthcare services. Third, we highlighted different security threats against each layer of IoT architecture along with the possible AI and non-AI-based solutions. Finally, we propose a methodology to develop reliable, resilient, and personalized healthcare services that address the identified weaknesses of existing approaches