Nanorobot-Based Intelligent Symptoms Analysis and Recommendation Framework in Edge Networks.

Nanorobots are microscopic robots that operate at the molecular and cellular level and can potentially revolutionize fields such as medicine, manufacturing, and environmental monitoring due to their precision. However, the challenge for researchers is to analyze the data and provide a constructive recommendation framework instantly, as most nanorobots demand on-time and near-edge processing. To tackle this challenge, this research presents a novel edge-enabled intelligent data analytics framework called Transfer Learning Population Neural Network (TLPNN) to predict glucose levels and associated symptoms from invasive and non-invasive wearable devices. The TLPNN is designed to be unbiased in predicting symptoms during the initial phase but later modified based on the best-performing neural networks during the learning phase. The effectiveness of the proposed method is validated using two publicly available glucose datasets with various performance metrics. The simulation results demonstrate the effectiveness of the proposed TLPNN method over existing ones.

Deep Transfer Learning for Communicable Disease Detection and Recommendation in Edge Networks.

Considering the increasing number of communicable disease cases such as COVID-19 worldwide, the early detection of the disease can prevent and limit the outbreak. Besides that, the PCR test kits are not available in most parts of the world, and there is genuine concern about their performance and reliability. To overcome this, in this paper, we develop a novel edge-centric healthcare framework integrating with wearable sensors and advanced machine learning (ML) model for timely decisions with minimum delay. Through wearable sensors, a set of features have been collected that are further preprocessed for preparing a useful dataset. However, due to limited resource capacity, analyzing the features in resource-constrained edge devices is challenging. Motivated by this, we introduce an advanced ML technique for data analysis at edge networks, namely Deep Transfer Learning (DTL). DTL transfers the knowledge from the well-trained model to a new lightweight ML model that can support the resource-constraint nature of distributed edge devices. We consider a benchmark COVID-19 dataset for validation purposes, consisting of 11 features and 2 Million sensor data. The extensive simulation results demonstrate the efficiency of the proposed DTL technique over the existing ones and achieve 99.8% accuracy while diseases prediction.

An Intrusion Detection Mechanism for Secured IoMT Framework Based on Swarm-Neural Network.

The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient's health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient's health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework. The major outcome of the proposed strategy is to identify the attacks during data transmission through a network and analyze the health data efficiently at the edge of the network with higher accuracy. The proposed Swarm-NN strategy is evaluated with a real-time secured dataset, namely the ToN-IoT dataset that collected Telemetry, Operating systems, and Network data for IoT applications and compares the performance over the standard classification models using various performance metrics. The test results demonstrate that the proposed Swarm-NN strategy achieves 99.5% accuracy over the ToN-IoT dataset.

iCovidCare: Intelligent health monitoring framework for COVID-19 using ensemble random forest in edge networks.

The COVID-19 outbreak is in its growing stage due to the lack of standard diagnosis for the patients. In recent times, various models with machine learning have been developed to predict and diagnose novel coronavirus. However, the existing models fail to take an instant decision for detecting the COVID-19 patient immediately and cannot handle multiple medical sensor data for disease prediction. To handle such challenges, we propose an intelligent health monitoring and prediction framework, namely the iCovidCare model for predicting the health status of COVID-19 patients using the ensemble Random Forest (eRF) technique in edge networks. In the proposed framework, a rule-based policy is designed on the local edge devices to detect the risk factor of a patient immediately using monitoring Temperature sensor values. The real-time health monitoring parameters of different medical sensors are transmitted to the centralized cloud servers for future health prediction of the patients. The standard eRF technique is used to predict the health status of the patients using the proposed data fusion and feature selection strategy by selecting the most significant features for disease prediction. The proposed iCovidCare model is evaluated with a synthetic COVID-19 dataset and compared with the standard classification models based on various performance matrices to show its effectiveness. The proposed model has achieved 95.13% accuracy, which is higher than the standard classification models.