Multi-Access Edge computing (MEC) and Fog computing provide services to subscribers at low latency. There is a need to form a federation among 3GPP MEC and fog to provide better coverage to 3GPP subscribers. This federation gives rise to two issues—third-party authentication and application mobility—for continuous service during handover from 3GPP MEC to fog without re-authentication. In this paper, we propose: 1) a proxy-based state transfer and third-party authentication (PS3A) that uses a transparent proxy to transfer the authentication and application state information, and 2) a token-based state transfer and proxy-based third-party authentication (TSP3A) that uses the proxy to transfer the authentication information and tokens to transfer the application state from 3GPP MEC to the fog. The proxy is kept transparent with virtual counterparts, to avoid any changes to the existing 3GPP MEC and fog architectures. We implemented these solutions on a testbed and results show that PS3A and TSP3A provide authentication within 0.345–2.858s for a 0–100 Mbps proxy load. The results further show that TSP3A provides application mobility while taking 40–52% less time than PS3A using state tokens. TSP3A and PS3A also reduce the service interruption latency by 82.4% and 84.6%, compared to the cloudbased service via tokens and prefetching.

To handle the exponentially growing service expectations from mobile users and circumvent the band switching slow rate, device-to-device (D2D) communication is receiving much research attention in the Internet of Things (IoT). While the emerging D2D nodes can support heterogeneous frequency bands [radio frequency (RF) including 2.4 GHz/5 GHz wireless local area network (WLAN), 38-GHz millimeter wave (mmWave), and visible light communication (VLC)], the physical constraints (e.g., blocking) require the user devices to dynamically switch between the bands in order to avoid the loss of connectivity and throughput degradation. In this article, we investigate an effective online link selection in hybrid RF-VLC scenarios for direct user data handling. First, we model the multiband selection issue as a multiarmed bandit (MAB) problem. The source/relay node acts as a player who gambles to maximize its long-term feedback/reward via selecting suitable arms, i.e., available bands (WLAN, mmWave, or VLC). Then, we propose an online, energy-aware band selection (EABS) methodology by leveraging three theoretically guaranteed MAB techniques [upper confidence bound (UCB), Thompson sampling (TS), and minimax optimal stochastic strategy (MOSS)] to derive optimal band selection policies. Based on these adopted policies, we propose three algorithms, namely, EABS-UCB, EABS-TS, and EABS-MOSS, to implement the EABS strategy, respectively. Extensive simulations demonstrate our proposed algorithms’ superior performance compared to the traditional link selection schemes regarding energy efficiency, average throughput, and convergence rate. In particular, EABS-MOSS emerges as the best algorithm as it exhibits near-optimal performance due to its flexibility to both stochastic and adversarial environments.

Continuous low-rate monitoring is an important IoT application, which requires high-fidelity in observing signals with low frequency. However, most sensors exhibit noise that is inversely-proportional to spectral frequency (1/f noise). Because both the relevant signal and noise share the same spectral properties, standard linear filtering techniques cannot be used. We are looking into a special application for remote healthcare of the magnetic field sensing of cardiac activity, magnetocardiography (MCG). For such an application, we need to develop a noise separation method, that is also resource-efficient. Previously, we demonstrated AI-based removal of 1/f noise in MCG by a convolutional neural network coupled with gated recurrent units. However, it needs a large amount of data for training, requiring significant training time and computational power. In this work, we employ reservoir computing (RC) for noise-removal, while being conservative in computing resources.

To combat the novel coronavirus (COVID-19) spread, the adoption of technologies including the Internet of Things (IoT) and deep learning is on the rise. However, the seamless integration of IoT devices and deep learning models for radiograph detection to identify the presence of glass opacities and other features in the lung is yet to be envisioned. Moreover, the privacy issue of the collected radiograph data and other health data of the patients has also arisen much concern. To address these challenges, in this paper, we envision a federated learning model for COVID-19 prediction from radiograph images acquired by an X-ray device within a mobile and deployable screening resource booth node (RBN). Our envisioned model permits the privacy-preservation of the acquired radiograph by performing localized learning. We further customize the proposed federated learning model by asynchronously updating the shallow and deep model parameters so that precious communication bandwidth can be spared. Based on a real dataset, the effectiveness of our envisioned approach is demonstrated and compared with baseline methods.

Multihop device-to-device (D2D)-enabled relay networks are envisaged to be utilized by the Internet of Things (IoT) and massive machine-type communication (mMTC) traffic for the purpose of offloading data in beyond fifth-generation (B5G) networks. The emerging challenge of spectrum scarcity and overloading of cellular base stations can be addressed using such relay nodes in terms of spectrum and energy efficiency. In order to improve spectral efficiency, in this article, we intend to employ several frequency bands concurrently in the relay node rather than the traditional concept of specifying one channel on a specific band at a time. A deep learning-based predictive channel selection method is leveraged to unravel the potential challenges associated with the dynamic channel conditions in the multiband relay networks. For predicting the most appropriate channel based on its quality, signal-to-interference-plus-noise-ratio (SINR) is adopted as the metric, which is predicted by the proposed convolutional neural network (CNN) model. The best modulation and coding rates of the predicted band are attained in order to transmit the packets received from the source or previous relay node to the successive relay node/destination. Two proactive channel assignment strategies, referred to as controlled and smart prediction schemes, are employed to exhibit the performance of the shallow and deep-CNN models. The proposed model is evaluated on multiple publicly available data sets from diverse network systems and compared with several machine/deep learning methods. Our proposal leads to encouraging results for proactively predicting the conditions of the channels and choosing the most suitable ones in multiband relay systems.

Traditionally, the Internet of Things (IoT) devices, deployed on the ultra-edge of the network, lack computation, and energy resources. In this paper, we press on the need to go beyond the realms of traditional edge computing (e.g., limited to user-smartphones) and investigate how to incorporate intelligence into the ultra-edge IoT sensors. Among numerous use-cases, we select a mobile Health (mHealth) scenario where we conceptualize a smart IoT sensor to collect and intelligently process single-channel Electrocardiogram (ECG) signals to detect arrhythmia, a heart-condition often associated with morbidity and even mortality. The arrhythmia detection can be regarded as a non-linear Delay Differential Equation (DDE) time-series analysis problem, and the conventional solutions to this problem are not suitable for integration with IoT sensors due to rigorous pre-processing steps. As a solution, a Convolutional Neural Network (CNN)-based, lightweight Arrhythmia classification system is proposed in the paper without the need for noise-filtering and feature extraction steps. Four classes of the heartbeats are considered to comply with the ANSI/AAMI EC57:1998 standard. The proposed system's performances and generalization potential are assessed using three datasets from PhysioNet trained on a deep learning workstation and then transferred to virtualized micro-controllers connected to IoT sensors. The proposed deep learning model exhibits encouraging performance (accuracy 95.27%) in heartbeat classification. Experimental and numerical results demonstrate that the proposed deep learning technique outperforms conventional DDE-based optimization techniques and machine learning techniques such as K-Nearest Neighbor (KNN), and random forest (RF).