Abstract Extensive research has been conducted on the automatic classification of sleep stages utilizing deep neural networks and other neurophysiological markers. However, for sleep specialists to employ models as an assistive solution, it is necessary to comprehend how the models arrive at a particular outcome, necessitating the explainability of these models. This work proposes an explainable unified CNN-CRF approach (SleepXAI) for multi-class sleep stage classification designed explicitly for univariate time-series signals using modified gradient-weighted class activation mapping (Grad-CAM). The proposed approach significantly increases the overall accuracy of sleep stage classification while demonstrating the explainability of the multi-class labeling of univariate EEG signals, highlighting the parts of the signals emphasized most in predicting sleep stages. We extensively evaluated our approach to the sleep-EDF dataset, and it demonstrates the highest overall accuracy of 86.8% in identifying five sleep stage classes. More importantly, we achieved the highest accuracy when classifying the crucial sleep stage N1 with the lowest number of instances, outperforming the state-of-the-art machine learning approaches by 16.3%. These results motivate us to adopt the proposed approach in clinical practice as an aid to sleep experts.

Low latency, resource efficiency, and data privacy are some of the crucial requirements in modern communication networks. Federated learning can efficiently address these issues by utilizing the data at the network edge and processing massive amounts of data in parallel at the edge devices, thus ensuring data privacy and low latency. For a large-scale federated learning task spanning many devices, challenges arise due to device heterogeneity, data variability, and limited network resources. Optimal selection of edge devices participating in federated learning is essential to attaining resilient, reliable, and resource-efficient edge networks. In this context, this article proposes an optimal device selection method to minimize redundant data training and improve network resource utilization without affecting the performance of federated learning over resource-constrained edge networks. The proposed optimal device selection method aims to minimize network resource demands while maximizing data diversity within the aggregated model. The performance of the proposed federated learning framework is evaluated using a publicly available image data set of handwritten digits, EMNIST (an extended version of the MNIST data set). Experimental results indicate that the proposed framework can obtain accuracy convergence performance on par with conventional federated learning methods while significantly reducing device usage (up to$50\%$) and resource utilization (up to$30\%$) while reaching 99% of achievable accuracy. The proposed method can therefore be effectively applied to resource-constrained edge networks.

Automatic sleep stage classification can play a vital role when measuring sleep quality and diagnosing different sleep-related ailments. Several automated sleep stage identification algorithms have been proposed using various physiological signals. However, most of these methods use hand-crafted features or multiple Electroencephalography (EEG) signals. This work proposes a one-dimensional convolutional autoencoder (1D-CAE) based on a single-channel EEG signal for sleep stage identification. A total of five 1-D CAEs models are implemented, and each model is trained to reconstructs a specific sleep stage with the lowest reconstruction error, thus enabling the sleep stage identification based on this error. Furthermore, the proposed approach is evaluated on the Sleep EDF expanded datasets and achieved an overall classification accuracy of 87.2% using a single-channel EEG FPz-Cz signal. Also, our approach demonstrated the highest sleep stage identification accuracy compared with the recent algorithms, especially for sleep stage N1, a short period that transitions between sleep stages during a sleep cycle.

This paper presents the design of a data-driven algorithm for a multi-user channel aware single step signal detection system in Non-Orthogonal Multiple Access (NOMA) wireless communication networks. In a NOMA downlink system, the Successive Interference Cancellation (SIC) decoding process can be performed at the receiver, where users are decoded based on their channel behavior. The SIC detection accuracy largely depends on the estimation accuracy of the Channel State Information (CSI) which is highly difficult in general due to the non-linear and non-stationary nature of the real wireless channels. Moreover, SIC performance is limited by the receiver complexity and error propagation problems. In this paper, we design a Deep Learning (DL) based NOMA receiver that considers jointly the CSI and detects the originally transmitted symbols of the multiple users. This approach differs from the existing NOMA receivers that use SIC decoding because our proposed DL-based channel aware single-step signal detection method allows each user to detect their symbols using their respective Deep Neural Network (DNN) in a single step. The DNNs are first trained offline using data generated from the channel realizations and labelled symbol data, and then they are tested in terms of recovering new transmitted symbols directly. Our simulation results show that the DL-based approach provides a better symbol detection performance than the SIC.

Improving the energy-efficiency of remotely deployed sensor nodes in agriculture wireless networks is very challenging due to a lack of access to energy grid. Network clustering and limiting the amount of sensor data are among the various methods to improve the lifetime of these sensor nodes. In this work, an optimal sensor-selection scheme is proposed to improve the Quality of Service in clustered agriculture networks. the proposed method selects a limited number of sensor nodes to be active in each cluster. It considers the estimator performance while selecting limited sensor nodes for environmental monitoring. the optimal sensor-selection process considers the information of remaining energy of sensor nodes in agriculture wireless networks. the proposed method selects a limited number of nodes in each cluster while maximizing the estimator performance at the receiver. Our cluster based sensor-selection scheme optimizes the energy-efficiency in a distributed manner. Network clustering also ensures a uniform sensor-selection over a network area. Extensive experiments are conducted to analyse the performance of the proposed optimal sensor-selection method over different network scenarios. Our experimental results indicate significant improvements in energy-efficiency of agriculture wireless sensor networks and motivate the proposed method in practice.

Development of energy-efficient wireless sensor networks is crucial in the deployment of IoT and IIoT for modern day applications like smart home, smart vehicles, and smart industries. Several methods like network clustering, mobile sink deployment and dynamic sensing rate have been used in improving the energy-efficiency of wireless sensor networks in IoT framework. However, these methods have been developed independently which can lead to certain network issues like reduced lifetime, network breakdown among others. In this work, an energy-efficient method that optimizes mobile sink scheduling while concurrently providing dynamic buffer management is proposed. A cooperative network model that incorporates node clustering and mobile sink deployment in variable node sensing rate scenario is first developed. However, in such cooperative network models, mobile sink scheduling and buffer overflow management which causes information loss become challenging. This is primarily due to limited buffer size, variable sensing rate of the nodes, and the unavailability of mobile sink at all times near a cluster. Therefore, a reinforcement Q-learning framework is developed for scheduling the mobile sink while minimizing the information loss caused by buffer overflow in each cluster of a clustered WSN. More specifically, the network behaviour is learnt in the context of buffer overflow using Q-learning approach. The proposed method computes the adaptive halt-times for the mobile sink based on information loss and buffer overflow in each cluster. Performance of the proposed joint mobile sink scheduling and dynamic buffer management method is evaluated on a medium scale WSN. A clustered wireless sensor network with a total of 600 sensor nodes is considered for performance evaluation. The proposed method is shown to learn the variable node sensing rate in a reasonable amount of time using convergence analysis. Numeric evaluations indicate that the proposed method minimizes the information loss in a medium scale wireless sensor network while improving the network lifetime simultaneously. The proposed cooperative network model also outperforms in terms of energy-efficiency when compared to conventional WSN. The results are motivating enough for the use of cooperative network model in practical WSNs for IoT applications.

Energy-efficient data aggregation is a challenging problem in asynchronous wireless sensor networks. Asynchronous behaviour of sensor nodes is generally due to adaptive duty cycling and it leads to information loss, buffer overflow and poor quality of services. To overcome these issues, a joint mobile sink scheduling and data aggregation scheme is proposed in this work. A reinforcement learning framework is developed herein for budgeting the energy of mobile sink while minimizing the information loss in each cluster of a clustered WSN. More specifically, a Q-learning approach is used to learn the network behaviour over time and compute adaptive halt-times for the mobile sink based on active number of nodes in each cluster. Experiments on joint mobile sink scheduling and data aggregation are conducted on a medium scale WSN. Experimental results indicate that proposed method minimizes the information loss in an asynchronous wireless sensor network. It is also observed that mobile sink performs the data gathering operation with limited energy consumption while maximizing network lifetime.

The mobility of smart and connected devices in Internet of Things brings the challenge of reliable data forwarding. In this work, a relay-node selection method is proposed for mobility-tolerant data forwarding. The proposed relay-node selection method uses the connectivity information in time-varying networks. The connectivity information of IoT devices is modelled using homogeneous Poisson point processes. The connectivity duration information of all the devices with their neighbours is updated continuously. Based on connectivity information, an optimal relay-node is selected as a data forwarding node. The online relay-node selection from a source node to the base station establishes a data forwarding path. The proposed method selects the relay-nodes based on joint optimization of two network parameters, data latency and packet loss risk. The simulation results show that the proposed method significantly improves the data latency and the packet loss risk for data forwarding over time-varying IoT networks.

Energy conservation in sensor nodes of an IoT framework is a challenging problem. Wake-up radio, a new technology being developed by IEEE, describes a broad solution to this challenge. In this paper, we address the issue of optimal sensor nodes selection to maximize the energy-efficiency of a WSN for IoT applications. A wake-up protocol is then applied on the optimally selected-sensor nodes. The node selection is formulated as an optimization problem with spatio-temporal correlation constraints thus minimizing redundant data transfer. Subsequently, a MLE method is used to solve the problem. Additional novelty of this work is the development of a wake-up protocol over hexagonal grids when a mobile sink is used for data aggregation leading to improved energy-efficiency. The impact of the proposed method on decision errors in IoT applications is also discussed. Extensive experiments are performed to analyse the energy-efficiency, estimation error, modelling accuracy, network lifetime of WSN in the context of IoT applications. Results indicate an improvement in energy-efficiency when compared to conventional methods.