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Wireless sensor networks have become pervasive in various applications, including environmental monitoring, smart cities, precision agriculture, and healthcare. In particular, linear wireless sensor networks that span considerable distances are increasingly deployed for applications such as pipeline monitoring and transportation systems. However, ensuring the reliability of long wireless sensor networks poses significant challenges due to the unique characteristics of the network topology and the constraints imposed by the resource-constrained sensor nodes. There is a notable lack of methods for the analysis and optimization of the reliability of large-scale sensor networks, and in this paper, we partially fill this gap. We propose a method for assessing the tradeoff between redundancy of forward error correction and reliable packet delivery. We also consider approaches to improve the reliability of linear sensor networks based on increasing topology connectivity and provide a corresponding method for the exact calculation of the all-terminal reliability. Numerical experiments demonstrate the relevance and effectiveness of our results. This research contributes to a deeper understanding of the underlying principles governing the reliability of long wireless sensor networks and provides valuable insights for the design, optimization, and management of such networks in real-world applications.

In this study, we explore a hybrid non-orthogonal multiple access and time division multiple access (NOMA-TDMA) approach designed to maximize sum throughput in a wireless powered Internet of Things (IoT) network (WPIN). Hybrid access points send energy signals to users on downlink, and users in various groups utilize that harvested energy to transmit information on uplink. This process is facilitated by the NOMA-TDMA scheme wherein users of the same group use NOMA for simultaneous transmissions, and separate time slots are assigned to each group through TDMA. Under this hybrid NOMA-TDMA scheme, the main objective is to enhance the network’s sum throughput by jointly optimizing both the allocation of time for downlink and uplink and the downlink beamforming vectors. Given the complex interdependence of variables, the problem is inherently non-convex, making it difficult to solve numerically. Therefore, we reformulate the problem as a bi-level programming problem—the outer-level sub-problem addresses beamforming vectors using a genetic algorithm while the inner-level sub-problem deals with the allocation of downlink and uplink time through the Lagrange multiplier method. Numerical results show that the proposed hybrid NOMA-TDMA scheme outperforms baseline schemes like orthogonal multiple access and equal time allocation, in terms of the sum throughput of the WPIN.

Conventional communications systems centered on data prioritize maximizing network throughput using Shannon’s theory, which is primarily concerned with securely transmitting the data despite limited radio resources. However, in the realm of edge learning, these methods frequently fall short because they depend on traditional source coding and channel coding principles, ultimately failing to improve learning performance. Consequently, it is crucial to transition from a data-centric viewpoint to a task-oriented communications approach in wireless system design. Therefore, in this paper, we propose efficient communications under a task-oriented principle by optimizing power allocation and edge learning-error prediction in an edge-aided non-orthogonal multiple access (NOMA) network. Furthermore, we propose a novel approach based on the ant colony optimization (ACO) algorithm to jointly minimize the learning error and optimize the power allocation variables. Moreover, we investigate four additional benchmark schemes (particle swarm optimization, quantum particle swarm optimization, cuckoo search, and butterfly optimization algorithms). Satisfactorily, simulation results validate the superiority of the ACO algorithm over the baseline schemes, achieving the best performance with less computation time. In addition, the integration of NOMA in the proposed task-oriented edge learning system obtains higher sum rate values than those achieved by conventional schemes.

In today’s digital age, coverage prediction is essential for optimizing wireless networks and improving user experience. While numerous path loss models and advanced machine learning algorithms have been developed to achieve high prediction performance, they predominantly operate within a centralized learning paradigm. While effective, this conventional approach often suffers from scalability and privacy limitations that are critical to the successful deployment of wireless maps. Conversely, in this paper, we propose a novel decentralized approach based on a federated learning long short-term memory (LSTM) model to accurately predict network coverage in indoor environments. The proposed FedLSTM is a method that allows multiple users, or clients, to train the model without sharing their personal data directly with a central server. In an experimental setup, we used real data collected from numerous clients moving along different paths. The FedLSTM model is evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and R2. Furthermore, compared to a centralized counterpart, FedLSTM shows a slight increase in RMSE from 2.4 dBm to 2.5 dBm and an increase in MAE from 1.7 dBm to 1.9 dBm. In addition, we evaluate the proposed FedLSTM considering variations in the number of participating clients and the number of local training epochs. The results show that even devices with limited computational power can meaningfully contribute to the training of the federated model, with fewer epochs achieving competitive results. Graphical analyses of the radio environment maps (REMs) generated by both FedLSTM and the centralized LSTM highlight their similarities. However, FedLSTM provides client privacy while reducing communication overhead and server strain.

The integration of wireless powered communication network (WPCNs) with intelligent reflecting surface (IRS) technology has emerged as a promising solution for enhancing the energy and spectral efficiency of the network. Recent studies have explored the benefits of active and passive reflecting element surfaces in various networks. However, most existing works on IRS-assisted WPCNs mainly focus on comparing an active IRS to single/multiple IRSs or to a hybrid IRS comprising active and passive elements on the same surface. In this paper, we explore a hybrid, active and passive IRS-assisted WPCN which can significantly improve network capacity. By introducing a distributed pair of active and passive IRSs, signal amplification and multiple reflection links can boost the wireless link capacity. To enhance network performance, this new framework utilizes both active and passive IRS elements to optimize wireless energy transfer (WET) on the downlink and wireless information transfer (WIT) on the uplink. However, designing a joint deployment of active and passive IRS elements becomes more challenging due to the joint operations of downlink WET and uplink WIT in WPCNs. To address the complexity of the non-convex optimization problem associated with the hybrid IRS-aided architecture, we use a systematic divide-and-conquer methodology. This decomposes the main problem into three interconnected sub-problems that are solved sequentially using sophisticated techniques. Furthermore, the study examines two practical deployment scenarios for active and passive IRS elements, namely ‘Hybrid Case-1’ (where active IRS is placed near the hybrid access point (H-AP) and passive IRS is placed near wireless devices) and ‘Hybrid Case-2’ (where active IRS is placed near wireless devices and passive IRS is placed near the H-AP). Extensive simulations and numerical analysis demonstrate the superiority of our proposed hybrid IRS-based framework over single active IRS and conventional double passive IRSs-assisted network, making it a promising and compelling solution for practical WPCN deployments, ensuring enhanced network performance and throughput fairness in wireless power and communications technologies.

Security is an important consideration when delivering information-aware messages to vehicles that are far away from the current location of the information-sending vehicle. This information helps the receiver to save fuel and time by making wise decisions to avoid damaged or blocked roads. To ensure the safety and security of this type of information using blockchain technology, we propose a new cognitive vehicular communication scheme to transfer messages from source to destination. Due to spectrum scarcity in vehicular networks, there needs to be a wireless medium available for every communication link since vehicles require it to communicate. The primary user (PU) makes a public announcement about a free channel to all secondary users nearby and only gives it to authentic vehicles. The authenticity of vehicles is guaranteed by a roadside unit (RSU) that offers secure keys to any vehicle that joins this blockchain network. Those who participate in this network must pay a certain amount and receive rewards for their honesty that exceed the amount spent. To test the performance of various parameters, the proposed scheme utilizes the Ethereum smart contract and compares them to blockchain and non-blockchain methods. Our results show a minimum delivery time of 0.16 s and a minimum overhead of 350 bytes in such a dynamic vehicle environment.

The Internet of Things (IoT) is witnessing a surge in sensor-equipped devices. The data generated by these IoT devices serve as a critical foundation for informed decision-making, real-time insights, and innovative solutions across various applications in everyday life. However, data reliability is often compromised due to the vulnerability of sensors to faults arising from harsh operational conditions that can adversely affect the subsequent operations that depend on the collected data. Hence, the identification of anomalies within sensor-derived data holds significant importance in the IoT context. This article proposes a sensor fault detection method using a Long Short-Term Memory autoencoder (LSTM-AE). The AE, trained on normal sensor data, predicts a 20-step window, generating three statistical features via SHapley Additive exPlanations from the estimated steps. These features aid in determining potential faults in the predicted steps using a machine learning classifier. A secondary classifier identifies the type of fault in the sensor signal. Experimentation on two sensor datasets showcases the method’s functionality, achieving fault detection accuracies of approximately 93% and 97%. It is possible to attain a perfect fault classification performance by slightly modifying the feature calculation approach. In a univariate prediction scenario, our proposed approach demonstrates good fault detection and classification performance.

Particularly in Internet of Things (IoT) scenarios, the rapid growth and diversity of network traffic pose a growing challenge to network intrusion detection systems (NIDs). In this work, we perform a comparative analysis of lightweight machine learning models, such as logistic regression (LR) and k-nearest neighbors (KNNs), alongside other machine learning models, such as decision trees (DTs), support vector machines (SVMs), multilayer perceptron (MLP), and random forests (RFs) with deep learning architectures, specifically a convolutional neural network (CNN) coupled with bidirectional long short-term memory (BiLSTM), for intrusion detection. We assess these models’ scalability, performance, and robustness using the NSL-KDD and UNSW-NB15 benchmark datasets. We evaluate important metrics, such as accuracy, precision, recall, F1-score, and false alarm rate, to offer insights into the effectiveness of each model in securing network systems within IoT deployments. Notably, the study emphasizes the utilization of lightweight machine learning models, highlighting their efficiency in achieving high detection accuracy while maintaining lower computational costs. Furthermore, standard deviation metrics have been incorporated into the accuracy evaluations, enhancing the reliability and comprehensiveness of our results. Using the CNN-BiLSTM model, we achieved noteworthy accuracies of 99.89% and 98.95% on the NSL-KDD and UNSW-NB15 datasets, respectively. However, the CNN-BiLSTM model outperforms lightweight traditional machine learning methods by a margin ranging from 1.5% to 3.5%. This study contributes to the ongoing efforts to enhance network security in IoT scenarios by exploring a trade-off between traditional machine learning and deep learning techniques.

Bioacoustic event detection is a demanding endeavor involving recognizing and classifying the sounds animals make in their natural habitats. Traditional supervised learning requires a large amount of labeled data, which are hard to come by in bioacoustics. This paper presents a few-shot learning (FSL) method incorporating transductive inference and data augmentation to address the issues of too few labeled events and small volumes of recordings. Here, transductive inference iteratively alters class prototypes and feature extractors to seize essential patterns, whereas data augmentation applies SpecAugment on Mel spectrogram features to augment training data. The proposed approach is evaluated by using the Detecting and Classifying Acoustic Scenes and Events (DCASE) 2022 and 2021 datasets. Extensive experimental results demonstrate that all components of the proposed method achieve significant F-score improvements of 27% and 10%, for the DCASE-2022 and DCASE-2021 datasets, respectively, compared to recent advanced approaches. Moreover, our method is helpful in FSL tasks because it effectively adapts to sounds from various animal species, recordings, and durations.

The reconfigurable intelligent surfaces (RIS) represent a transformative technology in wireless communication, offering a novel approach to managing and enhancing radio signal propagation. By dynamically adjusting their electromagnetic properties, RIS can significantly improve the performance and efficiency of 5G and beyond communication systems. In this paper, we study a cognitive RIS-aided non-orthogonal multiple access (NOMA) network that serves multiple users and improves spectrum efficiency. Our analysis assumes a secondary network operates under multi-primary user constraints and interference from the primary source. We derive approximation closed-form formulas for outage probability (OP), and system throughput. To obtain further insights, an asymptotic expression for OP is computed by taking into account two power configurations at the source. Additionally, numerical results show the effects of important factors on performance, confirming the accuracy of the theoretical derivation. According to the simulation results, performance by the system under consideration might be improved considerably by combining a RIS and NOMA, particularly when compared to an orthogonal multiple access scheme.

In mobile communications network planning (and designing any radio system), coverage prediction helps network operators optimize cellular networks to improve customer experience. Accordingly, several path-loss models have been proposed that depend on many conditions, such as suitable selection of the terrain for each model, the height of the receiver and transmitter above ground and the distance between them, and the presence of obstacles. This may increase the prediction error between actual and estimated values, which change according to the propagation model selected. To overcome these problems, we propose a novel approach to mobile coverage prediction based on an extremely randomized trees regressor (ERTR) algorithm. In addition, we construct a radio environment map (REM) over a Google Earth digital map to improve visualization of the results and to easily detect coverage holes and traffic hotspots. For this purpose, we utilize a dataset with real measurements collected from Victoria Island and Ikoyi in Lagos, Nigeria. For performance evaluation, we use k-fold cross-validation based on four error metrics: relative error, root mean squared error, mean absolute error, and ${R^{2}}$ score. The proposed ERTR scheme achieves the best performance in terms of accuracy and computational load in predicting the reference signal received power and the received signal strength indicator value. We prove this with extensive simulation analysis and by comparing the error metrics of the proposed ERTR approach with an existing method widely used to perform coverage prediction, called ordinary kriging. We also compared seven machine learning regression algorithms, namely, random forest, a bagging regressor, support vector regression, k-nearest neighbors, a deep neural network, Gaussian process regression, and the decision tree.

In this paper, we study a cognitive radio (CR)-enabled wireless powered communication network (WPCN) with an intelligent reflecting surface (IRS). A cognitive wireless powered communication network (CWPCN) consisting of one secondary wireless powered communication network, in which a hybrid access point broadcasts wireless energy to multiple users on downlink and receives information signals on uplink, shares the same spectrum with the existing primary wireless communication network. In this paper, we consider an underlay CWPCN in which the secondary network is regulated by a given interference temperature constraint (ITC) such that interference with the primary network is kept no greater than a predefined threshold. To enhance the performance of the CWPCN, we consider an IRS-assisted network in which multiple passive reflection elements are configured to reflect the signals in any desired phase/direction. Our main goal is to maximize the sum throughput of secondary users while managing the interference constraint. For this, we jointly optimize the uplink and downlink phase shift matrices of the IRS elements with optimal time slots for wireless energy transfer (WET) on downlink and wireless information transfer on uplink. In finding the optimal solution, the formulated optimization problem is non-convex, complex, and intractable. In this paper, we propose an alternating optimization (AO)-based solution with a successive convex approximation (SCA) technique. We show through simulation results that the proposed IRS-assisted network provides a significant enhancement in performance over the conventional CWPCN.

This comprehensive review paper explores the intricate integration of biosensors with multi-hop Internet of Things (IoT) systems, representing a paradigm shift in healthcare through real-time remote patient monitoring. The strategic deployment of biosensors in different locations in medical facilities, intricately connected to multiple microcontrollers, serves as a cornerstone in the establishment of robust multi-hop IoT networks. This paper highlights the role of this multi-hop IoT network, which efficiently facilitates the seamless transmission of vital health data to a centralized server. Crucially, the utilization of cloud connectivity emerges as a linchpin in this integration, providing a secure and scalable platform for remote patient monitoring. This cloud-based approach not only improves the accessibility of critical health information but also transcends physical limitations, allowing healthcare providers to monitor patients in real-time from any location. This paper highlights the transformative potential of this integration in overcoming traditional healthcare limitations through real-time remote patient monitoring.

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to human error. Recognizing this limitation, the rise in deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time–frequency domain along with pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time–frequency (CWT) images processed using autoencoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: a remarkable 100% accuracy in most of the binary classifications, exceeding 95% accuracy in three-class and four-class challenges, and a commendable rate, exceeding 93.5% for the five-class classification.

In this article, we consider a multi-user multiple-input single-output underlay cognitive radio system with simultaneous wireless information and power transfer (SWIPT) based on the rate-splitting multiple access (RSMA) framework. The system model is composed of a set of secondary users that only decode information, and another set of secondary users that simultaneously decode information and harvest energy based on a power-splitting (PS) ratio. Precoders are designed to minimize the transmission power of the secondary transmitter subject to a minimum rate requirement, an energy harvesting requirement, and maximum allowable interference with the primary network. The optimization problem is non-convex and challenging. Thus, we divide it into two subproblems where the outer problem is solved by a deep neural network (DNN)-based scheme with an autoencoder, and the inner problem is solved based on the semidefinite relaxation (SDR) technique. The inner problem takes the solution of the DNN-based scheme to provide the precoder vectors and PS ratios based on SDR, where a penalty function is proposed to guarantee feasible solutions to the problems. Our simulation results prove that the proposed framework based on RSMA outperforms the conventional methods and can achieve performance close to that of the optimal solutions, with a significant reduction in computational complexity.

Cooperative networks integrating non-orthogonal multiple access (NOMA) and simultaneous wireless information power transfer (SWIPT) are emerging technologies that have been investigated as potential techniques to support the proliferation of the Internet of Things (IoT) and to obtain greener communications. In this sense, the optimization of resource allocation schemes is crucial to improve the performance of future cooperative wireless networks. However, conventional optimization methods that attempt to find the optimal solution may entail high computational complexity. Therefore, we propose a low-complexity particle swarm optimization (PSO)-based scheme to solve the resource allocation problem in a cooperative non-linear SWIPT-enabled NOMA system with a non-linear energy harvesting (EH) user. Specifically, we consider two optimization problems. First, we minimize transmission power, and second, we maximize energy efficiency subject to meeting quality-of-service (QoS) constraints. The problems are non-convex and challenging to solve. Furthermore, we develop the optimal solution based on convex optimization and the exhaustive search (ES) method to validate the results of the proposed PSO-based framework. Afterward, we investigate the performance of five swarm intelligence-based baseline schemes and evaluate an additional low-complexity solution based on the cuckoo search (CS) technique. For comparison purposes, we use orthogonal multiple access (OMA), equal power splitting (EPS), and time fixed (TF) baseline schemes. To our satisfaction, the proposed SWIPT NOMA network outperforms the benchmark schemes, and the proposed PSO-based framework achieves the nearest performance to the optimal scheme with lower complexity than obtained by the comparative swarm intelligence techniques and from convex optimization with the ES method.

Recently, the demand for spectral and energy efficiency has significantly been increased along with new breakthroughs in programmable meta-material techniques. The integration of an intelligent reflecting surface (IRS) into simultaneous wireless information and power transfer (SWIPT) systems has attracted much attention from operators in advanced wireless communication networks (WCNs) such as fifth-generation (5G) and sixth-generation (6G) networks. In addition, an IRS-assisted SWIPT system faces many security risks that can easily be compromised by eavesdroppers. In this paper, we investigate the physical-layer secure and transmission optimization problem in an IRS-assisted SWIPT system where a power-splitting (PS) scheme is installed in the user equipment (UE). In particular, our purpose is to maximize the system secrecy rate by jointly finding optimal solutions for transmitter power, PS factor of UE, and phase shifts matrix of IRS under the required minimum harvested energy and maximum transmitter power. We propose the alternating optimization (AO)-based scheme to obtain optimal solutions. The proposed AO-based scheme can effectively solve both convex and non-convex problems; however, applying them in practice still poses some difficulties due to the complexity and long computation time. This is because many mathematical transformations are used and the optimal solution needs a number of iterations to achieve convergence. Therefore, we also propose 5 types of data and DNN structures to potentially achieve efficiency in computations by using a deep learning (DL)-based approach. The simulation results indicate that the proposed IRS scheme provides an improvement in terms of the average secrecy rate (ASR) by up to 38.91% when the number of reflecting elements is high (30 elements) compared to a scheme without an IRS. We also observe that the DL-based approach not only provides similar performance to the AO-based scheme but it also significantly reduces computation time.

Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health issues from human sleep data. Manual sleep staging requires human expertise, which is occasionally prone to error and variation. In recent times, availability of polysomnography data has aided progress in automatic sleep-stage classification. In this paper, a hybrid deep learning model is proposed for classifying sleep and wake states based on a single-channel electroencephalogram (EEG) signal. The model combines an artificial neural network (ANN) and a convolutional neural network (CNN) trained using mixed-input features. The ANN makes use of statistical features calculated from EEG epochs, and the CNN operates on Hilbert spectrum images generated during each epoch. The proposed method is assessed using single-channel Pz-Oz EEG signals from the Sleep-EDF database Expanded. The classification performance on four randomly selected individuals shows that the proposed model can achieve accuracy of around 96% in classifying between sleep and wake states from EEG recordings.

This paper considers uplink solar-powered cognitive radio networks (CRNs) where multiple secondary users (SUs) transmit data to a secondary base station (SBS) by sharing a licensed channel of a primary system. A deep Q-learning (DQL) algorithm, which combines non-orthogonal multiple access (NOMA) and time division multiple access (TDMA) techniques, is proposed to maximize the long-term throughput of the system. By using our scheme, the agent (i.e. the SBS) can obtain the optimal decision by interacting with the environment to learn about system dynamics. Simulation results validate the superiority of the performance under the proposed scheme, compared with traditional schemes.

Fault detection of rotating engine components in the aircraft engine is a challenging task that must constantly be monitored to provide aviation safety. In this paper, we propose a novel approach based on multi-layer perceptron (MLP) to detect in real time the degree of faults in a turbine engine disk due to a crack. To further improve detection accuracy while reducing computational complexity, the recursive feature elimination (RFE) is applied as a potent feature selection method. Satisfactorily, simulation results show that the proposed framework is robust to changes in operating conditions and outperforms comparative approaches.

A cognitive radio network (CRN) is a novel solution that promises to solve the spectrum scarcity problem and enhance spectrum utilization. However, unsecured CRN can easily be manipulated in order to attack legacy users on the communication channel. As a result, the network’s performance significantly degrades. Therefore, communication channel security is an important issue that needs to be addressed in a CRN. In this work, we focus on improving the security of multi-channel communication in a CRN, while various jammers try to access channels of interest to prevent SUs from using them. By using game-theoretic concepts and by defining states, actions, and players’ rewards, we propose game–based schemes that find the best channel for the secondary users (SUs) in order to avoid jammer’s attacks on communication channels. Accordingly, the problem is finding the optimal channel to maximize the long-term reward of the SU where communication channels are not used by the primary users (PUs) and are not jammed by attackers. In addition, the idea of transfer learning might be applied to the problem under consideration, and thus, a transfer Game-Actor-Critic (TGACT) scheme is proposed, which uses the transferred knowledge in a double-game period to accelerate the learning process and provide performance improvement in channel selection. Finally, the performance of the proposed schemes is simulated with different configurations. The simulation results show that the proposed schemes are quite resistant to jammer attacks, and achieve better performance compared to other channel selection schemes.

The objective of this study is to classify the rice grains of seventeen different varieties popularly planted in Vietnam. Image processing is used to extract color, morphological, and texture features of the rice grains. Five feature subsets are formed, namely, morphological, basic color, clustering color, statistical, and gray level co-occurrence matrix (GLCM). These subsets and combined sets are evaluated for classification ability with a support vector machine (SVM). A dataset of 248 features, including a total of color, morphological, and texture features classified with the SVM gives an overall accuracy of 88.29%. To decrease the number of used features and to improve the classification accuracy, the proposed method combining binary particle swarm optimization (BPSO) and the SVM, called BPSO+SVM, is applied to the dataset. In the results, classification accuracy from BPSO+SVM reaches 93.94% using only 96 selected features. The obtained result shows the proposed method achieves higher classification accuracy than the SVM alone, and the required number of features is only 39% of the total dataset. This result can be applied for developing an automatic classification and identification system of rice varieties.

In this paper, we propose deep learning–based energy beamforming in a multi-antennae wireless powered communication network (WPCN). We consider a WPCN where a hybrid access point (HAP) equipped with multiple antennae broadcasts an energy-bearing signal to wireless devices using energy beamforming. We investigate the joint optimization of the time allocation for wireless energy transfer (WET) and wireless information transfer (WIT) with the design for energy beams while minimizing the transmit power at the HAP for efficient use of its available resources. However, this is a non-convex problem, and it is numerically intractable to solve it. In the literature, the traditional approach to solving this problem is based on an iterative algorithm that incurs high computational and time complexity, which is not feasible for real-time applications. We study and analyze a deep neural network (DNN)-based scheme and propose a faster and more efficient approach for the fair approximation of a near-optimal solution to this problem. To train the proposed DNN, we acquire training data samples from a sequential parametric convex approximation (SPCA)-based iterative algorithm. Instead of acquiring data samples and training the DNN, which is highly complex, we use offline training for the DNN to provide a faster solution to the real-time resource allocation optimization problem. Through the simulation results, we show the proposed DNN scheme provides a fair approximation of the traditional SPCA method with low computational and time complexity.

The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast system, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the network performance by opportunistically allocating the primary channels to the devices for data transmissions. Firstly, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process with the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.

This paper examines the optimal spectrum competing strategy for a virtual network operator in cognitive cellular networks with energy-harvesting base stations. In the scenario for this study, multiple cognitive virtual network operators (CVNOs) obtain spectrum resources from a mobile network operator via spectrum sensing and leasing in order to provide data services to their subscribers. Compared to traditional spectrum leasing via long-term contract, spectrum acquired by sensing is usually cheaper but is unreliable due to the stochastic activities of the licensed users. The CVNOs need to determine the optimal sensing and leasing amount to satisfy the needs of subscribers while guaranteeing a low leasing cost. We aim to find an efficient spectrum sensing and leasing scheme for a CVNO in order to maximize its utility in the long run. The problem is first formulated as the framework of a sequential decision process considering the dynamics of users’ activities, spectrum prices, and harvested energy. We then develop a deep reinforcement learning algorithm that uses deep neural networks as function approximators so the CVNO can learn the optimal decision policy by interacting with the environment. We analyze the performance of our proposed scheme through extensive simulations. The experiment results show that the proposed mechanism can significantly improve the CVNO’s long-term benefit compared to other learning and non-learning methods.

In this study, a proposed descriptor based on the improved local ternary patterns (ILTP) that also uses the color properties of rice varieties is presented. Not only gray-scale intensity, but R, G, and B color components of the rice grains are considered. Combining a support vector machine (SVM) with the proposed descriptor for classification of 17 rice varieties planted in Vietnam gives an overall accuracy of 95.53%. To evaluate and compare the effectiveness of the proposed descriptor with other analysis techniques for rice varieties classification, texture descriptors based on local binary pattern and local ternary patterns are combined with SVM to classify these 17 rice varieties. Experiment results show that the classification accuracy from the SVM using the proposed descriptor is significantly higher than using ILTP or other texture descriptors from the literature. This technique can be used to build an automatic system of rice varieties identification and classification.

In this paper, we propose a radio environment map (REM) update methodology based on clustering and machine learning for indoor coverage. We use real measurements collected by the TurtleBot3 mobile robot using the received signal strength indicator (RSSI) as a measure of link quality between transmitter and receiver. We propose a practical framework for timely updates to the REM for dynamic wireless communication environments where we need to deal with variations in physical element distributions, environmental factors, movements of people and devices, and so on. In the proposed approach, we first rely on a historical dataset from the area of interest, which is used to determine the number of clusters via the K-means algorithm. Next, we divide the samples from the historical dataset into clusters, and we train one random forest (RF) model with the corresponding historical data from each cluster. Then, when new data measurements are collected, these new samples are assigned to one cluster for a timely update of the RF model. Simulation results validate the superior performance of the proposed scheme, compared with several well-known ML algorithms and a baseline scheme without clustering.

Smart grids have become susceptible to cyber-attacks, being one of the most diversified cyber-physical systems. Measurements collected by the supervisory control and data acquisition system can be compromised by a smart hacker, who can cheat a bad-data detector during state estimation by injecting biased values into the sensor-collected measurements. This may result in false control decisions, compromising the security of the smart grid, and leading to financial losses, power network disruptions, or a combination of both. To overcome these problems, we propose a novel approach to cyber-attacks detection, based on an extremely randomized trees algorithm and kernel principal component analysis for dimensionality reduction. A performance evaluation of the proposed scheme is done by using the standard IEEE 57-bus and 118-bus systems. Numerical results show that the proposed scheme outperforms state-of-art approaches while improving the accuracy in detection of stealth cyber-attacks in smart-grid measurements.

Rising energy demand and the disproportionate utilization of fossil fuels not only result in power imbalance and economic drain but also raise environmental concerns. Under these challenging circumstances, microgrids provide a tactical solution by adopting distributed energy resources at user end. However, this solution is not effective without enough participation by these end users (prosumers) for sustainable energy growth in microgrids. This paper presents a behavioral control theory and various psychological motivational models to improve prosumers' participation up to the desired level. A framework for peers' management within a community is also presented. The coalition-based game theory is employed for fair and trustworthy inter-trading which lead to the formation of grand coalition by satisfying all the defined motivational models. Various trading systems i.e. feed in tariff system, peer-to-peer trading with and without storage systems, and demand-side management-based peer-to-peer trading systems are used for energy inter-trading with minimum involvement with the grid. Finally, the proposed system is validated through simulations of various game theoretic-based peer-to-peer trading systems. Simulation results show a considerable reduction in average expenses for energy demand and carbon emissions with improved earnings for peers.

Air pollution has become a serious problem in all megacities. It is necessary to continuously monitor the state of the atmosphere, but pollution data received using fixed stations are not sufficient for an accurate assessment of the aerosol pollution level of the air. Mobility in measuring devices can significantly increase the spatiotemporal resolution of the received data. Unfortunately, the quality of readings from mobile, low-cost sensors is significantly inferior to stationary sensors. This makes it necessary to evaluate the various characteristics of monitoring systems depending on the properties of the mobile sensors used. This paper presents an approach in which the time of pollution detection is considered a random variable. To the best of our knowledge, we are the first to deduce the cumulative distribution function of the pollution detection time depending on the features of the monitoring system. The obtained distribution function makes it possible to optimize some characteristics of air pollution detection systems in a smart city.

Integration of the Internet into the entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called the Internet of Things (IoT). However, the ubiquitous and wide-range IoT networks make them prone to cyberattacks. One of the main types of attack is a denial of service (DoS), where the attacker floods the network with a large volume of data to prevent nodes from using the services. An intrusion detection mechanism is considered a chief source of protection for information and communications technology. However, conventional intrusion detection methods need to be modified and improved for application to the IoT owing to certain limitations, such as resource-constrained devices, the limited memory and battery capacity of nodes, and specific protocol stacks. In this paper, we develop a lightweight attack detection strategy utilizing a supervised machine learning-based support vector machine (SVM) to detect an adversary attempting to inject unnecessary data into the IoT network. The simulation results show that the proposed SVM-based classifier, aided by a combination of two or three incomplex features, can perform satisfactorily in terms of classification accuracy and detection time.

Anomaly identification for internal combustion engine (ICE) sensors has become an important research area in recent years. In this work, a proposed indirect fuzzy Lyapunov-based computed ratio observer integrated with a support vector machine (SVM) was designed for sensor fault classification. The proposed fuzzy Lyapunov-based computed ratio observer integrated with SVM has three main layers. In the preprocessing (first) layer, the resampled root mean square (RMS) signals are extracted from the original signals to the designed indirect observer. The second (observation) layer is the principal part with the proposed indirect fuzzy sensor-fault-classification technique. This layer has two sub-layers: signal modeling and estimation. The Gaussian autoregressive-Laguerre approach integrated with the fuzzy approach is designed for resampled RMS fuel-to-air-ratio normal signal modeling, while the subsequent sub-layer is used for resampled RMS fuel-to-air-ratio signal estimation using the proposed fuzzy Lyapunov-based computed ratio observer. The third layer, for residual signal generation and classification, is used to identify ICE sensor anomalies, where residual signals are generated by the difference between the original and estimated resampled RMS fuel-to-air-ratio signals. Moreover, SVM is suggested for residual signal classification. To test the effectiveness of the proposed method, the results are compared with two approaches: a Lyapunov-based computed ratio observer and a computed ratio observer. The results show that the accuracy of sensor anomaly classification by the proposed fuzzy Lyapunov-based computed ratio observer is 98.17%. Furthermore, the proposed scheme improves the accuracy of sensor fault classification by 8.37%, 2.17%, 6.17%, 4.57%, and 5.37% compared to other existing methods such as the computed ratio observer, the Lyapunov-based computed ratio observer, fuzzy feedback linearization observation, self-tuning fuzzy robust multi-integral observer, and Kalman filter technique, respectively.

The integration of computing and modern wireless communications techniques is enabling prolific intelligent monitoring and efficient control of electric power systems in the frameworks of smart grids. In parallel, an enhanced reliance on such technologies has increased the susceptibility of today's smart grids to cyber-assaults. Recently, a new type of assault, termed covert cyber deception assault, has been introduced to infringe upon the integrity of smart grid data. Such assaults are designed and initiated by hackers who have considerably good knowledge of the power network topology and the security measures in place, and therefore, these assaults cannot be effectively detected by the bad-data detectors in traditional state estimators. In this paper, we propose a supervised machine learning-based scheme to detect a covert cyber deception assault in the state estimation-measurement feature data that are collected through a smart-grid communications network. The distinctive characteristic of the paper is that we use a genetic algorithm-based feature selection in our scheme to improve detection accuracy and reduce computational complexity. The proposed detection scheme is evaluated using standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus test systems. Through performance analysis, it is shown that the proposed scheme provides a significant improvement in covert cyber deception assault detection accuracy, compared with existing machine learning-based schemes.

In this paper, a physical-layer-security scheme for an underlay relay-based cognitive radio network (CRN) that uses orthogonal frequency-division multiplexing (OFDM) as the medium access technique is proposed. Resource allocation in relay-aided CRNs becomes a hard problem especially if it is under security threat. Different from conventional relay-based OFDM schemes, in the paper, we consider the relay network which has two dedicated relay nodes; one relay which is capable of subcarrier mapping forward the received signal to the destination and the other sends a jamming signal to add noise to the signal received by the eavesdropper. Optimization is performed under a unified framework where power allocation at the source node, power allocation, and subcarrier mapping in the relay network are optimized to maximize the secrecy rate of the CRN while satisfying the maximum transmission power constraints and the interference threshold of the PU. The power allocation problem at the forwarding relaying node is a nonconvex optimization problem. Therefore, at first, the optimization problem is simplified and a closed-form solution is obtained which satisfies the maximum PU interference constraint. Afterward, the optimization problem is solved for satisfying the maximum transmission power constraint. An algorithm is also proposed for subcarrier mapping at the forwarding relaying node. The proposed power allocation method and a subcarrier mapping scheme have low complexity, compared with the baseline schemes. Finally, simulation results are provided for different parameters to show the performance improvement of the proposed scheme in terms of secrecy rate.

Multi-access edge computing has become a strategic concept of the Internet of Things. The edge computing market has reached USD several billion and is growing intensively. In the edge-computing paradigm, most of the data is processed close to, or at the edge of, the network. This greatly reduces the computation and communication load of the network core. Moreover, edge computing provides better support for user privacy. On the other hand, an increase in data processing locations will proportionately increase the attack surface. An edge node can be put out of service easily by being flooded with spoofed packets owing to limited capacities and resources. Furthermore, wireless edge nodes are quite vulnerable to energy exhaustion attacks. In this situation, traditional network security mechanisms cannot be used effectively. Therefore, a tradeoff between security and efficiency is needed. This study considered the requirements under which the use of an intrusion detection system (IDS) is justified. To the best of our knowledge, this is a first attempt to combine IDS quality, system performance degradation due to IDS operations, and workload specificity into a unified quantitative criterion. This paper is an extended version of a report published in the proceedings of the ICCSA 2020 and differs from it in many ways. In particular, this paper considers novel mathematical problems regarding the deployment strategies for an IDS and the corresponding inverse problems and provides closed-form solutions for a few previously unsolved problems.

In this paper, the combination of an indirect self-tuning observer, smart signal modeling, and machine learning-based classification is proposed for rolling element bearing (REB) anomaly identification. The proposed scheme has three main stages. In the first stage, the original signal is resampled, and the root mean square (RMS) signal is extracted from it. In the second stage, the normal resampled RMS signal is approximated using the AutoRegressive with eXternal Uncertainty (ARXU) technique. Moreover, the nonlinearity of the bearing signal is solved using the combination of the ARXU and the machine learning-based regression, which is called AMRXU. After signal modeling by AMRXU, the RMS resampled signal is estimated using a combination of the proportional multi-integral (PMI) technique, the variable structure (VS) Lyapunov technique, and a self-tuning network-fuzzy system (SNFS). Finally, in the third stage, the difference between the original signal and the estimated one is calculated to generate the residual signal. A machine learning-based classification technique is utilized to classify the residual signal. The Case Western Reserve University (CWRU) dataset is used to evaluate anomaly identification performance of the proposed scheme. Regarding the experimental results, the average accuracy for REB crack identification is 98.65%, 97.7%, 97.35%, and 97.67%, respectively, when the motor torque loads are 0-hp, 1-hp, 2-hp, and 3-hp.

This paper deals with the problem of fault detection and diagnosis in sensors considering erratic, drift, hard-over, spike, and stuck faults. The data set containing samples of the above mentioned fault signals was acquired as follows: normal data signals were obtained from a temperature-to-voltage converter by using an Arduino Uno microcontroller board and MATLAB. Then, faults were simulated in normal data to get 100 samples of each fault, in which one sample is composed of 1000 data elements. A support vector machine (SVM) was used for data classification in a one-versus-rest manner. The statistical time-domain features, extracted from a sample, were used as a single observation for training and testing SVM. The number of features varied from 5 to 10 to examine the effect on accuracy of SVM. Three different kernel functions used to train SVM include linear, polynomial, and radial-basis function kernels. The fault occurrence event in fault samples was chosen randomly in some cases to replicate a practical scenario in industrial systems. The results show that an increase in the number of features from 5 to 10 hardly increases the total accuracy of the classifier. However, using ten features gives the highest accuracy for fault classification in an SVM. An increase in the number of training samples from 40 to 60 caused an overfitting problem. The k-fold cross-validation technique was adopted to overcome this issue. The increase in number of data elements per sample to 2500 increases the efficiency of the classifier. However, an increase in the number of training samples to 400 reduces the capability of SVM to classify stuck fault. The receiver operating characteristics curve comparison shows the efficiency of SVM over a neural network.

This paper studies a simultaneous wireless information and power transfer multi-input single-output cognitive radio network in which a multi-antenna secondary transmitter sends data streams to multiple single-antenna secondary receivers (SRs) equipped with a power-splitting (PS) structure for information decoding and energy harvesting in the presence of multiple single-antenna primary users (PUs). First, the max-min fair SRs' harvested energy problem is formulated and solved by combining the tight semidefinite relaxation (SDR)-based solution of the transmit power minimization problem with the bisection search method. Second, the balancing problem examines the tradeoff between the worst-user harvested energy at the SR and the interference power at the PU. The proposed solution for this challenging non-convex problem includes two steps. First, the problem with fixed PS ratios is solved using the SDR technique and the tight solution is proved; then, the approximately optimal PS ratios are found using the particle swarm optimization method. Additionally, the closed-form solutions of transmit power minimization and harvested energy maximization problems are derived for the special case where only one SR and one PU are present. Finally, the numerical results demonstrate the effectiveness of the proposed approaches in comparison with two baseline schemes.

Underwater acoustic sensor networks (UW-ASNs) have recently been proposed for exploring the underwater resources and gathering the scientific data from the aquatic environments. UW-ASNs are faced with different challenges, such as high propagation delay, low bandwidth, and high energy consumption. However, the most notable challenge is perhaps how to efficiently forward the packets to the surface sink by considering the energy constrained sensor devices. The opportunistic routing concept may provide an effective solution for the UW-ASNs by the cooperation of the relay nodes to forward the packets to the surface sink. In this paper, the energy consumption problem is addressed and an energy-efficient cooperative opportunistic routing (EECOR) protocol is proposed to forward the packets toward the surface sink. In the EECOR protocol, a forwarding relay set is firstly determined by the source node based on the local information of the forwarder and then, a fuzzy logic-based relay selection scheme is applied to select the best relay based on considering the energy consumption ratio and the packet delivery probability of the forwarder. In the UW-ASNs, most of the energy is wasted due to the collisions amongst sensor nodes during the packet transmission. To alleviate the packet collisions problem, we have designed a holding timer for each of the forwarder to schedule the packets transmission toward the surface sink. We have performed our extensive simulations of the EECOR protocol on the Aqua-sim platform and compared with existing routing protocols in terms of average packet delivery ratio, average end-to-end delay, average energy consumption, and average network lifetime.

Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

In this paper, we aim to provide reliable user connectivity and enhanced security for computation task offloading. Physical layer security is studied in a wireless-powered non-orthogonal multiple access (NOMA) mobile edge computing (MEC) system with a nonlinear energy-harvesting (EH) user and a power beacon (PB) in the presence of an eavesdropper. To further provide a friendly environment resource allocation design, wireless power transfer (WPT) is applied. The secure computation efficiency (SCE) problem is solved by jointly optimizing the transmission power, the time allocations for energy transfer, the computation time, and the central processing unit (CPU) frequency in the NOMA-enabled MEC system. The problem is non-convex and challenging to solve because of the complexity of the objective function in meeting constraints that ensure the required quality of service, such as the minimum value of computed bits, limitations on total energy consumed by users, maximum CPU frequency, and minimum harvested energy and computation offloading times. Therefore, in this paper, a low-complexity particle swarm optimization (PSO)-based algorithm is proposed to solve this optimization problem. For comparison purposes, time division multiple access and fully offloading baseline schemes are investigated. Finally, simulation results demonstrate the superiority of the proposed approach over baseline schemes.

In this paper, we propose a learning-based solution for resource allocation in a wireless powered communication network (WPCN). We provide a study and analysis of a deep neural network (DNN) which can reasonably effectively approximate the iterative optimization algorithm for resource allocation in the WPCN. In this scheme, the deep neural network provides an optimized solution for transmitting power with different channel coefficients. The proposed deep neural network accepts the channel coefficient as an input and outputs minimized power for this channel in the WPCN. The DNN learns the relationship between input and output and gives a fairly accurate approximation for the transmit power optimization iterative algorithm. We exploit the sequential parametric convex approximation (SPCA) iterative algorithm to solve the optimization problem for transmit power in the WPCN. The proposed approach ensures the quality of service (QoS) of the WPCN by managing user throughput and by keeping harvested energy levels above a defined threshold. Through numerical results and simulations, it is verified that the proposed scheme can best approximate the SPCA iterative algorithms with low computational time consumption.

Non-orthogonal multiple access (NOMA) is considered a potential technique in fifth-generation (5G). Nevertheless, it is relatively complex when applying NOMA to a massive access scenario. Thus, in this paper, a hybrid NOMA/OMA scheme is considered for uplink wireless transmission systems where multiple cognitive users (CUs) can simultaneously transmit their data to a cognitive base station (CBS). We adopt a user-pairing algorithm in which the CUs are grouped into multiple pairs, and each group is assigned to an orthogonal sub-channel such that each user in a pair applies NOMA to transmit data to the CBS without causing interference with other groups. Subsequently, the signal transmitted by the CUs of each NOMA group can be independently retrieved by using successive interference cancellation (SIC). The CUs are assumed to harvest solar energy to maintain operations. Moreover, joint power and bandwidth allocation is taken into account at the CBS to optimize energy and spectrum efficiency in order to obtain the maximum long-term data rate for the system. To this end, we propose a deep actor-critic reinforcement learning (DACRL) algorithm to respectively model the policy function and value function for the actor and critic of the agent (i.e., the CBS), in which the actor can learn about system dynamics by interacting with the environment. Meanwhile, the critic can evaluate the action taken such that the CBS can optimally assign power and bandwidth to the CUs when the training phase finishes. Numerical results validate the superior performance of the proposed scheme, compared with other conventional schemes.

Security is considered a critical issue in the deployment of 5G networks because of the vulnerability of information that can be intercepted by eavesdroppers in wireless transmission environments. Thus, physical layer security has emerged as an alternative for the secure enabling of 5G technologies and for tackling this security issue. In this paper, we study the secrecy energy efficiency (SEE) in a downlink underlying cognitive radio (CR)—non-orthogonal multiple access (NOMA) system with a cooperative relay. The system has an energy-harvesting (EH) user and an eavesdropper, where the transmitter provides direct communication with a close secondary user and a distant secondary user via the relay. Our objective is to maximize the SEE of the CR-NOMA system under the constraints of a minimum information rate for the secondary users, a minimum amount of energy harvested by the EH user, and maximum power availability at the transmitter and the relay that still prevents them from causing unacceptable interference with the primary user. The proposed solution to maximize the SEE is based on the low-computational—complexity particle swarm optimization (PSO) algorithm. For validation purposes, we compare the optimization outcomes obtained by the PSO algorithm with the optimal exhaustive search method. Furthermore, we compare the performance of our proposed CR-NOMA scheme with the conventional orthogonal multiple access (OMA) scheme.

Recently, simultaneous wireless information and power transfer (SWIPT) systems, which can supply efficiently throughput and energy, have emerged as a potential research area in fifth-generation (5G) system. In this paper, we study SWIPT with multi-user, single-input single-output (SISO) system. First, we solve the transmit power optimization problem, which provides the optimal strategy for getting minimum power while satisfying sufficient signal-to-noise ratio (SINR) and harvested energy requirements to ensure receiver circuits work in SWIPT systems where receivers are equipped with a power-splitting structure. Although optimization algorithms are able to achieve relatively high performance, they often entail a significant number of iterations, which raises many issues in computation costs and time for real-time applications. Therefore, we aim at providing a deep learning-based approach, which is a promising solution to address this challenging issue. Deep learning architectures used in this paper include a type of Deep Neural Network (DNN): the Feed-Forward Neural Network (FFNN) and three types of Recurrent Neural Network (RNN): the Layer Recurrent Network (LRN), the Nonlinear AutoRegressive network with eXogenous inputs (NARX), and Long Short-Term Memory (LSTM). Through simulations, we show that the deep learning approaches can approximate a complex optimization algorithm that optimizes transmit power in SWIPT systems with much less computation time.

Mobile-data traffic exponentially increases day by day due to the rapid development of smart devices and mobile internet services. Thus, the cellular network suffers from various problems, like traffic congestion and load imbalance, which might decrease end-user quality of service. This work compensates for the problem of offloading in the cellular network by forming device-to-device (D2D) links. A game scenario is formulated where D2D-link pairs compete for network resources. In a D2D-link pair, the data of a user equipment (UE) is offloaded to another UE with an offload coefficient, i.e., the proportion of requested data that can be delivered via D2D links. Each link acts as a player in a cooperative game, with the optimal solution for the game found using the Nash bargaining solution (NBS). The proposed solution aims to present a strategy to control different parameters of the UE, including harvested energy which is stored in a rechargeable battery with a finite capacity and the offload coefficients of the D2D-link pairs, to optimize the performance of the network in terms of throughput and energy efficiency (EE) while considering fairness among links in the network. Simulation results show that the proposed game scheme can effectively offload mobile data, achieve better EE and improve the throughput while maintaining high fairness, compared to an offloading scheme based on a maximized fairness index (MFI) and to a no-offload scheme.

We consider a Sensor-Aided Cognitive Radio Network (SACRN) in which sensors capable of harvesting energy are distributed throughout the network to support secondary transmitters for sensing licensed channels in order to improve both energy and spectral efficiency. Harvesting ambient energy is one of the most promising solutions to mitigate energy deficiency, prolong device lifetime, and partly reduce the battery size of devices. So far, many works related to SACRN have considered single secondary users capable of harvesting energy in whole slot as well as short-term throughput. In the paper, we consider two types of energy harvesting sensor nodes (EHSN): Type-I sensor nodes will harvest ambient energy in whole slot duration, whereas type-II sensor nodes will only harvest energy after carrying out spectrum sensing. In the paper, we also investigate long-term throughput in the scheduling window, and formulate the throughput maximization problem by considering energy-neutral operation conditions of type-I and -II sensors and the target detection probability. Through simulations, it is shown that the sensing energy consumption of all sensor nodes can be efficiently managed with the proposed scheme to achieve optimal long-term throughput in the window.

The integration of different networks has attracted significant attention in academia. Both terrestrial and maritime communications systems have been attracting keen interest for ways to deal with various applications. As the environment of cognitive vehicular and maritime networks is extremely dynamic, these networks suffer with a long delay because of intermittent links while providing services for different applications. To this end, here we introduce the integration of cognitive vehicular and maritime networks to design a coastal smart city by utilizing software-defined networking, network function virtualization, and fog computing under the same infrastructure. This novel integrated cognitive coastal city fulfills the demand of each application user in a hybrid environment with a quicker response time. The idea is to combine vehicular and maritime communications to meet different user demands. Different virtual networks are launched by network function virtualization, and are managed and controlled by a software-defined networking controller. From the integration of software-defined networking, network function virtualization, and fog computing, both vehicular and marine users are provided with stable paths to meet each application’s demands.

As one of the most diversified cyber-physical systems, the smart grid has become more decumbent to cyber vulnerabilities. An intelligently crafted, covert, data-integrity assault can insert biased values into the measurements collected by a sensor network, to elude the bad data detector in the state estimator, resulting in fallacious control decisions. Thus, such an attack can compromise the secure and reliable operations of smart grids, leading to power network disruptions, economic loss, or a combination of both. To this end, in this paper, we propose a novel idea for the reconstruction of sensor-collected measurement data from power networks, by removing the impacts of the covert data-integrity attack. The proposed reconstruction scheme is based on a latterly developed, unsupervised learning algorithm called a denoising autoencoder, which learns about the robust nonlinear representations from the data to root out the bias added into the sensor measurements by a smart attacker. For a robust, multivariate reconstruction of the attacked measurements from multiple sensors, the denoising autoencoder is used. The proposed scheme was evaluated utilizing standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems. Simulation results confirm that the proposed scheme can handle labeled and non-labeled historical measurement data and results in a reasonably good reconstruction of the measurements affected by attacks.

The cognitive radio network (CRN) is vulnerable to various newly-arising attacks targeting the weaknesses of cognitive radio (CR) communication and networking. In this paper, we focus on improving the secrecy performance of CR communications in a decentralized, multiple-channel manner while various eavesdroppers (EVs) try to listen to their private information. By choosing the best channel, the secondary user (SU) aims at mitigating the effects of eavesdropping and other SUs that compete for the same channel. Accordingly, the problem of finding the best channel that maximizes the secrecy rate for the SU is formulated as the framework of multiple repeated games where both the SU and the EVs try to maximize their own performance. In this case, the secrecy rate of an SU is defined based on the expected rewards of the SUs and the EVs. In the paper, we propose a repeated games-based scheme that can provide the best channel for the SU to avoid eavesdropping attacks and also minimize interference from other SUs that compete for the same channel. The simulation results demonstrate that the proposed scheme can combat a physical layer attack from EVs quite well and can provide much better performance, in comparison with other conventional channel selection schemes.