This article presents advances in resource allocation for downlink non-orthogonal multiple access (NOMA) systems, focusing on user pairing and power allocation algorithms. The former pairs the users ...to obtain high capacity gain by exploiting the channel gain difference between the users, while the latter allocates power to users in each cluster to balance system throughput and user fairness. Additionally, the article introduces the concept of cluster fairness and proposes the divide-and-next-largest-difference-based user pairing algorithm to distribute the capacity gain among the NOMA clusters in a controlled manner. Furthermore, performance comparison between multiple-input multiple-output NOMA (MIMO-NOMA) and MIMO orthogonal multiple access (MIMO-OMA) is conducted when users have pre-defined quality of service. Simulation results are presented, which validate the advantages of NOMA over OMA. Finally, the article provides avenues for further research on resource allocation for downlink NOMA.
•The accurate prediction of heart disease is essential to treat the patient efficiently.•Feature fusion can provide rich healthcare data for heart disease diagnosis.•Existing systems cannot handle ...high-dimensional datasets related to heart disease.•Traditional methods are failed to extract valuable features for heart treatment.•A smart system is proposed to enhance the accuracy of heart disease diagnosis.
The accurate prediction of heart disease is essential to efficiently treating cardiac patients before a heart attack occurs. This goal can be achieved using an optimal machine learning model with rich healthcare data on heart diseases. Various systems based on machine learning have been presented recently to predict and diagnose heart disease. However, these systems cannot handle high-dimensional datasets due to the lack of a smart framework that can use different sources of data for heart disease prediction. In addition, the existing systems utilize conventional techniques to select features from a dataset and compute a general weight for them based on their significance. These methods have also failed to enhance the performance of heart disease diagnosis. In this paper, a smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches. First, the feature fusion method combines the extracted features from both sensor data and electronic medical records to generate valuable healthcare data. Second, the information gain technique eliminates irrelevant and redundant features, and selects the important ones, which decreases the computational burden and enhances the system performance. In addition, the conditional probability approach computes a specific feature weight for each class, which further improves system performance. Finally, the ensemble deep learning model is trained for heart disease prediction. The proposed system is evaluated with heart disease data and compared with traditional classifiers based on feature fusion, feature selection, and weighting techniques. The proposed system obtains accuracy of 98.5%, which is higher than existing systems. This result shows that our system is more effective for the prediction of heart disease, in comparison to other state-of-the-art methods.
Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for performance enhancement in next-generation cellular communications. Compared to orthogonal frequency division ...multiple access, which is a well-known high-capacity orthogonal multiple access technique, NOMA offers a set of desirable benefits, including greater spectrum efficiency. There are different types of NOMA techniques, including power-domain and code-domain. This paper primarily focuses on power-domain NOMA that utilizes superposition coding at the transmitter and successive interference cancellation at the receiver. Various researchers have demonstrated that NOMA can be used effectively to meet both network-level and user-experienced data rate requirements of fifth-generation (5G) technologies. From that perspective, this paper comprehensively surveys the recent progress of NOMA in 5G systems, reviewing the state-of-the-art capacity analysis, power allocation strategies, user fairness, and user-pairing schemes in NOMA. In addition, this paper discusses how NOMA performs when it is integrated with various proven wireless communications techniques, such as cooperative communications, multiple-input multiple-output, beamforming, space-time coding, and network coding among others. Furthermore, this paper discusses several important issues on NOMA implementation and provides some avenues for future research.
•The accurate detection of traffic accidents is essential to reduce serious injuries and fatalities.•Sensors and social networking platforms can provide rich traffic data to detect traffic ...accident.•Sensor-based systems provide limited information and may fail to detect traffic accident quickly.•Traditional algorithms might not extract valuable data for traffic event detection.•A smart system is proposed for real-time traffic accident detection and condition analysis.
Accurate detection of traffic accidents as well as condition analysis are essential to effectively restoring traffic flow and reducing serious injuries and fatalities. This goal can be obtained using an advanced data classification model with a rich source of traffic information. Several systems based on sensors and social networking platforms have been presented recently to detect traffic events and monitor traffic conditions. However, sensor-based systems provide limited information, and may fail owing to the long detection times and high false-alarm rates. In addition, social networking data are unstructured, unpredictable, and contain idioms, jargon, and dynamic topics. The machine learning algorithms utilized for traffic event detection might not extract valuable information from social networking data. In this paper, a social network–based, real-time monitoring framework is proposed for traffic accident detection and condition analysis using ontology and latent Dirichlet allocation (OLDA) and bidirectional long short-term memory (Bi-LSTM). First, the query-based search engine effectively collects traffic information from social networks, and the data preprocessing module transforms it into structured form. Second, the proposed OLDA-based topic modeling method automatically labels each sentence (e.g., traffic or non-traffic) to identify the exact traffic information. In addition, the ontology-based event recognition approach detects traffic events from traffic-related data. Next, the sentiment analysis technique identifies the polarity of traffic events employing user’s opinions, which helps determine accurate conditions of traffic events. Finally, the FastText model and Bi-LSTM with softmax regression are trained for traffic event detection and condition analysis. The proposed framework is evaluated using traffic-related data, comparing OLDA and Bi-LSTM with existing topic modeling methods and traditional classifiers using word embedding models, respectively. Our system outperforms state-of-the-art methods and achieves accuracy of 97 %. This finding demonstrates that the proposed system is more efficient for traffic event detection and condition analysis, in comparison to other existing systems.
•The available classical ontology-based systems are inadequate and limit the information extraction from the internet.•An ontology with fuzzy logic is effective technology for precise information ...extraction from blurred data environment.•We proposed fuzzy domain ontology with SVM to extract feature’s opinion from reviews and to compute polarity.•The result of opinion mining by using SVM with FDO for online large data set is better than SVM-based existing systems.•The proposed system thoroughly explains the feature extraction and polarity computation.
With the explosion of Social media, Opinion mining has been used rapidly in recent years. However, a few studies focused on the precision rate of feature review’s and opinion word’s extraction. These studies do not come with any optimum mechanism of supplying required precision rate for effective opinion mining. Most of these studies are based on Naïve Bayes, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and classical ontology. These systems are still imperfect for classifying the feature reviews into more degrees of polarity terms (strong negative, negative, neutral, positive and strong positive). Further, the existing classical ontology-based systems cannot extract blurred information from reviews; thus, it provides poor results. In this regard, this paper proposes a robust classification technique for feature review’s identification and semantic knowledge for opinion mining based on SVM and Fuzzy Domain Ontology (FDO). The proposed system retrieves a collection of reviews about hotel and hotel features. The SVM identifies hotel feature reviews and filter out irrelevant reviews (noises) and the FDO is then used to compute the polarity term of each feature. The amalgamation of FDO and SVM significantly increases the precision rate of review’s and opinion word’s extraction and accuracy of opinion mining. The FDO and intelligent prototype are developed using Protégé OWL-2 (Ontology Web Language) tool and JAVA, respectively. The experimental result shows considerable performance improvement in feature review’s classification and opinion mining.
Wearable sensors and social networking platforms play a key role in providing a new method to collect patient data for efficient healthcare monitoring. However, continuous patient monitoring using ...wearable sensors generates a large amount of healthcare data. In addition, the user-generated healthcare data on social networking sites come in large volumes and are unstructured. The existing healthcare monitoring systems are not efficient at extracting valuable information from sensors and social networking data, and they have difficulty analyzing it effectively. On top of that, the traditional machine learning approaches are not enough to process healthcare big data for abnormality prediction. Therefore, a novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy. The proposed big data analytics engine is based on data mining techniques, ontologies, and bidirectional long short-term memory (Bi-LSTM). Data mining techniques efficiently preprocess the healthcare data and reduce the dimensionality of the data. The proposed ontologies provide semantic knowledge about entities and aspects, and their relations in the domains of diabetes and blood pressure (BP). Bi-LSTM correctly classifies the healthcare data to predict drug side effects and abnormal conditions in patients. Also, the proposed system classifies the patients’ health condition using their healthcare data related to diabetes, BP, mental health, and drug reviews. This framework is developed employing the Protégé Web Ontology Language tool with Java. The results show that the proposed model precisely handles heterogeneous data and improves the accuracy of health condition classification and drug side effect predictions.
•Smartphones, wearable sensors, and social networks provide a new approach to collect patient data.•Continuous patient monitoring generates a large amount of unstructured healthcare data.•Existing approaches cannot deal with huge amounts of healthcare data extracted from various sources.•Traditional ML techniques are unable to handle extracted healthcare data for abnormality prediction.•A big data analytics engine is proposed to precisely analyze different sources of healthcare data.
Social networks play a key role in providing a new approach to collecting information regarding mobility and transportation services. To study this information, sentiment analysis can make decent ...observations to support intelligent transportation systems (ITSs) in examining traffic control and management systems. However, sentiment analysis faces technical challenges: extracting meaningful information from social network platforms, and the transformation of extracted data into valuable information. In addition, accurate topic modeling and document representation are other challenging tasks in sentiment analysis. We propose an ontology and latent Dirichlet allocation (OLDA)-based topic modeling and word embedding approach for sentiment classification. The proposed system retrieves transportation content from social networks, removes irrelevant content to extract meaningful information, and generates topics and features from extracted data using OLDA. It also represents documents using word embedding techniques, and then employs lexicon-based approaches to enhance the accuracy of the word embedding model. The proposed ontology and the intelligent model are developed using Web Ontology Language and Java, respectively. Machine learning classifiers are used to evaluate the proposed word embedding system. The method achieves accuracy of 93%, which shows that the proposed approach is effective for sentiment classification.
•Social networks provide a new approach to collect data regarding transportation.•Sentiment analysis can make observations of social data to examine transportation.•Current text mining techniques are unable to generate the topics accurately.•Document representation is another challenging tasks in sentiment analysis.•We proposed a new topic modeling and word embedding system for sentiment analysis.
Wireless body area network (WBAN) has been recognized as one of the promising wireless sensor technologies for improving healthcare service, thanks to its capability of seamlessly and continuously ...exchanging medical information in real time. However, the lack of a clear in-depth defense line in such a new networking paradigm would make its potential users worry about the leakage of their private information, especially to those unauthenticated or even malicious adversaries. In this paper, we present a pair of efficient and light-weight authentication protocols to enable remote WBAN users to anonymously enjoy healthcare service. In particular, our authentication protocols are rooted with a novel certificateless signature (CLS) scheme, which is computational, efficient, and provably secure against existential forgery on adaptively chosen message attack in the random oracle model. Also, our designs ensure that application or service providers have no privilege to disclose the real identities of users. Even the network manager, which serves as private key generator in the authentication protocols, is prevented from impersonating legitimate users. The performance of our designs is evaluated through both theoretic analysis and experimental simulations, and the comparative studies demonstrate that they outperform the existing schemes in terms of better trade-off between desirable security properties and computational overhead, nicely meeting the needs of WBANs.
Abstract
This paper discusses the equivalent circuit model and functional verification of an integrated antenna system as its main focus. The integrated antenna system consists of two independent ...antenna systems, namely the Cognitive Radio antenna and the Ultra Wide Band Multiple Iinput Multiple Output antenna. This article is split into two parts: The first part discusses the equivalent circuit of an integrated antenna system by optimizing the RLC values. The developed lumped equivalent circuit model produces the NB resonant frequencies, which is the same as the S-parameter obtained through EM simulation. The second part of this paper aims to discuss the experimental verification of an integrated CR antenna system with Bayesian learning-based spectrum sensing algorithms using Universal Software Radio Peripheral devices. Real-time sensing and communication functionalities are visualized in the LABVIEW monitor. The integrated antenna system is fabricated and measured after the simulation.
This paper investigates simultaneous wireless information and power transfer (SWIPT) for a decode-and-forward (DF) full-duplex relay (FDR) network. A battery group consisting of two batteries is ...applied to utilize the relay-harvested energy for FDR transmission. The virtual harvest-use model and the harvest-use-store model are considered, respectively. By switching between two batteries for charging and discharging with the aid of power splitting (PS), concurrent source and relay transmissions can overcome spectral efficiency loss compared with half-duplex relay (HDR)-assisted PS-SWIPT. The outage probability for the virtual harvest-use model is presented in an exact integral form and the optimal PS (OPS) ratio that maximizes the end-to-end signal-to-interference-plus-noise ratio (e-SINR) is characterized in closed form via the cubic formula. The fundamental tradeoff between the e-SINR and recycled self-power is quantified. The OPS ratios and the corresponding outage probabilities in noise-limited and interference-limited environments are also derived. In the harvest-use-store model, a greedy switching (GS) policy is implemented with energy accumulation across transmission blocks. The OPS ratio of the GS policy is presented and the corresponding outage probability is derived by modeling the relay's energy levels as a Markov chain with a two-stage state transition. Numerical results verify the performance improvement of the proposed scheme over HDR-assisted PS-SWIPT in terms of outage probability and average throughput.
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