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Smart cities and urbanization use enormous IoT devices to transfer data for analysis and information processing. These IoT can relate to billions of devices and transfer essential data from their surroundings. There is a massive need for energy because of the tremendous data exchange between billions of gadgets. Green IoT aims to make the environment a better place while lowering the power usage of IoT devices. In this work, a hybrid deep learning method called "Green energy-efficient routing (GEER) with long short-term memory deep Q-Network is used to minimize the energy consumption of devices. Initially, a GEER with Ant Colony Optimization (ACO) and AutoEncoder (AE) provides efficient routing between devices in the network. Next, the long short-term memory deep Q-Network based Reinforcement Learning (RL) method reduces the energy consumption of IoT devices. This hybrid approach leverages the strengths of each technique to address different aspects of energy-efficient routing. ACO and AE contribute to efficient routing decisions, while LSTM DQN optimizes energy consumption, resulting in a well-rounded solution. Finally, the proposed GELSDQN-ACO method is compared with previous methods such as RNN-LSTM, DPC-DBN, and LSTM-DQN. Moreover, we critically analyze the green IoT and perform implementation and evaluation. [ABSTRACT FROM AUTHOR]

With the widespread use of Internet of Things (IoT) in various applications and several security vulnerabilities reported in them, the security requirements have become an integral part of an IoT system. Authentication and access control are the two principal security requirements for ensuring authorized and restricted accesses to limited and essential resources in IoT. The built-in authentication mechanism in IoT devices is not reliable, because several security vulnerabilities are revealed in the firmware implementation of authentication protocols in IoT. On the other hand, the current authentication approaches for IoT that are not firmware are vulnerable to some security attacks prevalent in IoT. Moreover, the recent access control approaches for IoT have limitations in context-awareness, scalability, interoperability, and security. To mitigate these limitations, there is a need for a robust authentication and access control system to safeguard the rapidly growing number of IoT devices. Consequently, in this paper, we propose a new secure unified authentication and access control system for IoT, called SUACC-IoT. The proposed system is based around the notion of capability, where a capability is considered as a token containing the access rights for authorized entities in the network. In the proposed system, the capability token is used to ensure authorized and controlled access to limited resources in IoT. The system uses only lightweight Elliptic Curve Diffie-Hellman Ephemeral (ECDHE), symmetric key encryption/decryption, message authentication code and cryptographic hash primitives. SUACC-IoT is proved to be secure against probabilistic polynomial-time adversaries and various attacks prevalent in IoT. The experimental results demonstrate that the proposed protocol's maximum CPU usage is 29.35%, maximum memory usage is 2.79% and computational overhead is 744.5 ms which are quite acceptable. Additionally, in SUACC-IoT, a reasonable communication cost of 872 bits is incurred for the longest message exchanged. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%. [ABSTRACT FROM AUTHOR]

The multi-objective grasshopper optimization algorithm (MOGOA) is a relatively new algorithm inspired by the collective behavior of grasshoppers, which aims to solve multi-objective optimization problems in IoT applications. In order to enhance its performance and improve global convergence speed, the algorithm integrates simulated annealing (SA). Simulated annealing is a metaheuristic algorithm that is commonly used to improve the search capability of optimization algorithms. In the case of MOGOA, simulated annealing is integrated by employing symmetric perturbation to control the movement of grasshoppers. This helps in effectively balancing exploration and exploitation, leading to better convergence and improved performance. The paper proposes two hybrid algorithms based on MOGOA, which utilize simulated annealing for solving multi-objective optimization problems. One of these hybrid algorithms combines chaotic maps with simulated annealing and MOGOA. The purpose of incorporating simulated annealing and chaotic maps is to address the issue of slow convergence and enhance exploitation by searching high-quality regions identified by MOGOA. Experimental evaluations were conducted on thirteen different benchmark functions to assess the performance of the proposed algorithms. The results demonstrated that the introduction of simulated annealing significantly improved the convergence of MOGOA. Specifically, the IDG (Inverse Distance Generational distance) values for benchmark functions ZDT1, ZDT2, and ZDT3 were smaller than the IDG values obtained by using MOGOA alone, indicating better performance in terms of convergence. Overall, the proposed algorithms exhibit promise in solving multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such an environment. In this paper, we propose a malware detection mechanism in IoT-enabled industrial multimedia environment with the help of machine-learning approach, which is named as MADP-IIME. MADP-IIME uses four different types of machine learning methods (i.e., naive bayes, logistic regression, artificial neural networks (ANN) and random forest) to detect the presence of malware attacks successfully. Furthermore, MADP-IIME performs better than other related existing schemes and achieves 99.5 % detection and 0.5 % false positive rate. In addition, the conducted security analysis proves the resilience of the proposed MADP-IIME against different types of malware attacks. [ABSTRACT FROM AUTHOR]

Agricultural industry is one of the most vital industries that has a major contribution to the economy due to its share in the Gross Domestic Product (GDP) and as a source of employment. The past few decades have seen immense change in the operation of agricultural sector with the introduction of precision farming in conjunction with Internet of Things (IoT). The application of such advancements is highly based on exchange of messages between various devices in the farming. This paper aims to study the security scenarios applicable in husbandry through the analysis of possible attacks and threats. The testbeds available for agriculture based on IoT have been studied. An architecture for smart farming is proposed which is independent of the underlying technologies that may be used and the requirements of security have been laid out based on the proposed architecture. A literature survey of security protocols for various subsectors of security in smart agriculture along with authentication protocols in smart applications provides a detailed direction of the progress in each of farming security sub-areas and identifies the dearth of existing protocols. The current progress in development of IoT-based tools and systems from industry has also been studied. [ABSTRACT FROM AUTHOR]

Complex network analysis helps in finding hidden patterns within a graph network. This concept is extended for knowledge graphs to identify hidden concepts using state-of-the-art network analysis techniques. In this paper, a profiling knowledge graph is analyzed to identify hidden concepts which result in the identification of implicit communities within a campus network. The proposed work is verified with the interesting results achieved by applying different metrics using a state-of-the-art network analysis algorithm. The results of the proposed work are mapped in the domain of digital advertisement to answer intelligent semantic queries. Various factors of centrality measures identify the prospective influencers within a campus network. Moreover, bridge analysis determines amplifier nodes in the knowledge graph that will help in the digital advertisement. The proposed work concludes with a discussion on link prediction. It shows the future interactions to design digital advertising campaigns through billboards. [ABSTRACT FROM AUTHOR]

In recent times, wireless communication systems have been considered by a fixed spectrum allocation policy, where administrative agencies provide wireless spectrum to licensees on a long-term basis for high geographical areas. Cognitive radio networks (CRN) will provide a large bandwidth to mobile users. However, CRN networks impose challenges due to security issues and spectrum management issues. Hence, in this paper, blockchain based security enhancement and spectrum sensing method is developed for managing the spectrum as well as identify the malicious user in the CRN. In the CRN, spectrum sensing is a fundamental requirement which affected by the malicious user. The malicious user is attacking the general signal detection of network and disturbs the accuracy of the system performance. The occurrence of a malicious user in CRN sends false sensing data which decreases the presentation of the system. Blockchain-based security and spectrum sensing is achieved in the CRN network which empowers the system performance. The blockchain-based method is utilized to identify the malicious user in the CRN by an Adaptive threshold spectrum energy detection algorithm. The proposed method is implemented in MATLAB and performance is evaluated by performance metrics such as the probability of detection, false alarm probability, sensing performance gain, total error probability, missed detection probability, number of selected sensing nodes, average network throughput, and energy efficiency. The proposed method is compared by existing methods such as Friend or Foe and Tidal Trust Algorithm. [ABSTRACT FROM AUTHOR]

Data leakage is a problem that companies and organizations face every day around the world. Mainly the data leak caused by the internal threat posed by authorized personnel to manipulate confidential information. The main objective of this work is to survey the literature to detect the existing techniques to protect against data leakage and to identify the methods used to address the insider threat. For this, a literature review of scientific databases was carried out in the period from 2011 to 2022, which resulted in 42 relevant papers. It was obtained that from 2017 to date, 60% of the studies found are concentrated and that 90% come from conferences and publications in journals. Significant advances were detected in protection systems against data leakage with the incorporation of new techniques and technologies, such as machine learning, blockchain, and digital rights management policies. In 40% of the relevant studies, significant interest was shown in avoiding internal threats. The most used techniques in the analyzed DLP tools were encryption and machine learning. [ABSTRACT FROM AUTHOR]

In current digital era, users of various social media upload photos which usually contain tremendous amount of private information on daily basis. Though the private information contained within photos can assist enterprises to provide users with better services, it is also at the risk of being disclosed. Especially, with deep learning techniques developed for object detection tasks, users' privacy can be extracted with no difficulty. Therefore, we propose an approach to prevent DNN detectors from detecting private objects, especially human body. An algorithm is developed by exploiting an inherent vulnerability of deep learning models known as the adversarial sample problem, and is integrated under a general framework which is also proposed in this work. We evaluate our method on the task of reducing the performance of DNN detectors on PASCAL VOC dataset. Our proposed algorithm can reduce the recall of human detection from 81.1% to 18.0%, while having few effects on pixel value. The results show that our proposed method performs remarkably well on preventing privacy from being exposed by DNN detectors, while causing very limited degradation to the visual quality of images. [ABSTRACT FROM AUTHOR]

Amidst the global pandemic and catastrophe created by 'COVID-19', every research institution and scientist are doing their best efforts to invent or find the vaccine or medicine for the disease. The objective of this research is to design and develop a deep learning-based multi-modal for the screening of COVID-19 using chest radiographs and genomic sequences. The modal is also effective in finding the degree of genomic similarity among the Severe Acute Respiratory Syndrome-Coronavirus 2 and other prevalent viruses such as Severe Acute Respiratory Syndrome-Coronavirus, Middle East Respiratory Syndrome-Coronavirus, Human Immunodeficiency Virus, and Human T-cell Leukaemia Virus. The experimental results on the datasets available at National Centre for Biotechnology Information, GitHub, and Kaggle repositories show that it is successful in detecting the genome of 'SARS-CoV-2' in the host genome with an accuracy of 99.27% and screening of chest radiographs into COVID-19, non-COVID pneumonia and healthy with a sensitivity of 95.47%. Thus, it may prove a useful tool for doctors to quickly classify the infected and non-infected genomes. It can also be useful in finding the most effective drug from the available drugs for the treatment of 'COVID-19'. [ABSTRACT FROM AUTHOR]

Signboard detection and recognition is an important task in automated context-aware marketing. Recently many scripting languages like Latin, Japanese, and Chinese have been effectively detected by several machine learning algorithms. As compared to other languages, outdoor Urdu text needs further attention in detection and recognition due to its cursive nature. Urdu detection and recognition are also difficult due to a wide variety of illuminations, low resolution, inconsistent font styles, color, and backgrounds. To overcome the deficiency of Urdu text detection from the outdoor environment, we have proposed a new Urdu-text signboard dataset with 467 ligature categories, containing a 30 + K images for recognition and 700 base images with annotation are created for detection. We also propose a methodology, that consists of 3-phases. In first phase text regions containing Urdu ligatures from shop-signboard images are detected by a faster regional convolutional neural network (FasterRCNN) using pre-trained CNNs like Alexnet and Vgg16. In the second phase detected regions from the first phase are clustered to identify unique ligatures in a dataset. Lastly in the third phase, all detected regions are recognized by 18-layer convolutional neural network trained model. The proposed system has successfully achieved the precision and recall of 87% and 96% respectively using vgg16 model for detection. For the classification of ligatures, a recognition rate of 97.50% is achieved. Recognition of ligatures was also evaluated using bilingual evaluation understudy (BLEU), and achieved an encouraging score of 0.96 on the newly developed Urdu-Signboard dataset. [ABSTRACT FROM AUTHOR]

The usage of 5G-enabled IoT devices is rising exponentially as humans tend to shift towards a more automated lifestyle. A significant amount of IoT devices is expected to join the network as IoT has allowed interconnection and transmission among global devices which has resulted in generation of enormous diverse data. There is a requirement for a real-time, latency-specific, and network efficient computing paradigm in 5G-enabled smart city infrastructure. Fog computing presents trustworthy solutions to tackle these issues by combining edge users. They store, control, communicate, configure, measure, and manage big data produced by IoT devices. In this survey, the authors have performed a comprehensive study on fog computing and have classified various such paradigms. The authors have performed an extensive evaluation of features, along with the algorithmic and architectural packages deployed in the framework. This survey covers various 5G-enabled Industrial IoT (I-IoT) application settings and unleashes various fog framework-based solutions for numerous real-world application challenges in sustainable smart city infrastructures. Numerous contributions of fog computing towards latency-sensitive applications like healthcare 4.0, smart conveyance, smart waste management, smart energy handling, etc. has also been discussed. Fog computing framework apart from the abilities, also inherits various security flaws from cloud computing paradigm, and these flaws needs to be rectified in the interest of the end user. This survey presents a comprehensive review of state-of-the-art literature schemes to preserve the integrity of data in sustainable smart city infrastructure. Diverse phrases employed for investigating numerous security and privacy concerns in 5G enabled technologies are discussed in a sophisticated approach. [ABSTRACT FROM AUTHOR]

Content-centric networking (CCN) has emerged as a promising future Internet architecture that brings effective content retrieval mechanism. In CCN, the contents are accessed using their names instead of searching for the host location in the network. To improve Quality-of-Service for the users, CCN offers the in-network caching capability that places the incoming contents in the intermediate on-path nodes. During content retrieval, the in-network caching reduces network delay and bandwidth requirements as the requester access the content from the nearest node having a copy of the required content. Therefore, it is crucial to control the content caching decisions in the CCN as the efficiency of the caching scheme largely affects the performance of the network. In this context, a novel content placement scheme is proposed that considers node degree centrality and content provider distance based on the network bandwidth parameters for effective content placement decisions. A heuristic approach has been proposed that investigates the compound effect of these parameters to minimize average delay and network traffic during retrieval of the requested content. The proposed caching scheme has been implemented on the Abilene network topology and evaluated for different content access patterns and caching capacities. Extensive simulation results demonstrate the superiority of the proposed scheme on the existing peer schemes for various performance parameters such as average network hop-count, delay, and network traffic load. [ABSTRACT FROM AUTHOR]

With rapid development of voice control technology, making speech recognition more precisely in various IoT domains have been an intractable problem to be solved. Since there are various conversation scenes, understanding the context of a dialog scene is a key issue of voice control systems. However, the reality is available training data for dialog system are always insufficient. In this paper, we mainly solve the problem of data lacking in dialog systems by data augmentation technique. A Generative Adversarial Network(GAN)-based model is proposed and the data are augmented effectively. It can generate from text to text, enhance the original data with text retelling, and improve the robustness of parameter estimation of unknown data by using the sample data generated by GAN model. A new N-gram language model is used to evaluate multiple recognition candidates of speech recognition, and the candidate sentences with the highest evaluation scores are selected as the final result of speech recognition. Our data enhancement algorithm based on the Generative Model is verified by the experiments. In the result of model comparison test, the error rates of data set THCHS30 and AISHELL are 3.3% and 5.1% which are lower than that of the baseline system. [ABSTRACT FROM AUTHOR]

At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19 disease. It is essential to identify and classify the disease through the use of image processing techniques. So, a new intelligent disease diagnosis model is in need to identify the COVID-19. In this view, this paper presents a novel IoT enabled Depthwise separable convolution neural network (DWS-CNN) with Deep support vector machine (DSVM) for COVID-19 diagnosis and classification. The proposed DWS-CNN model aims to detect both binary and multiple classes of COVID-19 by incorporating a set of processes namely data acquisition, Gaussian filtering (GF) based preprocessing, feature extraction, and classification. Initially, patient data will be collected in the data acquisition stage using IoT devices and sent to the cloud server. Besides, the GF technique is applied to remove the existence of noise that exists in the image. Then, the DWS-CNN model is employed for replacing default convolution for automatic feature extraction. Finally, the DSVM model is applied to determine the binary and multiple class labels of COVID-19. The diagnostic outcome of the DWS-CNN model is tested against Chest X-ray (CXR) image dataset and the results are investigated interms of distinct performance measures. The experimental results ensured the superior results of the DWS-CNN model by attaining maximum classification performance with the accuracy of 98.54% and 99.06% on binary and multiclass respectively. [ABSTRACT FROM AUTHOR]

Early detection, early diagnosis and classification of the cancer type facilitates faster disease management of patients. Cervical cancer is fourth most pervasive cancer type which affects life of many people worldwide. The intent of this study is to automate cancer diagnosis and classification through deep learning techniques to ensure patients health condition progress timely. For this research, Herlev dataset was utilized which contains 917 benchmarked pap smear cells of cervical with 26 attributes and two target variables for training and testing phase. We have adopted combination of convolutional network with variational autoencoder for data classification. The usage of variational autoencoder reduces the dimensionality of data for further processing with involvement of softmax layer for training. The results have been obtained over 917 cancerous image type pap smear cells, where 70% (642) allocated for training and remaining 30% (275) considered for test data set. The proposed architecture achieved variational accuracy of 99.2% with 2*2 filter size and 99.4% with 3*3 filter size using different epochs. The proposed hybrid variational convolutional autoencoder approach applied first time for cervical cancer diagnosis and performed better than traditional machine learning methods. [ABSTRACT FROM AUTHOR]

Neuronal excitation enables identifying the features of an electroencephalogram (EEG) signal for motor imagery detection. We propose a novel feature extraction algorithm supported by short-term cepstrum-based inverse filtration of neuronal excitation of the active synapse. The maximum power of the estimated neuronal excitation is subjected to a two-class Bayesian probabilistic classifier. The feature extraction algorithm with the Bayesian probabilistic classifier significantly improves the brain–computer interface performance compared with that of other conventional methods of EEG signal processing such as wavelet with a Bayesian classifier, autocorrelation and CSP filter with a naïve Bayes classifier over the BCI competition II and IV datasets. Consequently, this neuronal excitation feature allows the authors to develop a motor imagery neuro-feedback system; the performance of which achieves 87.2% average classification accuracy, which is 14% greater than that of the wavelet-based algorithm and 6.2% greater than that of the TRSP-based algorithm, with 53 ms of processing time allotted for each instruction in a real-time experiment. However, brain signal variation across different subjects and sessions significantly impairs decision accuracy. Our neuronal excitation base feature extraction algorithm minimizes these variations in classification accuracy. [ABSTRACT FROM AUTHOR]

Museums have adapted their traditional ways of providing services with the advent of novel digital technologies to match up with the pace and growing needs of current industry revolution. Mixed Reality has revitalized interpretation of numerous domains by offering immersive experiences in digital and real world. In the proposed study, an attempt was made to enrich user's museum experience with relevant multimedia information and for building a better connection with the artifacts with in Taxila Museum in Pakistan, which has beautifully preserved the Gandhara civilization. The proposed solution is an Augmented Reality (AR)-based smartphone application which recognizes artifacts using Deep Learning in real time and retrieve supportive multimedia information for the visitors. To provide user with exact content, convolutional neural networks (CNN) will be applied to correctly recognize artifacts. The significance of proposed application is compared with traditional human guided or free user tours through user-centric questionnaire-based survey. The evaluation is carefully performed using relevant evaluation models including Museum Experience Scale (MES) and triptych model of interactivity. The findings of the study are discussed and assessed comprehensively using statistical methods to highlight its significance. [ABSTRACT FROM AUTHOR]

For the purpose of usability feature extraction and prediction, an innovative metaheuristic algorithm is introduced. Generally, the term "usability" is defined by the several researchers with respect to the hierarchical-based software usability model and it has become one of the important methods in terms of software quality. In hierarchically based software, its usability factors, attributes, and its characteristics are combined. The paper presented an algorithm, i.e., modified crow search algorithm (MCSA) mainly for extraction of usability features from hierarchical model with the optimal solution under the search for useful features. MCSA is an extension of original crow search algorithm (CSA), which is a naturally inspired algorithm. The mechanism of this algorithm is based on the process of hiding food and prevents theft and hence introduced this CSA in the field of software engineering practices as an inspiration. The algorithm generates a particular number of selected features/attributes and is applied on software development life cycles models, finding out the best among them. The results of the presented algorithm are compared with the standard binary bat algorithm (BBA), original CSA, and modified whale optimization algorithm (MWOA). The outcomes conclude that the proposed MCSA performs well than the standard BBA and original CSA as the proposed algorithms generate fewer number of feature selection equal to 17 than 18 in BBA, 23 in CSA, and 19 in MWOA. [ABSTRACT FROM AUTHOR]

Routing in Opportunistic Internet of Things networks (OppIoTs) is a challenging task because of intermittent connectivity between devices and the lack of a fixed path between the source and destination of messages. Recently, machine learning (ML) and reinforcement learning (RL) have been used with great success to automate processes in a number of different problem domains. In this paper, we seek to fully automate the OppIoT routing process by using the Policy Iteration algorithm to maximize the possibility of message delivery. Moreover, we model the OppIoT environment as a Markov decision process (MDP) replete with states, actions, rewards, and transition probabilities. The proposed routing protocol, RLProph, is able to optimize the routing process via the optimal policy obtained by solving the MDP using Policy Iteration. Through extensive simulations, we show that RLProph outperforms a number of ML-based and context-aware routing protocols on a multitude of performance criteria. [ABSTRACT FROM AUTHOR]

In addition to changing service management, smart devices connect people and objects around them and collect data from them on and on, in order to construct the notion of a smart city. Such data produced by embedded devices and automatically transmitted over the Internet provides people with the information to make decisions. A smart grid is one of the most popular applications for a smart city. Due to the insecurity of the wireless channels, the security of data transmission in a smart grid has become a hot issue nowadays. Many schemes for data protection have been proposed, but weaknesses exist generally. We present a new data transmission scheme for a smart grid among the smart meter (SM), the electricity utility (EU), and the trusted authority (TA). The EU can obtain the power consumption of each SM, but cannot get the real identity of the SM. To keep the privacy of the user, if the consumption value is over the threshold in special time span or identity of SM is required for public affairs, TA could track the identity in time. Formal proof with random oracle model and security analysis are expressed to show the security of the proposed scheme. Via the performance and network simulation, it is easy to see that our scheme is practical for a smart city. [ABSTRACT FROM AUTHOR]

Among the numerous alternatives used in the world of risk balance, it highlights the provision of guarantees in the formalization of credit agreements. The objective of this paper is to compare the achievement of fuzzy sets with that of artificial neural network-based decision trees on credit scoring to predict the recovered value using a sample of 1890 borrowers. Comparing with fuzzy logic, the decision analytic approach can more easily present the outcomes of the analysis. On the other hand, fuzzy logic makes some implicit assumptions that may make it even harder for credit-grantors to follow the logical decision-making process. This paper leads an initial study of collateral as a variable in the calculation of the credit scoring. The study concludes that the two models make modelling of uncertainty in the credit scoring process possible. Although more difficult to implement, fuzzy logic is more accurate for modelling the uncertainty. However, the decision tree model is more favourable to the presentation of the problem. [ABSTRACT FROM AUTHOR]

The combination of two overlapping technologies (bigdata and cloud computing) helps easy access to the evolving applications. In this context, there is a serious requirement of ensuring the transmission of data securely in order to improve the productivity over the public channel. Since the data collected by various sources are strictly private and confidential, there is also a great requirement to deal with the privacy preservation of the bigdata. To handle this issue, a new privacy-preserving bigdata collection technique in cloud computing environment, called 2PBDC, has been designed, which allows secure communication between the bigdata gateway nodes and the cloud servers. 2PBDC is shown to be secure against various known attacks against an active/passive adversary through the formal security verification as well as informal security analysis. A detailed comparative study among 2PBDC and other existing schemes has been conducted. This study shows that 2PBDC offers a better trade-off among the security and functionality features and communication and computation overheads while these are compared with other schemes. [ABSTRACT FROM AUTHOR]

The state-of-art broadband THz sources can contribute to the development of short-range 6G communications. This paper has demonstrated the feasibility of forming the controllable sequence of THz subpulses in the temporal domain and the corresponding quasidiscrete spectrum by the interference of two THz pulses with an exponential chirp. Moreover, due to small time delay between these pulses the temporal and spectral structures are similar to each other (so-called "linkage relation"). This will benefit information encoding in the THz range. The calculated metrics for the prototype communication channel based on the proposed method are competitive with existing short-range THz CW channels. [ABSTRACT FROM AUTHOR]

This paper introduces a nature-inspired optimized algorithm called modified moth-flame optimization (MMFO) for usability feature selection. To determine quality of software usability plays a significant role. This model contains various usability factors that are divided into several features, which have some characteristics, thus making a hierarchical model. Here, the authors have introduced MMFO (Modified Moth-flame optimization algorithm) for the selection of usability features to get an optimal solution MMFO is an extension of moth-flame optimization algorithm (MFO), which is based on the navigation method of moths called transverse orientation and to the best of our knowledge; this algorithm is introduced in software engineering practices. The selected features and accuracy of proposed MMFO is compared with the original MFO and other related optimization techniques. The results shows that the proposed nature-inspired optimization algorithm outperforms the other related optimizers as it generates a fewer number of selected features and having low accuracy. [ABSTRACT FROM AUTHOR]

A recent trend of peering at geo-diversified Internet exchange points (IXPs) has empowered decades-old proposal of inter-networking and opened up new avenues of business ventures. IP-transit, cloud direct and remote peering are a few important amongst numerous proposals of service provisioning capitalizing on this peering infrastructure support across domains. Enduring these business proposals becomes a challenging task, especially when the increased dependency of enterprises over the Internet is affirmed. Volatile traffic priorities necessitate different strategies of flow management for each pattern of enterprise traffic. Providing diverse service guarantees to each traffic class require careful selection of resource allocation and compliance of inter-domain policies. In this paper, we propose a novel orchestration framework that helps to stitch end-to-end traffic engineering compliant multiple paths. The framework enables prioritized management of various traffic classes in a centralized manner by employing software defined networking paradigm. Abstraction of multi-graph from the inter-connectivity of peering anchors helps to gear service provisioning spanning across multiple domains. Beside presenting details of our framework, we have articulated use cases highlighting the efficacy of our proposal. We have observed a maximum increase of 26.52% in throughput using proposed model compared with an optimization formulation from literature. Our results imply transparent utility of this formulation for various network topologies and traffic loads. [ABSTRACT FROM AUTHOR]

Video captioning is currently considered to be one of the simplest ways to index and search data efficiently. In today's era, suitable captioning of video images can be facilitated with deep learning architectures. The focus of past research has been on providing image captions; however, the generation of high-quality captions with suitable semantics for different scenes has not yet been achieved. Therefore, this work aims to generate well-defined and meaningful captions to images and videos by using convolutional neural networks (CNN) and recurrent neural networks in combination. Beginning with the available dataset, features of images and videos were extracted using CNN. The extracted feature vectors were then utilized to generate a language model with the involvement of long short-term memory for individual word grams. The generated meaningful captions were trained using a softmax function, for performance computation using some predefined evaluation metrics. The obtained experimental results demonstrate that the proposed model outperforms existing benchmark models. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO), a significant reduction in mortality and maternal morbidity has occurred in developed countries over the past decades. In contrast, these rates remain high in developing countries. Smart mobile-health (m-health) applications that use machine learning (ML) approaches are necessary tools for pregnancy monitoring in an accessible, reliable, and cost-efficient manner, making the prediction of high-risk situations possible during gestation. This paper, therefore, proposes the development, performance evaluation, and comparison of ML algorithms based on Bayesian networks capable of identifying at-risk pregnancies based on the symptoms and risk factors presented by the patients. A performance comparison of several Bayes-based ML algorithms determined the best-suited algorithm for the prediction, identification, and accompaniment of hypertensive disorders during pregnancy. The contribution of this study focuses on finding a smart classifier for the development of novel mobile devices, which presents reliable results in the identification of problems related to pregnancy. Through the well-known cross-validation method, this proposal is evaluated and compared with other recent approaches. The averaged one-dependence estimators presented better results on average than the other approaches. These findings are key to improving the health monitoring of women suffering from high-risk pregnancies around the world. Thus, this study can contribute to a reduction of both maternal and fetal deaths. [ABSTRACT FROM AUTHOR]

Mobile health (m-Health) system architectures are typically based on mobile and wireless communications, and use mobile devices with data exchange supported by Web services (WS). Although m-Health systems offer mobility as a potential and precious resource they also present several challenged issues and constraints, such as, battery and storage capacity, broadcast constraints, interferences, disconnections, noises, limited bandwidths, and network delays. Furthermore, constant mobility and often-required Internet connectivity also exposes and compromises the privacy and confidentiality of the m-Health system information. This paper proposes a novel data encryption solution for mobile health systems, considering a novel and early-proposed cooperation strategy. This encryption solution, tries to guarantee the best confidentiality, integrity, and authenticity of m-Health systems users data. The paper also presents a performance evaluation study comparing the performance an m-Health application with and without the DE4MHA. [ABSTRACT FROM AUTHOR]

The article investigates policies for helping emergency-centre authorities for dispatching resources aimed at reducing goals such as response time, the number of unattended calls, the attending of priority calls, and the cost of displacement of vehicles. Pareto Set is shown to be the appropriated way to support the representation of policies of dispatch since it naturally fits the challenges of multi-objective optimization. Understanding which are the best policies of dispatching as well as workload balancing are very important in the context FOG computing, which aims to distribute in part workload and services on fog devices (such as hardened routers, switches, IP video cameras, etc.). Fog-computing systems produce an ever-increasing service request impacting, for instance, to the power consumption on cloud servers. On the other hand, it is equally crucial to guarantee the quality of service (e.g., latency requirements) of end users. By means of the concept of Pareto dominance a set with objectives may be ordered in a way that guides the dispatch of resources. Instead of manually trying to identify the best dispatching strategy, a multi-objective evolutionary algorithm coupled with an Emergency Call Simulator uncovers automatically the best approximation of the optimal Pareto Set that would be the responsible for indicating the importance of each objective and consequently the order of attendance of the calls. The scenario of validation is a big metropolis in Brazil using one-year of real data from 911 calls. Comparisons with traditional policies proposed in the literature are done as well as other innovative policies inspired from different domains as computer science and operational research. The results show that strategy of ranking the calls from a Pareto Set discovered by the evolutionary method is a good option because it has the second best (lowest) waiting time, serves almost 100% of priority calls, is the second most economical, and is the second in attendance of calls. That is to say, it is a strategy in which the four dimensions are considered without major impairment to any of them. [ABSTRACT FROM AUTHOR]

Nowadays, there is a huge and growing concern about security in information and communication technology among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. In this study, the main objective is to review the most important aspects pertaining to anomaly detection, covering an overview of a background analysis as well as a core study on the most relevant techniques, methods, and systems within the area. Therefore, in order to ease the understanding of this survey's structure, the anomaly detection domain was reviewed under five dimensions: (1) network traffic anomalies, (2) network data types, (3) intrusion detection systems categories, (4) detection methods and systems, and (5) open issues. The paper concludes with an open issues summary discussing presently unsolved problems, and final remarks. [ABSTRACT FROM AUTHOR]

Precision agriculture is the mechanism which controls the land productivity and maximizes the revinue and minimizes the impact on sorroundings by automating the complete agriculture processes. This projected work relies on independent internet of things (IoT) enabled wireless sensor network (WSN) framework consisting of soil moisture (MC) probe, soil temperature measuring device, environmental temperature sensor, environmental humidity sensing device, CO2 sensor, daylight intensity device (light dependent resistor) to acquire real-time farm information through multi-point measurement. The projected observance technique consists of all standalone IoT-enabled WSN nodes used for timely data acquisitions and storage of agriculture information. The farm history is additionally stored for generating necessary action throughout the whole course of farming. The work summarizes the optimum usage of irrigation by the precise management of water valve using neural network-based prediction of soil water requirement in 1 h ahead. Our proposed irrigation control scheme utilizes structural similarity (SSIM)-based water valve management mechanism which is used to locate farm regions having water deficiency. Moreover, a close comparative study of optimization techniques, like variable learning rate gradient descent, gradient descent for feedforward neural network-based pattern classification, is performed and the best practice is employed to forecast soil MC on hourly basis together with interpolation method for generating soil moisture content (MC) distribution map. Finally, SSIM index-based soil MC deficiency is calculated to manipulate the specified valves for maintaining uniform water requirement through the entire farm area. The valve control commands are again processed using fuzzy logic-based weather condition modeling system to manipulate control commands by considering different weather conditions. [ABSTRACT FROM AUTHOR]

A regulatory regime is dynamic and adapts to all aspects of market evolution: infrastructure development, service catalogue, delivery conditions, market shares of operators and opportunities provided by technology. The present article analyses implications of the regulation of next generation networks (NGN) in two well-differentiated areas in Spain: rural versus urban. Regulation will be applied to fixed networks and broadband Internet access services provided with them, and, within fixed networks, those that have come into existence as a result of access network technologies based on fiber optics. The results can serve as a reference for obtaining a better overview and reaching conclusions regarding what has been a global action over the current period in time for the electronic communications market within the European Union. The conclusions speak of a successful model because objectives to date (2017) are being reached, except for the purely economic aspect of return on investments. A long-term time frame is expected to be in place until 2023. With regard to the future, regulation must continue to address issues that were not issues initially, but which have become fundamental and have begun to be addressed: geographical discrimination, fiber loop unbundling, new wholesale prices resulting from adapting reference offers, and greater control of retail services. As a case of study, this article discusses what is happening in the province of Soria, where there are large uninhabited areas with a high percentage of the territory where the broadband infrastructure is very limited. This situation is opposed in the urban areas where the deployment of new NGN is moving forward quickly. [ABSTRACT FROM AUTHOR]

The need to implement techniques to monitor optical fiber links in passive optical networks (PONs), based on the principle of reflectometry, has motivated various research projects. Such studies are especially important for networks with tree topology or hierarchical topology, due to the difficulty to determine in which specific fiber branch there was rupture. This work proposes a novel model based on fiber Bragg grating to solve the complexity in the monitoring of point-multipoint networks. This mode uses multiple access techniques by division of optical codes in phase, to monitor the PONs. The work involved developing a novel PON monitoring model to detect fiber optic branches with problems that are analyzed by means of autocorrelation of optical pulses reflected by Bragg gratings. These Bragg gratings are strategically arranged in network terminal units. The results obtained through simulations demonstrate the viability of the proposed approach applied in Bragg grating 63 chips for identifies fault, in real time, in the network with 8, 16, 32 or 63 users. The system satisfactorily demonstrated its ability to monitor PONs, using fewer Bragg gratins and couplers, compared to the other models found in the literature, such as fault location in passive optical networks using T-Optical time-domain reflectometer and wavelength-selective isolators, remote coding scheme based on waveguide Bragg grating in power line communication splitter chip for PONs monitoring, optimal fiber link fault decision for optical 2D coding-monitoring scheme in passive optical networks, and centralized PONs monitoring scheme based on optical coding. [ABSTRACT FROM AUTHOR]

With the exponential growth of wireless users and their traffic demands, it is greatly increasing for the demand of the scarce spectrum resources in the communication networks. In order to enhance the performance of the wireless networks such as end-to-end delay, energy efficiency and throughput, the device-to-device (D2D) communication has been attracted more attention because the two devices in close proximity can communicate directly without traversing the central base station. However, most of users are very sensitive to the battery. Therefore, we aim to maximize the energy efficiency of wireless communication system in the context of underlaying device-to-device communication in this paper, We focus on the formulated power control and resource allocation problem which is non-convex in the fractional form. We reduce it from the power allocation of all users to the joint power and subchannel allocation of D2D users. Then, we tackle it by an iterative approximation algorithm leveraging to the properties of fractional programming. There are two studied cases for the subchannel allocation. One can be solved by the penalty function approach, and the other can be solved by the dual decomposition as well as sub-gradient method. Accordingly, we propose a dual-based algorithm in general. Numerical simulations demonstrate that the proposed algorithms outperform the conventional algorithm in terms of the energy efficiency. [ABSTRACT FROM AUTHOR]

An introduction is presented in which editor discusses various articles within the issue on topics including product model to reflect the recent purchasing patterns of customers, mobile application for a personal information disclosure attack on smartphones and Speech Recognition System (ASR).

Vehicular communications refer to a wide range of networks proposed for environments characterized by sparse connectivity, frequent network partitioning, intermittent connectivity, long propagation delays, asymmetric data rates, and high error rates. These environments may also be characterized by a potential non-available end-to-end path. To overcome these issues and improve the overall network performance, cooperation between network nodes must be severely considered. Nodes may cooperate by sharing their storage capacity, bandwidth, or even energy resources. However, nodes may be unwilling to cooperate due to a selfish behavior or to an intent to protect the integrity of their own resources. This selfish behavior significantly affects the functionality and performance of the network. This paper overviews the most recent advances related with cooperation on vehicular networks. It also studies the impact of different cooperation levels in the performance of Vehicular Delay-Tolerant Networks (VDTNs). It was shown that scenarios with a higher number of cooperative nodes present the best results in terms of bundle delivery delay. [ABSTRACT FROM AUTHOR]