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Surface material identification using hyperspectral imaging (HSI) analysis is a crucial and challenging issue in remote sensing. Researchers widely recognize that exploiting spectral-spatial data outperforms using spectral pixel-wise methods. Most of the work focuses on machine learning techniques to address issues with HSI categorization. The semi-supervised learning model, for example, was utilized in early research to address the issue of the expenses associated with training samples during HSI classification. Nevertheless, these approaches and techniques have certain drawbacks. The suggested framework must be improved in terms of hardware and computational capacity to enhance security. While integrated patch-based convolutional neural network (CNN) can help overcome these issues, due to the complicated geographical distribution of many targets, there are some downsides, such as using spectra as odorless vectors. The supervised learning techniques categorize the inputs by using a collection of appropriate examples for each class, referred to as training samples. For this purpose, samples must be obtained, however, the process is expensive and time-consuming. Deep learning techniques for evaluating spatial spectrum data have significantly advanced, yet security challenges remain in remote sensing with HSI. In this article, we introduce a novel HSI classification method, termed Deep-Detect, which leverages deep learning models with attention mechanisms to tackle these challenges. We use two weighting factors that may be obtained flexibly during the training phase to integrate both spectral and spatial properties, whose importance differs for various objects and scenarios. Additionally, we proposed a spatial and spectral fully convolutional networks (Spe-FCNs) attention mechanism that focuses attention on the input patches’ most informative regions. To verify performance, we used the northern Italy dataset from Pavia University and compared our proposed model with state-of-the-art HSI classification models. Our findings show that the proposed Deep-Detect model enhances classification accuracy while addressing key security concerns in HSIs.

Artificial intelligence (AI) is one of the key technologies emerging in the Industrial Revolution that could protect against cybersecurity threats. AI is a key component of big data analytics and enables accurate real-time data analysis. AI can analyze big data, but it has some issues with security, privacy, and centralization of data. Moreover, cybercriminals continue to advance, so law enforcement faces more threats. With traditional cybersecurity solutions, sophisticated cyber-attacks are harder to detect and defend against. In complex cyberspaces, AI algorithms mine valuable features from data. However, the data on the Internet is scattered and controlled by different parties, making it challenging to authorize and validate its use. The AICyber-Chain model is presented in this paper for securely storing, calculating, and distributing data on the Internet at an enterprise scale. In a large-scale Internet environment, our proposed AICyber-Chain model integrates three key components to ensure a more secure cyberspace, enhancing AI, namely: Firstly, blockchain-based data sharing guarantees ownership at a large scale, enabling real-time data sharing. Secondly, a platform powered by AI makes cyberspace more trustworthy. Thirdly, sharing data or services rewards participants financially, which promotes sharing. We also discuss a typical use scenario, an alternative deployment method, and its security and commercial efficacy. Also, we simulated our model on Ethereum’s official test network, called Rinkeby, to demonstrate its practicality and efficiency. This model speeds up authentication by 1.8 times compared to the centralized model. In addition, our proposed solution reduces gas consumption by 20 to 25%. Our paper aims to serve as a guide and reference point for cybersecurity researchers and industry practitioners, especially from an intelligent computing or AI-based technical standpoint.

The global surge in population, coupled with the continuous emergence of new digital devices, has significantly increased the worldwide demand for electricity, intensifying the existing energy crisis. Microgrids are pivotal in addressing the current needs of utility industries and driving innovation. However, numerous challenges remain, particularly in developing countries where inadequate infrastructure hampers electricity generation, transmission, and distribution. A potential solution lies in energy sharing, which offers mutual benefits to both microgrid operators and consumers. The integration of Internet of Things (IoT) technology within microgrids enhances real-time monitoring and management of energy resources, thereby improving both efficiency and reliability. Microgrids have the capability to share electricity resources with other microgrids, local communities, or contribute surplus energy to the national grid. However, maintaining the integrity of participants in these energy exchanges is crucial. It is necessary to distinguish between trustworthy and potentially malicious microgrids or consumers. To address this issue, the implementation of a MicroTrust mechanism is proposed. The results demonstrate that MicroTrust effectively evaluates the trustworthiness of CCs and various requesting consumer nodes, providing a robust solution for secure and reliable energy sharing.

Model-driven development (MDD) in the Artificial Intelligence of Things (AIoT) domain faces significant challenges in ensuring the consistency and preservation of model properties during transformations, often leading to system inconsistencies. This research introduces the Property Preservation Framework (PPF), a novel approach fortified with a Markov chain methodology, specifically designed to address these challenges. The PPF integrates formal procedures and constraint-checking methods to systematically validate and preserve model properties, thereby enhancing the reliability of AIoT systems. Through empirical evaluations, the framework has demonstrated its ability to efficiently determine and verify model characteristics at various transformation stages, significantly reducing the incidence of property violations. The results indicate that the PPF not only ensures overall consistency and reliability but also optimizes resource allocation, thereby enhancing data efficiency during the property validation and preservation processes. These advancements make a substantial contribution to the domain of MDD, providing developers with the methodology to execute model transformations that accurately reflect system requirements and behaviors in AIoT ecosystems. The findings underscore the potential of the PPF to revolutionize AIoT development by ensuring high-quality, dependable, and efficient modeling outcomes.

In order to improve cybersecurity in newly developed network infrastructures, this research investigates the integration of blockchain technology with zero-trust security concepts. The zero-trust paradigm ensures continuous authentication across entities, in contrast to standard security models that often presuppose trust based on a network environment. Blockchain is used to decentralize and impose authentication intensity of communication clarity and honesty. The study compares the performance of the zero trust model enhanced by blockchain to traditional security systems in a number of parameters, such as intrusion detection rates and security breach reaction times, using extensive simulations. The findings demonstrate that the blockchain-enhanced zero-trust architecture performs better than conventional systems in both identifying and countering threats and methodically handling a large volume of transactions when under pressure. These conclusions, which emphasize significant advancements in security applications and system resilience, are predicated on the use of blockchain in zero-trust systems. Subsequent investigations will endeavor to enhance these technologies and investigate their utilization in networks across diverse intricate scenarios.

Mexico exhibits one of the highest rates of identity theft in relation to bank accounts, with 56% of cardholders encountering fraudulent processes. Consequently, financial institutions have elevated security measures in their authentication protocols and implemented more robust procedures. To address this issue, this study advocates a multifactor authentication approach wherein an algorithm incorporates the user’s location as a transparent authentication factor. Traditional multifactor authentication techniques typically necessitate intricate combinations of factors, including passwords, biometrics, external hardware, or time-based one-time passwords, to enhance security levels, albeit inadvertently introducing cumbersome steps. This research proposes a simple authentication implementation that retains the benefits of complex multifactor procedures -such as precision, accuracy, and security-, with the additional feature of utilizing an imperceptible location factor. The experimental phase encompasses the development of two functional prototypes: a software implementation on the Android environment -tested in 79 distinct locations-, and an implementation on Arduino hardware -tested in four locations-. These experiments were conducted in real-world scenarios, spanning a 21-day period, and involving data collection from different participants. The most significant result of this research is intricately linked to the runtime of each authentication process, where the average time elapsed from the user input stage to the completion of the validation section is 1.76 seconds. This time optimization is primarily attributed to the integration of native Android libraries. The findings demonstrate that it is possible to attain 100% accuracy for all secure locations with a 12-meter radius in 1.76 seconds in the case of the Android app. As for the Arduino-based security box, a 100% accuracy rate in all opening attempts is possible by employing a radius of 30 meters. The research endeavors to emphasize the versatility and applicability of the proposed solution for integration into diverse real-world scenarios where the preservation of data integrity holds utmost importance.

Text emotion detection is a pivotal aspect of natural language processing, with wide-ranging applications involving human-computer interactions. Machine learning agents have been trained with supervised methods, thus relying on labeled datasets. However, the arbitrary selection of emotion models while labeling such datasets poses significant challenges in the performance and generalizability of the produced machine learning predictors, primarily when evaluated against unseen data, as it effectively introduces bias to the process. This study investigates the impact of emotion model selection on the efficacy of machine learning systems for text emotion detection. Eight labeled datasets were employed to train linear regression, feedforward neural network, and BERT-based deep learning models. Results demonstrated a notable decrease in accuracy when models trained on one dataset were tested on others, underscoring the inherent incompatibilities in labeling across datasets. To prove that the emotion model significantly impacts predictors’ performance, we propose a standardized emotion label mapping utilizing James Russell’s circumplex model of affect that turns the emotion model into a parameter rather than a fixed element. Cross-dataset testing with this shared emotion mapping yielded significant, non-negligible changes in accuracy (both improvement and degradation). This fact highlights the impact of the emotion model (traditionally arbitrarily selected) during machine learning training and performance, arguing that improvements in accuracy reported in related research literature might be due to differences in the used emotion model rather than the new algorithms introduced.

Sentiment analysis (SA) and text emotion detection (TED) are two computer techniques used to analyze text. SA categorizes text into positive, negative, or neutral opinions, while TED can identify a wide array of emotional states, allowing an automated agent to respond appropriately. These techniques can be helpful in areas such as employee and customer management, online support, and customer loyalty, where identifying human emotions is crucial. Among other approaches, research has been conducted using machine learning (ML) algorithms, and labeled datasets have been created to train these models. Current state-of-the-art research for supervised ML algorithms reports good performance for TED (approximately 80% accuracy) and even better results for SA (above 90%). After conducting an extensive review of 30 surveys, the primary objective of this manuscript is to point out that most of these articles (94%) focus heavily on comparing the applied computational methods (the algorithm). At the same time, relatively diminished attention is paid to three other critical factors, namely the selection of an appropriate emotion model (mentioned only in 23% of cases), the corpora utilized for training (30%), and the data source employed during analysis and evaluation (20%). The lack of standardization across these essential elements presents a significant challenge when performing meaningful performance comparisons among algorithms. Consequently, the absence of a unified framework for comparison hampers the practical implementation of SA and TED techniques within mission-critical scenarios within real-world mission-critical scenarios.

The traditional Know Your Customer (KYC) procedure used by banks is deemed unreliable and costly. Therefore, the adoption of emerging technologies is essential for banking firms’ future prospects. One such technology that has gained widespread acceptance is Blockchain, which is known for its reliability and security across various fields. This study aims to investigate how the implementation of Blockchain technology can modify the existing banking business, particularly the KYC document verification process, by storing and monitoring of information. The current need for an optimized KYC system is paramount; one that is coupled with a secure and trustworthy technology like Blockchain that can withstand fraudulent activities while also overcoming scalability and privacy challenges. The article analyzes previous relevant works, which highlight how the implementation of Blockchain technology eliminates the need for intermediaries, thereby reducing the possibility of malicious activities and errors that may occur when there are multiple manual tasks involved.

The use of Unmanned Aerial Vehicles (UAVs) or drones has grown rapidly in both civilian and military operations over the last few decades. Multi-UAV systems are preferred to single-UAV systems, as they are more efficient and cost-effective when completing missions collaboratively. However, like other ad-hoc networks, UAV networks face the challenge of effective communication. Existing research in mobile ad-hoc networks (MANETs) and vehicular ad-hoc networks (VANETs) does not fully address the unique characteristics of UAV networks, which can exhibit varying levels of dynamism, intermittent links, and fluid topology. Moreover, drones need to communicate with each other and ground stations in Flying Ad-hoc Networks. Therefore, routing protocols in such networks must select the most effective paths for communication while ensuring reliable and stable data transmission. This article examines several recently developed routing protocols in UAV communication, detailing their construction methods. As routing is quite challenging in UAVs, where communication performance depends on various factors, this research introduces performance metrics to analyze the efficiency of these protocols. Routing protocols in UAV networks must adapt to high mobility, dynamic topology, intermittent links, power constraints, and changing link quality. Since the lifespan of UAV nodes is limited, seamless handovers are crucial, and the energy efficiency of protocols at different layers should also be considered. Although the reviewed protocols address several aspects of designing routing protocols for UAV communication, some challenges remain. Thus, this article provides a comprehensive exploration of routing protocols in UAV communication, along with a discussion of some open research areas.

The Internet of Things (IoT) has revolutionized the world with its diverse applications and smart connected devices. These IoT devices communicate with each other without human intervention and make life easier in many ways. However, the independence of these devices raises several significant concerns, such as security and privacy preservation due to malicious and compromised nodes within the network. Trust management has been introduced as a less computationally intensive alternative to traditional approaches such as cryptography. The proposed FedTrust approach addresses these challenges by designing a method for identifying malicious and compromised nodes using federated learning. FedTrust trains edge nodes with a provided dataset and forms a global model to predict the abnormal behavior of IoT nodes. The proposed approach utilizes a novel trust dataset consisting of 19 trust parameters from three major components: knowledge, experience, and reputation. To reduce the computational burden, FedTrust employs the concept of communities with dedicated servers to divide the dataset into smaller parts for more efficient training. The proposed approach is extensively evaluated in comparison to existing approaches in terms of accuracy, precision, and other metrics to validate its performance in IoT networks. Simulation results demonstrate the effectiveness of FedTrust by achieving a higher rate of detection and prediction of malicious and compromised nodes.

Driver’s stress detection is a critical research area that helps reduce the likelihood of traffic accidents and driver’s health complexities due to prolonged stress. Previous work in this area is heavily based on traditional machine learning models that classify the driver’s stress levels using handcrafted features extraction techniques. Extracting the best features using these approaches is always a challenging task. Recently, deep learning techniques have emerged for constructing reliable features automatically and classifying the classes with high accuracy. However, large deep learning models face gradient exploding or vanishing problems. Moreover, acquiring a large dataset for training an entire network from scratch is also a challenging task. This paper is based on the deep transfer learning technique to avoid these problems and to reduce computational cost and time. Seven models are proposed for real-world driver’s stress levels detection using Electrocardiogram (ECG) signals. Different Convolutional Neural Network (CNN)-based pre-trained networks are used to classify the driver’s three stress levels. The time-frequency ECG components for the three stress levels are obtained as scalogram images using a normalized Continuous Wavelet Transform (CWT) filter bank and Morse wavelet. Results show that Model 5 based on Xception outperforms the GoogLeNet, DarkNet-53, ResNet-101, InceptionResNetV2, DenseNet-201, and InceptionV3 based models by 11.32%, 11.32%, 9.45%, 7.54%, 5.66%, and 1.88% respectively and achieves 98.11% overall validation accuracy. Ranking estimation using fuzzy logic approach shows that Xception based Model 5 achieves the highest rank for driver’s high and medium stress levels, while DenseNet-201 based Model 4 achieves the highest rank for low-stress level detection among the other models.

Information Technology (IT) is one of the fast-growing industries. On the one hand, IT organizations get benefits from their skilled and talented employees to increase their annual growth and productivity. On the other hand, IT and software organizations are facing a lot of challenges because of high employee turnover intention. The main causes of high employee turnover intention are lack of management support, low level of compensation plans, poor relationship with supervisors, limited development and training programs, and work stress. Therefore, the current study aims to identify and empirically evaluate the factors of turnover intention in Pakistan ’s software and IT industries. For this purpose, a systematic literature review is conducted to identify turnover intention factors from the literature. For the empirical evaluation, a survey is conducted to evaluate these identified factors. In the survey, a total of two hundred and fifty professionals from Pakistan ’s IT and software industry participated either through online Google Form or printed survey questionnaires. The collected data were analyzed using regression analysis performed in SPSS and Warp-PLS. The findings of this study illustrate that the recruitment & section, team & management support, performance & career management, salary & compensation, employee commitment, job security, recognition, organizational demographics, and personal demographics with mediating the role of job satisfaction have a significant impact on IT professionals’ turnover intention except for age and gender. The current study also depicts that there was a significant correlation between SLR and empirical results (r = 0.627, P = 0.039). It has been concluded that IT professionals prefer to work long tenure in the organization if they are offered better facilities in terms of compensation plans, career growth, and management support. The practical implication of this study is helpful for IT and software organizations’ top management to retain skilled employees and reduce their loss in annual revenue.

The way services offered by cloud computing gets its unprecedented and undisputed popularity, so its security concerns. Among them the storage as service model (SaaS) is of the forefront of these concerns. SaaS liberates individuals and enterprises from management of IT infrastructure and data centers to concentrate on their core business. Because of untrusted and out-of-premise architecture users are reluctant to outsource their personal and important data. Encryption before outsourcing addresses some of these issues but at the same time strips the data of its useful operation such as sharing and searching. Now to address this issue, the combination of keyword based searchable encryption (KSE) and attribute-based encryption (ABE) leads to an attribute-based keyword searching (ABKS). The resultant combined concept is capable of fine-grained search operation in the multi-owner/multi-user (M/M) setting. However, the underlying costly pairing operation and complex secret sharing mechanism of ABE makes it unsuitable in practical application for resource-limited devices. On top of it, in most of the existing ABKS schemes the size of the secret key and its associated pairing operation linearly expands to the number of attributes. This paper aims at presenting a novel ABKS scheme with pairing-free access verification and constant size secret key based on AND gate access structure and ciphertext-policy (CP) framework. The security of the proposed work is reduced to the standard Decisional Diffie-Hellmen (DDH) assumption, and also collision free and error tolerant. Finally, the performance evaluation and experimental results shows that the proposed scheme improved the overall efficiency and communication overhead.

The electronic applications of financial institutions like banks and insurance companies use either token-based, biometric-based, or knowledge-based password scheme to keep the confidential information of their customers safe from hackers. The knowledge-based password scheme's resistance, particularly its reformation-based password scheme against shoulder surfing attacks, is comparatively better than the other two because its password can be entered in crowded places without fear of shoulder surfers. However, the available reformation based passwords involve mental computation making their usability difficult. Furthermore, they also need an extra device like earphones during password entry causing to create a gap for information leakage. Moreover, most of the passwords store passwords' actual content on a server database that causes penetration in the financial institutions' database. In this article, a reformation-based password scheme involving no mental computation and using no extra device is proposed. The proposed scheme works on the password characters' indices, which change dynamically after each login process. It gets the password characters' indices from the end-user and obtains his password characters' indices from the database. Next, the textual passwords are formed from the user-provided indices and those obtained from the database. The textual passwords are then compared, and if found match, then login is succeeded, otherwise failed. Our proposed password scheme's experimental results on the password data set showed better security and usability compared to state-of-art password schemes.

The defocus blur concept adds an artistic effect and enables an enhancement in the visualization of image scenery. Moreover, some specialized computer vision fields, such as object recognition or scene restoration enhancement, might need to perform segmentation to separate the blurred and non-blurred regions in partially blurred images. This study proposes a sharpness measure comprised of a Local Binary Pattern (LBP) descriptor and Pulse Coupled Neural Network (PCNN) component used to implement a robust approach for segmenting in-focus regions from out of focus sections in the scene. The proposed approach is very robust in the sense that the parameters of the model can be modified to accommodate different settings. The presented metric exploits the fact that, in general, local patches of the image in blurry regions have less prominent LBP descriptors than non-blurry regions. The proposed approach combines this sharpness measure with the PCNN algorithm; the images are segmented along with clear regions and edges of segmented objects. The proposed approach has been tested on a dataset comprised of 1000 defocused images with eight state-of-the-art methods. Based on a set of evaluation metrics, i.e., precision, recall, and F1-Measure, the results show that the proposed algorithm outperforms previous works in terms of prominent accuracy and efficiency improvement. The proposed approach also uses other evaluation parameters, i.e., Accuracy, Matthews Correlation Coefficient (MCC), Dice Similarity Coefficient (DSC), and Specificity, to assess better the results obtained by our proposal. Moreover, we adopted a fuzzy logic ranking scheme inspired by the Evaluation Based on Distance from Average Solution (EDAS) technique to interpret the defocus segmentation integrity. The experimental outputs illustrate that the proposed approach outperforms the referenced methods by optimizing the segmentation quality and reducing the computational complexity.

Recently, Attribute-Based Encryption (ABE) is used as a baseline technology for keyword searching mechanisms to cater users’ security and privacy-related concerns who outsource their data to the powerful and resource-rich cloud server. Almost all the existing Attribute-Based Keyword Searching (ABKS) schemes either operate on a single-keyword or multiple; conjunctive-keyword search setting. These schemes work on the premise of an “all-or-nothing” retrieval mode, which greatly limits users’ searching capabilities. The most recent work incorporates multi-keyword ranked search in attribute-based encryption, which improves user search results accuracy and the flexibility of retrieval mode. However, these schemes demand high computation overhead, which ultimately suffers searching efficiency. This dilemma prevents further research and application of ABKS schemes, especially mobile-resource constrained devices such as mobile phones. This paper proposes an Online/Offline-aided Attribute-Based Multi-keyword Search (OOABMS) scheme to delegate most laborious computation operations to the offline phase before acquiring the attribute-based access control policy or keywords. An online phase then quickly assembles the pre-computed index or trapdoor with the required specifics when it becomes known. Theoretical comparison and simulation results show that our scheme is more efficient and practical in the real world scenario for mobile cloud computing in terms of computation overhead and flexibility.

Wireless Body Area Networks (WBANs) are emerging in the livestock industry for remote monitoring of cattle using wireless body sensors (WBS). The random mobility of animals acting as nodes causes the network’s topology to change rapidly, originating from scalability and reliability issues. Stable transmission of acquired data to the base station requires an intelligent clustering mechanism that reduces the energy consumption and fulfills the network’s constraints. Several clustering techniques are available as a solution, but these techniques yield numerous cluster heads, resulting in more energy utilization. Higher energy utilization lessens the effective life of WBSs and increases monitoring costs. This paper presents a metaheuristic approach for selecting optimal clusters in WBANs to realize an energy-efficient routing protocol for livestock health and behavior monitoring. The proposed approach employs Ant Lion Optimizer (ALO) to select the optimal clusters for different pasturage sizes using sensors of different transmission ranges considering user’s preferences about cluster density. The proposed technique with ALO is compared with other recent techniques such as Ant Colony Optimization, Grasshopper Optimization, and Moth Flame Optimization. The comparison results show the proposed technique’s effectiveness in realizing energy-efficient protocols of WBANs for remote monitoring applications.

Emerging Unmanned Aerial Vehicles (UAVs) have applications for traffic monitoring, public safety, surveillance, agriculture, health services. Collaborative UAVs can form flying ad hoc networks, although such networks are especially vulnerable to security vulnerabilities due to open access media and limited power. Very recently, Khan et al. presented an Identity-Based Generalized Signcryption having Multi-access Edge computing to secure Flying Ad hoc Networks (FANETs). First, this paper presents the cryptanalysis of the Khan et al. scheme and shows that their scheme does not provide message confidentiality, Authenticity, and integrity. Second, it presents an improved scheme as well. The comparison of the improved scheme with the state of the art schemes based on security and cost shows, it is efficient, provably secure against security attacks, and suitable for multi-access edge computing empowered FANETs.

Speaker identification refers to the process of recognizing human voice using artificial intelligence techniques. Speaker identification technologies are widely applied in voice authentication, security and surveillance, electronic voice eavesdropping, and identity verification. In the speaker identification process, extracting discriminative and salient features from speaker utterances is an important task to accurately identify speakers. Various features for speaker identification have been recently proposed by researchers. Most studies on speaker identification have utilized short-time features, such as perceptual linear predictive (PLP) coefficients and Mel frequency cepstral coefficients (MFCC), due to their capability to capture the repetitive nature and efficiency of signals. Various studies have shown the effectiveness of MFCC features in correctly identifying speakers. However, the performances of these features degrade on complex speech datasets, and therefore, these features fail to accurately identify speaker characteristics. To address this problem, this study proposes a novel fusion of MFCC and time-based features (MFCCT), which combines the effectiveness of MFCC and time-domain features to improve the accuracy of text-independent speaker identification (SI) systems. The extracted MFCCT features were fed as input to a deep neural network (DNN) to construct the speaker identification model. Results showed that the proposed MFCCT features coupled with DNN outperformed existing baseline MFCC and time-domain features on the LibriSpeech dataset. In addition, DNN obtained better classification results compared with five machine learning algorithms that were recently utilized in speaker recognition. Moreover, this study evaluated the effectiveness of one-level and two-level classification methods for speaker identification. The experimental results showed that two-level classification presented better results than one-level classification. The proposed features and classification model for identifying a speaker can be widely applied to different types of speaker datasets.

The communications in the millimeter wave (mmWave) band, e.g., WiGig (60 GHz), is considered as one of the main components of 5G and beyond 5G (B5G) networks. However, it is characterized by short range transmissions along with high susceptibility to path blockage, e.g., human shadowing. Thus, mmWave multi-hop relaying using device to device (D2D) connections turns to be an efficient solution to extend its communication range and to route around blockages. In this context, relay probing is essential to discover/explore the candidate multi-hop routes from source-to-destination and then select the best route among them. However, a trade-off exists between relay probing and the required overhead coming from mmWave beamforming training. In this paper, taking advantage of the multi-band μW /mmWave relay nodes, an efficient multi-hop relay probing scheme is proposed for mmWave D2D routing. In this scheme, the collected μW received signal strengths (RSSs) among the distributed relay nodes are used to estimate the probability of their mmWave signal-to-noise power ratios (SNRs) while considering line-of-sight (LOS) and non-LOS (NLOS) path availabilities. Then, based on a proposed probabilistic metric, a hierarchical search algorithm is proposed to jointly discover the relay nodes along the candidate multi-hop routes and enumerate the number of routes expected to maximize the spectral efficiency of the whole path from source-to-destination. This is done in an offline phase, and only the relay nodes located within the pre-selected multi-hop routes are requested to do online relay probing. Numerical analysis confirms the superiority of the proposed mmWave multi-hop relay probing scheme over the candidate techniques.

The IoT devices produce a massive amount of content compared to the host-centric (IP-based) network. The IP-based network has no strengthened capabilities to handle the growing traffic of the IoT network. The existing research is focusing on proposing Information Centric Network (ICN) framework with potential replacement of IP-based network. Using the caching technique in ICN based IoT significantly strengthen the resource-constrained IoT devices, by reducing the access time with optimized energy consumption. Thus, this paper proposes a caching scheme for IoT contents, known as Central Control Caching (CCC) Scheme. This scheme positions a central entity between different autonomous systems that manage caching and updates the content, by keeping information in a table regarding different contents present on different autonomous systems. This information is utilized when cached contents are exchanged among different autonomous systems. Furthermore, when contents becomes stale, the same information is used to identify the autonomous systems with a copy of the outdated content. The simulation results show that the proposed scheme has outperformed in terms of reduced energy consumption, response latency, and cache-hit compared to state-of-the-art works.

In the modern era business intelligence (BI) has a pivotal role in articulating a strategy and taking correct measures based on data. Business intelligence plays a pivotal role in an inevitable decision support system that enables the enterprise to perform analysis on data and throughout the process of business. Machine learning predicts the forecasting of future demands of the enterprises. Demand forecasting is one of the main decision-making tasks of enterprise. For demand forecasting first raw sales data is collected from the market, then according to data, the future sale/product demands are forecasted. This prediction is based on collected data that compiles through different sources. The machine learning engine executes data from different modules and determines the weekly, monthly, and quarterly demands of goods/commodities. In demand forecasting, its perfect accuracy is non-compromising, the more accurate system model is more efficient. Furthermore, we test the efficiency by comparing the predicted data with actual data and determine the percentage error. Simulation results show that after applying the purposed solution on real-time organization data, we get up to 92.38 % accuracies for the store in terms of intelligent demand forecasting.

A smart city is a sustainable and effective metropolitan hub, that offers its residents high excellence of life through appropriate resource management. Energy management is among the most challenging problems in such metropolitan areas due to the difficulty and key role of energy systems. To optimize the benefit from the available megawatt-hours, it is important to predict the maximum electrical power output of a baseload power plant. This paper explores the method of a deep extreme learning machine to create a predictive model that can predict a combined cycle power plant's hourly full-load electrical output. An intelligent energy management solution can be achieved by properly monitoring and controlling these resources through the internet of things (IoT). The universe of artificial intelligence has produced many strides through deep learning algorithms and these methods were used for data analysis. Nonetheless, for further accuracy, deep extreme learning machine (DELM) is another candidate to be investigated for analyses of the data sequence. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows better results compared to previous investigations since previous studies could not meet the findings up to the mark and unable to predict power plant electrical energy output efficiently. During the investigation, it is shown that the proposed approach has the highest accuracy rate of 98.6% with 70% of training (33488 samples), 30% of test and validation (14352 examples). Simulation results validate the prediction effectiveness of the proposed scheme.

To recognize the basis of disease, it is essential to determine its underlying genes. Understanding the association between underlying genes and genetic disease is a fundamental problem regarding human health. Identification and association of genes with the disease require time consuming and expensive experimentations of a great number of potential candidate genes. Therefore, the alternative inexpensive and rapid computational methods have been proposed that can identify the candidate gene associated with a disease. Most of these methods use phenotypic similarities due to the fact that genes causing same or similar diseases have less variation in their sequence or network properties of protein-protein interactions based on-premises that genes lie closer in protein interaction network that causes the similar or same disease. However, these methods use only basic network properties or topological features and gene sequence information or biological features as a prior knowledge for identification of gene-disease association, which restricts the identification process to a single gene-disease association. In this study, we propose and analyze some novel computational methods for the identification of genes associated with diseases. Some advance topological and biological features that are overlooked currently are introducing for identifying candidate genes. We evaluate different computational methods on disease-gene association data from DisGeNET in a 10-fold cross-validation mode based on TP rate, FP rate, precision, recall, F-measure, and ROC curve evaluation parameters. The results reveal that various computational methods with advanced feature set outperform previous state-of-the-art techniques by achieving precision up to 93.8%, recall up to 93.1%, and F- measure up to 92.9%. Significantly, we apply our methods to study four major diseases: Thalassemia, Diabetes, Malaria, and Asthma. Simulation results show that the proposed Deep Extreme Learning Machine (DELM) gives more accurate results as compared to previously published approaches.

Wireless Body Area Network is an emerging technology that is used primarily in the area of healthcare applications. It is a low-cost network having the capability of transportability and adaptability. It can be used in location independent and long-term remote monitoring of people without disturbing their daily activities. In a typical WBAN system, sensing devices are either implanted or etched into the human body that continuously monitors his physiological parameters or vital signs. In such a network, trusts among the stakeholders (healthcare providers, users, and medical staff, etc.) are found of high importance and regarded as the critical success factor for the reliability of information exchange among them. In remote patient monitoring, the implementation of trust and privacy preservation is crucial, as vital parameters are being communicated to remote locations. Nonetheless, its widespread use, WBAN, has severe trust and privacy risks, limiting its adaptation in healthcare applications. To address trust and privacy-related issues, reliable communication solutions are widely used in WBANs. Given the motivation, in this paper, we have proposed a trust-based communication scheme to ensure the reliability and privacy of WBAN. To ensure reliability, a cooperative communication approach is used, while for privacy preservation, a cryptography mechanism is used. The performance of the proposed scheme is evaluated using MATLAB simulator. The output results demonstrated that the proposed scheme increases service delivery ratio, reliability, and trust with reduced average delay. Furthermore, a fuzzy-logic method used for ranking benchmark schemes, that has been concluded that the proposed scheme has on top using comparative performance ranking.

Proxy signcryption is essential security primitive for emerging secure communication such as e-business, mobile agents, online voting, contract signing, and online auction. It combines the functionality of a proxy signature and encryption to achieve basic security features maintaining a low computational and communicational cost. Ming proposed Proxy Signcryption (PSC) scheme in the standard computational model, claimed it to be secured against: (1) Indistinguishable Chosen Ciphertext Attack (IND-CCA) under the Decisional Bi-linear Diffie-Hellman (DBDH) assumption (2) Existentially Unforgeable Chosen Message Attack (EUF-CMA) under the Computational Diffie Hellman (CDH) assumption. This paper first provides a security analysis to check the correctness and validity of the said PSC scheme. Furthermore, it proves PSC is vulnerable to the launched cryptanalysis attacks. It is established that the PSC is neither semantically secured against IND-CCA nor existentially secured against EUF-CMA in its defined security model. Secondly, we propose an improved new proxy signcryption scheme (N-PSC) based on Elliptic Curve Cryptosystem (ECC) without bi-linear pairing secure against IND-CCA and EUF-CMA for Type-1 adversary A1 in the standard computational model. It is also proved that the new proposed N-PSC scheme achieves an extra security property of judge verification in case of signature dispute between the proxy correspondents, as well as it outperforms the existing states of the art schemes including the Ming scheme in terms of cost efficiency which makes the new proposed scheme suitable for scarce resources constraint proxy enabled communication applications.

A smart grid (SG) is an emerging technology that provides electricity in a cost-efficient and eco-friendly way. SG combined with distributed energy resources (DERs) plays a crucial role in extending the existing grid's capacity while mitigating carbon emissions. The potential sources of DERs include solar, wind, and tidal energy. Usually, these DERs are located far away from the grid and not necessarily tied to the grid system. However, the energy trading capabilities of a grid-tied DERs are getting attention, both from academia and industry. This bonding of grid-tied DERs helps to decrease the loss of surplus energy, build an energy storage capacity, and other operational charges. Energy-consuming flexible home tasks can be optimized coordinately with the operations of DERs to minimize the economic cost and CO2 emissions. In this work, our problem is multi-objective and we aim to reduce both electricity price and CO2 emission. We proposed a multi-objective self-adaptive multi-population based Jaya algorithm (PMO-SAMP-Jaya) to schedule the operations of flexible home tasks. Different pricing schemes have been applied to uncover the correlation between CO2 emission, economic cost, and pricing schemes. We assume a smart building, including 30 smart homes with PV and energy storage system (ESS) as DERs. Promising results have shown the effectiveness of our proposed scheme.

With the rising number of patients along with the same time, the comparative evolution in wireless technology has made Body Sensor Networks (BSNs) flourishing in the market. In BSNs, tiny biosensor nodes are deployed inside/outside of a human body to continuously monitor patient vital signs such as heartbeat rate, blood pressure, respiratory rate, and temperature. BSNs face various challenges due to their constrained nature environment. These are prioritized, delaying less, and secure patient data transmission using a public network, simultaneous reception of patient data by end-users, patient identity privacy, high overhead, and energy constraints. However, various researchers have worked in this domain, unable to cope with all these issues in one go. In this paper, we have proposed a novel cryptosystem covering the mentioned issues in a desirable way. For this purpose, we offer a novel concept of a lightweight and secure attribute-based multi-receiver generalized signcryption scheme that can adaptively work as an encryption mode, a signature mode, or a signcryption mode with a single algorithm that consume fewer resources by using shorter keys size of Hyper-elliptic Curve Cryptography (HECC). Similarly, we design a modified priority-based scheduling algorithm to delay less emergency patient data. Multiple end-users access the encoded data simultaneously by defining an access policy using AND & OR logic gates. Furthermore, we simulate our scheme using the AVISPA tool and demonstrate that our proposed scheme can meet the security requirements such as data confidentiality (IND-CCA), integrity, unforgeability (EUF-CMA), patient data authenticity, forward secrecy, and non-repudiation distinctly in the Random Oracle Model (ROM). Due to lower processing costs and transmission overhead, this scheme is more efficient and suitable for the resource-constrained environment of BSNs.

The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user's anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information.

Internet of Things (IoT) is proposed and used in diverse application domains. In IoT, nodes commonly have a low capacity to maintain security on their own expenses, which increases the vulnerability for several attacks. Many approaches have been proposed that are based on privacy and trust management to reduce these vulnerabilities. Existing approaches neglect the aspects of cross-domain node communications and the significance of cross-domain trust management. In this paper, we propose a Holistic Cross-domain trust management model (HoliTrust) that is based on multilevel central authorities. To provide multilevel security, the HoliTrust divides domains into communities on the basis of similarities and interests. Every community has its dedicated server to calculate and manage the degree of trust. In addition, these domains also have their dedicated servers to manage their specific domains, to communicate with the trust server, and to sustain trust among other domain servers. The trust sever is introduced in the HoliTrust that controls the domains, calculates the domain trust, manages the trust values, and distributes standard trust certificates to domains based on a degree of trust. Trust computation is performed on the basis of direct and indirect trust parameters. Furthermore, if a trustor communicates through the community, then the community server includes community trust of the trustee during the trust evaluation. If the communication of the trustor is across the domain, then the community server includes the domain trust along with the community trust of the trustee comprising direct and indirect observations. The overall trust evaluation of communities and domains is time-driven and the responsible authority computes trust after a specific interval of time. We have also compared the HoliTrust with the existing trust mechanisms by focusing on several holistic trust objectives, such as trust relation and decision, data perception trust, and privacy preservation.

Millimeter wave (mmWave) band, e.g., WiGig (60 GHz), is considered as a promising candidate of future 5G networks. Thanks to its directional communication by means of antenna beamforming, WiGig is best fitted for short range device to device (D2D) communications, which highly boost the capacity of future 5G networks. However, the use of direct beamforming training (BT) in the process of D2D neighbor discovery (ND) results in large overhead, high-energy consumptions, and low throughput of the whole WiGig D2D networks. In this paper, as standardized WiGig devices are multiband capable, i.e., it includes the unlicensed μW band (i.e., 2.4 and 5 GHz) and the unlicensed WiGig band (i.e., 60 GHz), an efficient paradigm for WiGig D2D networks will be proposed. In this paper, the wide coverage μW band is used to assist the construction of WiGig D2D links. By which, each multiband WiGig device decides if it is neighbor device is in its WiGig (60 GHz) range or not based on the μW received signal strength (RSS) from its neighbor while considering the effect of WiGig path blocking. If both devices are discoverable in the WiGig band, the WiGig D2D data link will be constructed between them under the control of the wide coverage μW D2D link. The detailed management protocol of the proposed multiband WiGig D2D network in addition to the proposed WiGig ND algorithm is given. Mathematical and simulation analysis confirm the superiority of the proposed multiband WiGig D2D network over that using only WiGig band with WiGig direct ND.

The automotive industry is growing day by day and personal vehicles have become a significant transportation resource now. With the rise in private transportation vehicles, getting a free space for parking one's car, especially in populated areas, has not only become difficult but also results in several issues, such as: (i) traffic congestion, (ii) wastage of time, (iii) environmental pollution, and most importantly (iv) unnecessary fuel consumption. On the other hand, car parking spaces in urban areas are not increasing at the same rate as the vehicles on roads. Therefore, smart car parking systems have become an essential need to address the issues mentioned above. Several researchers have attempted to automate the parking space allocation by utilizing state-of-the-art technologies. Significant work has been done in the domains of Wireless Sensor Networks (WSN), Cloud Computing, Fog Computing, and Internet of Things (IoT) to facilitate the advancements in smart parking services. Few researchers have proposed methods for smart car parking using the cloud computing infrastructures. However, latency is a significant concern in cloud-based applications, including intelligent transportation and especially in smart car parking systems. Fog computing, bringing the cloud computing resources in proximate vicinity to the network edge, overcomes not only the latency issue but also provides significant improvements, such as on-demand scaling, resource mobility, and security. The primary motivation to employ fog computing in the proposed approach is to minimize the latency as well as network usage in the overall smart car parking system. For demonstrating the effectiveness of the proposed approach for reducing the lag and network usage, simulations have been performed in iFogSim and the results have been compared with that of the cloud-based deployment of the smart car parking system. Experimental results exhibit that the proposed fog-based implementation of the efficient parking system minimizes latency significantly. It is also observed that the proposed fog-based implementation reduces the overall network usage in contrast to the cloud-based deployment of the smart car parking.

This paper proposes a spectRum Aware cRoss-layEr (RARE) medium access control protocol for cognitive radio ad-hoc networks. The RARE protocol initially splits the network into clusters, where cluster formation is defined as maximum edge biclique problem. Besides, in order to maintain the integrity of the cluster-based network, super-frame structure, and topology maintenance protocols are also presented in this paper. Moreover, RARE also integrates a delay-aware routing protocol, where the routing protocol is defined as a weighted graph problem. It is anticipated that clusters in RARE adapt themselves dynamically with respect to spectrum availability and nodes mobility. Furthermore, the routing protocol in RARE is expected to select stable paths while ensuring faster data delivery from a source node to the destination. Simulation is conducted to evaluate the performance of the proposed RARE protocol, where it is found that RARE outperforms existing approaches by maintaining a lesser number of clusters and a steady number of common channels. The simulation results also show that route selection in RARE proves to be more stable with the lowest packet transmission delay compared with the other approaches.

In vehicular ad hoc networks (VANETs), trust establishment among vehicles is important to secure integrity and reliability of applications. In general, trust and reliability help vehicles to collect correct and credible information from surrounding vehicles. On top of that, a secure trust model can deal with uncertainties and risk taking from unreliable information in vehicular environments. However, inaccurate, incomplete, and imprecise information collected by vehicles as well as movable/immovable obstacles have interrupting effects on VANET. In this paper, a fuzzy trust model based on experience and plausibility is proposed to secure the vehicular network. The proposed trust model executes a series of security checks to ensure the correctness of the information received from authorized vehicles. Moreover, fog nodes are adopted as a facility to evaluate the level of accuracy of event's location. The analyses show that the proposed solution not only detects malicious attackers and faulty nodes, but also overcomes the uncertainty and imprecision of data in vehicular networks in both line of sight and non-line of sight environments.

A novel on-demand cluster-based hybrid routing protocol for cognitive radio ad hoc network with non-uniform node distribution is proposed in this paper. At first, a novel spectrum-aware clustering mechanism is introduced. The proposed clustering mechanism divides node into clusters based on three values: spectrum availability, power level of node, and node stability. Therefore, clusters are formed with the highest stability to avoid frequent reclustering. Later, a routing algorithm is introduced to minimize the delay while achieving acceptable delivery ratio. In this paper, routing is defined as a multi-objective optimization problem to combine different individual routing metrics to form a global metric. Simulation results show that our proposed routing algorithm can guarantee a lower delay and a higher packet delivery ratio than conventional routing protocols for cognitive radio ad hoc networks. Due to our routing design specification, such as power consideration and low delay, it can be suitable to be adopted for Internet of Things applications.