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Blockchain is a technology that has gained popularity in recent years due to its immutability and decentralized features. Various industries have started integrating this technology into their business models, resulting in the development of various types of blockchain networks. This paper reviews the latest academic advancements in sharding techniques within the various types of sharded blockchain networks implemented in the past five years to identify their strengths and weaknesses. The increasing volume of transactions in blockchain networks has caused issues in terms of scalability, transaction efficiency, storage, and security, which obstruct the global adoption of blockchain technology. Sharding in blockchain is a solution that aims to address these issues through various types of sharding techniques, such as network, transaction, or state sharding, along with the various types of networks and systems. The general definition of sharding is the partitioning of the network into subsets known as shards, which allows them to validate transactions in parallel, resulting in performance and scalability improvements of the existing network. The existing sharding techniques (before 2020) and the latest sharding techniques (2020–2024), along with their characteristics, advantages, and disadvantages, are also discussed. An analysis based on the performance evaluation of recent sharding techniques was conducted to further understand the impact of these techniques. The result of the review provides critical insights into the advancements in sharding techniques and fosters innovation while facilitating the widespread adoption of sharding-based solutions in the blockchain ecosystem.

The exponential growth of intelligent systems technologies, including Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), and Smart Connected Products, has intensified the difficulties of data governance. Organizations adopting these technologies face challenges in managing the volume, variety, and velocity of data while ensuring quality, security, compliance, and ethical integrity. This study investigates the state of big data governance (BDG) programs in organizations leveraging intelligent systems technologies, using a systematic literature review guided by PRISMA. A synthesis of insights from 74 peer-reviewed articles, conferences, and industry reports reveals both the significant benefits of BDG such as enhanced decision-making, operational efficiency, and regulatory compliance and persistent challenges, including fragmented governance frameworks, limited scalability, data quality issues, and ethical concerns. To address these gaps, this study proposes the big data governance maturity assessment model (BDG MAM), a novel framework developed to assess and enhance the maturity of BDG programs. The BDG MAM evaluates governance maturity across four key dimensions: people, process, data, and technology. It provides a structured roadmap for organizations to benchmark governance practices, prioritize improvements, and implement effective strategies. The model was validated through a pilot study conducted with a public higher learning institution, demonstrating its practical applicability and effectiveness in real-world scenarios. By bridging theoretical insights with practical implementation, this study advances academic discourse and provides practitioners with a robust approach to navigate the challenges of modern data governance practices, ensuring sustainable and effective management of heterogeneous data environment.

The Fourth Industrial Revolution (4IR) has ushered in a new era of efficiency across various domains, including the management of Road Traffic Congestion (RTC) in metropolitan cities. Numerous studies have elucidated how smart urban traffic management systems (TMS), encompassing 4IR technologies like Artificial Intelligent (AI), Machine Learning (ML), Internet of Things, and even Software-Define Networks (SDN) or Blockchain can optimize traffic flow and mitigate congestion. Given these results, there is a pressing need to evaluate these approaches and innovations in terms of their performance and sustainability. This review aims to scrutinize current technologies predicated on intelligent traffic management. It will examine the methodologies employed, the benefits derived from these systems, and potential challenges that may arise. The study has identified and categorized research based on the TMS procedure from the sensing process, traffic data evaluation, and solution developments. By providing a comprehensive overview of state-of-the-art in intelligent traffic management, this review seeks to contribute to the ongoing discourse on leveraging 4IR technologies for sustainable urban development.

The rapid growth of the Internet-of-things (IoT) in recent years has led to the development of new access technologies targeting low-power wide area networks (LPWANs). LPWAN is a promising solution for long-range and low-power IoT and machine-to-machine (M2M) communication applications. However, practical LPWAN deployments suffer from intense collisions due to uncoordinated transmissions, given the dense deployment of devices and wide coverage area. Therefore, this paper proposes a deep learning-based collision aware transmission priority scheduling technique (CA-PST) to mitigate high packet collision rate in ultra-dense wireless networks. The proposed CA-PST is applied to the low-range wide area network (LoRaWAN) platform where the LoRa gateway configures the nodes to a particular transmission protocol classes based on the predicted number of packet collisions. Aided by unsupervised learning clustering, the nodes located at higher transmission priority clusters will be allowed to communicate with the gateway using class C and therefore avoid packet collisions. Whereas nodes in lower transmission priority clusters communicate with the gateway using class A. Therefore, the CA-PST offers a fair trade-off between the packet delivery rate (PDR) and total energy consumption. Extensive simulation results show that using CA-PST can significantly improve the LPWAN reliability in terms of PDR in ultra-dense networks.

Sketch-based image retrieval (SBIR) utilizes sketches to search for images containing similar objects or scenes. Due to the proliferation of touch-screen devices, sketching has become more accessible and therefore has received increasing attention. Deep learning has emerged as a potential tool for SBIR, allowing models to automatically extract image features and learn from large amounts of data. To the best of our knowledge, there is currently no systematic literature review (SLR) of SBIR with deep learning. Therefore, the aim of this review is to incorporate related works into a systematic study, highlighting the main contributions of individual researchers over the years, with a focus on past, present and future trends. To achieve the purpose of this study, 90 studies from 2016 to June 2023 in 4 databases were collected and analyzed using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework. The specific models, datasets, evaluation metrics, and applications of deep learning in SBIR are discussed in detail. This study found that Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) are the most widely used deep learning methods for SBIR. A commonly used dataset is Sketchy, especially in the latest Zero-shot sketch-based image retrieval (ZS-SBIR) task. The results show that Mean Average Precision (mAP) is the most commonly used metric for quantitative evaluation of SBIR. Finally, we provide some future directions and guidance for researchers based on the results of this review.

Recent improvements formulated in the area of video captioning have brought rapid revolutions in its methods and the performance of its models. Machine learning and deep learning techniques are both employed in this regard. However, there is a lack of tracing the latest studies and their remarkable results. Although several studies have been proposed employing the ML and DL algorithms in different other areas, there is no systematic review utilizing the video captioning task. This study aims to examine, evaluate, and synthesize the primary studies into a thorough Systematic Literature Review (SLR) that provides a general overview of the methods used for video captioning. We performed the SLR to determine the research problems under which machine learning models were preferred over the deep learning models and vice versa. We collected a total of 1,656 studies retrieved from four electronic databases; Scopus, WoS, IEEE Xplore, and ACM, based on our search string from which 162 published studies passed the selection criteria related to one primary and two secondary research questions after a systematic process. Moreover, insufficient data collection and inefficient comparison of results are common issues identified during the review process. We conclude that the 2D/3D CNN for video feature extraction and LSTM for caption generation, METEOR and BLEU performance evaluation tools, and MSVD dataset are most frequently employed for video captioning. Our study is the pioneer in comparing the implementation of ML and DL algorithms employing the video captioning area. Thus, our study will accelerate the critical assessment of the state-of-the-art in other research fields of video analysis and human-computer interaction.

The usage of distributed Peer-to-Peer (P2P) networks has been growing steadily for a reasonable period. Various applications rely on the infrastructure of P2P networks, where nodes communicate to accomplish a task without the need for a central authority. One of the significant challenges in P2P networks is the ability of the network nodes to reach a consensus on a shared item; the challenge increases as time passes. Thus, this work proposes a new effective method for tweaking the Deep Reinforcement Learning (DRL) algorithm to train Deep Q Network (DQN) learning agents to reach a consensus among the P2P nodes. We propose various hierarchies of deep agents to address this crucial challenge in P2P networks. DRL is utilized to build and train agents; more precisely, DQN learning agents are constructed and trained. Two scenarios are proposed and evaluated. In the first scenario, one DQN agent is trained to find the consensus between the network nodes. In the second scenario, three hierarchies with different numbers of layers of agents are proposed and evaluated. In both scenarios, the P2P network used is a blockchain network. The best result was obtained using the third hierarchy of the second scenario; the overall accuracy of the model is 87.8%.

Activation functions are essential components in any neural network model; they play a crucial role in determining the network’s expressive power through their introduced non-linearity. Rectified Linear Unit (ReLU) has been the famous and default choice for most deep neural network models because of its simplicity and ability to tackle the vanishing gradient problem that faces backpropagation optimization. However, ReLU introduces other challenges that hinder its performance; bias shift and dying neurons in the negative region. To address these problems, this paper presents a novel composite monotonic, zero-centered, semi-saturated activation function called Hyperbolic cosine Linearized SquasHing function (HcLSH) with partial gradient-based sparsity HcLSH owns many desirable properties, such as considering the contribution of the negative values of neurons while having a smooth output landscape to enhance the gradient flow during training. Furthermore, the regularization effect resulting from the self-gating property of the positive region of HcLSH reduces the risk of model overfitting and ensures learning more robust expressive representations. An extensive set of experiments and comparisons is conducted that includes four popular image classification datasets, seven deep network architectures, and ten state-of-the-art activation functions. HcLSH exhibited the Top-1 and Top-3 testing accuracy results in 20 and 25 out of 28 conducted experiments, respectively, suppressing the widely used ReLU that achieved 2 and 5, and the reputable Mish that achieved 0 and 5 Top-1 and Top-3 testing accuracy results, respectively. HcLSH attained improvements over ReLU, ranging from 0.2% to 96.4% in different models and datasets. Statistical results demonstrate the significance of the enhanced performance achieved by our proposed HcLSH activation function compared to the competitive activation functions in various datasets and models regarding the testing loss Furthermore, the ablation study further verifies the proposed activation function’s robustness, stability, and adaptability for the different model parameter.

Predicting the data transfer throughput of cloud networks plays an important role in several resource optimization applications, such as auto-scaling, replica selection, and load balancing. However, constant short-term variations in cloud networks make the prediction of end-to-end data transfer throughput a very challenging task. The parameters that affect the throughput can be categorized into three different areas: end-system characteristics (e.g., disk I/O bandwidth, CPU utilization), network characteristics (e.g., network bandwidth, latency, background traffic, bandwidth shaping mechanisms), and dataset characteristics (e.g., average file size, dataset size). Although there are promising results in the literature using neural networks, the datasets are collected from network layer devices and memory-to-memory data transfers where end-system and dataset characteristics are not considered as part of the problem. Also, very few studies use multivariate time series data collected from cloud networks, and the variables differ from study to study. In this project, we collected multivariate time series data from Amazon Web Services (AWS) by conducting intra- and inter-region transfers between storage systems and compute resources using monitoring services. This dataset is unique in the sense that end-system metrics in addition to network throughput are collected from both source and destination systems. Different average file size, instance type, and regionality parameters provide various settings, making the dataset applicable to various types of prediction models. Our multivariate neural network models predict the one-step-ahead network throughput with ~3.7% and disk throughput with ~6.1% error rate, outperforming multivariate models with least-correlated variables and univariate models empowered by transfer learning.

Data centers serve as the backbone for cloud computing, enterprise services, and infrastructure-based offerings. One area of ongoing research in data center networking focuses on innovating new topologies for large-scale node connectivity. These topologies must incorporate fault-tolerant and efficient routing algorithms. Consequently, the data center network topology must dynamically adapt to ever-changing application requirements. While traditional topology designs often emphasize scalability, they are typically limited by the necessity for dedicated switches to manage server connections. The development of software technologies that distinguish server and switch roles offers a unique opportunity to reconsider design priorities, paving the way for a more balanced assessment of scalability, energy efficiency, and infrastructure costs. Moreover, certain network topologies fail to be cost-effective due to their structural intricacies, often requiring far more node connections than those that are practically necessary. To address these challenges, we introduce VacoNet: a new flexible data center network topology that organizes nodes into structurally similar clusters, interconnected by a novel physical structure algorithm. Boasting high bisection bandwidth, VacoNet delivers robust network capacity, even when encountering bottlenecks. Furthermore, to connect a given set of nodes, VacoNet uses a minimal number of cables and switches, thereby drastically reducing both infrastructure costs and energy consumption. Simulation results show that VacoNet can reduce error rates by 20% and slash infrastructure costs by 70% compared to existing solutions. Additionally, it performs tasks 30% faster, underscoring its superior performance.

The digital market trend is rapidly expanding due to key characteristics like decentralization, accessibility, and market diversity enabled by blockchain technology. This study proposes a Predictive Analytics System to provide simplified reporting for the three most popular cryptocurrencies with varying digits, namely ADA Cardano, Ethereum, and Binance coin, for ten days to contribute to this emerging technology. Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. Moreover, the research experiments are repeated several times to achieve the best results by employing hyperparameter tuning of each algorithm. This involves selecting an appropriate kernel and suitable data normalization technique for SVR, determining ARIMA’s (p, d, q) values, and optimizing the loss function values, number of neurons, hidden layers, and epochs in LSTM models. For the model validation, we utilize widely used evaluation techniques: Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R-squared. Results demonstrate that ARIMA outperforms the other models in all cases, accurately projecting the price variability within the actual price range. Conversely, Facebook Prophet exhibits good performance to some extent. The paper suggests that the ARIMA technique offers practical implications for market analysts, enabling them to make well-informed decisions based on accurate price projections.

This paper considers the problem of efficient network design for data collection from various sensors in smart environments using flying base stations, which are realized using unmanned aerial vehicles (UAVs), or drones. The system efficiency is enhanced by maximizing the number of served sensors using the minimum number of UAVs while satisfying particular network constraints. Towards this end, a joint optimization problem is formulated for UAVs placement and sensors assignment in smart environments with massive sensor deployment. Due to the complexity of the optimal solution, a probabilistic learning approach is utilized to find a near-optimal solution. Further, a non-death penalty constraint handling approach is used to deal with difficult and conflicting constraints. Monte Carlo simulation is performed to evaluate the performance of the proposed algorithm in various scenarios, and compare it with the optimal solution to validate the efficiency of the proposed solution. The presented numerical results show that the solutions obtained using the proposed algorithm are generally close or equal to the optimal solution for several scenarios, but with significant complexity reduction, which confirms the efficiency of the proposed algorithm. Moreover, the proposed solution shows significant performance improvement when compared with an efficient greedy algorithm.

For reliable digital evidence to be admitted in a court of law, it is important to apply scientifically proven digital forensic investigation techniques to corroborate a suspected security incident. Mainly, traditional digital forensics techniques focus on computer desktops and servers. However, recent advances in digital media and platforms have seen an increased need for the application of digital forensic investigation techniques to other subdomains. This includes mobile devices, databases, networks, cloud-based platforms, and the Internet of Things (IoT) at large. To assist forensic investigators to conduct investigations within these subdomains, academic researchers have attempted to develop several investigative processes. However, many of these processes are domain-specific or describe domain-specific investigative tools. Hence, in this paper, we hypothesize that the literature is saturated with ambiguities. To further synthesize this hypothesis, a digital forensic model-orientated Systematic Literature Review (SLR) within the digital forensic subdomains has been undertaken. The purpose of this SLR is to identify the different and heterogeneous practices that have emerged within the specific digital forensics subdomains. A key finding from this review is that there are process redundancies and a high degree of ambiguity among investigative processes in the various subdomains. As a way forward, this study proposes a high-level abstract metamodel, which combines the common investigation processes, activities, techniques, and tasks for digital forensics subdomains. Using the proposed solution, an investigator can effectively organize the knowledge process for digital investigation.

Question-answering (QA) systems aim to provide answers for given questions. The answers can be extracted or generated from either unstructured or structured text. Therefore, QA is considered an important field that can be used to evaluate machine text understanding. Arabic is a challenging language for many reasons; although it is spoken by more than 330 million native speakers, research on this language is limited. A few QA systems created for Arabic text are available. They were created to experiment on small datasets, some of which are unavailable. The research on QA systems can be expanded into different components of QA systems, such as question analysis, information retrieval, and answer extraction. The objective of this research is to analyze the QA systems created for Arabic text by reviewing, categorizing, and analyzing the gaps by providing advice to those who would like to work in this field. Six benchmark datasets are available for testing and evaluating Arabic QA systems, and 26 selected Arabic QA systems are analyzed and discussed in this research.

Mobile Forensics (MF) field uses prescribed scientific approaches with a focus on recovering Potential Digital Evidence (PDE) from mobile devices leveraging forensic techniques. Consequently, increased proliferation, mobile-based services, and the need for new requirements have led to the development of the MF field, which has in the recent past become an area of importance. In this article, the authors take a step to conduct a review on Mobile Forensics Investigation Process Models (MFIPMs) as a step towards uncovering the MF transitions as well as identifying open and future challenges. Based on the study conducted in this article, a review of the literature revealed that there are a few MFIPMs that are designed for solving certain mobile scenarios, with a variety of concepts, investigation processes, activities, and tasks. A total of 100 MFIPMs were reviewed, to present an inclusive and up-to-date background of MFIPMs. Also, this study proposes a Harmonized Mobile Forensic Investigation Process Model (HMFIPM) for the MF field to unify and structure whole redundant investigation processes of the MF field. The paper also goes the extra mile to discuss the state of the art of mobile forensic tools, open and future challenges from a generic standpoint. The results of this study find direct relevance to forensic practitioners and researchers who could leverage the comprehensiveness of the developed processes for investigation.

Database forensic investigation (DBFI) is an important area of research within digital forensics. It's importance is growing as digital data becomes more extensive and commonplace. The challenges associated with DBFI are numerous, and one of the challenges is the lack of a harmonized DBFI process for investigators to follow. In this paper, therefore, we conduct a survey of existing literature with the hope of understanding the body of work already accomplished. Furthermore, we build on the existing literature to present a harmonized DBFI process using design science research methodology. This harmonized DBFI process has been developed based on three key categories (i.e. planning, preparation and pre-response, acquisition and preservation, and analysis and reconstruction). Furthermore, the DBFI has been designed to avoid confusion or ambiguity, as well as providing practitioners with a systematic method of performing DBFI with a higher degree of certainty.

This paper proposes an adaptive large scale antenna system (ALSAS) for enhancing energy efficiency in low density wireless network scenarios. The proposed ALSAS comprises of two stages, a novel adaptive discontinuous transmission (ADTx) stage and an antenna array optimization (AAO) one. The basic idea is to utilize prior knowledge of the users' quality of service (QoS) requirements as well as precoding selection in the ADTx stage to maximize the transmitter hibernation periods subject to a certain complexity constraint. In the AAO stage, further power saving is achieved by reducing the number of active antenna elements subject to a certain QoS requirement. It is shown that, relative to conventional large scale antenna system (LSAS), the proposed ALSAS system achieves significant energy efficiency improvements under various scenarios. The results show that the proposed technique can provide energy efficiency improvement between 125% and 1124% in the suburban scenario, and between 196% and 952% in the rural scenario. It is also demonstrated that for rural environments with relatively small short inter-site-distance (ISD) values, ALSAS can provide up to 500% power saving for the fixed bit rate requirement case.

For every contact that is made in a database, a digital trace will potentially be left and most of the database breaches are mostly aimed at defeating the major security goals (Confidentiality, Integrity, and Authenticity) of data that reside in the database. In order to prove/refute a fact during litigation, it is important to identify suitable investigation techniques that can be used to link a potential incident/suspect to the digital crime. As a result, this paper has proposed suitable steps of constructing and Integrated Incident Response Model (IIRM) that can be relied upon in the database forensic investigation field. While developing the IIRM, design science methodology has been adapted and the outcome of this study has shown significant and promising approaches that could be leveraged by digital forensic experts, legal practitioners and law enforcement agencies. This is owing to the fact, that IIRM construction has followed incident investigation principles that are stipulated in ISO guidelines.

Database Forensic Investigation (DBFI) involves the identification, collection, preservation, reconstruction, analysis, and reporting of database incidents. However, it is a heterogeneous, complex, and ambiguous field due to the variety and multidimensional nature of database systems. A small number of DBFI process models have been proposed to solve specific database scenarios using different investigation processes, concepts, activities, and tasks as surveyed in this paper. Specifically, we reviewed 40 proposed DBFI process models for RDBMS in the literature to offer up-to-date and comprehensive background knowledge on existing DBFI process model research, their associated challenges, issues for newcomers, and potential solutions for addressing such issues. This paper highlights three common limitations of the DBFI domain, which are: 1) redundant and irrelevant investigation processes; 2) redundant and irrelevant investigation concepts and terminologies; and 3) a lack of unified models to manage, share, and reuse DBFI knowledge. Also, this paper suggests three solutions for the discovered limitations, which are: 1) propose generic DBFI process/model for the DBFI field; 2) develop a semantic metamodeling language to structure, manage, organize, share, and reuse DBFI knowledge; and 3) develop a repository to store and retrieve DBFI field knowledge.

Data collection and storage in a large size is done on a routine basis in any company or organization. To this end, wireless network infrastructure and cloud computing are two widely-used tools. With the use of such services, less time is needed to attain the required output, and also managing the jobs will be simpler for users. General services employ a unique identifier for the aim of storing data in a digital database. However, it may be associated with some limitations and challenges. There is a link between the unique identifier and the data holder, e.g., name, address, Identity card number, etc. Attackers can manipulate a unique identifier for stealing the whole data. To get the data needed, attackers may even eavesdrop or guess. It results in lack of data privacy protection. As a result, it is necessary to take into consideration the data privacy issues in any data digital data storage. With the use of current services, there is a high possibility of exposure and leak of data/information to an unauthorized party during their transfer process. In addition, attacks may take place against services; for instance spoofing attacks, forgery attacks, etc. in the course of information transaction. To address such risks, this paper suggests the use of a biometric authentication method by means of a palm vein during the authentication process. Furthermore, a pseudonym creation technique is adopted to make the database record anonymous, which can make sure the data is properly protected. This way, any unauthorized party cannot gain access to data/information. The proposed system can resolve the information leaked, the user true identity is never revealed to others.

In this paper, the performance of a promising technology for the next generation wireless communications, non-orthogonal multiple access (NOMA), is investigated. In particular, the bit error rate (BER) performance of downlink NOMA systems over Nakagami-m flat fading channels, is presented. Under various conditions and scenarios, the exact BER of downlink NOMA systems considering successive interference cancellation (SIC) is derived. The transmitted signals are randomly generated from quadrature phase shift keying (QPSK) and two NOMA systems are considered; two users' and three users' systems. The obtained BER expressions are then used to evaluate the optimum power allocation for two different objectives, achieving fairness and minimizing average BER. The two objectives can be used in a variety of applications such as satellite applications with constrained transmitted power. Numerical results and Monte Carlo simulations perfectly match with the derived BER analytical results and provide valuable insight into the advantages of optimum power allocation which show the full potential of downlink NOMA systems.

A label-free detection for dissolved carbon dioxide (dCO2) is developed using a tapered optical fiber sensor. The tapered region of the optical fiber is coated with the zinc oxide (ZnO) nanorice and used as a probe for dCO2 sensing. The sensor probe was exposed to different concentrations of dCO2 solution ranging from 10 to 100 ppm. ZnO nanorice can adsorb dCO2 via strong hydrogen bonding due to the presence of plenty of oxygen atoms on its surface layer. The interaction between ZnO nanorice and dCO2 changes the optical properties of the ZnO nanorice layer, resulting in the change in reflectance. From the experiment, the result shows that there is an improvement in the sensitivity of the sensor when higher concentration was used. A broad linear trend ranging from 0 to 60 ppm ( $R^{2} = 0.972$ ) is observed for the sensor probe that is coated with 1.0 M of ZnO nanorice compared with the 0.1 M and 0.5 M ZnO nanorice concentrations. The sensor sensitivity obtained is 0.008 mW/ppm. The sensor demonstrates a response and recovery time of 0.47 and 1.70 min, respectively. Good repeatability is obtained with the standard deviation in the range of 0.008–0.027. The average resolution calculated for this sensor is 4.595 ppm.

The security of sensitive information is vital in many aspects of multimedia applications such as Intelligent Transportation Systems (ITSs), where traffic data collection, analysis and manipulations is essential. In ITS, the images captured by roadside units form the basis of many traffic rerouting and management techniques, and hence, we should take all precautions necessary to deter unwanted traffic actions caused by malicious adversaries. Moreover, the collected traffic images might reveal critical private information. Consequently, this paper presents a new image encryption algorithm, denoted as ChaosNet, using chaotic key controlled neural networks for integration with the roadside units of ITSs. The encryption algorithm is based on the Lorenz chaotic system and the novel key controlled finite field neural network. The obtained cryptanalysis show that the proposed encryption scheme has substantial mixing properties, and thus cryptographic strength with up to 5% increase in information entropy compared to other algorithms. Moreover, it offers consistent resistance to common attacks demonstrated by nearly ideal number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), pixel correlation coefficient values, and robustness to cropped attacks. Furthermore, it has less than 0.002% difference in the NPCR and 0.3% in the UACI metrics for different test images.

Posted road speed limits contribute to the safety of driving, yet when certain driving conditions occur, such as fog or severe darkness, they become less meaningful to the drivers. To overcome this limitation, there is a need for adaptive speed limits system to improve road safety under varying driving conditions. In that vein, a visibility range estimation algorithm for real-time adaptive speed limits control in intelligent transportation systems is proposed in this paper. The information required to specify the speed limit is captured via a road side unit that collects environmental data and captures road images, which are then analyzed locally or on the cloud. The proposed analysis is performed using two image processing algorithms, namely, the improved dark channel prior (DCP) and weighted image entropy (WIE), and the support vector machine (SVM) classifier is used to produce a visibility indicator in real-time. Results obtained from the analysis of various parts of highways in Canada, provided by the Ministry of Transportation of Ontario, show that the proposed technique can generate credible visibility indicators to motorists. The analytical results corroborated by extensive field measurements confirmed the advantage of the proposed system when compared to other visibility estimation methods such as the conventional DCP and WIE, where the proposed system results exhibit about 25% accuracy enhancement over the other considered techniques. Moreover, the proposed DCP is about 26% faster than the conventional DCP. The obtained promising results potentiate the integration of the proposed technique in real-life scenarios.

Database forensics is a domain that uses database content and metadata to reveal malicious activities on database systems in an Internet of Things environment. Although the concept of database forensics has been around for a while, the investigation of cybercrime activities and cyber breaches in an Internet of Things environment would benefit from the development of a common investigative standard that unifies the knowledge in the domain. Therefore, this paper proposes common database forensic investigation processes using a design science research approach. The proposed process comprises four phases, namely: 1) identification; 2) artefact collection; 3) artefact analysis; and 4) the documentation and presentation process. It allows the reconciliation of the concepts and terminologies of all common database forensic investigation processes; hence, it facilitates the sharing of knowledge on database forensic investigation among domain newcomers, users, and practitioners.

Wireless network virtualization (WNV) and cloud radio access networks (CRANs) are promising technologies with the potential to be game changing for the fifth generation (5G) wireless networks. In particular, these technologies may have significant impact on the capital expenditure, quality of service provisioning, as well as spectral efficiency in 5G networks. These two technologies are mostly considered separately in previous works. This paper, however, investigates both the gains and requirements of integrating WNV with CRAN. In this paper, we propose WNV schemes for CRAN, where the objective is to maximize the overall system throughput and minimize delay. The proposed schemes are designed to maintain a high level of isolation between mobile network operators (MNOs), which allows the deployment of different scheduling polices by different MNOs, and managing intercell interference, which may lead to significant throughput gain. Overall, the results presented in this paper reveal that a joint CRAN-WNV architecture can be highly efficient when MNOs have unbalanced loads, because MNOs with high loads can seamlessly access the underutilized resources of underloaded MNOs. The throughput gain in unbalanced loads can be as much as 50% using optimal sharing schemes when compared with static sharing, and about 18% when compared with the WNV without CRAN. The resource allocation problem in the joint CRAN-WNV is formulated, and both optimal and low complexity suboptimal solutions are derived. The obtained results show that integrating the two technologies in a joint architecture can significantly improve the network performance. However, reducing the complexity by adopting efficient sharing techniques may have tangible impact on the throughput when compared with optimal sharing.