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Handling and quantifying two types of uncertainties, spatial and inherent, in land-cover segmentation using multiple satellite images constitutes a primary concern within the domain of segmenting multiple remote sensing images. Despite the comprehensive examination of the spatial uncertainty, the lack of a mathematical model addressing the inherent uncertainty in satellite images is notable. This article endeavors to address this gap by considering and focuses on the latter. Leveraging multiple high-resolution panchromatic remote sensing images as input data, the study aims to model all procedures in the image formation and preprocessing stages (applied to each input image before segmentation) as the producing factors of inherent uncertainty in the segmentation process. The input images, conceptualized as events in random processes, undergo fusion using the mean estimator, referred to as the proposed “ultrafusion” method in this article. The study demonstrates that the results of ultrafusion can be effectively modeled by normal-type fuzzy numbers. The parameters of these fuzzy numbers are estimated using the maximum likelihood method. Subsequently, fuzzy C-means, as the most renowned clustering method, is employed for segmentation. Analytical comparisons reveal the notable performance of the proposed algorithm in terms of accurate segmentation and computational efficiency. Affirming the efficacy of the proposed approach, simulation results validate the advantages by improving the overall accuracy, Kappa, and F1-score indices by about 0.86%, 0.52%, and 1.03%, respectively, in comparison with the most recent and similar method.

In recent years, the use of Software Defined Networking (SDN) has increased due to various network management requirements. Using SDN in computer network applications has brought several benefits to users, including lower operational costs, better hardware management, flexibility, and centralized network deployment. On the other hand, the Internet of Things (IoT) is another rapidly growing technology. Distributed and dynamic infrastructures are two critical characteristics of IoT. These characteristics lead to some challenges while using SDN in IoT in terms of security and privacy. In this paper, we address security and privacy issues and solutions for SDN-based IoT systems. We analyze the techniques used for defense in previous works to achieve an acceptable level of security and privacy protection in SDN-based IoT systems. In the data plane, SDN-based IoT papers have considered hashing and encryption techniques, in the control plane, certificate authority and access control have been analyzed, and in the application plane, attack detection, and authentication have been discussed. We also provide a statistical analysis of the existing work. This analysis shows that researchers have focused on certain areas more than others in recent years. The final analysis also highlights issues that previous researchers have ignored.

The manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of the smart cities. Application domains include the essential aspects of the smart cities including the energy, healthcare, transportation, security, and pollution. The research methodology presents a state-of-the-art taxonomy, evaluation and model performance where the ML algorithms are classified into one of the following four categories: decision trees, support vector machines, artificial neural networks, and advanced machine learning methods, i.e., hybrid methods, ensembles, and Deep Learning. The study found that the hybrid models and ensembles have better performance since they exhibit both a high accuracy and low overall cost. On the other hand, the deep learning (DL) techniques had a higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference was negligible, it can be concluded that using either of them is appropriate.

Recent technology has modeled VANET (vehicular adhoc network) communication well in terms of privileges to derive vehicular communication technologically to save time, energy, and money. Due to the increase in powerful technology in modern times, VANETs play a vital role in uplifting daily concerns across vehicles and vehicular identities. Hence, to tune VANETs to become compatible with traditional technologies and increase demand, VANETs require upgrading. The severity and frequency of unwanted occurrences have become a considerable concern for our day-to-day lives relating to vehicular position. Thus, verily updated methodologies or working procedures are needed for the future VANET interplay to eradicate such problems occurring through vehicular identities. This article outlines in technology related to VANETS, future developments, and coping issues by deriving comprehensive frameworks, workflow patterns, upgrading procedures including big data, fog computing, SDN (software defined networking), and SIoT (social Internet of Things). This article provides a high-level overview of a complete VANET upgrade solution to address future problem management issues under a range of acceptable scientific themes, indicators, and combinations.

As a global community, the Internet is comprised of thousands of administrative entities that operate and interact with each other. Transferring data among these entities is possible due to the process of routing, which is challenging due to the lack of centrality. Consequently, the Border Gateway Protocol (BGP) can play a vital role in the routing process as a central hub for disseminating routing information to the various autonomous systems. Yet, the BGP poses security vulnerability due to the difficulty of validation and authentication. Recent studies argue that it would be beneficial to apply the Software-Defined Networking (SDN) approach to address some of the BGP problems. The SDN can help handle BGP-based networks at a low cost and with minimal complexity. However, there are still many scientific and operational problems in this field of study. The main objective of this paper is to identify the challenges that the BGP facing with respect to the adoption of the SDN. The findings revealed that most researchers focused on improving convergence time, while other essential features such as scalability and privacy were overlooked.

Software-Defined Networking (SDN) and Blockchain are leading technologies used worldwide to establish safe network communication as well as build secure network infrastructures. They provide a robust and reliable platform to address threats and face challenges such as security, privacy, flexibility, scalability, and confidentiality. Driven by these assumptions, this paper presents an optimized energy-efficient and secure Blockchain-based software-defined IoT framework for smart networks. Indeed, SDN and Blockchain technologies have proven to be able to suitably manage resource utilization and to develop secure network communication across the IoT ecosystem. However, there is a lack of research works that present a comprehensive definition of such a framework that can meet the requirements of the IoT ecosystem (i.e. efficient energy utilization and reduced end-to-end delay). Therefore, in this research, we present a layered hierarchical architecture for the deployment of a distributed yet efficient Blockchain-enabled SDN-IoT framework that ensures efficient cluster-head selection and secure network communication via the identification and isolation of rouge switches. Besides, the Blockchain-enabled flow-rules record keeps track of the rules enforced in the switches and maintains the consistency within the controller cluster. Finally, we assess the performance of the proposed framework in a simulation environment and show that it can achieve optimized energy-utilization, end-to-end delay, and throughput compared to considered baselines, thus being able to achieve efficiency and security in the smart network.

The world is witnessing the daily emergence of a vast variety of online social networks and community detection problem is a major research area in online social network studies. The existing community detection algorithms are mostly edge-based and are evaluated using the modularity metric benchmarks. However, these algorithms have two inherent limitations. Firstly, they are based on a pure mathematical object which considers the number of connections in each community as the main measures. Consequently, a resolution limit and low accuracy in finding community members in often observed. Whereas, online social networks are dynamic networks and the key players are humans whose main attributes such as lifespan, geo-location, the density of interactions, and user weight, change over time. These attributes tend to influence the formation of user communities in any category of online social network. Secondly, the output structure of existing community detection algorithms is usually provided as a graph and dendrogram. A graph structure, is, however, characterized by a high memory complexity, and subsequently exponential search time complexity. Implementing dendrogram such a complex structure is complicated. To address memory complexity and the accuracy rate of the community detection issues, this paper proposes a new temporal user attribute-based algorithm, namely the recently largest interaction based on the attributes of a typical online social network user. Experimental results show that the proposed algorithm outperforms eight well-known algorithms in this domain.

Blockchain as emerging technology is revolutionizing several industries, and its abundant privileges have opened up a bunch of research directions in various industries; thereby, it has acquired many interests from the research community. The rapid evolution of blockchain research papers in recent years has resulted in a need to conduct research studies that investigate a detailed analysis of the current body of knowledge in this field. To address this need, a few review papers have been published to report the latest accomplishments and challenges of blockchain technology from different perspectives. Nonetheless, there has not been any bibliometric analysis of the state of the art in blockchain where Web of Science (WoS) has been taken into consideration as a literature database. Hence, a thorough analysis of the current body of knowledge in blockchain research through a bibliometric study would be needed. In this paper, we performed a bibliometric analysis of all Blockchain’s conference papers, articles, and review papers that have been indexed byWoS from 2013 to 2018. We have analyzed those collected papers against five research questions. The results revealed some valuable insights, including yearly publications and citations trends, hottest research areas, top-ten influential papers, favorite publication venues, and most supportive funding bodies. The findings of this paper offer several implications that can be used as a guideline by both fresh and experienced researchers to establish a baseline before initiating a blockchain research project in the future.