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In recent years driving violations have increased in big and crowded cities specially in developing countries. This has resulted in escalation of traffic congestions and number of accidents, making them difficult to handle by a central managing authority. New technologies can be used in order to mitigate this problem. Hence, in this paper, we propose a novel concept, called self-fining vehicle, using Blockchain infrastructure on top of the Vehicular Edge Computing, VEC. Self-fining vehicle is a vehicle that detects and records driving violations, issues tickets and pays fines without the need for centralized management system. Violations can be coded into OBU and detected by vehicular communications. The conceptual architecture of secure self-fining vehicle is proposed and discussed in this paper. The proposed architecture improves violation data security, processing speed and communication delay using encryption, authentication, Blockchain and edge computing, enhancing quality of service and consumers‘ quality of experience. The performance analysis of the proposed architecture proves its efficiency in terms of latency in service delivery.

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.

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. The unprecedented growth of energy consumption in data centers created critical concern in recent years for both the research community and industry. Besides its direct associated cost; high energy consumption also results in a large amount of CO2 emission and incurs extra cooling expenditure. The foremost reason for overly energy consumption is the underutilization of data center resources. In modern data centers, virtualization provides a promising approach to improve the hardware utilization level. Virtual machine placement is a process of mapping a group of virtual machines (VMs) onto a set of physical machines (PMs) in a data center with the aim of maximizing resource utilization and minimizing the total power consumption by PMs. An optimal virtual machine placement algorithm substantially contributes to cutting down the power consumption through assigning the input VMs to a minimum number of PMs and allowing the dispensable PMs to be turned off. However, VM Placement Problem is a complex combinatorial optimization problem and known to be NP-Hard problem. This paper presents an extensive review of virtual machine placement problem along with an overview of different approaches for solving virtual machine placement problem. The aim of this paper is to illuminate challenges and issues for current virtual machine placement techniques. Furthermore, we present a taxonomy of virtual machine placement based on various aspects such as methodology, number of objectives, operation mode, problem objectives, resource demand type and number of clouds. The state-of-the-art VM Placement techniques are classified in single objectives and multi-objective groups and a number of prominent works are reviewed in each group. Eventually, some open issues and future trends are discussed which serve as a platform for future research work in this domain.

Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions.

With recent advances of information and communication technology, smart city has been emerged as a new paradigm to dynamically optimize the resources in cities and provide better facilities and quality of life for the citizens. Smart cities involve a variety of components, including ubiquitous sensing devices, heterogeneous networks, large-scale databases, and powerful data centers to collect, transfer, store, and intelligently process real-time information. Smart cities can offer new applications and services for augmenting the daily life of citizens on making decisions, energy consumption, transportation, health-care, and education. Despite the potential vision of smart cities, security and privacy issues remain to be carefully addressed. This paper delineates a comprehensive survey of security and privacy issues of smart cities, and presents a basis for categorizing the present and future developments within this area. It also presents a thematic taxonomy of security and privacy issues of smart cities to highlight the security requirements for designing a secure smart city, identify the existing security and privacy solutions, and present open research issues and challenges of security and privacy in smart cities.

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.

Even though the cloud paradigm and its associated services has been adopted in various enterprise applications, there has been major issues with regard to authenticating users' critical data. Single Sign on (SSO) is a user authentication technique through which a server authenticates and allows a user to use a single aspect of login credentials, for example, to access multiple services in the cloud. Even though SSO reduces the number of logins that are needed over heterogeneous environments, the risk that might be associated with the security of SSO might be detrimental if, for example, a Man-in-the Middle (MITM) attacker manages to gain control of the SSO credentials. It is also possible to get the identity of the users who have logged into Active Directory or intranet and this identity can easily be used to log into other web-based applications, and this requires the use of the Security Assertion Mark-up Language (SAML). SAML is basically a standard that allows users to be logged into applications as per their sessions. The problem that this paper addresses is the lack of a proactive technique of hardening cloud-based SAML while combining SSO with a Multi-Factor Authentication (MFA) at the time of writing this paper. The authors have, therefore, proposed an effective approach that unifies SSO with MFA in this context. Based on the base score index conducted over Common Vulnerability Scoring System (CVSS), the architecture proves to be reliable, feasible and with better performance.

Cloud computing has arisen as the mainstream platform of utility computing paradigm that offers reliable and robust infrastructure for storing data remotely, and provides on demand applications and services. Currently, establishments that produce huge volume of sensitive data, leverage data outsourcing to reduce the burden of local data storage and maintenance. The outsourced data, however, in the cloud are not always trustworthy because of the inadequacy of physical control over the data for data owners. To better streamline this issue, scientists have now focused on relieving the security threats by designing remote data checking (RDC) techniques. However, the majority of these techniques are inapplicable to big data storage due to incurring huge computation cost on the user and cloud sides. Such schemes in existence suffer from data dynamicity problem from two sides. First, they are only applicable for static archive data and are not subject to audit the dynamic outsourced data. Second, although, some of the existence methods are able to support dynamic data update, increasing the number of update operations impose high computation and communication cost on the auditor due to maintenance of data structure, i.e., merkle hash tree. This paper presents an efficient RDC method on the basis of algebraic properties of the outsourced files in cloud computing, which inflicts the least computation and communication cost. The main contribution of this paper is to present a new data structure, called Divide and Conquer Table (D&CT), which proficiently supports dynamic data for normal file sizes. Moreover, this data structure empowers our method to be applicable for large-scale data storage with minimum computation cost. The one-way analysis of variance shows that there are significant differences between the proposed method and the existing methods in terms of the computation and communication cost on the auditor and cloud.

The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.

Fog computing has emerged as an attractive solution to the distributed application of the Internet of Things, and could provide low-latency, highly mobile, and geo-distributed services distinguished from the cloud. While fog computing offers numerous benefits, it also faces various security challenges, of which access control is one of the fundamental requirements since it is concerned with authorizing users to access resources in the open fog environment. This article provides a comprehensive survey of access control of users' data in the environment of fog computing with the aim of highlighting security problems and challenges. It discusses the definition, architecture, and characteristics of fog computing, based on which typical requirements and essential models of access control are addressed. Finally, it highlights known access control schemes in the environment of fog computing, and identifies existing unresolved problems as future directions.

Cloud computing is currently emerging quickly as a client-server technology structure and, currently, providing distributed service applications. However, given the availability of a diverse range of wireless access technologies, people expect continuous connection to the most suitable technology that matches price affordability and performance goals. Among the main challenges of modern communication is the accessibility to wireless networks using mobile devices, with a high service quality (QoS) based on preferences of the users. Past literatures contain several heuristic approaches that use simplified rules to look for the best network that is available. Nevertheless, attributes of mobile devices need algorithms that are quick and effective in order to select best available network near real-time. This study proposes a hybrid intelligent handover decision algorithm primarily founded on two main heuristic algorithms: Artificial Bee Colony or ABC as well as Particle Swarm Optimization or PSO named ABC-PSO to select best wireless network during vertical handover process. The ABC-PSO algorithm has been optimized to achieve small cost function that are powered using the IEEE 802.21 standard taking into account different available wireless networks, the application requirements and the user preferences to improve QoS. Numerical results demonstrate that the ABC-PSO algorithm compared to the related work has lower cost and delay, higher available bandwidth and less number of handover.

As healthcare is one of major socioeconomic problems in cities, mobile healthcare network becomes one of core components of smart cities, which would improve urban healthcare environment. However, there are wide privacy concerns as personal health information is outsourced to untrusted cloud servers. It is a promising method to encrypt the health data before outsourcing, but how to do diagnosis computations on the encrypted health data remains an important challenge. In this paper, we propose a general architecture of the mobile healthcare network, and define three typical secure medical computations, which include the average heart rate, the long QT syndrome detection, and the chi-square tests. To achieve computations on the ciphertext, we leverage fully homomorphic encryption (FHE) to encrypt the health data. Different from previous related works, we use more efficient Dowlin's FHE scheme to implement above three medical computations. In our implementation of the average heart rate, only one ciphertext is sent back to the receiver, so homomorphic decryption is needed once. We take an efficient l-bits comparator to implement the long QT syndrome detection, which only needs l XOR operations and one homomorphic multiplication. We first implement the chi-square tests by homomorphic additions and homomorphic multiplications, which can be used to study whether varicose veins is relevant to overweight. Extensive simulations and analytical results show the scalability and efficiency of our proposed scheme.

With the thriving growth of the cloud computing, the security and privacy concerns of outsourcing data have been increasing dramatically. However, because of delegating the management of data to an untrusted cloud server in data outsourcing process, the data access control has been recognized as a challenging issue in cloud storage systems. One of the preeminent technologies to control data access in cloud computing is Attribute-based Encryption (ABE) as a cryptographic primitive, which establishes the decryption ability on the basis of a users attributes. This paper provides a comprehensive survey on attribute-based access control schemes and compares each schemes functionality and characteristic. We also present a thematic taxonomy of attribute-based approaches based on significant parameters, such as access control mode, architecture, revocation mode, revocation method, revocation issue, and revocation controller. The paper reviews the state-of-the-art ABE methods and categorizes them into three main classes, such as centralized, decentralized, and hierarchal, based on their architectures. We also analyzed the different ABE techniques to ascertain the advantages and disadvantages, the significance and requirements, and identifies the research gaps. Finally, the paper presents open issues and challenges for further investigations. Studying the fundamental of ABE cryptosystem based on different criteria.Taxonomizing the ABE access control in cloud computing.Analysing the existing techniques critically based on taxonomy to identify the gaps.Identifying open issues and challenges of the existing ABE access control techniques.

Previous studies show that information security breaches and privacy violations are important issues for organisations and people. It is acknowledged that decreasing the risk in this domain requires consideration of the technological aspects of information security alongside human aspects. Employees intentionally or unintentionally account for a significant portion of the threats to information assets in organisations. This research presents a novel conceptual framework to mitigate the risk of insiders using deterrence and prevention approaches. Deterrence factors discourage employees from engaging in information security misbehaviour in organisations, and situational crime prevention factors encourage them to prevent information security misconduct. Our findings show that perceived sanctions certainty and severity significantly influence individuals' attitudes and deter them from information security misconduct. In addition, the output revealed that increasing the effort, risk and reducing the reward (benefits of crime) influence the employees' attitudes towards prevent information security misbehaviour. However, removing excuses and reducing provocations do not significantly influence individuals' attitudes towards prevent information security misconduct. Finally, the output of the data analysis also showed that subjective norms, perceived behavioural control and attitude influence individuals' intentions, and, ultimately, their behaviour towards avoiding information security misbehaviour.

Emerging technologies are playing a critical role in the evolution of healthcare systems by presenting eHealth to provide high-quality services and better health to wide-range of patients. Achieving the eHealth goals highly depends on employing modern information and communication technologies (ICTs) to securely and efficiently collect and transmit electronic health records (EHRs) and make them accessible to authorized users and healthcare providers. However, the adoption of EHRs in healthcare providers puts the patients’ privacy and their information security at risk of data breaches. The advent of smart contracts and blockchain technology paves a way for developing efficient EHR access control methods to support secure identification, authentication, and authorization of the clients. This paper delineates an extensive survey on the state-of-the-art blockchain-based access control methods in healthcare domain as a basis for categorizing the existing and future developments in access control area. A thematic taxonomy of the blockchain-based access control methods is also presented to recognize the security issues of the existing methods and highlight the fundamental security requirements to design a granular access control method. This paper also aims for examining the similarities and differences of the traditional access control methods and describes some substantial and outstanding issues and challenges as further directions.

Cloud computing has emerged as a new computing paradigm that offers great potential for storing data remotely. Presently, many organizations have reduced the burden of local data storage and maintenance by outsourcing data storage to the cloud. However, integrity and security of the outsourced data continues to be a matter of major concern for data owners due to the lack of control and physical possession over the data. To deal with this problem, researchers have proposed remote data auditing (RDA) techniques. However, the majority of existing RDA techniques is only applicable for static archived data and is not applicable for auditing or dynamically updating the outsourced data. They are also not applicable to big data storage because of the high computational overhead on the auditor. In this paper, we propose an efficient RDA technique based on algebraic signature properties for a cloud storage system that incurs minimum computational and communication costs. We also present the design of a new data structure-Divide and Conquer Table (DCT)—that can efficiently support dynamic data operations such as append, insert, modify, and delete. Our proposed data structure can be applied for large-scale data storage and will incur minimum computational cost. A comparison between our proposed method and other state-of-the-art RDA techniques shows that our method is secure and highly efficient in reducing the computational and communication costs on the server and the auditor.

Vehicular infotainment systems have attracted great interests from both academia and industry. A vehicular infotainment system includes two parts: 1) the information part and 2) the entertainment part, which are integrated together to provide a unified platform to drivers and passengers. Although some excellent works have been done on vehicular infotainment systems, there are still some significant challenges that need to be addressed. Recently, compressed sensing (CS) has emerged as a new technique, which could be used to tackle the challenges in vehicular infotainment systems. In this paper, we present a comprehensive survey and research challenges on the applications of CS in vehicular infotainment systems, including object tracking for driving safety, privacy protection and security, vehicular crowdsensing, vehicular communications, road traffic estimation, video streaming, and object recognition. In addition, we compare the traditional methods and CS methods in solving the main problems in vehicular infotainment systems. Moreover, research challenges of applying CS in vehicular infotainment systems are discussed.

Cloud computing has emerged as a popular computing model to process data and execute computationally intensive applications in a pay-as-you-go manner. Due to the ever-increasing demand for cloud-based applications, it is becoming difficult to efficiently allocate resources according to user requests while satisfying the service-level agreement between service providers and consumers. Furthermore, cloud resource heterogeneity, the unpredictable nature of workload, and the diversified objectives of cloud actors further complicate resource allocation in the cloud computing environment. Consequently, both the industry and academia have commenced substantial research efforts to efficiently handle the aforementioned multifaceted challenges with cloud resource allocation. The lack of a comprehensive review covering the resource allocation aspects of optimization objectives, design approaches, optimization methods, target resources, and instance types has motivated a review of existing cloud resource allocation schemes. In this paper, current state-of-the-art cloud resource allocation schemes are extensively reviewed to highlight their strengths and weaknesses. Moreover, a thematic taxonomy is presented based on resource allocation optimization objectives to classify the existing literature. The cloud resource allocation schemes are analyzed based on the thematic taxonomy to highlight the commonalities and deviations among them. Finally, several opportunities are suggested for the design of optimal resource allocation schemes.

Smart city has been emerged as a new paradigm to dynamically optimize the resources in cities and provide better facilities and quality of life for the citizens. Despite the potential vision of smart cities, security and privacy issues remain to be carefully addressed. In this paper, we present a comprehensive survey of security and privacy issues of smart cities, and categorize the present and future developments within this area. We also describe a thematic taxonomy of security and privacy issues of smart cities to highlight the requirements for designing a secure smart city, identify the existing security and privacy solutions, and present open research issues and challenges of security and privacy in smart cities.

During the last decade, researchers and developers have been attracted to Vehicular Ad-hoc Network (VANET) research area due to its significant applications, including efficient traffic management, road safety, and entertainment. Several resources such as communication, on-board unit, storage, computing, and endless battery are embedded in the vehicles, which are used for enhancing Intelligent Transportation Systems (ITSs). One of the crucial challenges for VANETs is to securely share an important information among vehicles. In some cases, the data owner is also not available and unable to control the data sharing process, i.e., sharing data with a new user or revoking the existing user. In this paper, we present a new method to address the data sharing problem and delegate the management of data to a Trusted Third Party (TPA) based on bilinear pairing technique. To achieve this goal, we use a cloud computing, as the mainstream platform of utility computing paradigm, to store the huge amount of data and perform the re-encryption process securely.

The popularity of network virtualization has recently regained considerable momentum because of the emergence of OpenFlow technology. It is essentially decouples a data plane from a control plane and promotes hardware programmability. Subsequently, OpenFlow facilitates the implementation of network virtualization. This study aims to provide an overview of different approaches to create a virtual network using OpenFlow technology. The paper also presents the OpenFlow components to compare conventional network architecture with OpenFlow network architecture, particularly in terms of the virtualization. A thematic OpenFlow network virtualization taxonomy is devised to categorize network virtualization approaches. Several testbeds that support OpenFlow network virtualization are discussed with case studies to show the capabilities of OpenFlow virtualization. Moreover, the advantages of popular OpenFlow controllers that are designed to enhance network virtualization is compared and analyzed. Finally, we present key research challenges that mainly focus on security, scalability, reliability, isolation, and monitoring in the OpenFlow virtual environment. Numerous potential directions to tackle the problems related to OpenFlow network virtualization are likewise discussed.