In the very near future, transportation will go through a transitional period that will shape the industry beyond recognition. Smart vehicles have played a significant role in the advancement of ...intelligent and connected transportation systems. Continuous vehicular cloud service availability in smart cities is becoming a crucial subscriber necessity which requires improvement in the vehicular service management architecture. Moreover, as smart cities continue to deploy diversified technologies to achieve assorted and high-performance cloud services, security issues with regards to communicating entities which share personal requester information still prevails. To mitigate these concerns, we introduce an automated secure continuous cloud service availability framework for smart connected vehicles that enables an intrusion detection mechanism against security attacks and provides services that meet users’ quality of service (QoS) and quality of experience (QoE) requirements. Continuous service availability is achieved by clustering smart vehicles into service-specific clusters. Cluster heads are selected for communication purposes with trusted third-party entities (TTPs) acting as mediators between service requesters and providers. The most optimal services are then delivered from the selected service providers to the requesters. Furthermore, intrusion detection is accomplished through a three-phase data traffic analysis, reduction, and classification technique used to identify positive trusted service requests against false requests that may occur during intrusion attacks. The solution adopts deep belief and decision tree machine learning mechanisms used for data reduction and classification purposes, respectively. The framework is validated through simulations to demonstrate the effectiveness of the solution in terms of intrusion attack detection. The proposed solution achieved an overall accuracy of 99.43% with 99.92% detection rate and 0.96% false positive and false negative rate of 1.53%.
The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 ...service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.
•A blockchain-empowered and centerless trusted service mechanism for the crowdsourcing system in 5G-enabled smart cities.•Crowdsourcing service process is divided into nine stages: initialization, ...task submission, task publication, task reception, scheme submission, scheme arbitration, payment, task rollback, and service compensation.•A smart contract has been used to controls the execution of each step in each stage, and the payment is completed by blockchain without the involvement of third-party central institutions.
With the development of 5G(5th generation mobile networks) technology, smart cities are an inevitable trend in modern city development. Among them, smart city services are the foundation of 5G-enabled smart cities. As an emerging and informational city service model, crowdsourcing has been widely used in our daily life. However, in the existing crowdsourcing systems, the requesters and the workers are usually required to use the crowdsourcing platform as the trust center, and the payment depends on the third-party central payment institutions, which have a massive security risk. Once these centers are attacked or do evil, it will bring higher losses to the crowdsourcing parties. These problems will negatively affect the further development of 5G-enabled smart cities. To address these issues, we propose a blockchain-empowered and decentralized trusted service mechanism for the crowdsourcing system in 5G-enabled smart cities. In the proposed mechanism, the crowdsourcing service process is divided into nine stages: initialization, task submission, task publication, task reception, scheme submission, scheme arbitration, payment, task rollback, and service compensation. The smart contract controls the execution of each step in each stage, and the payment is completed by blockchain without the involvement of third-party central institutions. Finally, we develop smart contracts to conduct experiments based on Ethereum and compare it with the existing crowdsourcing system. The experimental results show the effectiveness and applicability of the crowdsourcing system service mechanism without the central institutions.
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•In this research, state-of-the-art approaches based on supervised machine learning are presented to tackle aspect-based sentiment analysis (ABSA) challenges of Arabic Hotels’ reviews.•Two approaches ...of deep recurrent neural network (RNN) and support vector machine (SVM) are implemented and trained along with lexical, word, syntactic, morphological, and semantic features.•The proposed approaches are evaluated using a reference dataset of Arabic Hotels’ reviews annotated using an ABSA framework presented in the Semantic Evaluation workshop 2016 (SemEval-ABSA16).•Evaluation results show that the SVM approach outperforms the other deep RNN approach in the research investigated tasks (T1: aspect category identification (E#A allocation), T2: aspect opinion target expression (OTE) extraction, and T3: aspect sentiment polarity identification).•Whereas, when focusing on the execution time required for training and testing the models, the deep RNN was faster especially for the second task.
In this research, state-of-the-art approaches based on supervised machine learning are presented to address the challenges of aspect-based sentiment analysis (ABSA) of Arabic Hotels’ reviews. Two approaches of deep recurrent neural network (RNN) and support vector machine (SVM) are implemented and trained along with lexical, word, syntactic, morphological, and semantic features. The proposed approaches are evaluated using a reference dataset of Arabic Hotels’ reviews. Evaluation results show that the SVM approach outperforms the other deep RNN approach in the research investigated tasks (T1: aspect category identification, T2: aspect opinion target expression (OTE) extraction, and T3: aspect sentiment polarity identification). Whereas, when focusing on the execution time required for training and testing the models, the deep RNN execution time was faster, especially for the second task.
Fog-to-fog communication has been introduced to deliver services to clients with minimal reliance on the cloud through resource and capability sharing of cooperative fogs. Current solutions assume ...full cooperation among the fogs to deliver simple and composite services. Realistically, each fog might belong to a different network operator or service provider and thus will not participate in any form of collaboration unless self-monetary profit is incurred. In this paper, we introduce a fog collaboration approach for simple and complex multimedia service delivery to cloud subscribers while achieving shared profit gains for the cooperating fogs. The proposed work dynamically creates short-term service-level agreements (SLAs) offered to cloud subscribers for service delivery while maximizing user satisfaction and fog profit gains. The solution provides a learning mechanism that relies on online and offline simulation results to build guaranteed workflows for new service requests. The configuration parameters of the short-term SLAs are obtained using a modified tabu-based search mechanism that uses previous solutions when selecting new optimal choices. Performance evaluation results demonstrate significant gains in terms of service delivery success rate, service quality, reduced power consumption for fog and cloud datacenters, and increased fog profits.
With the advancement in the development of the Internet of Things (IoT) technology, as well as the industrial IoT, various applications and services are benefiting from this emerging technology such ...as smart healthcare systems, virtual realities applications, connected and autonomous vehicles, to name a few. However, IoT devices are known for being limited computation capacities which is crucial to the device’s availability time. Traditional approaches used to offload the applications to the cloud to ease the burden on the end user’s devices, however, greater latency and network traffic issues still persist. Mobile Edge Computing (MEC) technology has emerged to address these issues and enhance the survivability of cloud infrastructure. While a lot of attempts have been made to manage an efficient process of applications offload, many of which either focus on the allocation of computational or communication protocols without considering a cooperative solution. In addition, a single-user scenario was considered. Therefore, we study multi-user IoT applications offloading for a MEC system, which cooperatively considers to allocate both the resources of computation and communication. The proposed system focuses on minimizing the weighted overhead of local IoT devices, and minimize the offload measured by the delay and energy consumption. The mathematical formulation is a typical mixed integer nonlinear programming (MINP), and this is an NP-hard problem. We obtain the solution to the objective function by splitting the objective problem into three sub-problems. Extensive set of evaluations have been performed so as to get the evaluation of the proposed model. The collected results indicate that offloading decisions, energy consumption, latency, and the impact of the number of IoT devices have shown superior improvement over traditional models.
Green Internet of things (GIoT) generally refers to a new generation of Internet of things design concept. It can save energy and reduce emissions, reduce environmental pollution, waste of resources, ...and harm to human body and environment, in which green smart device (GSD) is a basic unit of GIoT for saving energy. With the access of a large number of heterogeneous bottom-layer GSDs in GIoT, user access and control of GSDs have become more and more complicated. Since there is no unified GSD management system, users need to operate different GIoT applications and access different GIoT cloud platforms when accessing and controlling these heterogeneous GSDs. This fragmented GSD management model not only increases the complexity of user access and control for heterogeneous GSDs, but also reduces the scalability of GSDs applications. To address this issue, this article presents a blockchain-empowered general GSD access control framework, which provides users with a unified GSD management platform. First, based on the World Wide Web Consortium (W3C) decentralized identifiers (DIDs) standard, users and GSD are issued visual identity (
VID
). Then, we extended the GSD-DIDs protocol to authenticate devices and users. Finally, based on the characteristics of decentralization and non-tampering of blockchain, a unified access control system for GSD was designed, including the registration, granting, and revoking of access rights. We implement and test on the Raspberry Pi device and the FISCO-BCOS alliance chain. The experimental results prove that the framework provides a unified and feasible way for users to achieve decentralized, lightweight, and fine-grained access control of GSDs. The solution reduces the complexity of accessing and controlling GSDs, enhances the scalability of GSD applications, as well as guarantees the credibility and immutability of permission data and identity data during access.
•Introduce a trustworthy smart city service delivery solution at the edge of the Internet.•Improve the performance and the security aspects of smart city services.•Provide a detailed evaluation of ...the framework with support of machine learning.•Provide a detailed state of the art of smart city services supported by IoT and Edge computing.
The proliferation of smart city and Internet of Things (IoT) applications has introduced numerous challenges related to network performance, reliability, and security. Moreover, the distributed nature of the smart city and IoT infrastructure hs led to issues in regards to service availability, reliability, sustainability and security. Edge computing provides a decentralized computing and communication framework for different types of applications such as intelligent transportation systems, cognitive assistance, health and social services. Edge computing helps in improving the performance of such applications and reduces the end-to-end latency incurred for such time-critical applications. In this article, we introduce a trustworthy smart city service delivery solution at the edge of the network. The solution uses a collaborative technique between distributed edge servers and privacy mediator nodes with the support of an intrusion detection system to enhance the availability, reliability and security of smart city applications. Simulation results show a reduction in the delay per service request by 39.2% for highly dense environments, and up to 62.6% for lightly dense environments. Moreover, the solution reduces the dropped service requests below 2% with high accuracy and detection rates and low false-negative rates.
The intelligent and connected transportation system (ICTS) is a significant and mandatory component of the smart city architecture. Multimedia content sharing, vehicle power management, and road ...navigation are all examples of ICTS services. As smart cities continue to deploy different technologies to improve the performance and diversity of vehicular cloud services, one of the main issues that prevails is efficient and reliable service discovery and selection for smart vehicles. Furthermore, cloud service providers (SPs) are limited to the availability, variety and quality of services made available to vehicular cloud subscribers. Smart vehicles rely on a number of SPs to acquire the required services while moving. It therefore becomes challenging for vehicular cloud subscribers to acquire services that meet their quality of experience (QoE) preferences. This paper introduces a new service provision scheme to provide continuous availability of diversified cloud services targeting vehicular cloud users through a cluster-based trusted third party (TTP) framework. TTPs act as cloud service mediators between cloud service subscribers and providers. Vehicles that are considered to have similar patterns of movement and service acquisition characteristics are grouped into service-specific clusters. TTPs communicate with service providers and cluster heads to negotiate for services with high QoE characteristics. A location prediction method is adopted to determine a vehicle's future location and allow services to be negotiated for before the vehicle's arrival. We provide simulation results to show that our approach can adequately discover and deliver cloud services with increased QoE results, minimal overhead burden and reduced end-to-end latency.
The Internet of Things (IoT) paradigm has integrated the sensor network silos to the Internet and enabled the provision of value-added services across these networks. These smart devices are now ...becoming socially conscious by following the social Internet of Things (SIoT) model that empowers them to create and maintain social relationships among them. The Social Internet of Vehicle (SIoV) is one application of SIoT in the vehicular domain that has evolved the existing intelligent transport system (ITS) and vehicular ad-hoc networks (VANETs) to the next phase of Intelligent by adding socializing aspect and constant connectivity. SIoV generates a massive amount of real-time data enriched with context and social relationship information about vehicles, drivers, passengers, and the surrounding environment. Therefore, the role of privacy management becomes essential in SIoV, as data is collected and stored at different layers of its architecture. The challenge of privacy is aggravated because the dynamic nature of SIoV poses a major threat in its adoption. Motivated by the need to address these aspects, this paper identifies the challenges involved in managing privacy in SIoV. Furthermore, the paper analyzes the privacy issues and factors that are essential to be considered for preserving privacy in SIoV environments from different perspectives including the privacy of a person, behavior and action, communication, data and image, thoughts and feelings, location and space, and association. In addition, the paper discusses the blockchain-based solutions to preserve privacy for SIoV.
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