/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Due to the current structure of digital factory, it is necessary to build the smart factory to upgrade the manufacturing industry. Smart factory adopts the combination of physical technology and cyber technology and deeply integrates previously independent discrete systems making the involved technologies more complex and precise than they are now. In this paper, a hierarchical architecture of the smart factory was proposed first, and then the key technologies were analyzed from the aspects of the physical resource layer, the network layer, and the data application layer. In addition, we discussed the major issues and potential solutions to key emerging technologies, such as Internet of Things (IoT), big data, and cloud computing, which are embedded in the manufacturing process. Finally, a candy packing line was used to verify the key technologies of smart factory, which showed that the overall equipment effectiveness of the equipment is significantly improved.

Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT). With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.

Differential Privacy Techniques for Cyber Physical Systems: A Survey

With the advances in telecommunications, more and more devices are connected to the Internet and getting smart. As a promising application scenario for carrier networks, vehicular communication has enabled many traffic-related applications. However, the heterogeneity of wireless infrastructures and the inflexibility in protocol deployment hinder the real world application of vehicular communications. SDN is promising to bridge the gaps through unified network abstraction and programmability. In this research, we propose an SDN-based architecture to enable rapid network innovation for vehicular communications. Under this architecture, heterogeneous wireless devices, including vehicles and roadside units, are abstracted as SDN switches with a unified interface. In addition, network resources such as bandwidth and spectrum can also be allocated and assigned by the logically centralized control plane, which provides a far more agile configuration capability. Besides, we also study several cases to highlight the advantages of the architecture, such as adaptive protocol deployment and multiple tenants isolation. Finally, the feasibility and effectiveness of the proposed architecture and cases are validated through traffic-trace-based simulation.

SDVN: enabling rapid network innovation for heterogeneous vehicular communication

The potential of crowdsourcing for complex problem solving has been revealed by smartphones. Nowadays, vehicles have also been increasingly adopted as participants in crowd-sourcing applications. Different from smartphones, vehicles have the distinct advantage of predictable mobility, which brings new insight into improving the crowdsourcing quality. Unfortunately, utilizing the predictable mobility in participant recruitment poses a new challenge of considering not only current location but also the future trajectories of participants. Therefore, existing participant recruitment algorithms that only use the current location may not perform well. In this paper, based on the predicted trajectory, we present a new participant recruitment strategy for vehicle-based crowdsourcing. This strategy guarantees that the system can perform well using the currently recruited participants for a period of time in the future. The participant recruitment problem is proven to be NP-complete, and we propose two algorithms, a greedy approximation and a genetic algorithm, to find the solution for different application scenarios. The performance of our algorithms is demonstrated with traffic trace dataset. The results show that our algorithms outperform some existing approaches in terms of the crowdsourcing quality.

High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility

Vehicular Ad hoc Network (VANET) is an intermittently connected mobile network in which message propagation is quite challenging. Conventional routing protocols proposed for VANET are usually in greedy or optimum fashion. Geographical forwarding only uses local information to make the routing decision which may lead to long packet delay, while link-based forwarding has better performance but requires much more overheads. To disseminate message efficiently in VANET, a routing protocol which has both short delivery delay time and low routing overhead is required. In this paper, we proposed a SDN-based routing framework for efficiently message propagation in VANET. Software-Defined Networking (SDN) is an emerging technology that decouples the control plane from the data forwarding plane in switches and collects all the control planes into a central controller. In SDN-based routing framework, the central controller gathers network information from switches and computes optimal routing paths for switches based on the global network information. Since switches don’t need to exchange routing information with each other, the routing overhead is much lower. This paper is the first to propose a SDN-based routing framework for efficiently message propagation in VANET. A new algorithm is developed to find the global optimal route from the source to the destination in VANET with dynamic network density. We demonstrate, through the simulation results, that our proposed framework significantly outperforms the related protocols in terms of both delivery delay time and routing overhead.

SDN-Based Routing for Efficient Message Propagation in VANET

New-generation industries heavily rely on big data to improve their efficiency. Such big data are commonly collected by smart nodes and transmitted to the cloud via wireless. Due to the limited size of smart node, the shortage of energy is always a critical issue, and the wireless data transmission is extremely a big power consumer. Aiming to reduce the energy consumption in wireless, this article introduces a potential breach from data redundancy. If redundant data are no longer collected, a large amount of wireless transmissions can be cancelled and their energy saved. Motivated by this breach, this article proposes a compressive-sensing-based collection framework to minimize the amount of collection while guaranteeing data quality. This framework is verified by experiments and extensive realtrace- driven simulations.

Embracing big data with compressive sensing: a green approach in industrial wireless networks

Sensing and networking have been regarded as key enabling technologies of future smart vehicles. Sensing allows vehicles to be context awareness, while networking empowers context sharing among ambients. Existing vehicular communication solutions mainly rely on homogeneous network, or heterogeneous network via data offloading. However, today’s vehicular network implementations are highly heterogeneous. Therefore, conventional homogeneous communication and data offloading may not be able to satisfy the requirement of the emerging vehicular networking applications. In this paper, we apply the software-defined network (SDN) to the heterogeneous vehicular networks to bridge the gaps. With SDN, heterogeneous network resources can be managed with a unified abstraction. Moreover, we propose an SDN-based wireless communication solution, which can schedule different network resources to minimize communication cost. We investigate the problems in both single and multiple hop cases. We also evaluate the proposed approaches using traffic traces. The effectiveness and the efficiency are validated by the results.

Zongjian He

Papers

SDVN: enabling rapid network innovation for heterogeneous vehicular communication

High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility

SDN-Based Routing for Efficient Message Propagation in VANET

Embracing big data with compressive sensing: a green approach in industrial wireless networks

Cost-Efficient Sensory Data Transmission in Heterogeneous Software-Defined Vehicular Networks