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.

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.

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 real-trace-driven simulations.

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.

RFID is a major prerequisite for the IoT, which connects physical objects with the Internet. Unknown tag identification is a fundamental problem in large-scale IoT systems, such as automatic stock management and object tracking. Recently, several protocols have been proposed to discern unknown tags. In this article, we overview the underlying mechanism of previous protocols, and pinpoint the challenging issues together with possible solutions. Then we propose a scheme using a Bloom filter that significantly reduces the data transmission during the identification process. We further present the preliminary results to illuminate the Bloom-filter- based architecture.