Zongjian He

SDVN: enabling rapid network innovation for heterogeneous vehicular communication

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.

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

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.

Distributed Sensing for High-Quality Structural Health Monitoring Using WSNs

Due to the low cost and ease of deployment, wireless sensor networks (WSNs) are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered structural health monitoring (SHM) systems. Different from other applications of WSNs such as environmental monitoring, SHM applications are much more data intensive and it is not feasible to stream the raw data back to the server due to the severe bandwidth and energy limitations of low-power sensor networks. In-network processing is a promising approach to address this problem but designing distributed versions for the sophisticated SHM algorithms is much more challenging because SHM algorithms are computationally intensive, and involve data-level collaboration of multiple sensors. In this paper, we select a classical SHM algorithm: the eigen-system realization algorithm (ERA), and propose a few distributed ERAs suitable for WSNs. In particular, we first design a method to incrementally calculate the ERA and then propose three schemes upon which the incremental ERA can be carried out along an Hamiltonian path, along a path in the minimum connected dominating set (MCDS) and along the shortest path tree (SPT). The efficacy of these schemes are demonstrated and compared through both simulation experiment. We believe the proposed schemes can also serve as a guideline when applying WSNs for other applications like SHM which are also data-intensive and involve sophisticated signal processing of collected information.

MICE: A Real-time Traffic Estimation Based Vehicular Path Planning Solution Using VANETs

Dynamic vehicular path planning using real-time traffic information have attracted the interest for both academic and industry. How to collect traffic information and make path planning decisions accordingly are two major problems. Existing works have addressed these issues using centralized or infrastructure based traffic collection approaches. However, existing works have certain weaknesses on efficiency and effectiveness. This paper introduced a novel dynamic vehicular path planning solution. The proposed solution does not rely on infrastructures to collect traffic information. Meanwhile, It utilizes density-speed traffic flow model to predict the traffic condition. In addition, a dynamic candidate path selection algorithm is developed to reduce the redundant data collection overhead. Extensive evaluations using large scale traffic trace based simulation have been performed. The results show that our solution outperforms some existing solutions in terms of communication efficiency and path planning effectiveness.

SDN-Based Routing for Efficient Message Propagation in VANET

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.

Cost-efficient traffic-aware data collection protocol in VANET

Data from running vehicles are invaluable to numerous ITS and urban computing applications. This paper studies the issue of collecting data from multiple vehicles to a roadside base station via vehicle-to-vehicle and vehicle-to-infrastructure communications. Existing data collection approaches in VANET have mainly focused on the network problems, such as packet loss, network clustering or data aggregation, but the impact of real-time traffic condition is barely considered. In this paper, we investigate the data collection problem in VANETs under rapid evolving traffic conditions. Our approach can adaptively choose to carry or forward the data packet, based on current traffic information. The objective is to minimize the network communication overhead while satisfying the data collection time constraint. We formulate the data collection problem as a scheduling optimization problem and prove it is NP-complete. An optimal dynamic programming solution and a genetic algorithm based heuristic solution are developed to solve the problem under different application scenarios. Extensive evaluations validate that our proposed solution outperforms some existing ones in terms of effectiveness and efficiency.

Exploiting Real-Time Traffic Light Scheduling with Taxi Traces

Traffic lights in urban area can significantly influence the efficiency and effectiveness of transportation. The real-time scheduling information of traffic lights is fundamentally important for many intelligent transportation applications, such as shortest-time navigation and green driving advisory. However, existing traffic light scheduling identification systems either entail dedicated infrastructures or depend on specialized traffic traces, which hinders the popularity and real world deployment. Differently, we propose to identify real-time traffic light scheduling by analyzing taxi traces that are widely accessible from taxi companies. The key idea is to exploit the periodicity in traffic patterns, which is directly affected by traffic lights. We also develop advanced algorithms to identify red/green lights duration and signal change time. We evaluate our solution using over one billion taxi records from Shenzhen, China. The evaluation results validate the effectiveness of our system.

To Carry or To Forward? A Traffic-Aware Data Collection Protocol in VANETs

Vehicles can provide useful data to many urban computing applications. This paper addresses the issue of collecting data from multiple vehicles to a roadside base station using VANET. The impacts of real-time traffic condition have not been widely discussed in literature. In this paper, we study the data collection problem under different traffic conditions. The objective is to minimize the network communication overhead while satisfying the data collection time constraint. We formulate the problem as an scheduling optimization problem. A dynamic programming based solution and a genetic algorithm based solution are developed to solve the problem for different application scenarios. The solution can adaptively choose to carry or forward data based on current traffic information. Evaluation shows that the proposed solution outperforms some existing ones in terms of effectiveness and efficiency.

ITrip: Traffic signal prediction using smartphone based community sensing

Providing drivers with traffic signal scheduling information in advance can enable many novel applications, such as optimal speed advisory and shortest trip planning. Existing solutions employ either infrastructure (e.g. wireless transmitter) or vision (e.g. cameras) based approaches. However, these solutions may be limited by high infrastructure cost or low air visibility. In this paper, we propose iTrip, a novel community sensing service that only utilizes smartphone accelerometer to detect and predict accurate traffic signal schedules. In iTrip, on-vehicle smartphones detect and report vehicles events, such as start and stop moving, to the server. Using the collected data contributed by a group of vehicles, iTrip can predict the traffic signal in near future by estimating the traffic signal schedule. We conduct extensive simulation under different traffic scenarios. Results show our proposed method is able to efficiently estimate the schedule with accuracy less than 1 second in a few signal cycles.

CeBR: Cost-efficient Industrial Positioning using Hybrid Bluetooth and RFID

Industrial positioning with low cost is an important proposal in IoT system. Most existing approaches pursue good accuracy regardless of high cost, which significantly affects practical use. In this paper, we propose a cooperative positioning scheme called CeBR, which is based on hybrid Bluetooth and RFID. We find such a hybrid system obtains high accuracy and achieves good recognition for missing objects occurring in consecutive readings. In our work, we have implemented CeBR with lower error distance benefiting from deployment policy and Radial Basis Function (RBF) based error model. We evaluate our proposals in the experiment. CeBR achieves excellent cost efficiency in the results, which is seen to bring benefits including cost savings and implementation simplicity to industry.