Nianbo Liu

PVA in VANETs: Stopped cars are not silent

In Vehicular Ad Hoc Networks (VANETs), the major communication challenge lies in very poor connectivity, which can be caused by sparse or unbalanced traffic. Deploying supporting infrastructure could relieve this problem, but it often requires a large amount of investment and elaborate design, especially at the city scale. In this paper, we propose the idea of Parked Vehicle Assistance (PVA), which allows parked vehicles to join VANETs as static nodes. With wireless device and rechargable battery, parked vehicles can easily communicate with one another and their moving counterparts. Owing to the extensive parking in cities, parked vehicles are natural roadside nodes characterized by large number, long-time staying, wide distribution, and specific location. So parked vehicles can serve as static backbone and service infrastructure to improve connectivity. We investigate network connectivity in PVA through theoretic analysis and realistic survey and simulations. The results prove that even a small proportion of PVA vehicles could overcome sparse or unbalanced traffic, and promote network connectivity greatly. Thus, PVA enhances VANETs from down to top, and paves the way for new hybrid networks with static and mobile nodes.

When Transportation Meets Communication: V2P over VANETs

Information interaction is a crucial part of modern transportation activities. In this paper, we propose the idea of Vehicle-to-Passenger communication (V2P), which allows direct, instant, and flexible communication between moving vehicles and roadside passengers. With pocket wireless devices, passengers can easily join VANETs as roadside nodes, and express their travel demands, e.g., taking a free ride or calling a taxi via radio queries over VANETs. Once a matched vehicle is found through the disseminated queries, the driver can decide whether to provide corresponding services, especially the carrying of passengers and goods. We investigate the main challenges in vehicle calling, establish a trip history model to predict vehicle movement, and develop typical query dissemination schemes to match the target vehicle in vehicular networks. With V2P over VANETs, vehicle transportation is capable of open and efficient P2P information interaction, and thus benefits from relevant efficiency improvement. Based on a realistic travel survey and simulation, we prove that vehicle calling is effective and efficient in casual carpooling and taxi calling.

Traffic-Aware Data Delivery Scheme for Urban Vehicular Sensor Networks

Vehicular sensor network (VSN) is a promising technology which could be widely applied to monitor the physical world in urban areas. In such a scenario, the efficient data delivery plays a central role. Existing schemes, however, cannot choose an optimal route, since they either ignore the impact of vehicular distribution on connectivity, or make some unreasonable assumptions on vehicular distribution. In this paper, we propose a traffic-aware data delivery scheme (TADS). The basic idea of TADS is to choose intersections to forward packets dynamically as the route from a source to destination based on link quality and remaining Euclidean distance to destination. Specifically, we first present an optimal utility function as the criteria of intersection selection. Besides the packet forwarding through intersections, we also propose an improved geographically greedy routing algorithm for packet forwarding in straightway mode. Moreover, in order to decrease the routing overhead brought by the traffic information gathering, we build a traffic condition prediction model to estimate the link quality. The simulation results show that our TADS outperforms existing works on packet delivery ratio, end-to-end delay, and routing overhead.

Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone

In current carpooling systems, drivers and passengers offer and search for their trips through available mediums, for example, accessing carpool website by smartphone, for finding a possible match of the journey. While efforts have been made to achieve fast matching for known trips, the need for accurate mobile tracking for individual users still remains a bottleneck. For example, drivers feel impatient to input their routes before driving, or centralized systems haves difficulties to track a large number of vehicles in real time. In this paper, we present the idea of Mobility Crowdsourcing (MobiCrowd), which leverages private smartphone to collect individual trips for carpooling, without any explicit effort on the part of users. Our scheme generates daily trips and mobility models for each user, and then makes carpooling zero-effort by enabling travel data to be crowdsourced instead of tracking vehicles or asking users to input their trips. With prior mobility knowledge, one users travel routes and positions for carpooling can be predicted according to the location of the time and other mobility context. Based on a realistic travel survey and simulation, we prove that our scheme can provide efficient and accurate position estimation for individual carpools.

An Efficient Data Dissemination Protocol with Roadside Parked Vehicles’ Assistance in Vehicular Networks

Data dissemination is a challenging problem in vehicular ad hoc networks (VANETs) due to the characteristics of VANETs such as highly dynamic topology, intermittent connectivity, and the road-constrained mobility. Observed from the fact that on-street parking is a common phenomenon in the city, the parked vehicles at roadside can also contribute their resources to the communications in network. In this paper, an efficient data dissemination protocol (EDP) for VANETS is proposed. Different from the existing works which consider mainly mobile vehicles or some expensive roadside infrastructure, EDP leverages the resources of parked vehicles at roadside to help in forwarding data. EDP groups the parked vehicles at roadside into a cluster, which buffers and relays data from mobile vehicles and manages the duplicates of data. Simulation results based on a real city map show that EDP achieves a higher delivery ratio and lower delivery delay.

Mobile content distribution with vehicular cloud in urban VANETs

Plenty of multimedia contents such as traffic images, surveillance video, music and movie will flood into vehicular ad hoc networks. However, content distribution over VANETs is not a easy task, due to the high mobility of vehicles and intermittent connectivity. Infrastructure-based scheme can relieve the problem, but with a large amount of investment. In this paper, we propose a mobile content distribution scheme based on roadside parking cloud (RPC), which is formed by the parked car on the roadside, and mobile cloud (MC), which is formed by moving cars on the road. According to a trip history model, a mobile car can estimate its following trajectory. When it wants to download the content, gateway node of the RPC will work out a downloading schedule, which tells it how much chunks it can download from which RPCs. Moreover, the helper of the mobile car in mobile cloud would deliver specified chunks to it when there is lack of RPC in the following trip. Simulation results show that cloud-based scheme performs better than inter-vehicle communication approach and cluster-based scheme.

DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration

Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving. However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations. Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered. The noises caused by hardware of the smartphone cannot be removed from the sensor reading. In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration. We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation. Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven. DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control. We evaluate the effectiveness of our scheme under realistic environments. The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively. In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range.

Starvation avoidance mobile energy replenishment for wireless rechargeable sensor networks

The breakthrough progress of wireless charging technology provides a significant opportunity to solve the energy constrained problem in wireless sensor networks. However, most of existing mobile energy replenishment schemes either cannot well adapt to the high diversity of energy consumption or leave out of consideration about the fairness of charging response, and thus may still suffer from non-negligible performance degradation resulted from energy starvation. Particularly when there is a large number of charging requirements, the energy starvation may bring about quite a number of sensor nodes invalid due to energy depletion. In this paper, we explore the energy starvation issue while provisioning energy for wireless sensor networks and propose a Starvation Avoidance Mobile Energy Replenishment scheme (SAMER) which can avoid energy starvation through calculating and considering the maximum tolerable latency of each charging requirement. The simulation results show that SAMER scheme can effectively solve the energy starvation problem and achieve efficient mobile energy supplement for wireless sensor networks.

A Low-Cost Vehicle Anti-Theft System Using Obsolete Smartphone

In modern society, vehicle theft has become an increasing problem to the general public. Deploying onboard anti-theft systems could relieve this problem, but it often requires extra investment for vehicle owners. In this paper, we propose the idea of PhoneInside, which does not need a special device but leverages an obsolete smartphone to build a low-cost vehicle anti-theft system. After being fixed in the vehicle body with a car charger, the smartphone can detect vehicle movement and adaptively use GPS, cellular/WiFi localization, and dead reckoning to locate the vehicle during driving. Especially, a novel Velocity-Aware Dead Reckoning (VA-DR) method is presented, which utilizes map knowledge and vehicle’s turns at road curves and intersections to estimate velocity for trajectory computation. Compared to traditional dead reckoning, it reduces accumulated errors and achieves great improvement in localization accuracy. Furthermore, based on the learning of the driving history, our system can establish individual mobility model for a vehicle and distinguish abnormal driving behaviors by a Long Short Term Memory (LSTM) network. With the help of ad hoc authentication, the system can identify vehicle theft and send out timely alarming and tracking messages for rapid recovery. The realistic experiments running on Android smartphones prove that our system can detect vehicle theft effectively and locate a stolen vehicle accurately, with average errors less than the sight range.

Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model

In mHealth field, accurate breathing rate monitoring technique has benefited a broad array of healthcare-related applications. Many approaches try to use smartphone or wearable device with fine-grained monitoring algorithm to accomplish the task, which can only be done by professional medical equipment before. However, such schemes usually result in bad performance in comparison to professional medical equipment. In this paper, we propose DeepFilter, a deep learning-based fine-grained breathing rate monitoring algorithm that works on smartphone and achieves professional-level accuracy. DeepFilter is a bidirectional recurrent neural network (RNN) stacked with convolutional layers and speeded up by batch normalization. Moreover, we collect 16.17 GB breathing sound recording data of 248 hours from 109 and another 10 volunteers to train and test our model, respectively. The results show a reasonably good accuracy of breathing rate monitoring.