Daxin Tian

An adaptive vehicular epidemic routing method based on attractor selection model

Vehicular ad hoc networks (VANETs) is an emerging technology that can support many vehicular safety and comfort applications through intervehicle communications. However, VANETs could face a great challenge arising from rapidly varying network topology. It is an essential issue to design an efficient and reliable solution for message dissemination in dynamic VANETs. In this paper, we propose an epidemic routing method with capability of self-adapting to the highly dynamic nature of VANETs. To ensure a good trade-off between reachability and efficiency of message dissemination, an adaptive probabilistic infection and an adaptive limited-time forwarding mechanisms are also proposed for the epidemic broadcasting. Moreover, inspired by the self-adaptability and robustness of the cellular gene regulatory networks, we use the attractor selection mechanism to enhance routing messages in dynamic VANETs. Through comparative simulations under different traffic scenarios, we validate our proposed method and prove that it can guarantee message reachability and achieve high delivery efficiency in terms of message delivery ratio, average routing latency and cost.

A Dynamic and Self-Adaptive Network Selection Method for Multimode Communications in Heterogeneous Vehicular Telematics

With the increasing demands for vehicle-to-vehicle and vehicle-to-infrastructure communications in intelligent transportation systems, new generation of vehicular telematics inevitably depends on the cooperation of heterogeneous wireless networks. In heterogeneous vehicular telematics, the network selection is an important step to the realization of multimode communications that use multiple access technologies and multiple radios in a collaborative manner. This paper presents an innovative network selection solution for the fundamental technological requirement of multimode communications in heterogeneous vehicular telematics. To guarantee the QoS satisfaction of multiple mobile users and the efficient utilization and fair allocation of heterogeneous network resources in a global sense, a dynamic and self-adaptive method for network selection is proposed. It is biologically inspired by the cellular gene network, which enables terminals to dynamically select an appropriate access network according to the variety of QoS requirements and to the dynamic conditions of various available networks. The experimental results prove the effectiveness of the bioinspired scheme and confirm that the proposed network selection method provides better global performance when compared with the utility function method with greedy optimization.

Optimal epidemic broadcasting for vehicular ad hoc networks

Dynamic nature of vehicular ad hoc networks VANETs creates great challenges for message dissemination. To satisfy the high reliability and low delivery latency requirements for safety applications in VANETs, a dynamic epidemic broadcasting model is presented in this paper. On the basis of the ordinary differential equations, the proposed optimal control scheme is analyzed to understand the epidemic broadcasting properties, which include the message delivery probability, the expected delivery latency, and the number of copies. The correctness of the theoretical analyses is confirmed by simulations, and the experiment results prove the efficiency of the optimal epidemic broadcasting model. Copyright

Position-Based Directional Vehicular Routing

Routing of data packets in vehicular ad hoc networks (VANETs) is challenging due to dynamic changes in the network topologies. As nodes in VANETs can obtain accurate position information from onboard Global Positioning System receivers, position-based routing is considered to be a very promising routing strategy for VANETs. This paper presents a novel Position-based Directional Vehicular Routing (PDVR) method. To make sure the packets can be sent to the destination in an efficient and stable route, PDVR selects the next-hop from vehicles traveling in the same direction as the forwarding vehicle based on their angular directions relative to the destination. We analyze the straight and the curve highway scenarios, and present the realizing algorithm based on position and velocity vectors. The method is evaluated using NS2 and compared with typical ad hoc routing protocol Ad hoc On-Demand Distance Vector (AODV), the position-based routing protocol Distance Routing Effect Algorithm for Mobility (DREAM), and VANETs routing based on the Cartesian space method. Simulation results show that PDVR can find and maintain more stable routes compared with the other routing protocols.

Robust Energy-Efficient MIMO Transmission for Cognitive Vehicular Networks

This work investigates a robust energy-efficient solution for multiple-input–multiple-output (MIMO) transmissions in cognitive vehicular networks. Our goal is to design an optimal MIMO beamforming for secondary users (SUs), considering imperfect interference channel-state information (CSI). Specifically, we optimize the energy efficiency (EE) of SUs, given that the transmission power constraint, the robust interference power constraint, and the minimum transmission rate are satisfied. To solve the optimization problem, we first characterize the uncertainty of CSI by bounding it in a Frobenius-norm-based region and then equivalently convert the robust interference constraint to a linear matrix inequality (LMI). Furthermore, a feasible ascent direction approach is proposed to reduce the optimization problem into a sequential linearly constrained semidefinite program, which leads to a distributed iterative optimization algorithm for deriving the robust and optimal beamforming. The feasibility and convergence of the proposed algorithm is theoretically validated, and the final experimental results are also supplemented to show the strength of the proposed algorithm over some conventional schemes in terms of the achieved EE performance and robustness.

Analysis of broadcasting delays in vehicular ad hoc networks

High mobility of nodes in vehicular ad hoc networks (VANETs) may lead to frequent breakdowns of established routes in conventional routing algorithms commonly used in mobile ad hoc networks. To satisfy the high reliability and low delivery-latency requirements for safety applications in VANETs, broadcasting becomes an essential operation for route establishment and repair. However, high node mobility causes constantly changing traffic and topology, which creates great challenges for broadcasting. Therefore, there is much interest in better understanding the properties of broadcasting in VANETs. In this paper we perform stochastic analysis of broadcasting delays in VANETs under three typical scenarios: freeway, sparse traffic and dense traffic, and utilize them to analyze the broadcasting delays in these scenarios. In the freeway scenario, the analytical equation of the expected delay in one connected group is given based on statistical analysis of real traffic data collected on freeways. In the sparse traffic scenario, the broadcasting delay in an n-vehicle network is calculated by a finite Markov chain. In the dense traffic scenario, the collision problem is analyzed by different radio propagation models. The correctness of these theoretical analyses is confirmed by simulations. These results are useful to provide theoretical insights into the broadcasting delays in VANETs. Copyright

A rear-end collision avoidance system of connected vehicles

The rear-end collision is one of the main types of accident, and it brings unnecessary casualties and property losses. Aiming at reducing the rear-end collision, some researchers focus on the front collision avoidance system. Generally, the rear-end collision avoidance system detects risk situation based on the information collected by radar or camera. When the following car is in risk of crash, the system will inform or warn the driver that the current speed is not safe, and help drivers to decelerate or brake. With the development of vehicle-to-vehicle communication (V2V), a new way to develop the rear-end collision avoidance system is put forward. By using the connected vehicles, the system can collect information and share message through the communicate equipment. In this paper, we introduce a collision avoidance system which is based on the vehicle-to-vehicle communication. This rear-end collision avoidance is with some driver behavior by using a car-following model based on risk perception. At last, we provide some experimental date about collision avoidance, and demonstrate the feasibility of this system in risk detection and crash avoidance.

System Design for Big Data Application in Emotion-Aware Healthcare

As the living standards improve and the health consciousness enhances, the healthcare industry has become a hot spot in nowadays society and some health monitoring systems emerge one after another in recent years. However, the mostly existing systems only focus on the logic reasoning but ignore the factor of the users emotion, which is regarded as an important factor to impact human health. In this paper, we design a system for big data application in emotion-aware healthcare (BDAEH), which pays attention to both the logic reasoning and the emotion computing. Meanwhile, the SDN the and 5G technologies are adopted in the BDAHE system to improve the resource utilization and the overall network performance of the system. The BDAEH system includes the following functions: healthcare data collection, healthcare data transmission, healthcare data storage, healthcare data analysis, and human-machine interaction. The healthcare data are generated by wearable devices or sensing-less sensors, and these healthcare data are regarded as the foundation to expand a series of data processing. The healthcare data transmission is performed through leveraging the SDN and the 5G technologies. In the data center, the related technologies based on cloud computing are utilized to store and analyze healthcare data, which obtains both the emotion and the health state of the users, and the relation between the emotion and the illness. Finally, the BDAEH system returns the analysis result to the users or the doctors for further treatment schemes or rehabilitation advice. The presented system is expected to validly improve the healthcare services by considering the emotion factor.

An Adaptive Fusion Strategy for Distributed Information Estimation Over Cooperative Multi-Agent Networks

In this paper, we study the problem of distributed information estimation that is closely relevant to some network-based applications, such as distributed surveillance, cooperative localization, and optimization. We consider a problem where an application area containing multiple information sources of interest is divided into a series of subregions in which only one information source exists. The information is presented as a signal variable, which has finite states associated with certain probabilities. The probability distribution of information states of all the subregions constitutes a global information picture for the whole area. Agents with limited measurement and communication ranges are assumed to monitor the area, and cooperatively create a local estimate of the global information. To efficiently approximate the actual global information using individual agents’ own estimates, we propose an adaptive distributed information fusion strategy and use it to enhance the local Bayesian rule-based updating procedure. Specifically, this adaptive fusion strategy is induced by iteratively minimizing a Jensen–Shannon divergence-based objective function. A constrained optimization model is also presented to derive minimum Jensen–Shannon divergence weights at each agent for fusing local neighbors’ individual estimates. Theoretical analysis and numerical results are supplemented to show the convergence performance and effectiveness of the proposed solution.

A VANETs routing algorithm based on Euclidean distance clustering

Routing is a challenging task in the ad hoc networks, especially in vehicular ad hoc networks (VANETs) where the network topology changes fast and frequently. Since the nodes in VANETs are vehicles, which can easily provide the required power to run GPS receiver to get the accurate information of their position, the position-based routing is found to be a very promising routing strategy for VANETs. In this paper we present a clustering routing algorithm for VANETs. The clustering method is based on the Euclidean distance, which uses the position information to divide the vehicles into clusters. Furthermore, only the same direction vehicles can be divided into the same cluster. To reduce the flooding of the routing control message and increase the stability of the route, the routing discovery is also restricted by the vehicles driving direction. We implement the routing algorithm in NS2 and compare it with AODV, the simulation results show that in the same VANETs environment, the algorithm not only generate fewer routing control overhead, but also maintain stable route to transfmit more data packets.