Jianshan Zhou

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

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.

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 Bayesian Compressive Sensing Vehicular Location Method Based on Three-Dimensional Radio Frequency

In vehicular ad hoc networks (VANETs) safety applications, vehicular position is fundamental information to achieve collision avoidance and fleet management. Now, position information is comprehensively provided by global positioning system (GPS). However, in the dense urban, due to multipath effect and signal occlusion, GPS-based positioning method potentially fails to provide accurate position information. For this reason, an assistant approach has been presented in this paper by using three-dimensional radio frequency, such as time of arrival (TOA) and direction of arrival (DOA). With the goal of providing an efficient and reliable estimation of vehicular position in general traffic scenarios, we propose a hybrid TOA/DOA positioning method based on Bayesian compressive sensing (BCS), which benefits from the realization of vehicle-to-roadside wireless interaction with the dedicated short range communication. The effectiveness of the proposed approach is proved through extensive experiments in several scenarios where different signal configurations and the noise conditions are taken into account. Moreover, some comparative experiments are also performed to confirm the strength of our proposed approach.

Self-Organized Relay Selection for Cooperative Transmission in Vehicular Ad-Hoc Networks

Cooperation is a promising paradigm to improve spatial diversity in vehicular ad-hoc networks. In this paper, we pose a fundamental question: How the greediness and selfishness of individual nodes impact cooperation dynamics in vehicular ad-hoc networks. We map the self-interest-driven relay selection decision-making problem to an automata game formulation and present a noncooperative game-theoretic analysis. We show that the relay selection game is an ordinal potential game. A decentralized self-organized relay selection algorithm is proposed based on a stochastic learning approach where each player evolves toward a strategic equilibrium state in the sense of Nash. Furthermore, we study the exact outage behavior of the multirelay decode-and-forward cooperative communication network. Closed-form solutions are derived for the actual outage probability of this multirelay system in both independent and identically distributed channels and generalized channels, which need not assume an asymptotic or high signal-to-noise ratio. Two tight approximations with low computational complexity are also developed for the lower bound of the outage probability. With the exact closed-form outage probability, we further develop an optimization model to determine optimal power allocations in the cooperative network, which can be combined with the decentralized learning-based relay selection. The analysis of the exact and approximative outage behaviors and the convergence properties of the proposed algorithm toward a Nash equilibrium state are verified theoretically and numerically. Simulation results are also given to demonstrate that the resulting cooperative network induced by the proposed algorithm achieves high energy efficiency, transmission reliability, and network-wide fairness performance.

Real-Time Vehicle Route Guidance Based on Connected Vehicles

With advances in connected vehicle technology, real-time vehicle route guidance systems gradually become indispensable equipments for drivers. Conventional route guidance systems are designed to direct a vehicle along the shortest path from the origin to the destination without considering the dynamic traffic information. Therefore the state-of-the-art route guidance systems incorporate real-time traffic information to find better paths. So, this paper presents a novel approach to realize the real-time vehicle route guidance. It focuses on the way to determine the optimal route based on the dynamic road segments division and the traditional Dijkstra algorithm. This approach divides the road to sub-sections with the traffic information, so the state of each segment can be easily figured out (i.e. average speed or average travel time). Then a dynamic road network graph can be drawn out. And traditional Dijkstra algorithm can find optimal route on it. A simulation is implemented to show optimal route at different time. It also compares with the traditional Dijkstra algorithm and the result is validated.

From cellular attractor selection to adaptive signal control for traffic networks

The management of varying traffic flows essentially depends on signal controls at intersections. However, design an optimal control that considers the dynamic nature of a traffic network and coordinates all intersections simultaneously in a centralized manner is computationally challenging. Inspired by the stable gene expressions of Escherichia coli in response to environmental changes, we explore the robustness and adaptability performance of signalized intersections by incorporating a biological mechanism in their control policies, specifically, the evolution of each intersection is induced by the dynamics governing an adaptive attractor selection in cells. We employ a mathematical model to capture such biological attractor selection and derive a generic, adaptive and distributed control algorithm which is capable of dynamically adapting signal operations for the entire dynamical traffic network. We show that the proposed scheme based on attractor selection can not only promote the balance of traffic loads on each link of the network but also allows the global network to accommodate dynamical traffic demands. Our work demonstrates the potential of bio-inspired intelligence emerging from cells and provides a deep understanding of adaptive attractor selection-based control formation that is useful to support the designs of adaptive optimization and control in other domains.

A Bio-Inspired QoS-Oriented Handover Model in Heterogeneous Wireless Networks

We propose a bio-inspired model for making handover decision in heterogeneous wireless networks. It is based on an extended attractor selection model, which is biologically inspired by the self-adaptability and robustness of cellular response to the changes in dynamic environments. The goal of the proposed model is to guarantee multiple terminals’ satisfaction by meeting the QoS requirements of those terminals’ applications, and this model also attempts to ensure the fairness of network resources allocation, in the meanwhile, to enable the QoS-oriented handover decision adaptive to dynamic wireless environments. Some numerical simulations are preformed to validate our proposed bio-inspired model in terms of adaptive attractor selection in different noisy environments. And the results of some other simulations prove that the proposed handover scheme can adapt terminals’ network selection to the varying wireless environment and benefits the QoS of multiple terminal applications simultaneously and automatically. Furthermore, the comparative analysis also shows that the bio-inspired model outperforms the utility function based handover decision scheme in terms of ensuring a better QoS satisfaction and a better fairness of network resources allocation in dynamic heterogeneous wireless networks.