Research On Adaptive Channel Congestion Control For Cooperative Vehicle Safety Systems

Cooperative Vehicle Safety Systems (CVSSs) is one of the most challenging applications in vehicular networking. The performance of vehicle tracking is the basis for CVSSs. CVSSs rely on IEEE 802.11p to periodically broadcast each vehicle's state information in order to track neighbors'positions. In vehicular networking, however, vehicles mobile environment affects the networking performance, i.e., high vehicle density causes IEEE 802.11p channel congestion and hence degrades the network performance. The degradation of the network performance in turn degrades vehicles tracking accuracy. This tight coupling relationship between vehicles mobility and communication process requires a coupled channel access control protocol for the effective allocation of channel resources. This dissertation focuses on the establishment of interactive models between vehicles mobile environment and IEEE 802.11p MAC protocol, and the design of coupled channel access control protocol, in order to make full and fair utilization of channel resources and hence improve the vehicle tracking accuracy under various traffic conditions. This dissertation studies the following problems:(1)Actions to control the transmission parameters in the existing congestion control solutions are undertaken after channel congestion status or vehicle density have been changed. These existing channel control strategies are not suitable to real-time vehicle tracking application. In order to overcome this problem, we propose a dynamical power control strategy based on Model Predictive Control. In this strategy, an information dissemination rate model that captures the dynamic nature of vehicle density is proposed. Based on the information dissemination model, the power control problem is transformed into a nonlinear programming problem for a time domain where the vehicle density is invariant. A distributed algorithm is then proposed to obtain the optimal solution for the programming problem. In order to deal with the effect of dynamic vehicle density, an adaptive power control algorithm is proposed by means of the vehicle density estimation. The algorithm finds the optimal power sequence in a rolling-horizon manner and hence, dynamically guides IEEE 802.11p to evolve towards the optimal information dissemination performance and hence obtain the high tracking accuracy.(2)Closer neighboring nodes are more dangerous and should be maintained with acceptable tracking accuracy. The existing resource allocation strategies do not ensure the acceptable tracking accuracy for closer neighboring nodes. In order to make full utilization of channel resources and provide the acceptable tracking accuracy for closer neighbors, we propose a feedback power control framework by taking into account the tracking requirements. The proposed framework consists of two parts:a prescriptive reference model and an adaptive power control model.The prescriptive reference model maps probability of successful reception of packets to vehicle tracking accuracy by considering the continuous time-varying nature of vehicle density. It is applied to predict the desired reception probability of packets based on the acceptable. vehicle tracking accuracy.The adaptive power control model evaluates the error between the desired network state and the current real network state and produces the real-time power control strategy. The real-time power control strategy will be finally implemented to guide the real vehicle tracking to the acceptable accuracy.(3)The existing channel control strategies use the max-min method to realize the fair channel resource allocation. This strategy does not take into account the objective of efficiency and hence cannot provide the optimal tracking accuracy for each vehicle. In order to overcome this problem, a utility function that defines the relationship between vehicle tracking accuracy and channel access probability is firstly proposed. Then, based on the utility function, a non-cooperative dynamic game model with a penalty function is established. In order to implement the game model on each vehicle, a distributed channel access algorithm is then proposed. This algorithm adjusts the contention window for each vehicle, and hence, guides each vehicle to the Pareto-optimal Nash equilibrium. This game-based channel access strategy can guarantee a fair share of channel resources while achieving the high tracking performance for all vehicles.(4)The current congestion control strategies do not take into account the effect of hidden nodes on the packets reception. Against this background, a dynamic feedback power controls scheme based on proportional-integral-derivative control is proposed. In this scheme, a dynamic information dissemination model that captures the dynamic vehicle density and the effect of the hidden nodes is proposed. This model qualities the network performance in terms of state information dissemination rate under the effect of dynamic vehicle density and hidden nodes. Then, a predictive model is presented to predict the ideal information dissemination rate by considering the hidden nodes. Finally, a feedback control model that adjusts the transmission power is introduced. The feedback control model integrates the ideal state of information dissemination rate and the current real state to generate real-time power control strategies, which improves the performance of the vehicle tracking under the effect of dynamic vehicle density and hidden nodes.