Showing papers by "Yunpeng Wang published in 2020"

A Game-Based Computation Offloading Method in Vehicular Multiaccess Edge Computing Networks

Abstract: Multiaccess edge computing (MEC) is a new paradigm to meet the requirements for low latency and high reliability of applications in vehicular networking. More computation-intensive and delay-sensitive applications can be realized through computation offloading of vehicles in vehicular MEC networks. However, the resources of a MEC server are not unlimited. Vehicles need to determine their task offloading strategies in real time under a dynamic-network environment to achieve optimal performance. In this article, we propose a multiuser noncooperative computation offloading game to adjust the offloading probability of each vehicle in vehicular MEC networks and design the payoff function considering the distance between the vehicle and MEC access point, application and communication model, and multivehicle competition for MEC resources. Moreover, we construct a distributed best response algorithm based on the computation offloading game model to maximize the utility of each vehicle and demonstrate that the strategy in this algorithm can converge to a unique and stable equilibrium under certain conditions. Furthermore, we conduct a series of experiments and comparisons with other offloading methods to analyze the effectiveness and performance of the proposed algorithms. The fast convergence and the improved performance of this algorithm are verified by numerical results.

Reliability-Optimal Cooperative Communication and Computing in Connected Vehicle Systems

Abstract: The emergence of vehicular networking enables distributed cooperative computation among nearby vehicles and infrastructures to achieve various applications that may need to handle mass data by a short deadline. In this paper, we investigate the fundamental problems of a cooperative vehicle-infrastructure system (CVIS): how does vehicular communication and networking affect the benefit gained from cooperative computation in the CVIS and what should a reliability-optimal cooperation be? We develop an analytical framework of reliability-oriented cooperative computation optimization, considering the dynamics of vehicular communication and computation. To be specific, we propose stochastic modeling of V2V and V2I communications, incorporating effects of the vehicle mobility, channel contentions, and fading, and theoretically derive the probability of successful data transmission. We also formulate and solve an execution time minimization model to obtain the success probability of application completion with the constrained computation capacity and application requirements. By combining these models, we develop constrained optimizations to maximize the coupled reliability of communication and computation by optimizing the data partitions among different cooperators. Numerical results confirm that vehicular applications with a short deadline and large processing data size can better benefit from the cooperative computation rather than non-cooperative solutions.

Trajectory reconstruction for freeway traffic mixed with human-driven vehicles and connected and automated vehicles

Abstract: The development of technologies related to connected and automated vehicles (CAVs) allows for a new approach to collect vehicle trajectory. However, trajectory data collected in this way represent only sampled traffic flow owing to low penetration rates (PRs) of CAVs and privacy concerns, and fail to provide a comprehensive picture of traffic flow. This study proposes a method to reconstruct vehicle trajectories in fully sampled traffic flow on freeways that consists of human-driven vehicles (HVs) and CAVs by using the mobile sensing data acquired from CAVs. The expected behavior of vehicles within the detection range of CAVs is determined based on the driving state classified by the Wiedemann model, i.e., free driving, emergency, closing, and following. If the actual behavior is different from the expected, it is deemed to be influenced by the undetected HVs. Then, new HVs are inserted based on the estimated local traffic density and speed of the freeway. The trajectories of the inserted HVs are further reconstructed by using the established update rules of cellular automation, i.e., uniform motion, acceleration, deceleration, randomization and position update. Last, the proposed method was examined by simulation experiments under different traffic densities and PRs of CAVs. The results show that the trajectories of fully sampled mixed traffic flow can be reconstructed reasonably well, not only under traffic conditions without explicit congestion but in congested environments.

Cycle-Based End of Queue Estimation at Signalized Intersections Using Low-Penetration-Rate Vehicle Trajectories

Abstract: Queue length is a crucial measure of intersection performance. Probe vehicles (PVs) with advanced sensors are capable of recording vehicle trajectories that can be used to estimate queue length, a technique of which has received considerable attention in the past decade. Noticeably, this technique usually requires high PV penetration rates (e.g., above 25%) in order to ensure estimation accuracy. Though the PVs are expected to increase, their penetration rate will still remain relatively low in the near future. Meanwhile, the initial queue length is another important factor that directly relates to queue dynamics at each cycle. However, most of the studies failed to adequately account for the effect of the initial queue on cyclic queue length estimation. To address the above challenges, this paper proposes a cycle-based end of queue estimation method using sampled vehicle trajectory data under relatively low penetration rates. Two major steps are involved: first, vehicle arrival process is modeled as a certain distribution in line with traffic conditions and an expectation maximum (EM) procedure is employed to estimate the arrival rate of each cycle; then, both ends of the queue and initial queue are estimated at each cycle based on shockwave theory. Microscopic traffic simulator VISSIM is utilized to examine the performance of the method. The experimental results reveal that the cycle-based end of the queue can be estimated with desirable accuracy in different scenarios, e.g., undersaturated, oversaturated, and queue spillback conditions. The comparison with the state-of-the-art methods further helps to verify the advantage of the method, especially under low-penetration-rate conditions.

Channel Access Optimization with Adaptive Congestion Pricing for Cognitive Vehicular Networks: An Evolutionary Game Approach

Abstract: Cognitive radio-enabled vehicular nodes as unlicensed users can competitively and opportunistically access the radio spectrum provided by a licensed provider and simultaneously use a dedicated channel for vehicular communications. In such cognitive vehicular networks, channel access optimization plays a key role in making the most of the spectrum resources. In this paper, we present the competition among self-interest-driven vehicular nodes as an evolutionary game and study fundamental properties of the Nash equilibrium and the evolutionary stability. To deal with the inefficiency of the Nash equilibrium, we design a delayed pricing mechanism and propose a discretized replicator dynamics with this pricing mechanism. The strategy adaptation and the channel pricing can be performed in an asynchronous manner, such that vehicular users can obtain the knowledge of the channel prices prior to actually making access decisions. We prove that the Nash equilibrium of the proposed evolutionary dynamics is evolutionary stable and coincides with the social optimum. Besides, performance comparison is also carried out in different environments to demonstrate the effectiveness and advantages of our method over the distributed multi-agent reinforcement learning scheme in current literature in terms of the system convergence, stability and adaptability.

Thermal Management of Prismatic Lithium-Ion Battery with Minichannel Cold Plate

Abstract: A battery thermal management system (BTMS) is crucial to guarantee that lithium-ion (Li-ion) batteries attain high performance, long life, and a high level of safety. To investigate an effe...

Reliable shortest path finding in stochastic time-dependent road network with spatial-temporal link correlations: A case study from Beijing

Abstract: In view of the time-dependent characteristic of travel times in road networks and the travel time reliability (TTR) requirements by different travelers, it is complicated and time-consuming to determine the reliable shortest path (RSP) in large-scale road networks. To search the RSP in stochastic and time-dependent (STD) network with spatial-temporal correlated link travel times, an efficient path finding algorithm is presented. First, the fitting test results based on floating car data show that it is more appropriate to characterize the travel time distributions (TTDs) of links using lognormal distributions. In order to quantify spatial-temporal correlations between links, correlation coefficients of link travel times are calculated. Also, influences of spatial distance (counted by the number of links), temporal distance (counted by the number of time intervals) and road type on link correlations is analyzed. Afterwards, the dynamic moment-matching method (DMM) is used to calculate the approximate path TTD when correlated link travel times are considered. Accounting for different travelers' risk tolerance, a dynamic-moment-matching-based A* algorithm (STCRSP-DMA*) is proposed to provide personalized path navigation for individual travelers. Last, numerical case studies based on abundant floating car data as well as a subsistent road network in Beijing are conducted to demonstrate the applicability and the computational advantage of the devised algorithm in solving RSP searching problems.

Cooperative channel assignment for VANETs based on multiagent reinforcement learning

Abstract: Dynamic channel assignment (DCA) plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion. However, channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes, the lack of centralized coordination, unknown global state information, and other challenges. To solve this problem, a multiagent reinforcement learning (RL) based cooperative DCA (RL-CDCA) mechanism is proposed. Specifically, each vehicular node can successfully learn the proper strategies of channel selection and backoff adaptation from the real-time channel state information (CSI) using two cooperative RL models. In addition, neural networks are constructed as nonlinear Q-function approximators, which facilitates the mapping of the continuously sensed input to the mixed policy output. Nodes are driven to locally share and incorporate their individual rewards such that they can optimize their policies in a distributed collaborative manner. Simulation results show that the proposed multiagent RL-CDCA can better reduce the one-hop packet delay by no less than 73.73%, improve the packet delivery ratio by no less than 12.66% on average in a highly dense situation, and improve the fairness of the global network resource allocation.