Drive-thru Internet has emerged as a fundamental approach for information/content distribution to vehicles on the go. In the drive-thru Internet, one vehicle may connect to multiple roadside units ...(RSUs) along its trip, and in the meantime, multiple vehicles may compete one RSU at the same time for transmissions. Therefore, both vehicles and RSUs need to optimize their connections to achieve their best utilities. In the perspective of RSUs, it is important to select optimal vehicles to transmit so as to maximize the global RSU system's utilization. For individual vehicles, it needs to wisely select RSUs along its trip to connect so as to minimize its download cost, e.g., energy consumption and bandwidth cost. This paper targets to address the two design goals in one framework using a game theoretic model. Specifically, we model the two-dimensional drive-thru Internet as a second-price sealed-bid auction. In each RSU cell, an adaptive reserve price scheme is designed such that the RSU is allowed to selectively provide connections to vehicles based on the network size, vehicle's transmission rate, urgency of download, and content popularity; the RSU finally obtains the optimal utility on a Bayesian Nash equilibrium of the auction. For individual vehicles, a finite-horizon Markov decision process has been developed to guide vehicles to optimally select RSUs to connect along their road trips. Using extensive simulations, we demonstrate that the proposed framework can achieve the highest utility for the RSUs compared with existing proposals. It can also help vehicles keep a higher utility and transmission ratio when going through a single RSU or multiple RSUs than the conventional schemes.
The increasing use of Internet of Things (IoTs) has brought more advantages in supplying power to electric vehicles (EVs). With the help of IoTs, EVs can be charged more easily by mobile charging ...stations (MCSs) compared with the fixed charging stations (FCSs). However, previous works in the management of power supply in FCSs have not been properly applied in MCSs, e.g., dynamic of EV users' arrival and variable power supply from MCSs in IoTs. In this paper, we study how to manage MCSs' supply power in IoTs under the condition that MCSs supply multiple kinds of power. First, considering the randomness of power supply and dynamic of EV users' arrival, we develop the dynamic framework of power supply and the economic model. Then, aiming to maximize the long-term average profits of MCSs, a stochastic optimization problem is formulated to decide the optimal strategy of power management. Based on the Lyapunov optimization theory, a Lyapunov-based online distributed algorithm is proposed to obtain the optimal solutions. Meanwhile, the performance of our proposed algorithm is analyzed and simulation results validate the effectiveness of our proposal.
An increasing number of connected vehicles (CVs) driving together with regular vehicles (RVs) on the road is an inevitable stage of future traffic development. As accurate traffic flow state ...detection is essential for ensuring safe and efficient traffic, the level of road intelligence is being enhanced by the mass deployment of roadside perception devices, which is capable of sensing the mixed traffic flow consisting of RVs and CVs. In this background, we propose a roadside radar and camera data fusion framework to improve the accuracy of traffic flow state detection, which utilizes relatively more accurate traffic parameters obtained from real-time communication between CVs and roadside unit (RSU) as calibration values for training the back propagation (BP) neural network. Then, with the perception data collected by roadside sensors, the BP neural network-based data fusion model is applied to all vehicles including RVs. Furthermore, considering the changes of road environments, a dynamic BP fusion method is proposed, which adopts dynamic training by updating samples conditionally, and are applied to fuse traffic flow, occupancy and RVs speed data. Simulation results demonstrate that for CVs data and all vehicles (including RVs) data, the proposed dynamic BP fusion method is more accurate than single sensor detection, entropy based Bayesian fusion method and traditional BP fusion without training by CVs. It can achieve smaller error, and the accuracies of vehicle speed, traffic flow, and occupancy are all above 95%.
The integration of Internet of things (IoT) and intelligent transportation system (ITS) is expected to improve the traffic efficiency and enhance the driving experience. However, due to the dynamic ...traffic environment and various types of vehicles, it is a challenge to perform vehicle classification and speed estimation with a single magnetic sensor. In this paper, based on a single low-cost magnetic sensor, a scheme is proposed to achieve vehicle classification and speed interval estimation by designing a two-dimensional convolutional neural network (CNN). Specifically, we extract the magnetic field data of each vehicle and then convert the collected data into a two-dimensional grayscale image. In this way, the images of vehicle signals with different types and driving speeds can be used as the input data to train the designed CNN model. With the designed CNN model, we classify the vehicles into 7 types and estimate the speed interval of each vehicle, where the speeds in the range of 10km/h-70km/h are divided into 6 intervals of size 10km/h. The performance of the proposed vehicle classification and speed estimation scheme is evaluated by experiments, where the experimental results show that the accuracy of vehicle classification and the accuracy of speed interval estimation are 97.83% and 96.85%, respectively.
The autonomous vehicles (AVs) in smart city, as intelligent mobile robots, are expected to provide diversified services to facilitate the life of citizens. However, the attributes of the services ...requested by users are different and the statuses of the AVs managed by different central servers are dynamically changed. To execute the services with the minimum cost based on the requirements of users and the statuses of AVs therefore becomes a challenge. In this article, we establish an intelligent multi-attribute service response framework in smart city based on the request of users and the response of AVs. In the first phase of the framework, each central server decides the minimum service execution cost (SEC) to respond to the user's service by considering the available resources of its AVs, where the minimization problems are formulated for the services with one attribute and the services with multiple attributes, respectively. To address the problems, the optimal AV selection (OAVS) algorithm for the services with one attribute and the OAVS-M algorithm for the services with multiple attributes are designed. In the second phase, based on the SEC of each central server, an auction game is developed to model the competition among the central servers to help the user select the optimal one to execute the service with the lowest service transaction price (STP). By achieving the Nash equilibrium of the game, the optimal strategy of each central server to win the chance for executing the service is obtained. The simulation results show that the designed framework can reduce the STP compared with the conventional schemes.
The software defined heterogeneous vehicular networks (SD-HetVNETs), which consist of cellular base stations (CBSs) and roadside units (RSUs), have emerged as a promising solution to address the ...fundamental problems imposed by the surge increase of vehicular content demand. However, due to the ever increasing requirement of the vehicles' quality of experience (QoE) and the network vendors' utilities, there come new challenges to motivate CBS to cooperate with RSU for content delivery in order to maximize their utilities and improve the efficiency of the networks. Therefore, in this paper, we propose a collaborative content delivery scheme to improve the utilities of the participants (i.e., CBS, RSU and vehicles) in the SD-HeVNETs, where the CBS can cooperate with RSUs by serving a group of vehicles with multicast technology. We first define the utility models to map the profits of the participants in the networks and formulate the utilities of CBS and RSU as two optimization problems. Then, we exploit the double auction game to motivate CBS to cooperate with RSU for the multicast assisted content delivery to address the two maximization problems. Next, the optimal bidding strategies of CBS and RSU in the game are analyzed when the Bayesian Nash equilibrium is achieved. With the optimal bidding strategies, both CBS and RSU can bid for the multicast assisted content delivery services to maximize their utilities based on the network status. Finally, the performance of the proposed cooperative scheme is evaluated by using simulations. The simulation results demonstrate that the utilities of all the participants in the networks can be enhanced and the efficiency of the networks can be improved.
Mobile charging stations (MCSs) can provide electric vehicles (EVs) with better charging services than the fixed charging stations, as the flexible and efficient charging sites can be available. ...However, how to schedule the tasks from the EVs and optimally place the MCSs becomes a new challenge. Therefore, in this paper we present a novel approach to help EVs' charging with MCSs through heterogeneous networks. Firstly, a novel heterogeneous network model is presented to improve the communication between EVs and MCSs by using macro cells and small cells. Next, a novel model is developed to make optimal decisions for MCSs to schedule the tasks from EVs. Then, a chaotic evolution particle swarm optimization (CEPSO) algorithm is presented to determine the optimal placement of MCSs based on the charging demand and the maintenance cost. Finally, the simulation experiments prove that the proposed approach can outperform the conventional methods.
The vehicle localization, which aims to identify a vehicle and then position the vehicle with a high precision, can be used to facilitate various applications and services in vehicular networks. ...Unfortunately, conventional localization systems, e.g., global positioning system (GPS), hardly meet the accuracy requirements especially in certain specific scenarios, such as tunnels. At the same time, Ultrahigh frequency (UHF) radio frequency identification (RFID) has become an efficient booster for internet of things (IoT) due to the desirable advantages, such as low cost, battery-free, and unique identification. In this paper, based on the UHF-RFID, we propose a novel real-time vehicle localization scheme in GPS-Less Environments. Considering the practical implementation of multiple RFID reader antennas on a vehicle is constrained, we adopt single antenna multi-frequency ranging scheme, in which the integer ambiguity problem is solved by the maximum-likelihood estimation (MLE)-based robust Chinese remainder theorem (CRT). With the reconstructed distances between the tags and the reader, the coordinates of the vehicle then can be calculated with the Levenberg-Marquardt (LM) algorithm. Furthermore, the computational complexities of the algorithms and the time consumption of the proposed scheme are analyzed. The experimental results demonstrate that the proposed scheme can track vehicle's location with error lower than 27 cm at the probability of 90%.
The autonomous vehicles (AVs), like that in knight rider, were completely a scientific fiction just a few years ago, but are now already practical with real-world commercial deployments. A salient ...challenge of AVs, however, is the intensive computing tasks to carry out on board for the real-time traffic detection and driving decision making; this imposes heavy load to AVs due to the limited computing power. To explore more computing power and enable scalable autonomous driving, in this paper, we propose a collaborative task computing scheme for AVs, in which the AVs in proximity dynamically share idle computing power among each other. This, however, raises another fundamental problem on how to incentivize AVs to contribute their computing power and how to fully utilize the pool of group computing power in an optimal way. This paper studies the problem by modeling the issue as a market-based optimal computing resource allocation problem. In specific, we develop a software-defined network (SDN) architecture and consider a star topology where a centered AV outsources its computing tasks to the surrounding AVs for its autonomous driving. A market mechanism is developed in which the surrounding AVs sell their computing power at a cost based on their local idle computing resources. Then, we classify the tasks requested by the centered AV into two types which are task with time to live (TTL) and task without TTL, respectively. With different task types, we define corresponding cost models of the centered AV and formulate them as two minimization problems. The optimal solutions of the problems are achieved to guide the centered AV to wisely allocate computing tasks to surrounding AVs towards minimal cost. Finally, the performance of the proposed scheme is evaluated using simulations, which show that the proposed scheme can result in the guaranteed computing performance yet the lowest costs compared with other conventional schemes.
In this paper, we investigate secure transmissions in integrated satellite-terrestrial communications and the green interference based symbiotic security scheme is proposed. Particularly, the ...co-channel interference induced by the spectrum sharing between satellite and terrestrial networks and the inter-beam interference due to frequency reuse among satellite multi-beam serve as the green interference to assist the symbiotic secure transmission, where the secure transmissions of both satellite and terrestrial links are guaranteed simultaneously. Specifically, to realize the symbiotic security, we formulate a problem to maximize the sum secrecy rate of satellite users by cooperatively beamforming optimizing and a constraint of secrecy rate of each terrestrial user is guaranteed. Since the formulated problem is non-convex and intractable, the Taylor expansion and semi-definite relaxation (SDR) are adopted to further reformulate this problem, and the successive convex approximation (SCA) algorithm is designed to solve it. Finally, the tightness of the relaxation is proved. In addition, numerical results verify the efficiency of our proposed approach.
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