Showing papers in "IEEE Transactions on Vehicular Technology in 2016"

Impact of User Pairing on 5G Nonorthogonal Multiple-Access Downlink Transmissions

Abstract: Nonorthogonal multiple access (NOMA) represents a paradigm shift from conventional orthogonal multiple-access (MA) concepts and has been recognized as one of the key enabling technologies for fifth-generation mobile networks. In this paper, the impact of user pairing on the performance of two NOMA systems, i.e., NOMA with fixed power allocation (F-NOMA) and cognitive-radio-inspired NOMA (CR-NOMA), is characterized. For F-NOMA, both analytical and numerical results are provided to demonstrate that F-NOMA can offer a larger sum rate than orthogonal MA, and the performance gain of F-NOMA over conventional MA can be further enlarged by selecting users whose channel conditions are more distinctive. For CR-NOMA, the quality of service (QoS) for users with poorer channel conditions can be guaranteed since the transmit power allocated to other users is constrained following the concept of cognitive radio networks. Because of this constraint, CR-NOMA exhibits a different behavior compared with F-NOMA. For example, for the user with the best channel condition, CR-NOMA prefers to pair it with the user with the second best channel condition, whereas the user with the worst channel condition is preferred by F-NOMA.

Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

Abstract: With the emergence of ever-growing advanced vehicular applications, the challenges to meet the demands from both communication and computation are increasingly prominent. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. Thus, solving this problem is of great importance. The existing solutions, such as cellular networks, roadside units (RSUs), and mobile cloud computing, are far from perfect because they highly depend on and bear the cost of additional infrastructure deployment. Given tremendous number of vehicles in urban areas, putting these underutilized vehicular resources into use offers great opportunity and value. Therefore, we conceive the idea of utilizing vehicles as the infrastructures for communication and computation, named vehicular fog computing (VFC), which is an architecture that utilizes a collaborative multitude of end-user clients or near-user edge devices to carry out communication and computation, based on better utilization of individual communication and computational resources of each vehicle. By aggregating abundant resources of individual vehicles, the quality of services and applications can be enhanced greatly. In particular, by discussing four types of scenarios of moving and parked vehicles as the communication and computational infrastructures, we carry on a quantitative analysis of the capacities of VFC. We unveil an interesting relationship among the communication capability, connectivity, and mobility of vehicles, and we also find out the characteristics about the pattern of parking behavior, which benefits from the understanding of utilizing the vehicular resources. Finally, we discuss the challenges and open problems in implementing the proposed VFC system as the infrastructures. Our study provides insights for this novel promising paradigm, as well as research topics about vehicular information infrastructures.

Compensation Topologies of High-Power Wireless Power Transfer Systems

Abstract: Wireless power transfer (WPT) is an emerging technology that can realize electric power transmission over certain distances without physical contact, offering significant benefits to modern automation systems, medical applications, consumer electronics, etc. This paper provides a comprehensive review of existing compensation topologies for the loosely coupled transformer. Compensation topologies are reviewed and evaluated based on their basic and advanced functions. Individual passive resonant networks used to achieve constant (load-independent) voltage or current output are analyzed and summarized. Popular WPT compensation topologies are given as application examples, which can be regarded as the combination of multiple blocks of resonant networks. Analyses of the input zero phase angle and soft switching are conducted as well. This paper also discusses the compensation requirements for achieving the maximum efficiency according to different WPT application areas.

Interworking of DSRC and Cellular Network Technologies for V2X Communications: A Survey

Abstract: Vehicle-to-anything (V2X) communications refer to information exchange between a vehicle and various elements of the intelligent transportation system (ITS), including other vehicles, pedestrians, Internet gateways, and transport infrastructure (such as traffic lights and signs). The technology has a great potential of enabling a variety of novel applications for road safety, passenger infotainment, car manufacturer services, and vehicle traffic optimization. Today, V2X communications is based on one of two main technologies: dedicated short-range communications (DSRC) and cellular networks. However, in the near future, it is not expected that a single technology can support such a variety of expected V2X applications for a large number of vehicles. Hence, interworking between DSRC and cellular network technologies for efficient V2X communications is proposed. This paper surveys potential DSRC and cellular interworking solutions for efficient V2X communications. First, we highlight the limitations of each technology in supporting V2X applications. Then, we review potential DSRC-cellular hybrid architectures, together with the main interworking challenges resulting from vehicle mobility, such as vertical handover and network selection issues. In addition, we provide an overview of the global DSRC standards, the existing V2X research and development platforms, and the V2X products already adopted and deployed in vehicles by car manufactures, as an attempt to align academic research with automotive industrial activities. Finally, we suggest some open research issues for future V2X communications based on the interworking of DSRC and cellular network technologies.

Multihop-Cluster-Based IEEE 802.11p and LTE Hybrid Architecture for VANET Safety Message Dissemination

Abstract: Several vehicular ad hoc network (VANET) studies have focused on communication methods based on IEEE 802.11p, which forms the standard for wireless access for vehicular environments. In networks employing IEEE 802.11p only, the broadcast storm and disconnected network problems at high and low vehicle densities, respectively, degrade the delay and delivery ratio of safety message dissemination. Recently, as an alternative to the IEEE 802.11p-based VANET, the usage of cellular technologies has been investigated due to their low latency and wide-range communication. However, a pure cellular-based VANET communication is not feasible due to the high cost of communication between the vehicles and the base stations and the high number of handoff occurrences at the base station, considering the high mobility of the vehicles. This paper proposes a hybrid architecture, namely, VMaSC–LTE, combining IEEE 802.11p-based multihop clustering and the fourth-generation (4G) cellular system, i.e., Long-Term Evolution (LTE), with the goal of achieving a high data packet delivery ratio (DPDR) and low delay while keeping the usage of the cellular architecture at a minimum level. In VMaSC–LTE, vehicles are clustered based on a novel approach named Vehicular Multihop algorithm for Stable Clustering (VMaSC). The features of VMaSC are cluster head (CH) selection using the relative mobility metric calculated as the average relative speed with respect to the neighboring vehicles, cluster connection with minimum overhead by introducing a direct connection to the neighbor that is already a head or a member of a cluster instead of connecting to the CH in multiple hops, disseminating cluster member information within periodic hello packets, reactive clustering to maintain the cluster structure without excessive consumption of network resources, and efficient size- and hop-limited cluster merging mechanism based on the exchange of cluster information among CHs. These features decrease the number of CHs while increasing their stability, therefore minimizing the usage of the cellular architecture. From the clustered topology, elected CHs operate as dual-interface nodes with the functionality of the IEEE 802.11p and LTE interface to link the VANET to the LTE network. Using various key metrics of interest, including DPDR, delay, control overhead, and clustering stability, we demonstrate the superior performance of the proposed architecture compared with both previously proposed hybrid architectures and alternative routing mechanisms, including flooding and cluster-based routing via extensive simulations in ns-3 with the vehicle mobility input from the Simulation of Urban Mobility. The proposed architecture also allows achieving higher required reliability of the application quantified by the DPDR at the cost of higher LTE usage measured by the number of CHs in the network.

Nonorthogonal Multiple Access in Large-Scale Underlay Cognitive Radio Networks

Abstract: In this paper, nonorthogonal multiple access (NOMA) is applied to large-scale underlay cognitive radio (CR) networks with randomly deployed users. To characterize the performance of the considered network, new closed-form expressions of the outage probability are derived using stochastic geometry. More importantly, by carrying out the diversity analysis, new insights are obtained under the two scenarios with different power constraints: 1) fixed transmit power of the primary transmitters (PTs); and 2) transmit power of the PTs being proportional to that of the secondary base station. For the first scenario, a diversity order of m is experienced at the mth-ordered NOMA user. For the second scenario, there is an asymptotic error floor for the outage probability. Simulation results are provided to verify the accuracy of the derived results. A pivotal conclusion is reached that by carefully designing target data rates and power allocation coefficients of users, NOMA can outperform conventional orthogonal multiple access in underlay CR networks.

Vehicle Lateral Dynamics Control Through AFS/DYC and Robust Gain-Scheduling Approach

Abstract: In this paper, we investigate the combined active front-wheel steering/direct yaw-moment control for the improvement of vehicle lateral stability and vehicle handling performance. A more practical assumption in this work is that the longitudinal velocity is not constant but varying within a range. Both the nonlinear tire model and the variation of longitudinal velocity are considered in vehicle system modeling. A linear-parameter-varying model with norm-bounded uncertainties is obtained. To track the system reference, a generalized proportional-integral (PI) control law is proposed. Since it is difficult to get the analytic solution for the PI gains, an augmented system is developed, and the PI control is then converted into the state-feedback control for the augmented system. Both the stability and the energy-to-peak performance of the augmented system are explored. Based on the analysis results, the controller-gain tuning method is proposed. The proposed control law and controller design method are illustrated via an electric vehicle model.

Investigation of Prediction Accuracy, Sensitivity, and Parameter Stability of Large-Scale Propagation Path Loss Models for 5G Wireless Communications

Abstract: This paper compares three candidate large-scale propagation path loss models for use over the entire microwave and millimeter-wave (mmWave) radio spectrum: the alpha–beta–gamma (ABG) model, the close-in (CI) free-space reference distance model, and the CI model with a frequency-weighted path loss exponent (CIF). Each of these models has been recently studied for use in standards bodies such as 3rd Generation Partnership Project (3GPP) and for use in the design of fifth-generation wireless systems in urban macrocell, urban microcell, and indoor office and shopping mall scenarios. Here, we compare the accuracy and sensitivity of these models using measured data from 30 propagation measurement data sets from 2 to 73 GHz over distances ranging from 4 to 1238 m. A series of sensitivity analyses of the three models shows that the four-parameter ABG model underpredicts path loss when relatively close to the transmitter, and overpredicts path loss far from the transmitter, and that the physically based two-parameter CI model and three-parameter CIF model offer computational simplicity, have very similar goodness of fit (i.e., the shadow fading standard deviation), exhibit more stable model parameter behavior across frequencies and distances, and yield smaller prediction error in sensitivity tests across distances and frequencies, when compared to the four-parameter ABG model. Results show the CI model with a 1-m reference distance is suitable for outdoor environments, while the CIF model is more appropriate for indoor modeling. The CI and CIF models are easily implemented in existing 3GPP models by making a very subtle modification—by replacing a floating non-physically based constant with a frequency-dependent constant that represents free-space path loss in the first meter of propagation. This paper shows this subtle change does not change the mathematical form of existing ITU/3GPP models and offers much easier analysis, intuitive appeal, better model parameter stability, and better accuracy in sensitivity tests over a vast range of microwave and mmWave frequencies, scenarios, and distances, while using a simpler model with fewer parameters.

Improving Traffic Flow Prediction with Weather Information in Connected Cars: A Deep Learning Approach

Abstract: Transportation systems might be heavily affected by factors such as accidents and weather. Specifically, inclement weather conditions may have a drastic impact on travel time and traffic flow. This study has two objectives: first, to investigate a correlation between weather parameters and traffic flow and, second, to improve traffic flow prediction by proposing a novel holistic architecture. It incorporates deep belief networks for traffic and weather prediction and decision-level data fusion scheme to enhance prediction accuracy using weather conditions. The experimental results, using traffic and weather data originated from the San Francisco Bay Area of California, corroborate the effectiveness of the proposed approach compared with the state of the art.

Radio Resource Management for D2D-Based V2V Communication

Abstract: Direct device-to-device (D2D) links are proposed as a possible enabler for vehicle-to-vehicle (V2V) communications, where the incurred intracell interference and the stringent latency and reliability requirements are challenging issues In this paper, we investigate the radio resource management problem for D2D-based V2V communication First, we analyze and transform the latency and reliability requirements of V2V communication into optimization constraints that are computable using only the slowly varying channel information This transformation opens up the possibility of extending certain existing D2D techniques to cater to V2V communication Second, we propose a problem formulation that fulfills the different requirements of V2V communication and traditional cellular communication Moreover, a Separate resOurce bLock and powEr allocatioN (SOLEN) algorithm is proposed to solve this problem Finally, simulations are presented to evaluate different schemes, which illustrate the necessity of careful design when extending D2D methods to V2V communication, as well as show promising performance of the proposed SOLEN algorithm

Electronic Stability Control Based on Motor Driving and Braking Torque Distribution for a Four In-Wheel Motor Drive Electric Vehicle

Abstract: An electronic stability control (ESC) algorithm is proposed for a four in-wheel motor independent-drive electric vehicle (4MIDEV) utilizing motor driving and regenerative braking torque distribution control to improve vehicle stability. A stability judgment controller, an upper level controller, and a torque distribution algorithm are designed for the ESC system. The stability judgment controller is designed to generate the desired yaw rate and sideslip angle for vehicle stability, and the control mode, which is normal driving mode or ESC mode, is set according to the driver inputs and measurement signal inputs. The upper level controller consists of a speed tracking controller, a yaw moment controller, and four wheel-slip controllers to calculate the desired value of traction force, the desired value of yaw moment, and the four respective net torque inputs of the four in-wheel motors. The torque distribution algorithm is designed to generate each motor driving torque or regenerative braking torque input for each wheel. An average torque distribution strategy, a tire-dynamic-load-based torque distribution strategy, and a minimum-objective-function-based optimal torque distribution strategy are used separately in the torque distribution algorithm to control the motor driving torque or regenerative braking torque for vehicle stability enhancement. The proposed ESC algorithm was implemented and evaluated in a CarSim vehicle model and a MATLAB/Simulink control model. The three proposed torque distribution strategies can be used to regulate the vehicle to perform the following tasks: “single lane change,” “double lane change,” and “snake lane change.” The simulation studies show that the yaw rate error root mean square [RMS $(\gamma-\gamma_\mathrm{-des})$ ] decreased, on average, by 75 percent using the proposed optimal torque distribution algorithm compared with that without using stability control.

Multiobjective Resource Allocation for Secure Communication in Cognitive Radio Networks With Wireless Information and Power Transfer

Abstract: In this paper, we study resource allocation for multiuser multiple-input–single-output secondary communication systems with multiple system design objectives. We consider cognitive radio (CR) networks where the secondary receivers are able to harvest energy from the radio frequency when they are idle. The secondary system provides simultaneous wireless power and secure information transfer to the secondary receivers. We propose a multiobjective optimization framework for the design of a Pareto-optimal resource allocation algorithm based on the weighted Tchebycheff approach. In particular, the algorithm design incorporates three important system design objectives: total transmit power minimization, energy harvesting efficiency maximization, and interference-power-leakage-to-transmit-power ratio minimization. The proposed framework takes into account a quality-of-service (QoS) requirement regarding communication secrecy in the secondary system and the imperfection of the channel state information (CSI) of potential eavesdroppers (idle secondary receivers and primary receivers) at the secondary transmitter. The proposed framework includes total harvested power maximization and interference power leakage minimization as special cases. The adopted multiobjective optimization problem is nonconvex and is recast as a convex optimization problem via semidefinite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. Moreover, two suboptimal resource allocation schemes for the case when the solution of the dual problem is unavailable for constructing the optimal solution are proposed. Numerical results not only demonstrate the close-to-optimal performance of the proposed suboptimal schemes but unveil an interesting tradeoff among the considered conflicting system design objectives as well.

A Threshold Anonymous Authentication Protocol for VANETs

Abstract: Vehicular ad hoc networks (VANETs) have recently received significant attention in improving traffic safety and efficiency. However, communication trust and user privacy still present practical concerns to the deployment of VANETs, as many existing authentication protocols for VANETs either suffer from the heavy workload of downloading the latest revocation list from a remote authority or cannot allow drivers on the road to decide the trustworthiness of a message when the authentication on messages is anonymous. In this paper, to cope with these challenging concerns, we propose a new authentication protocol for VANETs in a decentralized group model by using a new group signature scheme. With the assistance of the new group signature scheme, the proposed authentication protocol is featured with threshold authentication, efficient revocation, unforgeability, anonymity, and traceability. In addition, the assisting group signature scheme may also be of independent interest, as it is characterized by efficient traceability and message linkability at the same time. Extensive analyses indicate that our proposed threshold anonymous authentication protocol is secure, and the verification of messages among vehicles can be accelerated by using batch message processing techniques.

Comparison Study on SS and Double-Sided LCC Compensation Topologies for EV/PHEV Wireless Chargers

Abstract: This paper compares the characteristics of the series–series and double-sided Inductor-Capacitor-Capacitor (LCC) compensation topologies for electric vehicle (EV) wireless chargers. Both the well-tuned and mistuned topologies for the two compensation methods are analyzed in detail. The mistuning considered here is mainly caused by the variations of the relative position between the primary and secondary sides. The output power displacements caused by mistuning are compared for both compensation topologies, as well as the impacts of the load variations on the performances of the mistuned topologies. The voltage and current stresses on components are also studied. The comparative result shows that the double-sided LCC compensation topology is less sensitive to mistuning. A double-sided LCC-compensated EV wireless charger system with up to 7.7-kW output power is built to verify the analysis results. A peak efficiency of 96% from dc power source to battery load is achieved.

Dynamic Routing for Flying Ad Hoc Networks

Abstract: This paper reports experimental results on self-organizing wireless networks carried by small flying robots. Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy. This makes them a very attractive technology for many civilian and military applications. Due to the high mobility of the nodes, maintaining a communication link between the UAVs is a challenging task. The topology of these networks is more dynamic than that of typical mobile ad hoc networks (MANETs) and of typical vehicle ad hoc networks. As a consequence, the existing routing protocols designed for MANETs partly fail in tracking network topology changes. In this paper, we compare two different routing algorithms for ad hoc networks: optimized link-state routing (OLSR) and predictive OLSR (P-OLSR). The latter is an OLSR extension that we designed for FANETs; it takes advantage of the Global Positioning System (GPS) information available on board. To the best of our knowledge, P-OLSR is currently the only FANET-specific routing technique that has an available Linux implementation. We present results obtained by both media-access-control (MAC) layer emulations and real-world experiments. In the experiments, we used a testbed composed of two autonomous fixed-wing UAVs and a node on the ground. Our experiments evaluate the link performance and the communication range, as well as the routing performance. Our emulation and experimental results show that P-OLSR significantly outperforms OLSR in routing in the presence of frequent network topology changes.

Interference-Limited Resource Optimization in Cognitive Femtocells With Fairness and Imperfect Spectrum Sensing

Abstract: The use of cognitive-radio(CR)-enabled femtocell is regarded as a promising technique in wireless communications, and many studies have been reported on its resource allocation and interference management. However, fairness and spectrum sensing errors were ignored in most of the existing studies. In this paper, we propose a resource allocation scheme for orthogonal frequency division multiple access (OFDMA)-based cognitive femtocells. The target is to maximize the total capacity of all femtocell users (FUs) under given quality-of-service (QoS) and cotier/cross-tier interference constraints with imperfect channel sensing. To achieve the fairness among FUs, the minimum and maximum numbers of subchannels occupied by each user are considered. First, the subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing and applying cotier interference constraints, which is finally solved using a dual decomposition method. Based on the obtained solution, an iterative subchannel and power allocation algorithm is proposed. The effectiveness of the proposed algorithm in terms of capacity and fairness compared with the existing schemes is verified by simulations.

PHY-Layer Spoofing Detection with Reinforcement Learning in Wireless Networks

Abstract: In this paper, we investigate the PHY-layer authentication that exploits radio channel information (such as received signal strength indicators) to detect spoofing attacks in wireless networks. The interactions between a legitimate receiver and spoofers are formulated as a zero-sum authentication game. The receiver chooses the test threshold in the hypothesis test to maximize its utility based on the Bayesian risk in the spoofing detection, whereas the spoofers determine their attack frequencies to minimize the utility of the receiver. The Nash equilibrium of the static authentication game is derived, and its uniqueness is discussed. We also investigate a repeated PHY-layer authentication game for a dynamic radio environment. As it is challenging for the radio nodes to obtain the exact channel parameters in advance, we propose spoofing detection schemes based on Q-learning and Dyna-Q, which achieve the optimal test threshold in the spoofing detection via reinforcement learning. We implement the PHY-layer spoofing detectors over universal software radio peripherals and evaluate their performance via experiments in indoor environments. Both simulation and experimental results have validated the efficiency of the proposed strategies.

Small Cell Base Station Sleep Strategies for Energy Efficiency

Abstract: Small cell networks offer a promising and viable approach to meeting the increasing demand for high-data-rate wireless applications. With the expected increase in the number of small cell deployments, energy efficiency (EE) is a crucial system design parameter that demands consideration from an eco-sustainability perspective. One way to improve EE is to switch off small cell base stations (BSs) or to keep them in energy-saving mode while preserving the quality of service (QoS) experienced by users. With “bits/joule” as the metric, we aim to optimize EE with the introduction of several levels of sleep depths. Using a stochastic geometry-based heterogeneous cellular network (HCN) model, we derive coverage probability, average achievable rate, and EE in heterogeneous $K$ -tier wireless networks with different sleep modes for small cells. Then, we try to maximize EE under 1) a random sleeping policy and 2) a strategic sleeping policy, with constraints on both coverage probability and wake-up times. Due to the nonconvexity of EE, we propose an alternative low-complexity near-optimal solution by maximizing the lower bound of EE. We use an alternating iterative approach to solve the resulting multivariable optimization problem. Simulation results confirm the effectiveness of the scheme. With improvements of approximately 30% in EE with random sleeping policy, simulation indicates that instantaneous EE can be further improved by 15% with a strategic sleeping policy.

Operation Modes for the Electric Vehicle in Smart Grids and Smart Homes: Present and Proposed Modes

Abstract: This paper presents the main operation modes for an electric vehicle (EV) battery charger framed in smart grids and smart homes, i.e., present-day and new operation modes that can represent an asset toward EV adoption are discussed and proposed, respectively. Apart from the well-known grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operation modes, this paper proposes two new operation modes: home-to-vehicle (H2V), where the EV battery charger current is controlled according to the current consumption of the electrical appliances of the home (this operation mode is combined with G2V and V2G), and vehicle-for-grid (V4G), where the EV battery charger is used for compensating current harmonics or reactive power, simultaneously with the G2V and V2G operation modes. The vehicle-to-home (V2H) operation mode, where the EV can operate as a power source in isolated systems or as offline uninterruptible power supply to feed priority appliances of the home during power outages of the electrical grid, is presented in this paper and framed with the other operation modes. These five operation modes were validated through experimental results using a developed 3.6-kW bidirectional EV battery charger prototype, which was specially designed for these operation modes. This paper describes the developed EV battery charger prototype, detailing the power theory and the voltage and current control strategies used in the control system. This paper also presents experimental results for the various operation modes, both in steady state and during transients.

D-ACB: Adaptive Congestion Control Algorithm for Bursty M2M Traffic in LTE Networks

Abstract: When incorporating machine-to-machine (M2M) communications into the Third-Generation Partnership Project (3GPP) Long-Term Evolution (LTE) networks, one of the challenges is the traffic overload since many machine-type communication (MTC) devices activated in a short period of time may require access to an evolved node B (eNodeB) simultaneously. One approach to tackle this problem is by using an access class barring (ACB) mechanism with an ACB factor to defer some activated MTC devices transmitting their access requests. In this paper, we first present an analytical model to determine the expected total service time, i.e., the time used by all MTC devices to successfully access the eNodeB. In the ideal case that the eNodeB is aware of the number of backlogged MTC devices, we determine the optimal value of the ACB factor to reduce traffic overload. To better utilize the random access resources shared among human users and MTC devices in LTE networks, we propose to dynamically allocate a number of random access preambles for MTC devices. We further propose two dynamic ACB (D-ACB) algorithms for fixed and dynamic preamble allocation schemes to determine the ACB factors without a priori knowledge of the system backlog. Simulation results show that the proposed D-ACB algorithms achieve almost the same performance as the optimal performance obtained in the ideal case. The proposed D-ACB for dynamic preamble allocation algorithm can reduce both the total time to serve all MTC devices and the average number of random access opportunities required by each MTC device.

Adaptive Power Allocation Schemes for Spectrum Sharing in Interference-Alignment-Based Cognitive Radio Networks

Abstract: Interference alignment (IA) is a promising technique for interference management and can be applied to spectrum sharing in cognitive radio (CR) networks. However, the sum rate may fall short of the theoretical maximum, particularly at low signal-to-noise ratio (SNR), and the quality of service (QoS) of the primary user (PU) may not be guaranteed. In addition, power allocation (PA) in IA-based CR networks is largely ignored, which can further improve its performance. Thus, in this paper, PA in IA-based CR networks is studied. To guarantee the QoS requirement of the PU, its minimal transmitted power is derived. Then, we propose three PA algorithms to maximize the throughput of secondary users (SUs), the energy efficiency (EE) of the network, and the requirements of SUs, respectively, while guaranteeing the QoS of the PU. To reduce the complexity, the closed-form solutions of these algorithms are further studied in detail. The outage probability of the PU according to its rate threshold is also derived to analyze the performance of these algorithms. Moreover, we propose a transmission-mode adaptation scheme to further improve the PU's performance when its QoS requirement cannot be guaranteed at low SNR, and it can be easily combined with the proposed PA algorithms. Simulation results are presented to show the effectiveness of the proposed adaptive PA algorithms for IA-based CR networks.

A Hybrid Path Planning Method in Unmanned Air/Ground Vehicle (UAV/UGV) Cooperative Systems

Abstract: In this paper, we study the automatic ground map building and efficient path planning in unmanned aerial/ground vehicle (UAV/UGV) cooperative systems. Using the UAV, a ground image can be obtained from the aerial vision, which is then processed with image denoising, image correction, and obstacle recognition to construct the ground map automatically. Image correction is used to help the UGV improve the recognition accuracy of obstacles. Based on the constructed ground map, a hybrid path planning algorithm is proposed to optimize the planned path. A genetic algorithm is used for global path planning, and a local rolling optimization is used to constantly optimize the results of the genetic algorithm. Experiments are performed to evaluate the performance of the proposed schemes. The evaluation results show that our proposed approach can obtain a much less costly path compared to the traditional path planning algorithms such as the genetic algorithm and the A-star algorithm and can run in real-time to support the UAV/UGV systems.

A Bidirectional Nonisolated Multi-Input DC–DC Converter for Hybrid Energy Storage Systems in Electric Vehicles

Abstract: To process the power in hybrid energy systems using a reduced part count, researchers have proposed several multiinput dc–dc power converter topologies to transfer power from different input voltage sources to the output. This paper proposes a novel bidirectional nonisolated multi-input converter (MIC) topology for hybrid systems to be used in electric vehicles composed of energy storage systems (ESSs) with different electrical characteristics. The proposed converter has the ability to control the power of ESSs by allowing active power sharing. The voltage levels of utilized ESSs can be higher or lower than the output voltage. The inductors of the converter are connected to a single switch; therefore, the converter requires only one extra active switch for each input, unlike its counterparts, hence resulting in reduced element count. The proposed MIC topology is compared with its counterparts concerning various parameters. It is analyzed in detail, and then, this analysis is validated by simulation and through a 1-kW prototype based on a battery/ultracapacitor hybrid ESS.

Joint Spectrum and Power Allocation for D2D Communications Underlaying Cellular Networks

Abstract: This paper addresses the joint spectrum sharing and power allocation problem for device-to-device (D2D) communications underlaying a cellular network (CN). In the context of orthogonal frequency-division multiple-access systems, with the uplink resources shared with D2D links, both centralized and decentralized methods are proposed. Assuming global channel state information (CSI), the resource allocation problem is first formulated as a nonconvex optimization problem, which is solved using convex approximation techniques. We prove that the approximation method converges to a suboptimal solution and is often very close to the global optimal solution. On the other hand, by exploiting the decentralized network structure with only local CSI at each node, the Stackelberg game model is then adopted to devise a distributed resource allocation scheme. In this game-theoretic model, the base station (BS), which is modeled as the leader, coordinates the interference from the D2D transmission to the cellular users (CUs) by pricing the interference. Subsequently, the D2D pairs, as followers, compete for the spectrum in a noncooperative fashion. Sufficient conditions for the existence of the Nash equilibrium (NE) and the uniqueness of the solution are presented, and an iterative algorithm is proposed to solve the problem. In addition, the signaling overhead is compared between the centralized and decentralized schemes. Finally, numerical results are presented to verify the proposed schemes. It is shown that the distributed scheme is effective for the resource allocation and could protect the CUs with limited signaling overhead.

Secrecy Wireless Information and Power Transfer in Fading Wiretap Channel

Abstract: Simultaneous wireless information and power transfer (SWIPT) has recently drawn significant interest for its dual use of radio signals to provide wireless data and energy access at the same time. However, a challenging secrecy communication issue arises as the messages sent to the information receivers (IRs) may be eavesdropped upon by energy receivers (ERs), which are presumed to harvest energy only from received signals. To tackle this problem, the authors propose in this paper an artificial-noise (AN)-aided transmission scheme to facilitate the secrecy information transmission to IRs and, yet, meet the energy harvesting requirement for ERs, under the assumption that the AN can be canceled at IRs but not at ERs. Specifically, the proposed scheme splits the transmit power into two parts: to send the confidential message to the IR and an AN to interfere with the ER, respectively. Under a simplified three-node wiretap channel setup, the transmit power allocations and power splitting ratios over fading channels are jointly optimized to minimize the outage probability for delay-limited secrecy information transmission or to maximize the average rate for no-delay-limited secrecy information transmission, subject to a combination of average and peak power constraints (APC and PPC) at the transmitter (Tx) and an average energy harvesting constraint at the ER. Both the secrecy outage probability minimization and average rate maximization problems are shown to be nonconvex, and for each, the authors propose the optimal solution based on the dual decomposition and the suboptimal solution based on the alternating optimization. Furthermore, two benchmark schemes are introduced for comparison where the AN is not used at the Tx and where the AN is used but cannot be canceled by the IR, respectively. Finally, the performances of proposed schemes are evaluated by simulations in terms of various tradeoffs for wireless (secrecy) information versus energy transmissions.

Energy-Efficient Joint Resource Allocation and Power Control for D2D Communications

Abstract: In this paper, joint resource allocation and power control for energy-efficient device-to-device (D2D) communications underlaying cellular networks are investigated. The resource and power are optimized for maximization of the energy efficiency (EE) of D2D communications. Exploiting the properties of fractional programming, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. Then, an efficient iterative resource allocation and power control scheme is proposed. In each iteration, part of the constraints of the EE optimization problem are removed by exploiting the penalty function approach. We further propose a novel two-layer approach, which allows finding the optimum at each iteration by decoupling the EE optimization problem of joint resource allocation and power control into two separate steps. In the first layer, the optimal power values are obtained by solving a series of maximization problems through root finding, with or without considering the loss of cellular users' rates. In the second layer, the formulated optimization problem belongs to a classical resource-allocation problem with single allocation format, which admits a network flow formulation so that it can be solved to optimality. Simulation results demonstrate the remarkable improvements in terms of EE by using the proposed iterative resource allocation and power control scheme.

Filter-Based Adaptive Vibration Control for Active Vehicle Suspensions With Electrohydraulic Actuators

Abstract: Vehicle suspension systems are important for significantly improving passenger comfort and handling characteristics. A well-designed suspension system can improve the entire performance of the automobile chassis. In this paper, an adaptive vibration control strategy is proposed for nonlinear uncertain suspension systems to stabilize both the vertical and pitch motions of the car and, thus, to contribute to ride comfort. Simultaneously, ride holding performance is preserved within allowable limits in the controller design. Moreover, differing from the existing results, in most of which the effect of actuator dynamic is neglected, this paper considers the electrohydraulic systems as actuators to supply active forces into suspension systems. Furthermore, to overcome the “exploration of terms” problem existing in standard backstepping, a filter-based adaptive control strategy is subsequently proposed. Finally, a design example is shown to illustrate the effectiveness of the proposed active controllers, where different road conditions are considered in order to reveal the closed-loop system performance in detail.

Optimal Charging in Wireless Rechargeable Sensor Networks

Abstract: Recent years have witnessed several new promising technologies to power wireless sensor networks, which motivate some key topics to be revisited. By integrating sensing and computation capabilities to the traditional radio-frequency identification (RFID) tags, the Wireless Identification and Sensing Platform (WISP) is an open-source platform acting as a pioneering experimental platform of wireless rechargeable sensor networks. Different from traditional tags, an RFID-based wireless rechargeable sensor node needs to charge its onboard energy storage above a threshold to power its sensing, computation, and communication components. Consequently, such charging delay imposes a unique design challenge for deploying wireless rechargeable sensor networks. In this paper, we tackle this problem by planning the optimal movement strategy of the mobile RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. We first propose an optimal solution using the linear programming (LP) method. To further reduce the computational complexity, we then introduce a heuristic solution with a provable approximation ratio of (1 + θ)/(1 - e) by discretizing the charging power on a 2-D space. Through extensive evaluations, we demonstrate that our design outperforms the set-cover-based design by an average of 24.7%, whereas the computational complexity is O((N/e) 2 ). Finally, we consider two practical issues in system implementation and provide guidelines for parameter setting.

Characterization and modeling of a hybrid electric vehicle lithium-ion battery pack at low temperatures

Abstract: Although lithium-ion batteries have penetrated hybrid electric vehicles (HEVs) and pure electric vehicles (EVs), they suffer from significant power capability losses and reduced energy at low temperatures. To evaluate those losses and to make an efficient design, good models are required for system simulation. Subzero battery operation involves nonclassical thermal behavior. Consequently, simple electrical models are not sufficient to predict bad performance or damage to systems involving batteries at subzero temperatures. This paper presents the development of an electrical and thermal model of an HEV lithium-ion battery pack. This model has been developed with MATLAB/Simulink to investigate the output characteristics of lithium-ion batteries over the selected operating range of currents and battery capacities. In addition, a thermal modeling method has been developed for this model so that it can predict the battery core and crust temperature by including the effect of internal resistance. First, various discharge tests on one cell are carried out, and then, cell's parameters and thermal characteristics are obtained. The single-cell model proposed is shown to be accurate by analyzing the simulation data and test results. Next, real working conditions tests are performed, and simulation calculations on one cell are presented. In the end, the simulation results of a battery pack under HEV driving cycle conditions show that the characteristics of the proposed model allow a good comparison with data from an actual lithium-ion battery pack used in an HEV.

2FLIP: A Two-Factor Lightweight Privacy-Preserving Authentication Scheme for VANET

Abstract: Authentication in a vehicular ad-hoc network (VANET) requires not only secure and efficient authentication with privacy preservation but applicable flexibility to handle complicated transportation circumstances as well. In this paper, we proposed a Two-Factor LIghtweight Privacy-preserving authentication scheme (2FLIP) to enhance the security of VANET communication. 2FLIP employs the decentralized certificate authority (CA) and the biological-password-based two-factor authentication (2FA) to achieve the goals. Based on the decentralized CA, 2FLIP only requires several extremely lightweight hashing processes and a fast message-authentication-code operation for message signing and verification between vehicles. Compared with previous schemes, 2FLIP significantly reduces computation cost by 100–1000 times and decreases communication overhead by 55.24%–77.52%. Furthermore, any certificate revocation list (CRL)-related overhead on vehicles is avoided. 2FLIP makes the scheme resilient to denial-of-service attack in both computation and memory, which is caused by either deliberate invading behaviors or jammed traffic scenes. The proposed scheme provides strong privacy preservation that the adversaries can never succeed in tracing any vehicles, even with all RSUs compromised. Moreover, it achieves strong nonrepudiation that any biological anonym driver could be conditionally traced, even if he is not the only driver of the vehicle. Extensive simulations reveal that 2FLIP is feasible and has an outstanding performance of nearly 0-ms network delay and 0% packet-loss ratio, which are particularly appropriate for real-time emergency reporting applications.