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Vehicles in Internet of Vehicles (IoV) exchange information about location, environment, infotainment, as well as social information with other units via vehicular communication networks. This makes IoV with key social entities in the human–vehicle–infrastructure–roadside units (RSUs) as integrated intelligent transportation systems. Therefore, by identifying the cyber–physical–social features of IoV and presenting its complexity issues of both engineering and social dimensions, this article proposes and introduces the concept, architecture, and applications of parallel IoV (PIoV). Three main components of PIoV are demonstrated, which are artificial IoV to learn and describe the physical IoV, computation experiments to evaluate and predict the consequences and values of driving strategies, and parallel execution to prescribe the operation of the physical IoV. PIoV makes it possible to achieve safe, smart, effective, and efficient transportation management and control. The final objective of PIoV is to equip IoV with descriptive, predictive, and prescriptive intelligence based on the parallel intelligence approach.

Vehicular networks (VNs) have received great attention as one of the crucial supportive techniques for intelligent transportation systems (ITSs). However, the introduction of dynamic and complex human behaviors into VNs makes it a cyber-social-physical system. Thus, artificial systems, computational experiments, parallel executions-based parallel VNs (PVN) are proposed in this paper. The framework of PVN is then designed and presented, its characteristics and applications are demonstrated, and its related research challenges are discussed. PVN uses software-defined artificial VNs for modeling and representation, computational experiments for analysis and evaluation, and parallel execution for control and management. Thus, more reliable and efficient traffic status and ultrahigh data rate communications are obtained among vehicles and infrastructures, which is expected to achieve the descriptive intelligence, predictive intelligence, and prescription intelligence for VNs. The proposed PVN offers a competitive solution for achieving a smooth, safe, and efficient cooperation among connected vehicles in future ITSs.

On June 11th, 2017, the 28th IEEE Intelligent Vehicles Symposium (IV'2017) was held in Redondo Beach, California, USA. As one of the 8 workshops at IV'2017, the cyber-physical-social systems (CPSS)-based parallel driving (WS'08), organized by the State Key Laboratory for Management and Control of Complex Systems (SKL-MCCS), Institute of Automation, Chinese Academy of Sciences, China, Xi'an Jiaotong University, China, Tsinghua University, China, Indiana University- Purdue University Indianapolis, USA, and Cranfield University, U.K, has attracted both researchers and practitioners in intelligent vehicles. About 60-70 participants from various countries had extensive and deep discussions on definition, challenges and alternative solutions for CPSS-based parallel driving, and widely agreed that it is a novel paradigm of cloud-based automated driving technologies. Six speakers shared their ideas, studies, field applications, and vision for future along these emerging directions from software-defined vehicles to self-driving vehicles.

Radio resource management (RRM), which aims to satisfy the requirements of both mobile users and service providers, can be seen as one of the typical issues of cyber-physical-social system since the social factors, that is, the requirements and priorities of users are extremely important in heterogeneous networks. In this paper, we propose a novel resource allocation and access control mechanism based on parallel network architecture, which provides a high-bandwidth connectivity with guaranteed quality of service (QoS) for mobile users in a seamless manner. In this mechanism, multiple users are classified into several types according to their social property such as priorities and bandwidth requirements of different users. Compared with the general received signal strength (RSS)-based method, the proposed user priority (UP)-based method achieves three main advantages as follows: 1) it further balances the load of base stations (BSs) when the resource is sufficient; 2) it provides a mechanism called high priority users higher QoS when the network is heavily loaded compared to the RSS-based method; and 3) it hands over a few users from a heavily loaded BS to a lightly loaded one to allow more users to access this network. The simulation results confirm the advantages of the proposed UP-based mechanism and show that the simulation results of the Q-learning method are consistent with its theoretical analysis.

This paper is to discuss the state, trend, and frontiers of development of cyber-physical-social systems (CPSSs) in China. The demand for developing CPSS is discussed in detail, followed by the Artificial societies, Computational experiments, Parallel execution (ACP) approach for CPSS and knowledge automation. The development of ACP based on CPSS in transportation, energy, information, Internet of Things, and Internet of Minds (IoM) is discussed to demonstrate the cutting-edge applications in CPSS. Finally, the blockchainized IoM technology and the concepts of parallel society are described. This paper will contribute to the transition from the current social construct to a futuristic intelligent society.

Multiple-input multiple-output (MIMO) and cooperative communications have been attracted great attention for the improvements of communication capacity, power consumption, and transmission coverage. The conventional fixed relaying protocols, amplify-and-forward (AF) and decode-and-forward (DF), have their own advantages and disadvantages, i.e. AF performs better than DF for low signal-to-noise ratio (SNR) region, while the reverse is true for high SNR region. Therefore, this paper proposes an SNR-adaptive forward (SAF) relaying scheme obtaining the advantages of both AF and DF. Furthermore, the proposed SAF does not need to switch between AF and DF when SNR changes. The main idea is to adaptively derive the soft information at the cooperative relay nodes based on the information of the received signal and the SNR. Besides, based on the theoretical analysis and the simulation results, it is affirmed that the proposed SAF achieves superior performance than both AF and DF for all SNRs. Moreover, the performance gain would be improved with the increasing number of parallel cooperative relay nodes.

A novel method avoiding the complex transfer process is proposed to directly grow low-defect and few-layer graphene on different insulating substrates (SiO2, Al2O3, etc.) by remote catalyzation of Cu nanoparticles (NPs) using ambient pressure chemical vapor deposition (APCVD). The insulating substrates with special structure are used as templates to grow wrapped graphene sheets with special shapes. Hollow graphene species are obtained by removing the substrates. The prime feature of the proposed method is using Cu NPs as catalyst rather than metal foils. The Cu NPs play an important role in the remote catalyzation during the nucleation of graphene. This method can improve the quality and relatively decrease the growth temperature of the graphene on the insulating substrates, which displays the great potential of APCVD direct growth of graphene on dielectric substrates for electronic and photovoltaic applications.

With the development of communication technology, in order to meet the smart city's demand for intelligent infrastructure, the smart street lighting system based on NB-IoT is an effective method to realize the goal. This paper elaborates on the smart street lighting system's architecture, including the perception and control layer, the transport layer, the platform layer and the application layer. In addition, the actual system is built to verify. This system uses STM32 to build the perception and controller, and relies on China Telecom's NB-IoT communication network for the data transmission, finally using the IoT management platform-Huawei OceanConnect- to implement the connection management, the device management, the data calculation and the remote control functions. This system verifies the feasibility of the smart street lighting system based on NB-IoT technology, which provides the reference for the promotion and application. It is expected to provide intelligent, diversified, efficient and economical services for the smart cities.

Recently low power wide area network (LPWAN) is widely researched and deployed due to its excellent performance of supporting large coverage, low power consumption and massive capacity. LPWAN might offer brandnew solutions to Vehicle to anything (V2X) communications, which is faced with the challenge of supporting massive connections due to the dramatically increasing number of vehicles. In this paper, after surveying the existing V2X communication technologies, we compare the traditional technologies with the representative LPWAN technologies according to their performance metrics. After the careful comparison and selection, Long-Range (LoRa) and enhanced machine type communication (eMTC) are introduced to V2X communication due to their support for mobility. Moreover, their performance are evaluated in both V2I (vehicle-to-infrastructure) and V2V (vehicle-to-vehicle) communication environments via Monte Carlo simulations.

The coexistence of various protocols for current network equipment leads to extremely complex network systems, which not only limit the development of network technologies, but also cannot meet the growing demands for cloud computing, big data, and service visualization applications, just to name a few. As a new network architecture, parallel networks are expected to revolutionize this situation and meet the evolving demands for network services. The main idea of a parallel network is to leverage upon software-defined networking to construct artificial networks, and then effectively optimize the network system operations via the interactions between actual and artificial networks. The foundation of the parallel network is the theory of ACP, composed of artificial societies, computational experiments, and parallel execution. By the computational experiments and analysis of the artificial network, a control strategy based on network traffic flow can be continuously updated and tracked on a real-time basis; meanwhile, the collected operating status of the actual network can also be used to optimize the model of the artificial network. These strategies can be applied to all types of network equipment to control network operations at various levels; thus, it is possible to allocate the network resources more effectively, improve the management and utilization of resources, and then provide new network solutions to effectively address the constantly evolving network demands for network performance, scalability, and security.

A secure spatial modulation based on artificial noise (AN-SM) is proposed. Different from the traditional spatial modulation schemes, the legitimate channel state information is utilized to convey part of information bits instead of amplitude phase modulation (APM) constellation. By this way, the normal transmit signals will become artificial noise to the eavesdropper, and it is impossible for the eavesdropper to know the legitimate channel state information. Consequently, the secrecy transmission could be achieved. Moreover, all transmit power is used to transmit the legitimate signals without any energy waste. The secrecy rate is analyzed, and the numerical results show that the new AN-SM scheme could achieve high secrecy rate.

With increasing popularity of Internet of Vehicles (IoV), concerns for reliable and low complexity communication techniques are proposed due to the requirements of signal reliability and transmission delay for vehicles. Meanwhile, the explosive and pervasive use of social network applications further adds drivers’ social relationships and behavioural characteristics into it, and makes it a cyber-physical-social system (CPSS). This paper proposes and analyzes an improved forward scheme for full deplex cooperative vehicular networks in terms of its CPSS features. The proposed CPSS-based forwarding (CPSS-F) strategy forwards a soft estimate of the received signal based on social historic data at the relay node (vehicle/infrastructure) to the destination node (vehicle/infrastructure), which achieves improved reliability than the two conventional strategies in cooperative networks, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, the proposed CPSS-F relay achieves performance gains and complexity reduction compared to the conventional AF and DF. Experimental results further confirm the advantages of the proposed CPSS-F for cooperative vehicular networks. The proposed CPSS-F approach is easily extended to other cooperative vehicular social networks, for example, multi-way, half-duplex, or large-antenna networks.

Recent years have witnessed much interest in Low Power Wide Area (LPWA) technologies, which are gaining unprecedented momentum and commercial interest towards the realisation of the Internet of Things (IoT). Long Range (LoRa), as a representative LPWA technology, has the potential to satisfy the growing demand for the longer range and larger amount connectivities in vehicular communication networks. In this paper, LoRa is firstly applied into two typical vehicular networks, namely Vehicle-to-Infrastructure (V2I)and Vehicle-to-Vehicle (V2V), and performance of LoRa schemes with different parameter configurations are evaluated and compared. Further, Monte Carlo simulations indicate that the schemes equipped with higher bandwidth or lower spreading factor exhibit significant advantages in combating the fast fading caused by Doppler effect in networks.

In recent years, with the rapid development of Internet, more and more users shop and socialize online, which produces a large amount of traffic data. Analyzing users' online shopping behavior is important for merchants to improve profit. Social networks are widely used in studying the relationship among users. In this paper, we apply the concept of social networks to online shopping behavior, and present a novel network perspective on the interconnected nature of online interest and product, allowing us to capture the attribute of online products reflecting users' sociability. Sociability in this paper is not a traditional social connection but common interests(defined by produce visiting behavior) among users. First, we build an interconnected online interest-product network which includes online shopping based social network (OSSN), online product network and intermediate layer, and we define popular online products and non-popular online products according to the number of distinct visitors of one product. Then, we analyze some indicators about OSSN, and find that OSSN has similar characteristics with traditional social networks, such as small world feature and homophily. Finally, we analyze how online products reflect users' sociability. With the number of online products users have visited increasing, the number of neighbors with similar interests first presents a positive correlation, and then disappears, which implies that the number of users' neighbors depends on the attribute of online products rather than the number of online products users have browsed. We define a formula to measure the attribute of online products reflecting users' sociability, and find that there is a distinct difference among different categories of online products, and non-popular online products often have high value of attribute to reflect users' sociability and the value is low in popular online products.

Nowadays, with the fast growth of Internet of Vehicles (IoV), wireless communication techniques for IoV is becoming more and more important for vehicular networks. To achieve more reliable information and reduce the computational complexity, receiver detection is one of the most significant techniques. In this paper, several modified K-best detection algorithms are developed to take advantages of the list decoder, which provide a flexible performance and complexity tradeoff. Different from the existing algorithms, the proposed K-best algorithms could make use of the a priori probability to generate the list. Simulation results demonstrate that the proposed low complexity algorithms can achieve a significant performance gain over existing ones, especially for the networks with high order constellations.

As known, conventional sphere detection (SD) obtains lower computational complexity compared to the optimal maximum-likelihood (ML) detection. However, its computational complexity is still high especially for low Signal-to-Noise Ratio (SNR) region. Therefore, in this paper, a new low-complexity sphere detection algorithm is proposed for differential unitary space-time modulation systems with multiple antennas. The main idea of the proposed algorithm is to constraint the searching radius of the sphere by a heuristic SNR-dependent factor. The use of this factor provides a much reduced computational complexity and a near-optimal performance, and it keeps the complexity low over the entire range of SNRs. Furthermore, the benefits are expected to be more and more evident and useful with the increasing number of antennas, i.e., massive multiple-input multiple-output (MIMO) systems. Finally, the simulation results further demonstrate the advantages of the proposed low-complexity detection algorithm.

Large scale multiple input multiple output (MIMO), commonly referred to as massive MIMO, is one of the most important physical layer disruptions in network evolution that would lead to significant increase in spectral and energy efficiency envisioned for 5G networks. However, the computational complexity of massive MIMO system becomes the key problem for its practical implementation. As known, Differential Unitary Space-Time Modulation (DUSTM) has been proposed as an efficient modulation choice for such MIMO transmissions where channel state information (CSI) is not required at the receiver. However, traditional DUSTM implementation relies mainly on Maximum Likelihood Detection (MLD) achieving optimal performance, which is not easily compatible with massive MIMO systems due to its exponentially increasing complexity with increasing number of transmitting and receiving antennas. Therefore, in this paper, in order to overcome the aforementioned disadvantages, we propose a new detection algorithm named Relaxed-Bound K-Best Sphere Detection (RBKSD). Compared to the traditional K-Best Sphere Detection (KSD) algorithm, the main idea of our algorithm is to relax the bound K of the conventional KSD to be K + Γ, thereby achieving quasi-optimal performance with significantly reduced computational complexity. Simulation results confirm that the proposed RBKSD algorithm shows superior performance and reduced complexity compared to other notable schemes such as MLD, traditional sphere detection algorithm and the KSD.

With the rapid development of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications (V2X communications), reliable and low-complexity cooperative communication techniques are becoming more and more important due to the requirements of signal reliability and transmission delay for vehicles. Therefore, in this paper, an estimated relaying scheme based on soft information is proposed for cooperative vehicular networks, named by estimate-and-forward (EF) strategy. The proposed EF relay forwards the unconstrained minimum mean square error (MMSE) estimate of the received signal at the relay node (vehicle/infrastructure) to the destination node (vehicle/infrastructure), which outperforms amplify-and-forward (AF) in terms of performance, and achieves lower complexity than decode-and-forward (DF). Therefore, the proposed EF relay achieves a better trade-off between performance and complexity compared to the conventional AF and DF. Simulation results confirm the advantages of the proposed EF for cooperative vehicular networks.

The Capacitated Arc Routing Problem (CARP) is a challenging combinatorial optimization problem with wide real world applications. Since CARP is NP-hard, many heuristic and meta-heuristic approaches have been applied to solve CARP. In this paper, a novel extension memetic approach, named MAENS-Pls is proposed to improve the state-of-the-art algorithm — Memetic Algorithm with Extended Neighbourhood Search (MAENS) for CARP, in which using an adaptive probability to optimize the frequency and depth of local search. Based on the parameter optimization on all the 8 instances, experimental studies show that the proposed algorithm can significantly improve the average performance of MAENS, and there is an increase in the robust stability and convergence reliability. Furthermore, 4 new best known solutions are found by the proposed algorithm.

With the development of intelligent technologies, self-driving vehicles are considered as a promising solution against accident, traffic congestion and pollution problems. Intelligent vehicle techniques have been the research focus all over the world. However, full self-driving vehicles are still far away from its realization and extensive application due to safety requirements and cost considerations. As a novel breakthrough, PArallel VEhicles (PAVE) incorporate the ACP theory, which facilitates real-time interaction and optimization of the actual self-driving vehicles and the artificial ones. As a result, PAVE can maintain intelligent control of the actual self-driving vehicles and achieve the global optimization via software-defined self-driving vehicles, intelligent infrastructure construction, and parallel control center. Besides, PAVE can effectively reduce the cost of high-precision equipments on the actual self-driving vehicles via remote processing and intelligent road(side) infrastructure, and also achieve improved safety and reliability via remote control, guidance and planning.

An optimized relaying method for multi-antennas cooperative networks, estimate-and-forward (EF) strategy for MIMO relay, is proposed and analyzed in this paper. According to our theoretical analysis, it performs like amplify-and-forward (AF) for the low signal noise ratio (SNR) region and behaves like detect-and-forward (DF) for the high SNR region. For the relay networks with a large number of antennas and/or high order constellations, two approximate methods are proposed to reduce the complexity of the signal estimation at the relay. The first one uses a list sphere decoder to generate an estimate list and to obtain the approximate minimum mean squared error (MMSE) estimate based on the reduced list. The proposed list EF retains the advantages of the exact EF relay strategy in large MIMO relay networks at a negligible performance loss. The second algorithm computes the estimate of the symbols using Gaussian approximation to approximate the summation to be integral. We find the proposed EF scheme performs better than both AF and DF across all SNRs without switching algorithms for different SNRs.