By analogy with Internet of things, Internet of vehicles (IoV) that enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.

Industrial Internet of unmanned aerial vehicles (IIoUAVs) that enable autonomous inspection and measurement of anything anytime anywhere have become an essential component of the future industrial Internet of things (IIoT) ecosystem. In this paper, we investigate how to apply IIoUAVs for power line inspection in smart grid from an energy-efficiency perspective. First, the energy consumption minimization problem is formulated as a joint optimization problem, which involves both the large-timescale optimization, such as trajectory scheduling, velocity control, and frequency regulation, and the small-timescale optimization, such as relay selection and power allocation. Then, the original NP-hard problem is transformed into a two-stage suboptimal problem by exploring the timescale difference and the energy magnitude difference between the large-timescale and the small-timescale optimizations, and is solved by combining dynamic programming (DP), auction theory, and matching theory. Finally, the proposed algorithm is verified based on real-world map and realistic power grid topology.

Driven by the evolutionary development of automobile industry and cellular technologies, dependable vehicular connectivity has become essential to realize future intelligent transportation systems (ITS). In this paper, we investigate how to achieve dependable content distribution in device-to-device (D2D)-based cooperative vehicular networks by combining big data-based vehicle trajectory prediction with coalition formation game-based resource allocation. First, vehicle trajectory is predicted based on global positioning system and geographic information system data, which is critical for finding reliable and long-lasting vehicle connections. Then, the determination of content distribution groups with different lifetimes is formulated as a coalition formation game. We model the utility function based on the minimization of average network delay, which is transferable to the individual payoff of each coalition member according to its contribution. The merge and split process is implemented iteratively based on preference relations, and the final partition is proved to converge to a Nash-stable equilibrium. Finally, we evaluate the proposed algorithm based on real-world map and realistic vehicular traffic. Numerical results demonstrate that the proposed algorithm can achieve superior performance in terms of average network delay and content distribution efficiency compared with the other heuristic schemes.

Service caching refers to caching the necessary programs or/and the related databases for performing computational tasks at edge servers, which has been considered to save both computation and communication resources in mobile edge computing (MEC) systems. In this paper, we investigate computation service caching in a multi-unmanned aerial vehicle (UAV) enabled MEC system, where each UAV equipped with an edge server acts as an aerial computing platform to serve the ground devices. Furthermore, the UAVs can serve the devices cooperatively through the provided computing and caching resources. Aiming at minimizing the maximum task completion latency among all devices, we formulate a joint service caching, task offloading, communication and computation resource allocation, as well as UAV placement optimization problem, while guaranteeing the energy budget of all devices and UAVs. The problem is a mixed integer non-linear programming problem, and we decompose it into four sub-problems, and then propose an iterative algorithm based on block coordinate descent (BCD) and successive convex approximation (SCA) techniques to obtain near-optimal solution. Numerical results show that our proposed algorithm can achieve lower task completion latency than other baselines while guaranteeing better fairness among all devices.

Mobile edge computing (MEC) has been regarded as a promising technology to improve the quality of service (QoS) of users by offloading their computation-intensive and delay-sensitive tasks to the wireless network edge. Furthermore, applying non-orthogonal multiple access (NOMA) technology in heterogeneous networks (HetNets) has become a trend to improve system throughput and spectrum efficiency. Exploiting these benefits, we investigate the joint task offloading and resource allocation problem for MEC in NOMA-based HetNets. To minimize the energy consumption of all users, we jointly consider task offloading decision, local CPU frequency scheduling, power control, computation resource and subchannel resource allocation. The optimization problem is challenging due to the combinatorial nature of the offloading decision and its strong coupling with the resource allocation. We thus decouple the problem into two sub-problems of offloading decision and resource allocation, and propose an efficient approach to find the joint solution by solving these two sub-problems iteratively. Simulation results show that the proposed approach can achieve a near optimal performance, and outperform other benchmark approaches in terms of saving energy consumption of all users with an acceptable complexity.

Traditionally, the online retailer has adopted the wholesale pricing model to work with heterogeneous manufacturers, that is, the retailer buys the products from the competitive manufacturers and then resells them to customers. Recently, the increasing prevalence of online retailing has given rise to a novel agency pricing model; that is, the manufacturers sell their products directly to customers on the retailer’s platform, and the retailer charges a commission fee for each sale. Meanwhile, customer loyalty is an important determinant of long-term business success for the retail platform. Considering the customer with loyalty, this paper investigates how the chosen pricing model (wholesale pricing model or agency pricing model) affects the online retailer’s profit, industry profit, consumer surplus, and social welfare. We find that the latter model always leads to a lower retail price and higher consumer surplus. More important, we also show that the industry profit and social welfare increase when the online retailer switches from the former model to the latter one. However, the online retailer’s profit critically depends on customer loyalty. Specifically, when customer loyalty is strong enough, the online retailer should adopt the agency pricing model. Otherwise, the wholesale pricing model dominates.