The emerging technologies for connected vehicles have become hot topics. In addition, connected vehicle applications are generally found in heterogeneous wireless networks. In such a context, user terminals face the challenge of access network selection. The method of selecting the appropriate access network is quite important for connected vehicle applications. This paper jointly considers multiple decision factors to facilitate vehicle-to-infrastructure networking, where the energy efficiency of the networks is adopted as an important factor in the network selection process. To effectively characterize users' preference and network performance, we exploit energy efficiency, signal intensity, network cost, delay, and bandwidth to establish utility functions. Then, these utility functions and multi-criteria utility theory are used to construct an energy-efficient network selection approach. We propose design strategies to establish a joint multi-criteria utility function for network selection. Then, we model network selection in connected vehicle applications as a multi-constraint optimization problem. Finally, a multi-criteria access selection algorithm is presented to solve the built model. Simulation results show that the proposed access network selection approach is feasible and effective.

Developing non-terrestrial networks (NTN) in future wireless networks has been widely recognized to bring advanced communication services to remote and unserved areas. The Low-Earth-Orbit (LEO) constellation has emerged as a promising component for NTN to provide seamless and fast global connectivity. However, since natural dynamic features, the mobility management, in particular the handover (HO) between satellites, plays an important role in ensuring a stable and continuous data service for NTN. Motivated by this fact, this paper proposes a HO optimization strategy based on conditional handover (CHO) mechanism to enhance service continuity in LEO-based NTN. A reward function, related to link service time and service capability, is firstly designed to modify the monitoring conditions of target satellite candidates. The optimal target selection algorithm is proposed to obtain the maximum reward for each CHO. Then, a service continuity performance graph (SCG) model is constructed to predict different potential CHO combinations in service duration. On the basis of SCG, the HO sequence supporting a high-quality and stable data service is predictively calculated for each accessing user. Simulation results demonstrate that the proposed HO optimization scheme can obviously reduce handover rate under different NTN conditions and can better enhance NTN service continuity.

The satellite-terrestrial networks (STN) utilize the spacious coverage and low transmission latency of the Low Earth Orbit (LEO) constellation to transfer requested content for subscribers especially in remote areas. With the development of storage and computing capacity of satellite onboard equipment, it is considered promising to leverage in-network caching technology on STN to improve content distribution efficiency. However, traditional caching and distribution schemes are not suitable in STN, considering dynamic satellite propagation links and time-varying topology. More specifically, the unevenness of user distribution heightens difficulties for assurance of user quality of experience. To address these problems, we first propose a density-based network division algorithm. The STN is divided into a series of blocks with different sizes to amortize the data delivery costs. To deploy the caching satellites, we analyze the link connectivity and propose an approximate minimum coverage vertex set algorithm. Then, a novel cache node selection algorithm is designed for optimal subscriber matching. On the basis of time-varying network model, the STN cache content updating mechanism is derived to enable a stable and sustainable quality of user experience. The simulation results demonstrate that the proposed user-oriented STN content distribution scheme can obviously reduce the average propagation delay and network load under different network conditions and has better stability and self-adaptability under continuous time variation.

We propose a big data based analysis framework to analyze and extract network behaviors in cellular networks for Industry 4.0 applications from a big data perspective, using Hadoop, Hive, HBase, and so on. The data pre-handling and traffic flow extraction approaches are presented to construct effective traffic matrices. Accordingly, we can capture network behaviors in cellular networks from a network-wide perspective. Although there have been a number of prior studies on cellular network usage, to the best of our knowledge, this paper is a first study that characterizes network behaviors using the big data analytics to analyze a network big data of Call Detail Records (CDRs) over a longer duration (5 months), with more users (5 million), more records (several hundred million lines) and nationwide coverage. The call pattern analysis and network behavior extraction approaches are designed to perform big data analysis and feature extractions. Then the corresponding algorithms are proposed to characterize network behaviors, i.e., cellular call patterns and network resource usage. The detailed evaluation is proposed to validate our method. For example, we find that some unpopular calls can last longer time and thus consume more network resources.

The distributed energy system is used as the prototype of the energy Internet, including a variety of forms of energy networks, plenty of distributed equipment and energy storage equipment composed of energy flow, and real-time communication and data volume of information systems. As an important energy system that is closely related to people's lives, its security and stability is one of the cores of its development. With the access of a large number of distributed devices, the structure of the power system has changed greatly. The addition of various forms of energy network, distributed equipment, and energy storage equipment has made it more difficult for the energy Internet to achieve the coordination among and control over these devices. Regardless of the fluctuation of the power load and the sudden change of the thermal load, problems such as energy network failure and demand will affect the security and stability of the energy Internet. Traditional energy systems are independent of one another, while integrated energy systems include subsystems, such as the power system, thermal system, and natural gas system, which can complement one another in planning and operation. To improve the utilization rate of all kinds of energy, reduce the waste of energy, and cut the emission of pollutants, it is crucial to realize the economic utilization of energy as well as the safe and stable operation of the energy Internet.

Estimation of end-to-end network traffic plays an important role in traffic engineering and network plan-ning. The direct measurement of a network's traffic matrix consumes large amounts of network resources and is thus impractical in most cases. How to accurately construct traffic matrix remains a great challenge. This paper studies end-to-end network traffic reconstruction in large-scale networks. Applying compressive sensing theory, we propose a novel reconstruction method for end-to-end traffic flows. First, the direct measurement of partial Origin-Destination (OD) flows is determined by random measurement matrix, providing partial measurements. Then we use the K-SVD approach to obtain a sparse matrix. Combined with compressive sensing, this partially known OD flow matrix can be used to recover the entire end-to-end network traffic matrix. Simulation results show that the proposed method can reconstruct end-to-end network traffic with a high degree of accuracy. Moreover, in comparison with previous methods, our approach exhibits a significant performance improvement.