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Future communication networksrequire higher bandwidth, greater coverage, and better throughput. The Space-Air-Ground Integrated Network (SAGIN) has the advantage of wide-area coverage and can cover global communications. It can meet the needs of network resources for maritime activities and remote mountainous areas, which is of great significance to the realization of a new generation of communications networks. With the development of the network and the emergence of delay-sensitive applications such as the Internet of Things, improving the delay performance of the system has received extensive attention. SAGIN involves multiple networks and is more complex than other networks. If there is no reasonable management between different networks, it is easy to lead to difficult link deployment and high time delay. On the basis of research and customization of SFC technology, it can provide a wide range of services and other advantages. It has significant performance in application scenarios involving multi network integration. In order to solve the above problems, this paper studies the SAGIN architecture of SFC based on business types. A service function chain mapping method based on delays prediction is proposed. Calculate the delay of the deployment path and select the path with the lowest delay as the SFC mapping path. The service model is constructed according to the mapping path, and the network slices are divided based on the service type. The simulation results show that the SFC mapping algorithm based on time delay prediction is compared with the traditional SFC mapping scheme. The algorithm does not affect other indicators, the CPU resource utilization rate is 27.8 $\%$ higher, and the link resource utilization rate is 22.7 $\%$ higher. The service acceptance rate increased by 21.5 $\%$ , the latency performance increased by 38.2 $\%$ , and the total resource consumption is reduced by 25.2 $\%$. [ABSTRACT FROM AUTHOR]

Big data is the fuel for powering pervasive artificial intelligence (AI) applications. Aiming at promoting efficient cooperation in data market for big data, in this paper we propose a novel cooperative data purchase framework, by leveraging the power of the data user crowd and their intrinsic trustworthy collaboration relationships. For achieving efficient cooperative data purchase, we develop a comprehensive approach consisting of both data purchase group formation and selection. For the data purchase group formation, we partition the users into multiple data purchase groups for the purpose of budget pooling, by taking into account their data interest and budget levels, meanwhile respecting their underlying collaboration relationships and the maximum allowable group size for data sharing enforced by the data market platform. For the data purchase group selection, we construct a data purchase flow network formulation and devise a minimum cut based solution for selecting the proper set of data purchase groups to fully support their demands subject to the budget constraints. We extensively evaluate the performance of cooperative data purchase framework using both Erdos-Renyi and scale-free collaboration graphs. The numerical results demonstrate that the proposed framework can achieve superior performance, with more than ${40}\%$ and ${100}\%$ performance gain over the case without cooperation in terms of the total received payment and the number of satisfied users, respectively. [ABSTRACT FROM AUTHOR]

The introduction of fourth-generation wireless technologies has fueled the rapid development of cellular networks, significantly increasing the energy consumption and the expenditures of mobile network operators (MNOs). In addition, network underutilization during low-traffic periods (e.g., night zone) has motivated a new business model, namely, infrastructure sharing, which allows the MNOs to have their traffic served by other MNOs in the same geographic area, thus enabling them to switch off part of their network. In this paper, we propose a novel infrastructure-sharing algorithm for multioperator environments, which enables the deactivation of underutilized base stations during low-traffic periods. Motivated by the conflicting interests of the MNOs and the necessity for effective solutions, we introduce a game-theoretic framework that enables the MNOs to individually estimate the switching-off probabilities that reduce their expected financial cost. Our approach reaches dominant strategy equilibrium, which is the strategy that minimizes the cost of each player. Finally, we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved in multioperator environments, incentivizing the MNOs to apply the proposed scheme. [ABSTRACT FROM AUTHOR]

Mobile opportunistic networks make use of node mobility to provide occasional contact opportunities for mobile devices to deliver data. In this paper, we investigate the inherent properties of data disseminations in mobile opportunistic networks by answering the question regarding how far and how fast a packet can be disseminated in mobile opportunistic networks. This problem has been overlooked by recent research, but it is very important because it reveals the potential for mobile opportunistic networks to support emerging applications such as mobile commerce and emergency services that may involve time and location sensitive information dissemination. Our investigations are taken from the perspectives of small and large scales. From the perspective of small scale, the probability distributions of the minimum time needed by the data to spread to a given region, namely, \mathcalT, are deduced for both the one-copy case and the multiple-copy case. From the perspective of large scale, by using the large deviation theory, the probability distributions of \mathcalT are deduced for both the one-copy case and the multiple-copy case when the destination region is far enough from the data origin. [ABSTRACT FROM PUBLISHER]

This paper analyzes plug-in hybrid electric vehicle (PHEV) behavior, its impact on the electric grid, and possible charging opportunities using representative synthetic cycles with the consideration of daily driving schedules. The representative naturalistic cycles are synthesized through a stochastic process utilizing transition probability matrices extracted from naturalistic driving data collected in the Midwest region of the United States. The representativeness of the cycles is achieved through the subsequent statistical analysis. The distributions of the departure/arrival time and the rest time, analyzed from the real-world data at the key locations, complete the picture to analyze vehicle daily missions and the PHEV impact on the grid. PHEV simulation is used to determine the battery state of charge (SOC) distribution upon arrival. The results for typical locations such as residential, work, large business, and small business allow the assessment of the PHEV impact on the grid and possible charging opportunities during daily missions. [ABSTRACT FROM PUBLISHER]