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Software defined satellite-terrestrial networking has been identified as a promising approach to support the diversity of network services. As the fundamental issue to improve the flexibility of network management, the controller placement problem has been attracted increasing attentions for the integration of satellite and terrestrial networking. However, the impact of the dynamic coverage demands on the controller placement have not been well investigated in existed works, which makes them fail to adjust the coverage dynamically according to the actual demands, and leads to an obvious increase of networking response latency to the terminals. Aiming to address this issue, we propose a novel on-demand dynamic controller placement scheme, which can optimize the placement of controllers to improve networking response latency while meeting the dynamic coverage demands. Firstly, to optimize the number of controllers and meet the dynamic coverage demands, we define the coverage redundancy and propose the redundancy-based satellite subnet division method to establish the reliable satellite subnets. Secondly, we quantify the networking response latency of the distributed satellite subnet, and build an optimization mathematical model to optimize the number and location of controllers. Then, we formulate the controller placement problem into the capacitated facility location problem and build the mathematical model for it. Moreover, the on-demand dynamic approximation algorithm is proposed to obtain the approximation solution. Finally, the simulation results demonstrate that the proposed algorithm can effectively optimize the network latency compared with related algorithms.

© 2013 IEEE. In dense Wireless Local Area Networks (WLANs), high-density Access Points (APs) bring severe interference that seriously affects the experience of users, resulting in lower throughput and poor connection quality. Due to the heavy computation workload raised by the sizable networking systems and the difficulty in estimating instantaneous Channel State Information (CSI), existing works are hard to solve interference problem. In this paper, we propose a Joint Power control and Channel allocation based on Reinforcement Learning (JPCRL) algorithm combining with statistical CSI to reduce interference adaptively. Firstly, we analyze the correlation between transmit power and channel, and formulate the interference optimization as a Mixed Integer Nonlinear Programming (MINLP) problem. Secondly, we use the statistical CSI method to take the power and channel state as the state and action space, the overall throughput increment as the reward function of Q-learning, and obtain the optimal joint optimization strategy through off-line training. Moreover, for the periodic reinforcement learning process leading to resource consumption, we design an event-driven mechanism of Q-learning, which triggers online learning to refresh the optimal policy by event-driven condition and the consumption of computing resources can be reduced. The evaluation results show that the proposed algorithm can effectively improve the throughput compared with the existing scheme.

With the wide and prosperous application of software-defined tactical network (SDTN), multi-controller placement on software-defined tactical networks (SDTN) has proved to increase to the recurrent issues brought about by SDN logical centralized principle which arises in large network environments. It will cause new requirements to reduce the network delay cost. For example, in an alliance operation, we need to share information frequently. However, in traditional tactical networks, it is very complicated to manage the management domain under mobility. The implementation and placement on the multi-controller approach have left gaps for research especially to dynamic systems, and their networks do have controllers that receive nodes. In this paper, first, we proposed a system model considering the dynamic network scenario and established objective function. Secondly, we proposed the CP-A1-Approximation algorithm, analyzed this algorithm with two other different algorithms finding complexity and compared for reducing delay effectively.

In vehicular networks, the location-based services (LBSs) are very popular and essential for most vehicular applications. However, large number of location information sharing may raise location privacy leakage of in-vehicle users. Since the existing privacy protection mechanisms ignore the trajectory information, so that the location privacy of in-vehicle users and the trade-off between privacy and quality of service (QoS) cannot be effectively solved. In order to provide satisfactory privacy and QoS for in-vehicle users, in this paper, we propose an improved geo-indistinguishable location privacy protection scheme (GLPPS). Specifically, we first select an area of service retrieval (ASR) instead of user’s real location to send to LBS, which can protect location privacy while avoiding the leakage of trajectory. Secondly, we establish an income model based on Stackelberg between in-vehicle users and attackers, and design an IM-ASR algorithm based on the Iterative method and Maximin theorem, to achieve optimal trade-off between privacy and QoS. Finally, we prove that GLPPS satisfies \(\alpha \)-differential privacy. Moreover, the simulation results demonstrate that GLPPS can reduce loss of QoS while improving location privacy compared to other methods.

In this letter, we propose an access point (AP) sleeping and user offloading optimization scheme to improve energy efficiency in densely deployed WLANs. Through real trace analysis, we investigate AP energy efficiency to obtain the sleep-awake threshold, which is used to select sleep or awake APs according to real-time status information monitored on controller. Moreover, we formulate the user offloading problem as a reverse auction process to optimize energy efficiency of APs involved in offloading. Simulation results demonstrate that, comparing to traditional methods, our scheme can achieve up to 20% energy saving while maintaining effective system coverage and throughput.

In the big data era, web-based instruction should be a new normal of the teaching reform, the teaching of "Western Marxist Philosophy" is not an exception. It is the inevitable choice to adapt to the era development, the only road to promote the teaching innovation, the important source to enrich teaching contents and the realistic quest to strengthen and improve the ideological and propagating work in universities under the new situation. Multiple measures must be taken simultaneously to improve the effectiveness of the web-based instruction of "Western Marxist Philosophy". Increasing the input of funds is an important guarantee, the perfection of management mechanism is the powerful measure, the improvement of teaching level is the key line, and the cultivation of learning interest is the internal approach.