With the characteristics of low-latency, seamless coverage and high bandwidth, the Low Earth Orbit (LEO) satellite network has been the promising technology for the sixth-generation mobile communication (6G) networks, especially, the inter-satellite link can improve the flexibility of inter-satellite networking and routing. However, in the existing works, since the impact of link attributes on the satellite topology has not been well investigated, it is difficult to avoid the loss of topology information for the transmission path selection, which may aggravate the unreliability of routing path. In this paper, we propose a novel time-varying topology model for the LEO satellite network, to improve the adaptability of the satellite routing. Firstly, the weighted time-space evolution graph based on the link attributes is established to construct the time-varying topology model of LEO satellite networks. Then, the utility function of the link attributes is modelled and the multi-attribute decision-making is introduced to calculate the weight of each link attribute for the quantification of the link utility. Finally, based on the topology model, the inter-satellite link utility-based dynamic routing algorithm is proposed to improve the adaptability of satellite routing. The simulation results demonstrate that the proposed routing algorithm outperforms the existing routing algorithms in terms of packet drop rate, end-to-end delay and throughput.

As the sensor layer of Internet of Things (IOT), enormous amount of sensor nodes are densely deployed in a hostile environment to monitor and sense the changes in the physical space. Since sensor nodes are driven with limited power batteries, it is very difficult and expensive for wireless sensor networks (WSNs) to extend network lifetime. In order to achieve reliable data transmission in WSNs, energy efficient routing protocol is a crucial issue in extending the network lifetime of a network. However, traditional routing protocols usually propagate throughout the whole network to discover a reliable route or employ some cluster heads to undertake data transmission for other nodes, which both require large amount energy consumption. In this paper, to maximize the network lifetime of the WSN, we propose a novel energy efficient region source routing protocol (referred to ER-SR). In ER-SR, a distributed energy region algorithm is proposed to select the nodes with high residual energy in the network as source routing node dynamically. Then, the source routing nodes calculate the optimal source routing path for each common node, which enables partial nodes to participate in the routing process and balances the energy consumption of sensor nodes. Furthermore, to minimize the energy consumption of data transmission, we propose an effective distance-based ant colony optimization algorithm to search the global optimal transmission path for each node. Simulation results demonstrate that ER-SR exhibits higher energy efficiency, and has moderate performance improvements on network lifetime, packet delivery ratio, and delivery delay, compared with other routing protocols in WSNs.

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 convergence of telemedicine and electronic health (e-health) services, Consumer Internet of Things (CIoT) enabled smart healthcare has attracted much attention from researchers. Telemedicine and e-health care can help to eliminate geographical barriers and provide high-quality medicine services. Whereas, deterministic low-latency communication is a major problem in smart healthcare system with commercial network, since the latency can affect the process of diagnosis and treatment while producing inaccuracy. To address this issue, Time Sensitive Networking (TSN) is introduced in this article for bounded lowlatency network services. First, we present a Digital Twin based TSN (DTTSN) framework where digital modeling and intelligent simulation are performed under a virtual environment. Second, a digital twin scheduling model is created to predict the forwarding delay. In order to enhance model adaption in different scenarios, cycle generative adversarial network (CycleGAN) is adopted to train the scheduling model. Finally, the optimal routing strategy deduced in digital twin network is fed back to physical network for automated configuration. Simulation results verify that our digital twin network can promote visibility of operations and predictions of physical network scheduling and routing, while optimizing delay and balancing link load.

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.

With the development of vehicular network, location-based services (LBSs) provide increasing diversified services for drivers and passengers. When users enjoy the services, users’ location needs to be constantly updated to service providers, which causes the location information to be speculated and attacked by attackers. However, existing schemes don’t provide differentiated protection for users’ different locations, which may lead to the leakage of location information. Therefore, we propose a location privacy protection method to satisfy users’ personalized privacy needs with reasonable protection of their privacy. Firstly, we define a normalized decision matrix to describe the efficiency and privacy effects of a route, and establish a multi-attribute utility function to quantify the utility of different routes for route selection. Then, according to users’ personalized privacy protection need, we allocate the privacy budget for each query location on the selected route based on the distance between it and his nearest sensitive location. Experimental results demonstrate that compared to existing methods, our scheme can meet the user’s service requirements and achieve better service quality under the conditions of reasonable protection of their privacy.