Efficient path routing strategy for flows with multiple priorities on scale-free networks

Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks

With the advent of the 5G era, the number of Internet of Things (IoT) devices has surged, and the population’s demand for information and bandwidth is increasing. The mobile device networks in IoT can be regarded as independent “social nodes”, and a large number of social nodes are combined to form a new “opportunistic social network”. In this network, a large amount of data will be transmitted and the efficiency of data transmission is low. At the same time, the existence of “malicious nodes” in the opportunistic social network will cause problems of unstable data transmission and leakage of user privacy. In the information society, these problems will have a great impact on data transmission and data security; therefore, in order to solve the above problems, this paper first divides the nodes into “community divisions”, and then proposes a more effective node selection algorithm, i.e., the FL node selection algorithm based on Distributed Proximal Policy Optimization in IoT (FABD) algorithm, based on Federated Learning (FL). The algorithm is mainly divided into two processes: multi-threaded interaction and a global network update. The device node selection problem in federated learning is constructed as a Markov decision process. It takes into account the training quality and efficiency of heterogeneous nodes and optimizes it according to the distributed near-end strategy. At the same time, malicious nodes are screened to ensure the reliability of data, prevent data loss, and alleviate the problem of user privacy leakage. Through experimental simulation, compared with other algorithms, the FABD algorithm has a higher delivery rate and lower data transmission delay and significantly improves the reliability of data transmission.

An Optimal Flow Admission and Routing Control Policy for Resource Constrained Networks

Overloaded network devices are becoming an increasing problem especially in resource limited networks with the continuous and rapid increase of wireless devices and the huge volume of data generated. Admission and routing control policy at a network device can be used to balance the goals of maximizing throughput and ensuring sufficient resources for high priority flows. In this paper we formulate the admission and routing control problem of two types of flows where one has a higher priority than the other as a Markov decision problem. We characterize the optimal admission and routing policy, and show that it is a state-dependent threshold type policy. Furthermore, we conduct extensive numerical experiments to gain more insight into the behavior of the optimal policy under different systems’ parameters. While dynamic programming can be used to solve such problems, the large size of the state space makes it untractable and too resource intensive to run on wireless devices. Therefore, we propose a fast heuristic that exploits the structure of the optimal policy. We empirically show that the heuristic performs very well with an average reward deviation of 1.4% from the optimal while being orders of magnitude faster than the optimal policy. We further generalize the heuristic for the general case of a system with n (n>2) types of flows.

Effective traffic-flow assignment strategy on multilayer networks

An efficient flow assignment strategy is of great importance to alleviate traffic congestion on multilayer networks. In this work, by considering the roles of nodes' local structures on the microlevel, and the different transporting speeds of layers in the macrolevel, an effective traffic-flow assignment strategy on multilayer networks is proposed. Both numerical and semianalytical results indicate that our proposed flow assignment strategy can reasonably redistribute the traffic flow of the low-speed layer to the high-speed layer. In particular, preferentially transporting the packets through small-degree nodes on the high-speed layer can enhance the traffic capacity of multilayer networks. We also find that the traffic capacity of multilayer networks can be improved by increasing the network size and the average degree of the high-speed layer. For a given multilayer network, there is a combination of optimal macrolevel parameter and optimal microlevel parameter with which the traffic capacity can be maximized. It is verified that real-world network topology does not invalidate the results. The semianalytical predictions agree with the numerical simulations.