Multirobot flocking is crucial for safe and cooperative navigation, with wide applications in logistics, service delivery, and mobile surveillance. Despite significant progress, developing effective flocking strategies under complex conditions remains challenging. Communication is a vital technique for multirobot coordination. In this article, we propose refinement and enhancement of communication information (REIN), a novel deep reinforcement learning-based framework designed to improve communication effectiveness in leader–follower flocking systems through the REIN. First, regarding information refinement, a graph-based information refiner, integrating directed graph-structured communication with an innovative edge filter, is developed for selective multirobot interaction. It helps robots adaptively focus on relevant neighbors, considerably alleviating information overload. Second, for information enhancement, a cognition-aligned information enhancer is designed that boosts information expressiveness by encouraging team consensus. It utilizes two cascaded leader-related objectives to optimize information towards cognitive alignment among decentralized followers. Extensive comparisons with state-of-the-art approaches and ablation versions demonstrate the superiority of our framework. Physical experiments are also conducted to validate its practicality.

SDN (Software defined networking) provides effective technical methods for optimal resource management. However, there are resource conflicts frequent and serious in current related schemes because they mix elephant and mice flows on shared transmission paths. So, controllers in SDN have to be smart enough to detect elephant flows with low cost and then reroute elephant and mice flows in a feature-aware way. However, existing elephant flow detection schemes suffer from high bandwidth consumption and long detection time; and little literature considers mice-flow scheduling. In this paper, we propose an Efficient Sampling and Classification Approach (ESCA). Our ESCA significantly reduces sampling overhead through estimating the arrival interval of elephant flows and filtering out redundant samples, and efficiently classifies samples with a new supervised classification algorithm based on correlations among data flows. Then, based on our low-cost ESCA, we propose a novel load-balanced routing approach LBRouting that sets up paths for elephant and mice flows with different mechanisms. The theoretical analysis proofs our ESCA outperforms related schemes. Extensive experiment results further demonstrate that our ESCA can provide accurate detection with less sampled packets and shorter detection time; and our routing approach LBRouting significantly outperforms related proposals.

In most existing routing algorithms for mobile ad hoc cognitive networks (MACNets), nodes are configured with a fixed even maximal transmission power. Links set up by such the algorithms often suffer from excessive co-channel interference, which significantly downgrades performance of MACNets because the link stability highly depends on not only node mobility but also co-channel interference. In this paper, we investigate how to improve route stability through jointly considering routing with adaptive power adjustment and mobility control. We first propose a set of power adjustment policies which dynamically sets up transmission power of cognitive nodes to enable the duration of links potentially interfered as long as possible. We, then, design a routing metric called integrated link stability (ILS) to quantitatively measure the link stability. This novel metric ILS considers both node mobility and channel interference. Finally, we propose a Joint Stable Routing and Adaptive Power Adjustment (J-SRAPA) algorithm for multi-flow and multi-hop MACNets, with the objective of maximizing network throughput. J-SRAPA dynamically adjusts the transmission power in a distributed way to mitigate co-channel interference and to improve channel utilization ratio accordingly, during both route setup and data transmission. NS2-based simulation results demonstrate that our J-SRAPA significantly outperforms related routing algorithms in terms of network throughput, end-to-end transmission delay, and packet loss ratio; and the higher channel interference degree MACNets experience, the more improvement our J-SRAPA will bring to the networks.

Multipath routing can significantly improve the network throughput and end-to-end (e2e) delay. Network coding based multipath routing removes the complicated coordination among multiple paths so that it further enhances data transmission efficiency. Traditional network coding based multipath routing protocols, however, are inefficient for Low Earth Orbit (LEO) satellite networks with the long link delay and regular network topology. Considering these characteristics, in this paper, we first formulate the multipath cooperative routing problem, then propose a Network Coding based Multipath Cooperative Routing (NCMCR) protocol for LEO satellite networks to improve the throughput. We propose source-based and destination-based multipath cooperative routing algorithms, which deliver different parts of a data flow along multiple link-disjoint paths dynamically and cooperatively. Furthermore, we design an efficient No-Stop-Wait ACK mechanism for our NCMCR protocol to accelerate the data transmission, where a source node continuously sends subsequent batches before it receives ACK messages for the batches sent previously. Under the proposed acknowledgement mechanism, we theoretically analyze the number of coded packets that should be sent and the transmission times of each batch for successfully decoding a batch. NS2-based simulation results demonstrate that our NCMCR outperforms the most related protocols.

The actual queuing delay in satellite networks is hard to get due to long propagation. So, most existing routing algorithms take the expected queuing delay as the routing metrics so that links with short-time light traffic are often chosen when setting up routing tables, which results in that more packets could be sent to the nodes with short-time light traffic. In this paper, we propose a Queue State based Dynamical Routing (QSDR) mechanism for NGEO satellite networks. Instead of expected queuing delay, we model effective queuing delay through filtering short-time light traffic based on the proposed forgotten factor, which considers not only the traffic load but also their duration. To balance traffic load, we propose a dynamical route updating algorithm based on real-time queue states with route state model, which ensures that each satellite sends out packets as soon as possible and avoids congestion at current node. We develop a NS2-based simulation system to evaluate our QSDR. The results demonstrate that our QSDR outperforms related TLR and ELB in terms of packet drop rate, throughput and end-to-end delay.

Existing random access protocols designed for satellite networks have poor performance in short burst communications because of the difficulty on global time synchronization and frequent collisions. In this paper, we propose a Lightweight Retransmission (LwR) mechanism for random access in satellite networks to reduce collisions and get rid of synchronization requirement. In our LwR, only partial bits in a packet are retransmitted. Firstly, we formulate the lightweight retransmission problem and prove that it is NP-hard. Next, we focus on the construction of partial replicas, which is the core of our LwR, and propose regular and random construction methods. Especially, we prove the sufficient conditions for successfully decoding two conflicted packets by ZigZag. Finally, we propose an algebraic model and derive the upper and lower bounds of successfully decoding probability under different construction methods. Both theoretical analysis and experimental results reveal that the random construction method achieves higher decoding probability than the regular construction method. Simulation results also demonstrate that our LwR significantly outperforms related schemes designed for satellite networks.

Link stability significantly suffers from dynamical primary user activities and random node movement in mobile cognitive networks (MCNs). In multihop MCNs, it will become even worse due to potential interference among multiple links so that stable routing will become more important and also more challenging than that in traditional wireless networks. In this paper, we first formulate the joint topology control and stable routing (JTCSR) problem based on primary user (PU) activity prediction. Then, we propose a PU prediction model to reveal channel utilization patterns of PUs. Next, we propose a novel routing metric PU prediction-based stability metric (PPSM), which quantitatively measures PU activities and node mobility, and design a min-max PPSM matrix construction algorithm. Finally, we propose and develop a PU prediction-based (PP) JTCSR algorithm for maximizing network throughput, which can find out the most stable and the shortest path. Theoretical analysis validates the effectiveness and efficiency of our approach. NS2-based simulation results further demonstrate that our PP-JTCSR can generate stable and efficient topology through predicting PU activities quantitatively, and outperforms related proposals in terms of path stability, average throughput, and packet loss rate.