Joint Channel Assignment and Opportunistic Routing for Maximizing Throughput in Cognitive Radio Networks

In this paper, we consider the joint opportunistic routing and channel assignment problem in multi-channel multi-radio (MCMR) cognitive radio networks (CRNs) for improving aggregate throughput of the secondary users. We first present the nonlinear programming optimization model for this joint problem, taking into account the feature of CRNs-channel uncertainty. Then considering the queue state of a node, we propose a new scheme to select proper forwarding candidates for opportunistic routing. Furthermore, a new algorithm for calculating the forwarding probability of any packet at a node is proposed, which is used to calculate how many packets a forwarder should send, so that the duplicate transmission can be reduced compared with MAC-independent opportunistic routing & encoding (MORE) [11]. Our numerical results show that the proposed scheme performs significantly better that traditional routing and opportunistic routing in which channel assignment strategy is employed.Comment: 5 pages, 4 figures, to appear in Proc. of IEEE GlobeCom 201

Semi-Asynchronous Federated Edge Learning Mechanism via Over-the-air Computation

Over-the-air Computation (AirComp) has been demonstrated as an effective transmission scheme to boost the efficiency of federated edge learning (FEEL). However, existing FEEL systems with AirComp scheme often employ traditional synchronous aggregation mechanisms for local model aggregation in each global round, which suffer from the stragglers issues. In this paper, we propose a semi-asynchronous aggregation FEEL mechanism with AirComp scheme (PAOTA) to improve the training efficiency of the FEEL system in the case of significant heterogeneity in data and devices. Taking the staleness and divergence of model updates from edge devices into consideration, we minimize the convergence upper bound of the FEEL global model by adjusting the uplink transmit power of edge devices at each aggregation period. The simulation results demonstrate that our proposed algorithm achieves convergence performance close to that of the ideal Local SGD. Furthermore, with the same target accuracy, the training time required for PAOTA is less than that of the ideal Local SGD and the synchronous FEEL algorithm via AirComp

CROR: Coding-Aware Opportunistic Routing in Multi-Channel Cognitive Radio Networks

Cognitive radio (CR) is a promising technology to improve spectrum utilization. However, spectrum availability is uncertain which mainly depends on primary user's (PU's) behaviors. This makes it more difficult for most existing CR routing protocols to achieve high throughput in multi-channel cognitive radio networks (CRNs). Inter-session network coding and opportunistic routing can leverage the broadcast nature of the wireless channel to improve the performance for CRNs. In this paper we present a coding aware opportunistic routing protocol for multi-channel CRNs, cognitive radio opportunistic routing (CROR) protocol, which jointly considers the probability of successful spectrum utilization, packet loss rate, and coding opportunities. We evaluate and compare the proposed scheme against three other opportunistic routing protocols with multichannel. It is shown that the CROR, by integrating opportunistic routing with network coding, can obtain much better results, with respect to throughput, the probability of PU-SU packet collision and spectrum utilization efficiency.Comment: 6 pages, 8 figures, to appear in Proc. of IEEE GlobeCom 201

Asynchronous Federated Learning with Incentive Mechanism Based on Contract Theory

To address the challenges posed by the heterogeneity inherent in federated learning (FL) and to attract high-quality clients, various incentive mechanisms have been employed. However, existing incentive mechanisms are typically utilized in conventional synchronous aggregation, resulting in significant straggler issues. In this study, we propose a novel asynchronous FL framework that integrates an incentive mechanism based on contract theory. Within the incentive mechanism, we strive to maximize the utility of the task publisher by adaptively adjusting clients' local model training epochs, taking into account factors such as time delay and test accuracy. In the asynchronous scheme, considering client quality, we devise aggregation weights and an access control algorithm to facilitate asynchronous aggregation. Through experiments conducted on the MNIST dataset, the simulation results demonstrate that the test accuracy achieved by our framework is 3.12% and 5.84% higher than that achieved by FedAvg and FedProx without any attacks, respectively. The framework exhibits a 1.35% accuracy improvement over the ideal Local SGD under attacks. Furthermore, aiming for the same target accuracy, our framework demands notably less computation time than both FedAvg and FedProx

Peiminine Inhibits Glioblastoma in Vitro and in Vivo Through Cell Cycle Arrest and Autophagic Flux Blocking

Background/Aims: Glioblastoma multiforme (GBM) is the most devastating and widespread primary central nervous system tumour in adults, with poor survival rate and high mortality rates. Existing treatments do not provide substantial benefits to patients; therefore, novel treatment strategies are required. Peiminine, a natural bioactive compound extracted from the traditional Chinese medicine Fritillaria thunbergii, has many pharmacological effects, especially anticancer activities. However, its anticancer effects on GBM and the underlying mechanism have not been demonstrated. This study was conducted to investigate the potential antitumour effects of peiminine in human GBM cells and to explore the related molecular signalling mechanisms in vitro and in vivo Methods: Cell viability and proliferation were detected with MTT and colony formation assays. Morphological changes associated with autophagy were assessed by transmission electron microscopy (TEM). The cell cycle rate was measured by flow cytometry. To detect changes in related genes and signalling pathways in vitro and in vivo, RNA-seq, Western blotting and immunohistochemical analyses were employed. Results: Peiminine significantly inhibited the proliferation and colony formation of GBM cells and resulted in changes in many tumour-related genes and transcriptional products. The potential anti-GBM role of peiminine might involve cell cycle arrest and autophagic flux blocking via changes in expression of the cyclin D1/CDK network, p62 and LC3. Changes in Changes in flow cytometry results and TEM findings were also observed. Molecular alterations included downregulation of the expression of not only phospho-Akt and phospho-GSK3β but also phospho-AMPK and phospho-ULK1. Furthermore, overexpression of AKT and inhibition of AKT reversed and augmented peiminine-induced cell cycle arrest in GBM cells, respectively. The cellular activation of AMPK reversed the changes in the levels of protein markers of autophagic flux. These results demonstrated that peiminine mediates cell cycle arrest by suppressing AktGSk3β signalling and blocks autophagic flux by depressing AMPK-ULK1 signalling in GBM cells. Finally, peiminine inhibited the growth of U251 gliomas in vivo. Conclusion: Peiminine inhibits glioblastoma in vitro and in vivo via arresting the cell cycle and blocking autophagic flux, suggesting new avenues for GBM therapy