Search

In heterogeneous wireless networks, when multiple nodes need to share the same wireless channel, they face the issue of multiple access, which necessitates a Medium Access Control (MAC) protocol to coordinate the data transmission of multiple nodes on the shared communication channel. This paper presents Proximal Policy Optimization-based Multiple Access (PPOMA), a novel multiple access protocol for heterogeneous wireless networks based on the Proximal Policy Optimization (PPO) algorithm from deep reinforcement learning (DRL). Specifically, we explore a network scenario where multiple nodes employ different MAC protocols to access an Access Point (AP). The novel PPOMA approach, leveraging deep reinforcement learning, adapts dynamically to coexist with other nodes. Without prior knowledge, it learns an optimal channel access strategy, aiming to maximize overall network throughput. We conduct simulation analyses using PPOMA in two scenarios: perfect channel and imperfect channel. Experimental results demonstrate that our proposed PPOMA continuously learns and refines its channel access strategy, achieving an optimal performance level in both perfect and imperfect channel scenarios. Even when faced with suboptimal channel conditions, PPOMA outperforms alternative methods by achieving higher overall network throughput and faster convergence rates. In a perfect channel scenario, PPOMA's advantage over other algorithms is primarily evident in its convergence speed, reaching convergence on average 500 iterations faster. In an imperfect channel scenario, PPOMA's advantage is mainly reflected in its higher overall network throughput, with an approximate increase of 0.04. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) enhances network control but faces Distributed Denial of Service (DDoS) attacks due to centralized control and flow-table constraints in network devices. To overcome this limitation, we introduce a multi-path routing algorithm for SDN called Trust-Based Proximal Policy Optimization (TBPPO). TBPPO incorporates a Kullback–Leibler divergence (KL divergence) trust value and a node diversity mechanism as the security assessment criterion, aiming to mitigate issues such as network fluctuations, low robustness, and congestion, with a particular emphasis on countering DDoS attacks. To avoid routing loops, differently from conventional 'Next Hop' routing decision methodology, we implemented an enhanced Depth-First Search (DFS) approach involving the pre-computation of path sets, from which we select the best path. To optimize the routing efficiency, we introduced an improved Proximal Policy Optimization (PPO) algorithm based on deep reinforcement learning. This enhanced PPO algorithm focuses on optimizing multi-path routing, considering security, network delay, and variations in multi-path delays. The TBPPO outperforms traditional methods in the Germany-50 evaluation, reducing average delay by 20%, cutting delay variation by 50%, and leading in trust value by 0.5, improving security and routing efficiency in SDN. TBPPO provides a practical and effective solution to enhance SDN security and routing efficiency. [ABSTRACT FROM AUTHOR]

Thermocatalytic conversion of redundant CO2 to useful methanol is an attractive route to address both energy and environmental crises simultaneously. However, existing copper/oxide catalysts widely used in these thermocatalytic processes still suffer from low methanol yield under mild reaction conditions. In this work, we design inverse oxide/Cu catalysts to achieve superior thermal catalytic performance for CO2 hydrogenation. The optimized ZnO/Cu-1.0 catalyst exhibits maximum CH3OH selectivity of 83.4% and space–time yield (STY) of 170.9 g CH 3 OH kg cat - 1 h - 1 in CO2 hydrogenation at 210 °C, nearly twofold higher STY than the previous optimal inverse ZnO/Cu catalysts ( 89.6 g CH 3 OH kg cat - 1 h - 1 at 250 °C). Importantly, ZnO/Cu-1.0 catalyst displayed not only a satisfactory catalytic stability but also a superior CH3OH STY with a time-on-stream of 24 h. Such inverse configuration of catalysts will pave the way for new strategies to design high-performance thermocatalytic catalysts and promote their commercialization. Typical inverse ZnO/Cu-1.0 catalysts have been demonstrated and achieved to significantly facilitate the activation of inert CO2 molecules to produce methanol, due to its special physicochemical properties and strong ZnO–Cu interaction [ABSTRACT FROM AUTHOR]