Deep neural networks (DNNs) extract embeddings from multimodal data such as audio, images, and LiDAR, each with heterogeneous computational demands and representation abilities to support multimodal services such as audio-visual speech recognition. Reinforcement learning (RL)-based model selection and splitting schemes determine the model variants from DNNs and the components to offload in unimodal models to reduce inference latency, aiming for a three-fold trade-off among inference accuracy, computation cost, and communication overhead but ignore the heterogeneity of different modalities within multimodal DNNs. In this paper, we propose an RL-based edge-assisted multimodal inference scheme that optimizes model selection at modality level and edge-assisted policies, including collaborative servers and partition points for each feature extractor to perform multimodal DNNs on mobile devices. Based on the information complexity and historical influence of each modality, as well as real-time observations such as channel gain, and previous inference performance, the policy distributions are designed to maximize the utility, as a weighted sum of inference latency and energy consumption, and inference accuracy. Safe policy exploration further mitigates risks such as low inference accuracy, intolerable inference latency, and improper allocation of computational resources to specific modalities. We analyze the computational complexity affected by the number of model variants, edge servers, and partition points and derive performance bounds for inference latency, energy consumption, and utility under specific sample sizes and data rates. Experimental results show that the proposed schemes improve inference performance compared to benchmark schemes.

Reinforcement learning (RL) based flying ad-hoc network (FANET) routing enables unmanned aerial vehicles (UAVs) to choose the next-hop, but the quality of service (QoS) and energy efficiency have to be enhanced against jamming due to the inaccurate path quality estimation. In this paper, we propose an RL based anti-jamming FANET routing with QoS guarantee to optimize the transmit power and the originator message broadcast interval for the route discovery to estimate the path quality to select the next-hop from the routing table. Based on the path availability history, the channel conditions, the received jamming power and the transmission quality regarding the number of the received originator messages and the bit error rate during route discovery, the routing policy is selected to enhance the throughput and the energy efficiency. The backward estimation of the throughput in the utility evaluation addresses the delayed feedback from the destination. The performance bound is derived in terms of network topology and channel gain based on the Nash equilibrium of the cooperative game among the UAVs. Simulation results provide the performance gain of the throughput and energy consumption over the benchmarks.

Reinforcement learning (RL) based worm detection chooses the test threshold to evaluate the network traffic features such as the spectral flatness measure (SFM), but the detection of the evasive worm that modifies the scan rate and the worm propagation speed to manipulate the network traffic features is inaccurate due to the estimation and quantization error in the test threshold. In this paper, we propose an RL based accurate worm detection for smart grids that enables the control center to optimize the test threshold based on the number of meters, the infection time series and the number of connections to new destination IP addresses, besides the traffic log size received from each data concentrator and the number of the previously infected meters. A constraint on the maximum missed detection rate required by the smart grids is exploited in the detection policy distribution to support the reliable data transmission. A deep RL version addresses the quantization error in terms of the test threshold in the SFM evaluation and the infection time series in the state formulation, and compress the state space of the traffic log size received from a large number of data concentrators. Based on a worm detection game, the performance bound is provided under the specified detection window size and the propagation speed of evasive worm. Simulation results for 3600 meters show that the performance gain of the detection accuracy and latency against evasive worm over the benchmarks.

Reinforcement learning (RL) based flying ad-hoc network (FANET) routing enables unmanned aerial vehicles (UAVs) to choose the next-hop to forward the packets, but the quality of service (QoS) and energy efficiency have to be enhanced due to the inaccurate path quality estimation under jamming attacks. In this paper, we propose an RL based energy-efficient anti-jamming FANET routing scheme with QoS guarantee to optimize both the transmit power and the originator message broadcast interval for the route discovery to estimate the path quality to select the next-hop from the routing table. Based on the path availability history, the channel conditions and the received jamming power, as well as the transmission quality regarding the number of the received originator messages and the bit error rate during route discovery, the routing policy is selected to enhance the throughput and the energy efficiency under jamming attacks with changing power. The backward estimation of the throughput in the utility evaluation addresses the delayed feedback from the destination under large-scale networks. The deep neural networks are further designed to address the quantization error of the transmission quality and the channel gain for UAVs with high mobility to enhance the path exploration efficiency. In addition, the upper bound in terms of network topology and channel gain is derived based on the Nash equilibrium of the anti-jamming routing game. The proposed routing scheme is implemented to improve the image transmission quality against jamming in outdoor environments. Experimental results based on UAVs equipped with Raspberry Pi show the performance gain of the throughput and the energy consumption.

False data injection (FDI) attacks construct attack vectors to inject false data into tampered meters with the goal of falsifying state estimation, but resulting in low successful attack rate with high attack costs in terms of the number of tampered meters in large-scale smart grids, because the bad data detection at the control center chooses the dynamic detection thresholds to identify the modified meter measurements. In this article, we propose a reinforcement learning-based FDI attack scheme that optimizes both the tampered meters and the false data to enhance the success attack rate and injected errors while reducing attack costs. Based on meter measurements and previous performance, the attack vector is constructed to induce more errors in state estimation and bypass bad data detection. The performance bounds regarding the successful attack rate and the injected error are derived in terms of the number of bus phase angles, the susceptance of the transmission line, and the maximum false data based on the Nash equilibrium of the FDI game. Simulations performed on both the IEEE 14-bus and IEEE 118-bus systems demonstrate the performance gain over the benchmarks.

Edge-assisted inference that enables each unmanned aerial vehicle (UAV) to offload marine tasks for maritime applications such as target tracking and data collection, but the inference speed and energy efficiency are affected by sea surface movement and wave occlusions under broad area maritime environment with instability channel. In this paper, we propose an RL-based edge-assisted inference scheme for maritime UAV networks to optimize the deep neural networks partition point, the transmit power and the collaborative edge server to enhance the utility as the weighted sum of the data-related energy consumption and the inference latency. Based on the average sea wave height, the channel gain, the number of marine tasks and the battery level, the self-correcting mechanism makes a trade-off between the overestimation and the underestimation in collaborative inference policy without additional computational cost. The bounds of the data-related energy consumption and the inference latency are derived under the specific data rate and inference computation amounts. Simulation results show the effectiveness of the proposed edge-assisted inference scheme.

Unmanned aerial vehicle (UAV) plays an important role in fifth and sixth (5G/6G) communication systems, garnering significant research attention. This paper investigates a dynamic UAV-enabled secure ultra-reliable and low-latency communication (URLLC) system, where a UAV sends confidential data to a mobile device where another UAV cooperatively transmits jamming interference to confuse a potential eavesdropper. The transmission latency constraint, secure communication requirement and dynamic channel characteristics are considered into real-word communication environments. Then, a joint communication transmit power, jamming power, and trajectory optimization approach is proposed to maximize the system secrecy rate, simultaneously guaranteeing the URLLC requirement. To effectively address the non-convex problem, the URLLC constraint is transformed into a data rate constraint to ensure that the optimization problem is more manageable, and then present an efficient solution by employing an alternating approximation and successive convex optimization. Finally, simulation results verify that the presented joint optimization approach substantially outperforms other popular benchmarks in secrecy rate performance.