The emerging autonomous driving has stringent requirements for latency and reliability. In this paper, we propose a task-oriented real-time and robust feature compression and selection method in collaborative intelligence system. Our design, consisting of a three-dimensional channel compression (TDCC) module and a one-dimensional feature selection (ODFS) module, can efficiently reduce feature size in different dimensional spaces. The TDCC initially reduces the number of feature channels in three-dimensional space. Subsequently, the ODFS flattens the three-dimensional feature maps into a one-dimensional feature vector and selects the most effective feature dimensions of the vector for transmission. Specifically, in ODFS, the most relevant information is first aggregated into specific dimensions through an information-theory-based inductive loss function. Then, the important features are selected using the mask generated by the mask generator. Extensive experiments demonstrate that the proposed method outperforms the baseline in both communication latency and task accuracy, while exhibiting robustness against poor channel conditions.

Multiple different computation tasks may be simultaneously offloaded to mobile edge computing (MEC) servers in smart agriculture scenarios, where the redundant transmission of shared data among different tasks leads to insufficient utilization of system resources (i.e., computing resources, communication resources, and caching resources) and lagging processing efficiency. Existing schemes optimizing multitask computation offloading with shared data almost focus on identical tasks, which are difficult to apply in real-world scenarios with different tasks to meet various service demands of fairness, low latency, and low energy consumption. In this article, we propose a fair, real-time, and green collaborative optimization scheme of computation offloading and resource orchestration for multitask data sharing in smart agriculture based on multiagent deep reinforcement learning (MADRL), aiming to improve offloading efficiency and system resource utilization to meet diverse tasks’ service demands. First, a collaborative optimization problem of computation offloading and resource orchestration is formulated to minimize the system latency, energy consumption, and caching space occupancy under constraints of redundant data transmission and limited system resources. It is difficult for traditional optimization methods to solve the formulated optimization problem characterized by dynamics, high-dimensionality, nonlinearity, and mixed-integer. Then, we propose an MADRL algorithm named MATD3-CO-RO-MDS based on a hierarchical reward mechanism to solve it and approximate the optimal offloading and orchestration strategy. Finally, experimental results prove that our proposed algorithm achieves smaller latency, energy consumption, and caching space occupancy compared with existing algorithms. It even has a 76.7% advantage in reducing latency when more tasks participate in offloading.

Task-oriented semantic communication offers a promising solution for providing real-time computer vision services. However, existing research on semantic communication ignores the connection between the key performance indicators (KPIs) used to measure semantic encoding-decoding networks (i.e., inference accuracy) and wireless semantic communication networks (i.e., transmission latency). Therefore, the designed semantic communication schemes are difficult to simultaneously meet the requirements of low latency and high accuracy of emerging intelligent applications. In this paper, by deeply exploring the relationship and interdependencies between the two kinds of KPIs, we propose a real-time and efficient task-oriented end-to-end semantic communication scheme enabled by deep reinforcement learning (DRL) and information bottleneck to improve both inference and communication efficiency. Specifically, we initially use information bottleneck theory to model the optimal tradeoff between inference accuracy and communication latency, which is subsequently reformulated by variational inference to be differentiable and tractable. Then, we introduce DRL to address the non-differentiability of dynamic stochastic fading channels and channel mismatch between the training phase and deployment phase, enabling accurate selection of the most task-relevant semantic feature dimensions for transmission under dynamic fading channels. Finally, extensive experiments show that our proposed scheme achieves better performance in latency and accuracy than comparison methods.

Wireless video offloading in mobile-edge-computing (MEC)-enabled Industrial Internet of Things imposes a risk of exposing users' private data to eavesdroppers. It is difficult for existing secure video offloading schemes to simultaneously guarantee security, reduce latency and energy consumption in privacy-sensitive multicell scenarios where users are unwilling to offload data to other cells. In this article, a secure video offloading scheme based on multicell federated (MCF) deep reinforcement learning (DRL) is proposed to facilitate a secure, real-time, and efficient MEC network by efficient orchestration of limited resources. We formulate a collaborative optimization problem of video frame resolution and resources to minimize latency and energy consumption while maximizing the security rate subject to analytic accuracy and limited resources. To solve the formulated NP-hard problem, a MCF DRL algorithm based on the frameworks of multicell horizontal federated learning (FL) and hierarchical reward function-based twin delayed deep deterministic policy gradient (TD3) is proposed. First of all, hierarchical reward function-based TD3 is employed to solve the collaborative optimization NP-hard problem formulated for each single cell, where the optimal solution can be efficiently approached by the agent under the guidance of the innovatively designed hierarchical reward function. Then, multicell horizontal FL is applied on TD3 to obtain a model with higher model quality by averagely aggregating multiple individual TD3 models. Simulation results reveal that the proposed algorithm outperforms comparison algorithms in terms of utility, cost, latency, energy consumption, and security rate.

Federated learning (FL) provides a new paradigm for protecting data privacy by enabling model training at devices and model aggregation at servers. However, data information may be leaked to honest-but-curious aggregation servers by updated model parameters. The existing secure methods do not fully exploit the potentiality of data characteristics in enhancing security, which makes it impossible to optimize limited system resources overall to achieve secure, fair, and efficient FL systems. In this article, a DRL-based joint secure aggregation and resource orchestration scheme is proposed to guarantee security and fairness, and improve efficiency for hierarchical FL (HFL) assisted by untrusted mobile-edge computing (MEC) servers. We formulate a joint optimization problem of data size, payment, and resource orchestration, to maximize the long-term social welfare subject to secure aggregation and limited resources. Since the formulated problem is a complex mixed integer dynamic optimization problem with NP-hardness, where multiple mixed integer optimization variables are highly coupled in time-varying constraints and objective function, it is difficult to obtain its optimal solution via traditional optimization methods. Thus, we propose a hierarchical reward function-based DRL algorithm (MATD3) to guide the agents to approach the optimal policy of secure aggregation and resource orchestration. Simulation results show that the proposed algorithm MATD3 can achieve superior performance over comparison algorithms and the MEC-enabled HFL framework outperforms two-layer FL frameworks.

Federated learning (FL) provides a new paradigm for protecting data privacy in Industrial Internet of Things (IIoT). To reduce network burden and latency brought by FL with a parameter server at the cloud, hierarchical federated learning (HFL) with mobile edge computing (MEC) servers is proposed. However, HFL suffers from a bottleneck of communication and energy overhead before reaching satisfying model accuracy as IIoT devices dramatically increase. In this article, a deep reinforcement learning (DRL)-based joint resource allocation and IIoT device orchestration policy using nonorthogonal multiple access is proposed to achieve a more accurate model and reduce overhead for MEC-assisted HFL in IIoT. We formulate a multiobjective optimization problem to simultaneously minimize latency, energy consumption, and model accuracy under the constraints of computing capacity and transmission power of IIoT devices. To solve it, we propose a DRL algorithm based on deep deterministic policy gradient. Simulation results show proposed algorithm outperforms others.

Wireless offloading in mobile-edge-computing (MEC)-enabled Internet of Things (IoT) networks inevitably suffers the risk of eavesdropping. Physical-layer security (PLS) approaches can be applied to prevent eavesdropping. However, the existing PLS techniques are not well targeted for videos due to the fact that video’s distortion characteristics, which allow encoding parameters to be flexibly adjusted to enhance security in offloading, are ignored. A deep reinforcement learning (DRL)-based real-time, secure, and efficient video offloading scheme is proposed in this article, where video frame resolution, one key parameter of video’s distortion characteristics, is introduced and jointly optimized with PLS scheme to guarantee video’s security, improve users’ Quality of Experience (QoE) and save energy consumption. We formulate a joint optimization problem of video frame resolution selection, computation offloading, and resource allocation strategy, to minimize energy consumption and maximize QoE in terms of delay and analytic accuracy, while subject to security rate, computing capability, and transmission power. To solve the formulated NP-hard problem with the form of high-dimensional nonlinear mixed-integer programming, the hierarchical reward-function-based DRL (JVFRS-CO-RA-MADDPG) algorithm is proposed to guide the agents to obtain the optimal policy efficiently. Finally, the simulation results show that the proposed algorithm outperforms the existing algorithms in terms of delay, energy consumption, and security level.

In this paper the transmission security for multiuser multi-input single-output (MU-MISO)- Non-orthogonal multiple access (NOMA) downlink transmission is studied for untrusted users. We propose a security-enhanced user pairing scheme under the constraints of weak security to achieve high spectrum efficiency as well as weak system secrecy. We first formulate a sum-rate maximization problem for NOMA subject to weak security constraints, and reformulate the weak security requirement into constraints of the average bit error rate (BER) at any undesired user. Then we decompose the original optimization problem into a security-guaranteed user pairing problem and a conventional sum-rate maximization problem for NOMA, which is eventually solved by jointly downlink beamforming and power allocation. Simulation results compare the proposed NOMA scheme with zero-forcing (ZF) and the conventional MU-MISO beamforming, and reveal the performance gain of the proposed NOMA scheme in secrecy sum-rate.

Physical‐layer security is usually ensured by the injection of artificial noise (AN) at the legitimate source or destination when relays are cooperative but untrusted. In this work, in order to avoid the power‐consuming AN at the legitimate user pairs, the authors investigate multi‐pair two‐way relaying as a solution for secure transmission in wireless relay networks with untrusted relays. They formulate a generalised problem for the joint design of cooperative beamforming (CB) and AN at relays to maximise the secrecy sum‐rate in wireless networks with multi‐pair legal users, multiple untrusted relays as well as multiple non‐colluding eavesdroppers. Further, they reformulate, approximate and relax the original non‐convex problem into a sequence of convex sub‐problems based on semi‐definite relaxation (SDR) and Taylor series approximation. Particularly, for the reformulated problem they prove that the SDR is in fact tight, and hence propose an iterative algorithm for CB and AN design at the relays. In the simulations, they demonstrate the fast convergence and high achievable secrecy sum‐rate of the proposed algorithm. Also, they simulate and discuss the impact of the number of user pairs on the secrecy sum‐rate, and reveal the change of performance gain with the number of multiple user pairs.