Integrated sensing and communication (ISAC) is an emerging paradigm designed to support next-generation wireless services and applications. However, ISAC systems with limited computation capabilities are unable to handle computation-intensive and latency-sensitive tasks. This paper proposes a novel integrated sensing, communication, and computation (ISCC) network empowered by a reconfigurable intelligent surface (RIS) to mitigate the performance degradation caused by interference between radar sensing and uplink offloading. To effectively coordinate the cross-layer resource allocation among communication, sensing, and computation, we propose a resource scheduling problem. Specifically, we maximize the total computation rate while satisfying the sensing signal-to-noise ratio (SNR) requirement by jointly optimizing the energy allocation for local computing and offloading, the transmit and receive beamforming at the base station (BS), and the RIS reflective beamforming. To address this complex non-convex problem, we develop an efficient scheduling algorithm based on the block coordinate descent (BCD) framework. The iterative algorithm employs the fractional programming algorithm based on Lagrangian dual transform and quadratic transform, the generalized eigenvector methods, the convex relaxation techniques, and the successive convex approximation (SCA) algorithms to solve each subproblem separately. Experimental results demonstrate that the proposed scheme outperforms several baseline methods, confirming that RIS technology can effectively enhance system performance. In addition, we reveal the impact of various parameters on system performance.

Mobile edge computing (MEC) and digital twin (DT) technologies have been recognized as key enabling factors for the next generation of industrial Internet of Things (IoT) applications. In existing works, DT-assisted edge network resource optimization solutions mostly focus on short-term performance optimization, and long-term resource optimization has not been well studied. Thus, this paper introduces a digital twin-assisted heterogeneous edge network (DTHEN), aiming to minimize long-term energy consumption by jointly optimizing transmit power and computing resource. To solve the stochastic optimization problem, we propose a long-term queue-aware energy minimization (LQEM) scheme for joint communication and computing resource management. The proposed scheme uses Lyapunov optimization to transform the original problem with long-term time constraints into a deterministic upper bound problem for each time slot, decouples it into three independent sub-problems, and solves each sub-problem separately. We then theoretically prove the asymptotic optimality of the LQEM scheme and the tradeoff between system energy consumption and task queue backlog. Finally, experimental results verify the performance analysis of the LQEM scheme, demonstrating its superiority over several benchmark schemes, and reveal the impact of various parameters on the system.

Mobile edge computing (MEC) harvests the computation capability at the network edge to perform the computation intensive tasks for diverse IoT applications. Meanwhile, the unmanned aerial vehicle (UAV) has a great potential to flexibly enlarge the coverage, and enhance the network performance. Accordingly, it has been a promising paradigm to use the UAV to provide the edge computing service for massive IoT devices. This paper studies the path planning problem of a UAV-assisted edge computing network, where an UAV is deployed with an edge server to execute the computing tasks offloaded from multiple devices. We consider the mobility of devices, where a GaussMarkov random movement model is adopted. By taking the energy consumed for the dynamic flying and executing the tasks at the UAV into account, we formulate a path planning problem that aims to maximize the amount of offloaded data bits by the devices while minimizing the energy consumption of the UAV. To deal with the dynamic change of the complex environment, we apply the deep reinforcement learning (DRL) method to develop an online path planning algorithm based on double deep Q-learning network (DDQN). Extensive simulation results validate the effectiveness of the proposed DRL-based path planning algorithm in terms of the convergence speed and the system reward.