Driven by the exploding computing service demands from various intelligent mobile applications, an increasing amount of research efforts have been devoted to mobile edge computing (MEC). Meanwhile, unmanned aerial vehicles (UAVs) have found a great success in assisting existing wireless systems due to their flexibility and low cost. The advancements in these two closely related fields foster the development of the recently advocated UAV-assisted MEC paradigm, which is expected to bring unprecedented performance gain to the existing ground-based MEC systems. Nonetheless, existing works on UAV-assisted MEC mainly focus on the single-UAV scenarios and often assume static system states. In this paper, UAV swarm assisted MEC is considered where multiple collaborative UAVs are employed to help the terrestrial edge server to provide better edge computing services. However, the complexity of using existing methods to find the best dynamic coordination strategy in UAV swarm assisted MEC becomes intractable when the number of UAVs increases. To resolve this challenge, a novel decentralized deep reinforcement learning algorithm is proposed in this work, which can reduce the complexity by orders of magnitude. In addition, simulations are conducted to show that by using the proposed algorithm, the UAV swarm can efficiently learn a good dynamic coordination strategy and thus achieve a significantly better performance than the baseline scheme.
The recently advocated reconfigurable holographic surface (RHS) is anticipated to bring substantial improvement in communication throughput, especially for the multi-node scenarios. From this viewpoint, RHS is a natural fit to distributed edge computing, where the computation task is offloaded to multiple edge nodes for faster processing. Nonetheless, existing pioneering works on RHS mainly focus on its application in conventional wireless communications, and, to the best of our knowledge, its potential for elevating distributed edge computing performance remains unexplored. In this work, an RHS-aided distributed edge computing scheme is proposed, where the mobile device performs part of the task locally and exploits the RHS to distribute the rest to multiple edge nodes simultaneously. Considering both local computing latency and offloading latency, a latency minimization problem is formulated by jointly optimizing the task partitioning, the local computing frequency, the holographic beamforming of RHS, and the digital beamforming. To tackle this non-trivial non-convex problem, a joint communication and computation optimization algorithm is developed based on the generic block coordinate descent (BCD) framework. Particularly, the original problem is divided into three subproblems, and the task partitioning and local computing frequency, the holographic beamformer, and the digital beamformer are optimized efficiently and iteratively in each subproblem. In addition, simulations are conducted to validate the effectiveness of the proposed scheme.
Coded edge computing is envisioned as a promising solution to cope with the ever-increasing large-scale and computation-intensive mobile applications. Besides alleviating the computation straggling issue, task encoding in coded edge computing is also beneficial to the transmission of computation results. Nonetheless, existing pioneering works in this direction mainly take an information-theoretical perspective and assume the ideal scenarios of high signal-to-noise ratio. To the best of our knowledge, the issue of power control still remains largely unexplored for coded edge computing. In this work, two novel power control schemes are developed for coded edge computing in dynamic wireless environments, which apply to the repetition encoded task computing and the general linearly encoded task computing, respectively. However, the corresponding optimization problems turn out to be non-convex and highly non-trivial. To this end, by exploiting the underlying structural property, a novel partition-based iterative optimization method is developed to obtain the closed-form expression of the optimal dynamic power control strategy for repetition encoded task computing. For the case of more general linearly encoded task computing, the corresponding problem is transformed into a sum-of-ratio problem and then solved iteratively. Simulations are conducted to corroborate the effectiveness of the proposed schemes.
Abstract With the progress of engineering, technology viscous damper are widely used in the field of engineering structures. Its principle is that when the external normal disturbance on the structure, the viscous damper will not generate damping force on the structure; when there is a strong external disturbance (strong earthquake, strong wind), The dampers can produce a reaction force, which can reduce the damage of the structure and improve its seismic capability. In this paper, the new viscous damper has been experimentally tested, including: 1. Test for maximum damping force; 2. Test for damping coefficient, damping exponent; 3. hysteresis curve. By analyzing the experimental data, the actual performance parameters of the new viscous damper are obtained, which provides certain support for future mass production and use of the product.
Abstract In the fields of civil engineering, aerospace and mechanical manufacturing, inertia mass is a very common form of load. The research on the response of inertia mass under dynamic load can provide a strong basis for the research in the above fields. In this paper, in order to study the response of inertia mass under dynamic load, a high-speed dynamic actuator is used to carry out sinusoidal loading and impact loading on an inertia mass, and its response under dynamic load is studied, which can provide a basic basis for the follow-up study of inertia mass. It can be concluded from the response analysis of inertia mass that the motion of inertia mass satisfies the basic laws of kinematics and dynamics.
With the proliferation of delay-sensitive and computation-intensive mobile applications, recent years have witnessed a transition towards more advanced distributed edge computing. However, the diverse computation results among different edge nodes (ENs) in conventional distributed edge computing present challenges to cooperative transmission and may result in prolonged downlink latency. In this regard, replicated edge computing has emerged as a promising solution to perform computations repeatedly across multiple ENs, thereby facilitating cooperative transmission in the downlink. Nonetheless, this approach inevitably leads to additional computation cost. To encompass the advantages of both distributed edge computing and replicated edge computing, a novel partial replication based distributed edge computing is proposed in this work. Specifically, by partitioning the original task into a common part computed at all ENs and multiple private parts, with each computed by one EN, cooperative downlink transmission can be enabled without incurring excessive computation cost. To minimize the overall processing delay, a task partition and downlink transmit power allocation algorithm is developed based on alternating optimization. Simulation results are presented to validate the superiority of the proposed scheme.
The recently advocated replicated edge computing can effectively exploit the rich ambient computing resource to fulfill better edge intelligence applications. Specifically, in replicated edge computing, the same task is replicated and offloaded to multiple edge nodes so as to allow them to perform collaborative transmission to reduce communication latency. Nonetheless, existing pioneering works in this regard often assume the ideal high signal-to-noise ratio (SNR) scenarios and transmit power control is often ignored. In practice not only the edge nodes may have limited power budget but also the wireless channels may experience random fading. With this consideration, transmit power control is studied in this work for effective operations of replicated edge computing in more general dynamic wireless environments. However, highly nontrivial non-convex optimization is involved and existing solutions for conventional wireless communications may not suit due to the joint influence of both communication and computing on the long-term energy consumption. To address this technical challenge, by exploiting the underlying structural property, a novel partition-based iterative optimization method is developed, which, together with the generic Lyapunov optimization framework, allows us to obtain the closed-form expression for the optimal dynamic power control strategy. In addition, simulations are conducted to validate the effectiveness of the proposed scheme.
Mobile-edge computing (MEC) enables low-latency computing services by by deploying the computing resources at the logical edge of the network and allowing mobile users to wirelessly offload their computation-intensive tasks. Meanwhile, as user privacy is receiving increasing attention in the modern society, mitigating the privacy leakage caused by task offloading in MEC becomes imperative. In this paper, we develop a reinforcement learning (RL) based privacy-aware task offloading scheme that can synthetically take into account the data privacy, the usage pattern privacy, and the location privacy of the mobile users. To find the optimal offloading strategy, a novel two-timescale RL algorithm, dubbed as statistic prediction-post decision state-virtual experience (SP-PDS-VE), is proposed. The proposed algorithm can construct the state transition model of the underlying problem via the fast timescale learning and, in the meantime, uses the learned model to create a set of virtual experience for the slow timescale learning, so as to speed up the convergence and allow the mobile device to learn the optimal privacy-aware offloading strategy much faster. In addition to the analysis, simulations results are presented to corroborate the effectiveness of the proposed scheme.
The text puts forward a lot of concrete emergent measures according to different dangerous aspects such as the road and ground sinking,the circumjacent building's abnormity,underground pipe's rupture and the tunnel's collapse.
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