To address the complex multi-tasking application scenarios and unstable network topology of micro Unmanned Aerial Vehicle (UAV) clusters, this paper proposes a clustering algorithm MT-WCA. Firstly, the UAV nodes calculate their weight based on average connectivity difference, transmission capability, relative mobility and energy consumption. Then, under the constraint of cluster membership, the most suitable cluster head is elected to prolong the network's integrity time as much as possible. MT-WCA partitions task clusters rationally based on task objective information in the scenario, enabling the UAV cluster to complete tasks more effectively. The paper simulates multi-tasking work scenarios by MATLAB and compares the integrity time of UAV networks using different algorithms.
Currently, quantized neural networks have been widely applied in edge device model inference. In model inference using FPGAs, convolution operations are typically implemented using DSP that can provide 27×18bits operations. However, there is no general method for fully utilizing DSP to compute arbitrary low-precision quantized data in parallel. In the paper, we propose MDP, a universal solution that packs multiple multiplications in one DSP based on quantization precision and convolution parameters in multiple modes. To maximize the DSP multiplication performance, we design a data pipeline architecture based on GEMM. The experimental results show that, for single convolutional layer, MDP can decrease DSP resource utilization by 0.78× while maintaining the same level of parallelism. Compared to UltraNet, MDP achieves 1.56× latency improvement.
MAC protocols are important to ensure the quality of service of UAV ad-hoc networks, which are becoming more and more popular. Facing the dynamically changing network transmission environment, the adaptive MAC protocol becomes an effective solution. In this paper, a MAC protocol based on deep Q-learning is designed. The system integrates a contention-based MAC protocol and a scheduling-based MAC protocol. Using the deep Q-learning approach, it switches between CSMA and TDMA according to the current state of the UAV (e.g. throughput rate, latency, etc.). Two different network scenarios were designed and simulated to evaluate the performance of the designed MAC protocols. The results show that the performance of the designed adaptive MAC protocol outperforms that of a single protocol in terms of performance such as latency and throughput.
Multi-UAV intelligent system can reduce the casualty rate and improve work efficiency in special tasks. Therefore, its related fields have attracted extensive attention and research worldwide. In this paper, we proposed a Task-based LeaderSelection Algorithm (TLSA) and Topology Maintenance Algorithm (TMA) based on leader-follower model. TLSA supports to establish communication network belonging to each task rapidly through exchange messages between neighboring UAV. And the TMA is used to maintain the network topology during flight. The leaders and followers obtain their own flight coordinates and relevant parameters through publish-subscribe communication. Experiments verify the effectiveness of TLSA and TMA.
KEYWORDS: Strategic intelligence, Genetic algorithms, Distributed computing, Signal processing, Data processing, Genetics, Control systems, Intelligence systems, Radar, Data storage
This paper studies the deployment strategy for software components in distributed systems. Based on genetic algorithms, Intelligent Deployment Strategy is designed to optimize the allocation and deployment of software components to make distributed systems achieve load balancing and high efficiency. Simulation results show that using Intelligent Deployment Strategy can realize better allocation of system resources than common Round-Robin Scheduling strategy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.