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AI-Powered Real-Time Obstacle Detection for UAVs using Blockchain

AI-Powered Real-Time Obstacle Detection for UAVs using Block...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Aparna Kumari Patel Ansh Alkeshbhai Shah Akshat Nimesh Safaa Najah Saud Al-Humairi Prasun Kumar Sunil Kumar Prakriti Lohumi Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Faculty of Engineering Science and Technology Adani University Ahmedabad India Faculty of Information Sciences and Engineering Management and Science University Malaysia Department of Biotechnology SSET Sharda University Greater Noida U.P. India Department of AI and Data Science Guru Jambheshwar University of Science & Technology Hisar Haryana India Department of Artificial Intelligence and Data Science Bhagwan Parshuram Institute of Technology New Delhi Delhi India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

AI integration with UAVs has brought significant advancements in aerial control and operational safety, particularly in real-time obstacle detection—an essential aspect for navigating unknown environments. This work introduces an innovative AI-based solution for obstacle detection in UAVs, leveraging deep learning techniques to enhance precision and environmental awareness. The system’s architecture involves multiple layers, where UAVs first capture high-resolution images that undergo a processing pipeline including data pre-processing, augmentation, and labeling. A key element of this process is the use of Convolutional Neural networks (CNNs) to train models capable of identifying obstacles across various terrains. To ensure the integrity and security of the data, especially in complex multi-UAV systems, blockchain technology is integrated. Utilizing distributed Hash Tables (DHTs) and the Interplanetary File System (IPFS), this decentralized system creates a content-addressable database to store and authenticate unalterable records. Experimental analysis demonstrates that this system offers high accuracy in real-time obstacle detection, minimizing false positives and improving UAV safety.

关键词： Deep learning Autonomous aerial vehicles Real-time systems Blockchains Safety Reliability InterPlanetary File System Convolutional neural networks Artificial intelligence Drones

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19th IEEE international Symposium on parallel and distributed Processing with Applications, 11th IEEE international conference on Big Data and Cloud computing, 14th IEEE international conference on Social computing and Networking and 11th IEEE international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

19th IEEE International Symposium on Parallel and Distribute...

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19th IEEE international Symposium on parallel and distributed Processing with Applications, 11th IEEE international conference on Big Data and Cloud computing, 14th IEEE international conference on Social computing and Networking and 11th IEEE international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

ISBN: (纸本)9781665435741

The proceedings contain 222 papers. The topics discussed include: DRL-deploy: adaptive service function chains deployment with deep reinforcement learning;accuracy vs. efficiency: achieving both through hardware-aware quantization and reconfigurable architecture with mixed precision;cmss: collaborative modeling of safety and security requirements for network protocols;FGPA: fine-grained pipelined acceleration for depthwise separable CNN in resource constraint scenarios;Dyacon: JointCloud dynamic access control model of data security based on verifiable credentials;understanding the runtime overheads of deep learning inference on edge devices;and alleviating imbalance in synchronous distributed training of deep neural networks.

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Augmenting Scalability in ARACNe and CLR for Inferring Large Gene Regulatory networks from Expression Profiles

Augmenting Scalability in ARACNe and CLR for Inferring Large...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Softya Sebastian Swarup Roy Department of Computer Applications Network Reconstruction & Analysis (NETRA) Lab Sikkim University Gangtok India

ISBN: (纸本)9781665454025

Inference of gene regulatory networks (GRNs) is an essential step towards understanding the complex interactions of an organism. In this regard, many methods have been proposed to infer gene regulatory networks over the years. However, most traditional methods do not apply to real networks because of their inability to infer large networks. The Algorithm for the Reconstruction of Accurate Cellular networks (ARACNe) and the Context Likelihood of Relatedness (CLR) are popular methods for inferring GRNs. Their capability is restricted to a few hundred nodes despite their qualitative goodness. Therefore, we developed an effective parallel framework that augments the scalability of ARACNe and CLR to infer large networks in parallel without compromising their original predictive quality. In this paper, we describe the workflow and the results of the framework implementation for both methods. The framework is tested using synthetically generated DREAM Challenge networks. Results show that the new framework is highly scalable and effectively infers relatively large networks with no loss of true edges.

关键词： Cellular networks Scalability Grid computing Organisms

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Execution Time Optimization for Pipeline Deep Network Training on Multiple GPUs

Execution Time Optimization for Pipeline Deep Network Traini...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Bing-Jou Wu Ding-Yong Hong Pangfeng Liu Jan-Jan Wu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

As neural network models become gigantic, they increasingly demand more time and memory for training. To meet these demands, advanced parallel computing techniques have become essential. Our research focuses on hybrid parallelism, an extension of pipeline parallelism. Pipeline parallelism splits the neural network into sub-networks distributed across a sequence of processing units, enabling simultaneous processing of different data segments on each device. Hybrid parallelism extends this concept by allocating multiple devices to each sub-network. Our research focuses on optimizing hybrid parallelism by improving how the model is partitioned and how computational devices are assigned. We address these issues by modeling the neural network as a directed acyclic graph of tensor operators, and then demonstrating that optimally partitioning this graph is NP-complete. Then, we propose a two-step approach. The first step is to determine a sequence of nodes. The second step is dynamic programming, which partitions the sequence to maintain balance across the assigned devices. In transforming the graph into a sequence, we explore two methods: one employs topological sorting, while the other clusters non-sequential subgraphs. We apply both methods and select the more effective one based on performance outcomes. We implement our algorithm and conduct experiments. The results show substantial enhancements in both the speed of partitioning and training throughput, with speedups reaching up to 23 in partitioning time and a 1.3 -fold increase in training throughput.

关键词： Training Directed acyclic graph Computational modeling Neural networks Pipelines Computer architecture parallel processing Throughput Dynamic programming Topology

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FSD-Inference: Fully Serverless distributed Inference with Scalable Cloud Communication

FSD-Inference: Fully Serverless Distributed Inference with S...

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international conference on Data Engineering

作者： Joe Oakley Hakan Ferhatosmanoglu Department of Computer Science University of Warwick

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Serverless computing offers attractive scalability, elasticity and cost-effectiveness. However, constraints on memory, CPU and function runtime have hindered its adoption for dataintensive applications and machine learning (ML) workloads. Traditional ‘server-ful’ platforms enable distributed computation via fast networks and well-established inter-process communication (IPC) mechanisms such as MPI and shared memory. In the absence of such solutions in the serverless domain, parallel computation with significant IPC requirements is challenging. We present FSD-Inference, the first fully serverless and highly scalable system for distributed ML inference. We explore poten-tial communication channels, in conjunction with Function-as-a-Service (FaaS) compute, to design a state-of-the-art solution for distributed ML within the context of serverless data-intensive computing. We introduce novel fully serverless communication schemes for ML inference workloads, leveraging both cloud-based publish-subscribe/queueing and object storage offerings. We demonstrate how publish-subscribe/queueing services can be adapted for FaaS IPC with comparable performance to object storage, while offering significantly reduced cost at high parallelism levels. We conduct in-depth experiments on benchmark DNNs of various sizes. The results show that when compared to server-based alternatives, FSD-Inference is significantly more cost-effective and scalable, and can even achieve competitive performance against optimized HPC solutions. Experiments also confirm that our serverless solution can handle large distributed workloads and leverage high degrees of FaaS parallelism.

关键词： Publish subscribe systems Costs Runtime Computational modeling Scalability Serverless computing Publish-subscribe

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Efficiency Optimization Method of Wireless Federated Learning Considering Computational Capability and Channel State 23

Efficiency Optimization Method of Wireless Federated Learnin...

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23rd IEEE international conference on Communication Technology, ICCT 2023

作者： Pang, Guohao Li, Fengguo Zhu, Xiaorong College of Portland Nanjing University of Posts and Telecommunications Nanjing China China Mobile Company Shandong China College of Telecommunication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798350325959

Due to the explosive growth in the variety of smart mobile terminals in wireless networks, the increasing computing capability of mobile chips, and the public's growing concern for personal privacy, it is a better solution to decentralize the deep learning framework for mobile services that can enhance user experience to the mobile terminal layer. In this paper, we study the joint optimization problem of processor performance and channel state in a non-independent distribution scenario (non-IID), while considering the user's device experience problem to improve the battery efficiency of the terminal device (TD) as much as possible and maximize the efficiency of the federated learning (FL) system while ensuring low local upload latency. To improve the efficiency of wireless federated learning (WFL), we propose a specific and complete scheduling strategy involving both computational and communication aspects. First, we model the total problem and decouple it into several sub-problems to solve according to the nature of the variables. Then, we propose a Reduced Load algorithm (RL) to solve the task allocation problem and a dynamic bandwidth allocation strategy to solve the bandwidth allocation problem. Simulation results show that the proposed scheduling strategy can achieve higher learning performance with lower training latency and is capable of adaptively adjusting the bandwidth allocation to decrease upload latency. © 2023 IEEE.

关键词： Federated learning parallel and distributed algorithms resource allocation scheduling policies wireless communication

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A Multi-attention Based CNN-BiLSTM Intrusion Detection Model for In-vehicle networks

A Multi-attention Based CNN-BiLSTM Intrusion Detection Model...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Kai Gao Hao Huang Linhong Liu Ronghua Du Jinlai Zhang College of Automotive and Mechanical Engineering Changsha University of Science & Technology Changsha China

The development of vehicular networking technology continuously enhances the internet connectivity of modern vehicles. However, for in-vehicle networks, constant communication with the outside world dramatically increases vehicle security risks, posing serious security threats to automotive systems and occupants. This paper proposes a CNN-BiLSTM vehicular network intrusion detection model based on multi-head attention by analyzing the information security vulnerabilities in the Controller Area Network (CAN) bus. By extracting only the communication features of CAN messages, including CAN ID and Data field, this paper conducted a multiclass classification study on the real-time traffic dataset CarHacking. The model achieved a detection accuracy of 99.992%. The reduction of features effectively simplifies the model and improves its computation efficiency.

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distributed Online Service Coordination Using Deep Reinforcement Learning 41

Distributed Online Service Coordination Using Deep Reinforce...

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41st IEEE international conference on distributed computing Systems (ICDCS)

作者： Schneider, Stefan Qarawlus, Haydar Karl, Holger Paderborn Univ Paderborn Germany Fraunhofer ISST Dortmund Germany

ISBN: (纸本)9781665445139

Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).

关键词： Training Knowledge engineering Network topology Pipelines Reinforcement learning Quality of service Manuals

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DFAR: Dynamic-threshold Fault-tolerant Adaptive Routing for Fat Tree networks

DFAR: Dynamic-threshold Fault-tolerant Adaptive Routing for ...

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international conference on parallel and distributed Systems (ICPADS)

作者： Binyan Lan Fei Lei Dezun Dong Ke Wu Xiaoyun Zhang College of Computer National University of Defense Technology Changsha China

The routing algorithm is important for the design of high-performance interconnection networks. With the increasing size of high-performance computing (HPC) systems, the possibility of network component failures increases simultaneously. Fault tolerance becomes a more critical consideration for the routing algorithms, as failed network devices, mostly network links, corrupt the regularity of the topology. However, existing routing algorithms focus on load balancing, ignoring that when the network is faulty. Similarly, current fault-tolerant routing algorithms ensure the correct functionality when failures exist without considering the more challenging post-failure load balancing. In this paper, we co-design the load balancing and fault tolerance for adaptive routing algorithms in fat tree networks and propose DFAR, Dynamic-threshold Fault-tolerant Adaptive Routing. DFAR prioritizes D-Mod-K deterministic output ports by adding thresholds to other available candidates. We adopt a simulated gradient descent algorithm to dynamically update the thresholds according to the network state changes. More state information other than local queue occupancies is used in the thresholds optimization. Experiments with synthetic load show that DFAR improves throughput by up to 25% for network with considerable failures. For realistic MPI workloads, DFAR also improves performance by up to 22%.

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Sparse Vector Binding on Spiking Neuromorphic Hardware Using Synaptic Delays 22

Sparse Vector Binding on Spiking Neuromorphic Hardware Using...

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international conference on Neuromorphic Systems (ICONS)

作者： Renner, Alpha Sandamirskaya, Yulia Sommer, Friedrich T. Frady, E. Paxon Univ Zurich Inst Neuroinformat Zurich Switzerland Swiss Fed Inst Technol Zurich Switzerland Intel Neuromorph Comp Lab Zurich Switzerland Intel Neuromorph Comp Lab Hillsboro OR USA Univ Calif Berkeley Redwood Ctr Theoret Neurosci Berkeley CA USA

ISBN: (纸本)9781450397896

Vector Symbolic Architectures (VSA) were first proposed as connectionist models for symbolic reasoning, leveraging parallel and in-memory computing in brains and neuromorphic hardware that enable low-power, low-latency applications. Symbols are defined in VSAs as points/vectors in a high-dimensional neural state-space. For spiking neuromorphic hardware (and brains), particularly sparse representations are of interest, as they minimize the number of costly spikes. Furthermore, sparse representations can be efficiently stored in simple Hebbian auto-associative memories, which provide error correction in VSAs. However, the binding of spatially sparse representations is computationally expensive because it is not local to corresponding pairs of neurons as in VSAs with dense vectors. Here, we present the first implementation of a sparse VSA on spiking neuromorphic hardware, specifically Intel's neuromorphic research chip Loihi. To reduce the cost of binding, a delay line and coincidence detection are used, trading off space with time. We show as proof of principle that our network on Loihi can perform the binding operation of a classical analogical reasoning task and discuss the cost of different sparse binding operations. The proposed binding mechanism can be used as a building block for VSA-based architectures on neuromorphic hardware.

关键词： Spiking Neural networks Vector Symbolic Architecture (VSA) Hyperdimensional computing Neuromorphic Hardware Binding Coincidence detection Sparse distributed code

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