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OCTOANTS: A Heterogeneous Lightweight Intelligent Multi-Robot Collaboration System with Resource-constrained IoT Devices

OCTOANTS: A Heterogeneous Lightweight Intelligent Multi-Robo...

引用

ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Zhang, Qian Quan, Ruiyang Qimuge, Siqin Xia, Peimin Wang, Jiaheng Zan, Xin Wang, Fangshi Chen, Changchuan Wei, Qi Zhao, Huichan Liu, Xinjun Qiao, Fei Tsinghua Univ Beijing 100084 Peoples R China Xi An Jiao Tong Univ Xian 710049 Peoples R China Chongqing Univ Posts & Telecommun Chongqing 400065 Peoples R China Beijing Jiaotong Univ Beijing 100044 Peoples R China Univ Sci & Technol Beijing Beijing 100083 Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

As the focus on highly intelligent robots continues, a problem that cannot be ignored has emerged: resource constraints. Considering the game problem of resource limitation and the level of intelligence, we focus on lightweight intelligence. This work is a further refinement of our previous work, a heterogeneous lightweight intelligent multi-robot system. Inspired by the nature creatures "octopus" and "ants". first, we propose a heterogeneous centralized-distributed architecture, which can make robots collaboration more flexible and non-redundant. Second, to reflect lightweight intelligence, we use the Raspberry Pi, a low computing and power consumption internet of things (IoT) device, as a processing platform and first propose a quantitative definition of the lightweight intelligent system. Then, combining the centralized-distributed architecture and the lightweight computing platform, we propose an adapted algorithm called OCTOANTS and apply it to the simultaneous localization and mapping (SLAM) field. The OCTOANTS architecture consists of one brain and eight tentacles, which can achieve complex things with proper collaboration between them. Finally, we use heterogeneous cameras and heterogeneous algorithms to form a lightweight intelligent collaborative system that can run in the real world. On the low-grade platform Raspberry Pi our heterogeneous tentacles frame rate can reach 41fps and 99.8fps respectively, power consumption is only 2W and 1.2W. At the same time, our heterogeneous system is on average 7.2% more accurate than the state-of-the-art homogeneous system and can be applied to a wider range of application scenarios, demonstrating the superiority and feasibility of our OCTOANTS.

关键词： Simultaneous localization and mapping Power demand Collaboration Computer architecture sensor phenomena and characterization sensor systems Nanoscale devices

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distributed Consensus Problem in Federated Learning Paradigm 23

Distributed Consensus Problem in Federated Learning Paradigm

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yan, Xin Qin, Yiming Hu, Xiaodong School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China The University of Hong Kong The Faculty of Education Hong Kong Hong Kong

ISBN: (纸本)9781665494571

Federated learning (FL) framework facilitates more and more applications of deep learning algorithms on the existing network architectures, where the model parameters are aggregated in a centralized manner. However, some of FL participants are often inaccessible, such as in a power shortage or device dormant state. That will force us to explore the probability that the parameter aggregation is operated in an ad hoc manner, which is based on consensus computing. In this paper, we first propose a novel FL paradigm, which supports an ad hoc operation mode for FL participants. Second, a discrete-time dynamic equation and its control law are formulated to satisfy the demands from FL framework, with a quantized caching scheme designed to mask the uncertainties from asynchronous updates and measurement noises. Then, the consensus conditions and the convergence of the consensus protocol are deduced analytically, and a strategy of quantized caching to optimize the convergence speed is provided. Last, the theoretical results are validated by numerical simulations. © 2021 ieee.

关键词： Uncertainty Smart cities Delay effects Packet loss Collaborative work Particle measurements Numerical simulation

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t-path Based Reliable and Real-time Routing in Battery-Free Wireless sensor Network 23

t-path Based Reliable and Real-time Routing in Battery-Free ...

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作者： Chen, Kunyi Gao, Hong Cheng, Siyao Chen, Quan Li, Jianzhong Harbin Institute of Technology China Guangdong University of Technology China

ISBN: (纸本)9781665494571

Wireless sensor Networks are deployed to sense data from the physical world and transmit these data to the server(s). To reduce the maintenance cost of replacing batteries, Energy-Harvesting nodes are developed which can get recharged from Solar Power, Radio Frequency, and other environmental energy resources. We term the network consist of energy-harvesting nodes as Battery-Free WSNs (BF-WSNs). Environmental energy is inherently time-varying and exhibits a high degree of heterogeneity. These features bring challenges to routing design. In this paper, we focus on the reliability and real-time issues and propose an opportunistic strategy as time synchronization is hard and expensive to reach in BF-WSN. We first model BF-WSNs as a directed weighted multigraph and propose a novel definition called t-path to describe the time-varying availability feature of BF-nodes. Further, we show that the problem is NP-Hard. We present a centralized approximate algorithm and prove that the ratio bound is 2 under two conditions. This paper also presents a distributed algorithm. Comprehensive simulations have been performed to evaluate the performance of the proposed algorithms and the results show that the efficiency of our algorithms. © 2021 ieee.

关键词： Wireless sensor networks Simulation Reliability theory Approximation algorithms Routing Reliability engineering Real-time systems

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BYEE: Batch Fully Private Scheme for Coded distributed Matrix Multiplication 24

BYEE: Batch Fully Private Scheme for Coded Distributed Matri...

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24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data Science and systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Guojing Wang, Jin Zou, Xiaotian Li, Lingzhi Gu, Fei School of Computer Science and Technology Soochow University Suzhou215006 China Collaborative Innovation Center of Novel Software Technology and Industrialization China

ISBN: (纸本)9798350319934

Recently, user privacy in distributed computing has received increasing attention. Matrix multiplication is one of the fundamental high-frequency operations in distributed machine learning (e.g., gradient descent, linear regression). This paper studies the batch Fully Private distributed Matrix Multiplication (FPMM) problem. In batch FPMM, the user aims to compute multiple desired matrix multiplications from two public libraries which are stored across all workers. Batch FPMM requires that colluding workers cannot obtain the indexes of all desired ma-trices in the two public libraries (i.e., privacy constraint). In this paper, we first propose the Bath fullY privatE schemE (BYEE) to encode multiple desired matrix multiplications together based on the construction of bilinear complexity. We then perform a solid theoretical analysis to prove the privacy and decodability of BYEE. Multi-aspect analysis and extensive simulation results illustrate that BYEE can significantly reduce the computation latency than state-of-the-art schemes that do not jointly encode multiple desired matrix multiplications, especially when the number of desired matrix multiplications and the number of colluding workers are large. © 2022 ieee.

关键词： Libraries

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Effective Severity Assessment of Parkinson's Disease using Wearable sensors in Free-living IoT Environment 29

Effective Severity Assessment of Parkinson's Disease using W...

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29th ieee international conference on Parallel and distributed systems, ICPADS 2023

作者： Li, Ziheng Zhao, Yuting Qi, Jun Wang, Xulong Yang, Yun Yang, Po National Pilot School of Software Yunnan University Kunming China Xi'an JiaoTong-Liverpool University Department of Computing Suzhou China University of Sheffield Department of Computer Science Sheffield United Kingdom

ISBN: (纸本)9798350330717

Internet of Things (IoT) Wearable technology plays a crucial role in assisting the diagnosis of Parkinson's disease (PD), and an efficient model for auxiliary diagnosis of the severity of PD can help reduce the workload for doctors. However, due to the influence of data collection environments and annotators, noisy label data is inevitable, which may have a negative impact on modeling the severity of PD. To address the above challenges, on the one hand, we collected a large number of activity signal data of Parkinson's patients in free-living environments, and on the other hand, we proposed an efficient PD stage assessment framework, which includes a noisy label processing method to alleviate the noisy label negative impact. Specifically, we collected signal data from 15 healthy controls and 68 PD patients through 12 activities, and then we proposed a framework for noisy label detection and correction. The experimental results on real PD data sets demonstrated that the proposed framework achieve 75.9% accuracy in PD stage assessment and significantly improve the classification performance of different types of basic classifiers, which is better than other noisy label detection algorithms and other PD stage assessment frameworks. Overall, in this work, we focus on modeling PD severity in free-living environments using a single wearable sensor and reducing the negative impact of noisy label data to better help PD patients manage the disease. © 2023 ieee.

关键词： Noisy Label detection Parkinson's disease Disease stage diagnosis model Wearable sensor

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3DS: An Efficient DPDK-based Data Distribution Service for distributed Real-time Applications 24

3DS: An Efficient DPDK-based Data Distribution Service for D...

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作者： Xu, Tianyu Chen, Xianzhang Wu, Changze Wang, Jiapin Zheng, Rongwei Liu, Duo Tan, Yujuan Ren, Ao Li, Jian College of Computer Science Chongqing University Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350319934

Data Distribution Service (DDS) is a widely-used middleware for data transmission in distributed real-time applications, such as autonomous vehicles and robotics. However, the communication of existing DDS middlewares generally relies on the network stacks of the operating system, which brings long latency to the real-time applications. In this paper, we propose a DPDK-based DDS, dubbed 3DS, to improve the performance of communication of DDS middlewares. first, 3DS reduces the latency of communication between distributed real-time applications by avoiding the latency of kernel. Second, 3DS reduces the overhead of dynamic memory allocation and deallocation by a reserved memory pool. Finally, 3DS adopts a thread-exclusive CPU model to reduce the resource contention between threads. We implement and evaluate 3DS on real systems running Linux. The experimental results show that 3DS reduces 44% of the communication latency over FastDDS, which is the state-of-the-art DDS middleware used by ROS2. Moreover, the throughput of data transfer in 3DS can be 1.51 times that of FastDDS. © 2022 ieee.

关键词： Data transfer

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Weak Network Oriented Mobile distributed Storage: A Hybrid Fault-Tolerance Scheme Based on Potential Replicas 24

Weak Network Oriented Mobile Distributed Storage: A Hybrid F...

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作者： Wei, Xinlei Tan, Yujuan Liu, Duo Wu, Daitao Wu, Yu Chen, Xianzhang Li, Jian College of Computer Science Chongqing University Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China School of Electrical Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350319934

Node failure is one of the most typical issues in distributed storage systems. The classic fault-tolerance methods can meet the fault tolerance needs of systems deployed in edge storage, 5G IoT, and other high-performance data centers. However, mobile distributed systems contend with inconsistent network signals and relatively low bandwidth in weak network environments. The traditional fault-tolerance schemes have difficulty ensuring distributed storage and data repair simultaneously, posing a considerable challenge to data storage reliability. To address this problem, we proposed a hybrid fault-tolerance scheme based on potential replicas, called HFPR, which trades off storage overhead and performance for weak network mobile distributed storage systems. HFPR first stores data with the erasure code redundancy and then gradually increases data redundancy by reserving potential replicas, enabling the system to disperse network transmission pressure and reduce network transmission. To maximize replica utilization and control the storage overhead of potential replicas, we design a node state prediction mechanism and a file lifecycle management mechanism. Evaluation results show that, compared with the traditional hybrid fault-tolerant scheme with the same storage overhead, HFPR achieves better replica-level repair efficiency in data repair and a reduced probability of degraded reads by approximately 50%. During data distribution, HFPR can minimize data write amplification by up to 1.1× and additional network transfers by up to 77.5%. © 2022 ieee.

关键词： Fault tolerance

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PC-BNA: Parallel Convolution Binary Neural Network Accelerator 24

PC-BNA: Parallel Convolution Binary Neural Network Accelerat...

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作者： Liu, Haihui Zhang, Yuang Zheng, Xiangwei Yu, Mengxin An, Baoxu Liu, Xiaojie School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9798350319934

Binary neural network (BNN) is widely used in speech recognition, image processing and other fields to save memory and speed up computing. However, the accuracy of the existing binarization scheme in the realistic dataset is obviously low, and the input layer uses 32-bit floating point to avoid excessive precision loss, which requires additional computing units and increases the computational burden. Therefore, it is very important to improve the input layer, save computing resources and reduce precision loss. In this paper, we propose a parallel convolution binary neural network accelerator architecture (PC-BNA). Based on our proposed BNN model, we design an efficient field programmable gate array (FPGA) based accelerator. The input of the first layer is binarized, the traditional binary convolution layer is replaced by a parallel binary convolution, and the network building blocks are improved. The experimental results show that the proposed PC-BNA has higher accuracy and better performance on CIFAR-10. The image recognition accuracy is as high as 91.4%, which is superior to the state-of-art of the BNN accelerator. Compared to the state-of-the-art BNN model, when using the same model size, the use of look-up-table (LUT) saves 9.08%, and the digital signal processor (DSP) saves 27.7%. The results suggest that it is promising for PC-BNA in future high performance mobile artificial intelligence applications. © 2022 ieee.

关键词： Convolution

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(POSTER) OVERTAKE: Opportunistic Routing and Concurrent Transmissions for TSCH 16

(POSTER) OVERTAKE: Opportunistic Routing and Concurrent Tran...

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16th Annual international conference on distributed computing in sensor systems (dcoss)

作者： Harms, Oliver Landsiedel, Olaf Univ Kiel Kiel Germany Chalmers Univ Technol Gothenburg Sweden

ISBN: (数字)9781728143514

ISBN: (纸本)9781728143514

In this paper, we present OVERTAKE, an opportunistic routing protocol for Time-Slotted Channel Hopping (TSCH). OVERTAKE combines (1) opportunistic routing, (2) concurrent transmissions and (3) TSCH. We show that this novel combination enables low-latency, central scheduling withstanding node failures. Our initial results show its ability to withstand node failures of up to 40% of nodes of a flow while keeping minimal latency.

关键词： TSCH Opportunistic Routing Concurrent Transmissions Central Scheduling Wireless sensor-Actuator Networks (Industrial) Internet of Things

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MicaPen: A Pen to Write in Air Using Mica Motes 16

MicaPen: A Pen to Write in Air Using Mica Motes

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16th Annual international conference on distributed computing in sensor systems (dcoss)

作者： Pal, Amitangshu Temple Univ Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781728143514

This paper demonstrates a technique to write in air using Micaz motes. The application, named MicaPen is low-cost and can be used by the disabled who does not have fingers or limbs. Patients can wear the mote as a wrist watch or bracelet and move their hands based on what character they want to write. The mote detects the movement pattern using the accelerometer of MTS310 sensorboard and displays the characters on the screen. The application can also be extended in several applications including virtual touchscreen or remotely interact with different controlled devices.

关键词： Write in air accelerometer hand-gestures alphabets human-sensor interaction

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