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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

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Authentication Scheme Based on Non-Interactive Zero-Knowledge Proof for Mobile Health

Authentication Scheme Based on Non-Interactive Zero-Knowledg...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Edward Mensah Acheampong Shijie Zhou Yongjian Liao Emmanuel Antwi-Boasiako Isaac Amankona Obiri School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science University of Electronic Science and Technology of China Chengdu China

Digital technologies, such as wireless body area networks (WBANs) for mobile health (mHealth) applications, are expected to enhance the quality of the public health care system. Although mHealth can improve patients' quality of health by offering outpatient real-time health monitoring systems instead of being stuck in the hospital all the time to monitor chronic diseases. The major challenge in adopting mHealth is data security and privacy. The health data routed on the internet from the patient monitoring device to the health center for remote monitoring is vulnerable to confidentiality attacks. To handle this issue, we present an authentication scheme based on non-interactive zero-knowledge proof (NIZKP), which issues certificates and authenticates monitoring devices each time performing transactions without revealing sensitive information. Our authentication scheme provides a high level of security with a low computational cost, which is lightweight for WBANs.

关键词： Performance evaluation Wireless communication Wireless sensor networks Patient monitoring Hospitals Authentication Real-time systems

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Aerial-DeepSearch: distributed Multi-Agent Deep Reinforcement Learning for Search Missions 17

Aerial-DeepSearch: Distributed Multi-Agent Deep Reinforcemen...

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17th ieee international conference on Mobile Ad Hoc and Smart systems (ieee MASS)

作者： Sadhu, Vidyasagar Sun, Chuanneng Karimian, Arman Tron, Roberto Pompili, Dario Rutgers Univ New Brunswick Dept Elect & Comp Engn New Brunswick NJ 08854 USA Boston Univ Dept Mech Engn Boston MA USA

ISBN: (纸本)9781728198668

Search and Rescue (SAR) is an important part of several applications of national and social interest. Existing solutions for search missions in both terrestrial and aerial domains are mostly limited to single agent and specific environments;however, search missions can significantly benefit from the use of multiple agents that can quickly adapt to new environments. In this paper, we propose a framework based on Multi-Agent Deep Reinforcement Learning (MADRL) that realizes the actor-critic framework in a distributed manner for coordinating multiple Unmanned Aerial Vehicles (UAVs) in the exploration of unknown regions. One of the original aspects of our work is that the actors represent simulated or actual UAVs exploring the environment in parallel instead of traditional computer threads. Also, we propose addition of Long Short Term Memory (LSTM) neural network layers to the actor and critic architectures to handle imperfect communication and partial observability scenarios. The proposed approach has been evaluated in a grid world and has been compared against other competing algorithms such as Multi-Agent Q-Learning, Multi-Agent Deep Q-Learning to show its advantages. More generally, our approach could be extended to image-based/continuous action space environments as well.

关键词： Drones Multi-Agent Deep Reinforcement Learning Search and Rescue Wireless communication

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Digital Cloud Platform of distributed Data Storage in Sphere of Higher Education 13

Digital Cloud Platform of Distributed Data Storage in Sphere...

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13th international ELEKTRO conference (ELEKTRO)

作者： Perepelkin, Dmitry Saprykin, Aleksey Ivanchikova, Maria Kosorukov, Sergey Ryazan State Radio Engn Univ Fac Comp Engn Ryazan Russia

ISBN: (纸本)9781728175423

Current trends, such as an increase in the number of devices connected to the Internet, an exponential increase in the volume of information, the development of network and cloud technologies, are changing all areas of activity. The global increase in network traffic dictates the need for business to configure large-scale computing systems and networks. To introduce new digital services in various fields, innovative approaches to building computing infrastructures meant for the processing, transmission and storage of large data arrays are needed. The aim of this work is to develop a software component for building a digital cloud platform for distributed data storage in higher education. Current work proposes an architecture and software tools for distributed processing and storage of user data on a digital cloud platform of a higher educational institution. The main class diagrams of the developed cloud platform and the principles of interaction of its main components are described in detail. The functioning process and the features of building a network infrastructure of the developed digital cloud platform for distributed data storage are considered. The developed digital cloud platform implements the capabilities of immediate connection and deployment of new digital services through the use of a wide range of software components and open APIs.

关键词： distributed computing distributed data storage cloud computing digital cloud platform software defined networking

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DARL: distributed Reconfigurable Accelerator for Hyperdimensional Reinforcement Learning

DARL: Distributed Reconfigurable Accelerator for Hyperdimens...

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ieee international conference on Computer-Aided Design

作者： Hanning Chen Mariam Issa Yang Ni Mohsen Imani Department of Computer Science University of California Irvine CA USA

Reinforcement Learning (RL) is a powerful technology to solve decision-making problems such as robotics control. Modern RL algorithms, i.e., Deep Q-Learning, are based on costly and resource hungry deep neural networks. This motivates us to deploy alternative models for powering RL agents on edge devices. Recently, brain-inspired Hyper-Dimensional computing (HDC) has been introduced as a promising solution for lightweight and efficient machine learning, particularly for *** this work, we develop a novel platform capable of real-time hyper-dimensional reinforcement learning. Our heterogeneous CPU-FPGA platform, called DARL, maximizes FPGA’s computing capabilities by applying hardware optimizations to hyperdimensional computing’s critical operations, including hardware-friendly encoder IP, the hypervector chunk fragmentation, and the delayed model update. Aside from hardware innovation, we also extend the platform to basic single-agent RL to support multi-agents distributed learning. We evaluate the effectiveness of our approach on OpenAI Gym tasks. Our results show that the FPGA platform provides on average 20× speedup compared to current state-of-the-art hyperdimensional RL methods running on Intel Xeon 6226 CPU. In addition, DARL provides around 4.8× faster and 4.2× higher energy efficiency compared to the state-of-the-art RL accelerator while ensuring a better or comparable quality of learning.

关键词： Technological innovation Computer aided instruction Distance learning Computational modeling Hardware Real-time systems IP networks

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IntelliBVR - Intelligent large-scale video retrieval for objects and events utilizing distributed deep-learning and semantic approaches

IntelliBVR - Intelligent large-scale video retrieval for obj...

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ieee international conference on Big Data and Smart computing (BigComp)

作者： Alam, Aftab Khan, Muhammad Numan Khan, Jawad Lee, Young-Koo Kyung Hee Univ Dept Elect & Comp Engn Gyeonggi Do South Korea

ISBN: (纸本)9781728160344

There is increasing reliance on the intelligent CCTV systems for effective analysis and interpretation of the streaming data to recognize activities and to ensure public safety. Monitoring videos captured by surveillance cameras is always a challenging and time-consuming task. There is a need for automated analysis using computer vision methods in order to extract spatial and temporal features to assist the authorities. Once videos are processed using computer vision technologies, another issue is how to index the extracted low-level features to search, analyze, and browse? How to bridge the semantic gap between the low-level features in Euclidean space and temporal relation across videos in a multi-stream environment? Similarly, how to deal with petascale video in the cloud while extracting the low-level and high-level features? In order to address such issues, in this paper, we propose a layered architecture for large-scale distributed intelligent video retrieval while exploiting deep-learning and semantic approaches called IntelliBVR. The base layer is responsible for large-scale video data curation. The second and the third layer is supposed to process and annotate videos, respectively while using deep learning on the top of distributed in-memory computing engine. Finally, the knowledge curation layer, where the extracted low-level and high-level features are mapped to the proposed ontology so that it can be searched and retrieved using semantic rich queries. Finally, we implement and show results, which project the effectiveness of IntelliBVR.

关键词： ontology deep-learning video analytics distributed computing spark intelligent video search video ontology

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FLaPS: Federated Learning and Privately Scaling 17

FLaPS: Federated Learning and Privately Scaling

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17th ieee international conference on Mobile Ad Hoc and Smart systems (ieee MASS)

作者： Paul, Sudipta Sengupta, Poushali Mishra, Subhankar Natl Inst Sci Educ & Res Sch Comp Sci Bhubaneswar India Homi Bhabha Natl Inst Mumbai 400094 Maharashtra India Univ Kalyani Dept Stat Kalyani 741235 W Bengal India

ISBN: (纸本)9781728198668

Federated learning (FL) is a distributed learning process where the model (weights and checkpoints) is transferred to the devices that posses data rather than the classical way of transferring and aggregating the data centrally. In this way, sensitive data does not leave the user devices. FL uses the FedAvg algorithm, which is trained in the iterative model averaging way, on the non-iid and unbalanced distributed data, without depending on the data quantity. Some issues with the FL are, 1) no scalability, as the model is iteratively trained over all the devices, which amplifies with device drops;2) security and privacy trade-off of the learning process still not robust enough and 3) overall communication efficiency and the cost are higher. To mitigate these challenges we present Federated Learning and Privately Scaling (FLaPS) architecture, which improves scalability as well as the security and privacy of the system. The devices are grouped into clusters which further gives better privacy scaled turn around time to finish a round of training. Therefore, even if a device gets dropped in the middle of training, the whole process can be started again after a definite amount of time. The data and model both are communicated using differentially private reports with iterative shuffling which provides a better privacy-utility trade-off. We evaluated FLaPS on MMST, CIFAR10, and TINY-IMAGENET-200 dataset using various CNN models. Experimental results prove FLaPS to be an improved, time and privacy scaled environment having better and comparable after-learning-parameters with respect to the central and FL models.

关键词： Federated Learning(FL) distributed Learning(DL) Differential Privacy(DP) Federated Averaging(FedAvg) k-means Clustering

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A distributed Population Management Approach for Mobile Agent systems 1

A Distributed Population Management Approach for Mobile Agen...

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1st ieee international conference on Autonomic computing and Self-Organizing systems (ACSOS)

作者： Prosser, Bryan J. Fulp, Errin W. Wake Forest Univ Dept Comp Sci Winston Salem NC 27101 USA

ISBN: (纸本)9781728172774

Many mobile agent systems rely on a population of software agents roaming a network of nodes (visitation sites) performing various tasks, such as monitoring security or measuring connectivity. For these systems, the number of agents in the population is critical for maintaining desired visitation rates throughout the network;however, the population distribution may change dramatically in reaction to an event, such as issues within the network or adversarial activity. As a result, population management is needed to ensure the number of agents in a system is available to achieve the system objectives. Although centralized management can be used to maintain agent populations, these approaches are not resilient to failure or scalable to large systems. This paper introduces a novel approach for managing the population of agents by governing the death and birth of agents through the distributed examination of the expected visitation rates. Nodes in the network individually monitor local visitation rates to determine if new agents should be created or destroyed, while queue management is used to distribute agents and dampen population oscillation (cyclical death and rebirth of all agents). Since these two population management components are available at every node, the agent population is maintained in a decentralized fashion. Experimental results demonstrate the proposed population management approach can appropriately manage an agent population under various conditions including sudden agent loss and attack situations.

关键词： Mobile software agents distributed systems population management system monitoring

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Special Issue: Resilient distributed Estimator with Information Consensus for CPS Security 38

Special Issue: Resilient Distributed Estimator with Informat...

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38th ieee international conference on Computer Design (ICCD)

作者： Yu, Feng Hu, Yaodan Zhang, Teng Jin, Yier Univ Cent Florida Orlando FL 32816 USA Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9781728197104

In this paper, we study the collaboratively estimating problem of a discrete-time LTI system with a time-varying undirected communication graph among sensors. The performance of resilient state estimators developed for cyber-physical systems (CPS) degenerates if the sensor measurements are compromised. To obtain robustness for the estimation, we propose Resilient distributed Estimator with Information Consensus (RDEIC). RDEIC is a consensus-based resilient distributed algorithm that produces bounded state estimation errors with faulty sensors. Our algorithm converges to the true state in an attack-free scenario and it produces bounded estimation errors during an attack. The performance of the proposed algorithm is demonstrated with Matlab simulations.

关键词： Cyber Physical System Fault Data Injection Attack System Resilience

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Development of Blockchain-based e-Voting System: Requirements, Design and Security Perspective

Development of Blockchain-based e-Voting System: Requirement...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Md Jobair Hossain Faruk Bilash Saha Mazharul Islam Fazlul Alam Hossain Shahriar Maria Valero Akond Rahman Fan Wu Zakirul Alam Department of Software Engineering and Game Development Kennesaw State University USA Department of Information Technology Kennesaw State University USA Department of Computing Sultan Idris Education University Malaysia Department of Computer Science and Engineering Daffodil International University Bangladesh Department of Computer Science and Software Engineering Auburn University USA Department of Computer Science Tuskegee University USA Department of Computer and Information Sciences Fordham University USA

Elections make a fundamental contribution to democratic governance and are very important to select the appropriate person to determine the fate of a nation while a large number of citizens do not trust the ballot-paper-based voting system. As a result, e-Voting is being adopted by various countries throughout the world. With advantages, there are a number of constraints of e-voting systems, a single vulnerability can lead to large-scale manipulations of voting results. Leveraging and intersecting both cutting-edge technologies including biometric and blockchain technology can address the limitations of current e-Voting frameworks. In this research, we analyze the requirements for systems modeling of voting technology and possible opportunities to adopt biometrics and blockchain technology. Based on the requirements analysis, we propose a biometric-enabled and hyperledger fabric-based voting framework to automate identity verification that will ensure transparency and security of electronic voting. We utilize the software architecture analysis method and active reviews for intermediate designs for evaluating the proposed framework. Demonstration indicates the efficiency and quality attributes of architectural design that shall lead us to implement the system in real-world scenarios in future studies. Initially, we develop a Webbased prototype to demonstrate the framework. In future studies, we aim to implement on large scale and evaluate the prototype against the requirements and security standard.

关键词： System testing distributed ledger Prototypes Restful API systems modeling Fabrics Blockchains

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Efficient Execution of Dynamic Programming Algorithms on Apache Spark 22

Efficient Execution of Dynamic Programming Algorithms on Apa...

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ieee international conference on Cluster computing

作者： Javanmard, Mohammad Mahdi Ahmad, Zafar Zola, Jaroslaw Pouchet, Louis-Noel Chowdhury, Rezaul Harrison, Robert SUNY Stony Brook Stony Brook NY 11794 USA Univ Buffalo Buffalo NY USA Colorado State Univ Ft Collins CO 80523 USA

ISBN: (纸本)9781728166773

One of the most important properties of distributed computing systems (e.g., Apache Spark, Apache Hadoop, etc) on clusters and computation clouds is the ability to scale out by adding more compute nodes to the cluster. This important feature can lead to performance gain provided the computation (or the algorithm) itself can scale out. In other words, the computation (or the algorithm) should be easily decomposable into smaller units of work to be distributed among the workers based on the hardware/software configuration of the cluster or the cloud. Additionally, on such clusters, there is an important trade-off between communication cost, parallelism, and memory requirement. Due to the scalability need as well as this trade-off, it is crucial to have a well-decomposable, adaptive, tunable, and scalable program. Tunability enables the programmer to find an optimal point in the trade-off spectrum to execute the program efficiently on a specific cluster. We design and implement well-decomposable and tunable dynamic programming algorithms from the Gaussian Elimination Paradigm (GEP), such as Floyd-Warshall's all-pairs shortest path and Gaussian elimination without pivoting, for execution on Apache Spark. Our implementations are based on parametric multi-way recursive divide-&-conquer algorithms. We explain how to map implementations of those grid-based parallel algorithms to the Spark framework. Finally, we provide experimental results illustrating the performance, scalability, and portability of our Spark programs. We show that offloading the computation to an OpenMP environment (by running parallel recursive kernels) within Spark is at least partially responsible for a 2 - 5x speedup of the DP benchmarks.

关键词： Dynamic Programming Recursive Divide-&-Conquer distributed-memory computing Apache Spark I/O-efficiency Communication-efficiency Polyhedral Compilation

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