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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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DRS+: Load Shedding Meets Resource Auto-Scaling in distributed Stream Processing 22

DRS+: Load Shedding Meets Resource Auto-Scaling in Distribut...

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22nd ieee international conference on High Performance computing and Communications, 18th ieee international conference on Smart City and 6th ieee international conference on Data Science and systems, HPCC-SmartCity-DSS 2020

作者： Tang, Kailin Hao, Zhifeng Cai, Ruichu Fu, Tom Z. J. Yang, Yin Wang, Li Winslett, Marianne Zhang, Zhenjie Guangdong University of Technology CVTE Research China Foshan University China Guangdong University of Technology China Bigo Technology Pte. Ltd. Hamad Bin Khalifa University Qatar Advanced Digital Sciences Center University of Illinois at Urbana-Champaign United States PVoice Technology

ISBN: (纸本)9781728176499

distributed stream processing is nowadays a leading paradigm for managing massive streaming data and performing real-Time analytics on such streams. Since data volume and distribution in the input streams often change over time, a dynamic resource scheduler is often employed to ensure that the system meets response time constraints while being cost effective in terms of resource usage. Currently, resource schedulers in distributed streaming systems generally assume that each input must be completely processed, and allocate resources accordingly. In practice, this assumption can often be relaxed, since many stream analytics tasks do not require exact outputs. To our knowledge, however, no existing resource scheduler takes this fact into consideration, leading to unnecessary over-provisioning of *** paper presents DRS+, a novel dynamic resource scheduler that integrates load shedding into resource auto-scaling strategies. DRS+ is based on a unified model that establishes the relationship between response time, result accuracy and resource consumption, given the current workload statistics. Using this model, DRS+ computes the best resource allocation plan and load shedding strategy, and executes them through an efficient protocol that minimizes the computation and communication overhead at each operator. We have implemented DRS+ based on Apache Storm, and evaluated it using real dataset. The results demonstrate that DRS+ achieves low resource consumption and high result utility, while satisfying real-Time response constraints. © 2020 ieee.

关键词： Scheduling

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Towards an Autonomous, Power-Efficient Base Station for sensor Data Collection

Towards an Autonomous, Power-Efficient Base Station for Sens...

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Autonomic computing and Self-Organizing systems Companion (ACSOS-C), ieee international conference on

作者： Pierre-Louis Sixdenier Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU) Erlangen Germany

For a bio-monitoring project called AMMOD, we have to design an autonomous station powered by solar panels that gathers samples from sensors in remote places and transmit them to the cloud. In this paper, we give a brief overview of our research on designing an autonomous, self-managing base station to operate heterogeneous sensors and manage their data in a power-efficient and self-sustained way.

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EdgeLD: Locally distributed Deep Learning Inference on Edge Device Clusters 22

EdgeLD: Locally Distributed Deep Learning Inference on Edge ...

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作者： Xue, Feng Fang, Weiwei Xu, Wenyuan Wang, Qi Ma, Xiaodong Ding, Yi Beijing Jiaotong University School of Computer and Information Technology Beijing China Key Laboratory of Industrial Internet of Things Networked Control Ministry of Education Chongqing China Beijing Wuzi University School of Information Beijing China

ISBN: (纸本)9781728176499

Deep Neural Networks (DNN) have been widely used in a large number of application scenarios. However, DNN models are generally both computation-intensive and memory-intensive, thus difficult to be deployed on resource-constrained edge devices. Most previous studies focus on local model compression or remote cloud offloading, but overlook the potential benefits brought by distributed DNN execution on multiple edge devices. In this paper, we propose EdgeLD, a new framework for locally distributed execution of DNN-based inference tasks on a cluster of edge devices. In EdgeLD, DNN models' time cost will be firstly profiled in terms of computing capability and network bandwidth. Guided by profiling, an efficient model partition scheme is designed in EdgeLD to balance the assigned workload and the inference runtime among different edge devices. We also propose to employ layer fusion to reduce communication overheads on exchanging intermediate data among devices. Experiment results show that our proposed partition scheme saves up to 15.82% of inference time with regard to the conventional solution. Besides, applying layer fusion can speedup the DNN inference by 1.11-1.13X. When combined, EdgeLD can accelerate the original inference time by 1.77-3.57X on a cluster of 2-4 edge devices. © 2020 ieee.

关键词： Deep neural networks

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Privacy-Preservation in distributed Deep Neural Networks via Encryption of Selected Gradients 22

Privacy-Preservation in Distributed Deep Neural Networks via...

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作者： Emmanuel, Antwi-Boasiako Zhou, Shijie Liao, Yongjian Liu, Qihe University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9781728176499

In this paper, privacy preservation in distributed Deep Neural Networks (DDNN) is re-visited. We focus on the use of Homomorphic Encryption (HE) in the DDNN. Although HE provides some level of effective way to bring privacy to the DDNN, its use is usually associated with high overheads, especially communication cost between the Honest-but-Curious (HbC) cloud server and participants. To this end, we propose an alternative approach for privacy preservation in the DDNN. Ability to select and encrypt selected gradients homomorphically, is the main appeal of this paper and has proven to provide good privacy, reduce communication cost to a considerable level (ie. 0.04MB and 0.13MB when 1% and 10% of total gradients are selected, encrypted and shared respectively) whilst maintaining acceptable level of accuracy. A learning with error (LWE) based additive Homomorphic Encryption is used and comparison is made with closely related works to show how efficient our approach is. Furthermore, we propose the use of non-malleable code to protect the integrity of ciphertexts that are transmitted between participants and the HbC cloud server. This paper is the first to combine partial sharing and Homomorphic Encryption in the DDNN. © 2020 ieee.

关键词： Deep neural networks

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Internet of Things Security: Requirements, Attacks on SH-IoT Platform

Internet of Things Security: Requirements, Attacks on SH-IoT...

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international conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

作者： Mona A. El. zuway Hend M. Farkash College of Electrical and Electronics Technology-Benghazi Benghazi Libya

ISBN: (纸本)9781665482622

Smart building security systems typically consist of sensors and controllers that monitor power operating systems, alarms, camera monitoring, access controls, and many other important information and security systems. These systems are managed and controlled through online platforms. A successful attack on one of these platforms may result in the failure of one or more critical intelligent systems in the building. In this paper, the security requirements in the application layer of any IoT system were discussed, in particular the role of IoT platforms in dealing with the security problems that smart buildings are exposed to and the extent of their strength to reduce the attacks they are exposed to, where an experimental platform was designed to test the presence of security vulnerabilities and This was done by using the Zed Attack Proxy (ZAP) tool, according to the OWASP standards and security level assessment, and the importance of this paper comes as a contribution to providing information about the most famous IoT platforms and stimulating work to explore security concerns in IoT-based platforms.

关键词： Cloud computing Smart buildings Reliability engineering sensor systems Security Internet of Things Smart devices

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A distributed biometric authentication scheme based on blockchain 3

A distributed biometric authentication scheme based on block...

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3rd ieee international conference on Blockchain (Blockchain)

作者： Toutara, Foteini Spathoulas, Georgios Univ Thessaly Dept Comp Sci & Biomed Informat Lamia Greece Norwegian Univ Sci & Technol NTNU Dept Informat Secur & Commun Technol Gjovik Norway

ISBN: (纸本)9780738104959

Biometric authentication is the preferred authentication scheme in modern computing systems. While it offers enhanced usability, it also requires cautious handling of sensitive users' biometric templates. In this paper, a distributed scheme that eliminates the requirement for a central node that holds users' biometric templates is presented. This is replaced by an Ethereum/IPFS combination to which the templates of the users are stored in a homomorphically encrypted form. The scheme enables the biometric authentication of the users by any third party service, while the actual biometric templates of the user never leave his device in non encrypted form. Secure authentication of users in enabled, while sensitive biometric data are not exposed to anyone. Experiments show that the scheme can be applied as an authentication mechanism with minimal time overhead.

关键词： biometric authentication blockchain homomorphic encryption privacy

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Designing a Method for Constructing distributed Open ACS Based on the ESP-NOW Wireless Protocol

Designing a Method for Constructing Distributed Open ACS Bas...

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2020 international Russian Automation conference, RusAutoCon 2020

作者： Yukhimets, Dmitry Sych, Alexander Sakhnenko, Andrey Institute of Automation and Control Processes Feb Ras Laboratory of Robotic Systems Vladivostok Russia Institute of Marine Technology Problems Feb Ras Laboratory of Intelligent Control Systems Vladivostok Russia Far Eastern Federal University Department of Automation and Control Vladivostok Russia

ISBN: (纸本)9781728161303

A new approach to the implementation of open distributed automatic control systems based on use of cheap computing nodes from the manufacturer Espressif, exchanging data via the ESP-NOW wireless protocol, is proposed. Due to the used data transfer protocol, it became possible to quickly and losslessly transfer data packets between computing nodes, which favorably affected the operation of the entire system as a whole. The solution proposed in the work allows the use of distributed automatic control systems (ACS) to control complex mechatronic objects, such as industrial manipulators, and allows location of computing nodes at remote points in the workspace. This fact is confirmed by the performance indicators obtained during testing of a real system. The paper proposes the basic architecture of such a system and describes the features of the implementation of individual nodes, presents diagrams of the classes of microprograms contained in computing nodes. © 2020 ieee.

关键词： distributed parameter control systems

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26th ieee international conference on Parallel and distributed systems (ieee ICPADS)

作者： Long, Saiqin Li, Zhetao Liu, Zihao Deng, Qingyong Oh, Sangyoon Komuro, Nobuyoshi Xiangtan Univ Sch Comp Sci Key Lab Hunan Prov Internet Things & Informat Sec Hunan Int Sci & Technol Cooperat Base Intelligent Xiangtan Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab PDL Changsha Peoples R China Ajou Univ Dept Comp & Informat Engn Suwon South Korea Chiba Univ Grad Sch Adv Integrat Sci Chiba Japan

ISBN: (纸本)9781728190747

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： deduplication cloud storage system similarity clustering data partitioning block fingerprint

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A Novel Approach to Optimized Cluster Head Selection in Heterogeneous WSNs for IoT Deployments

A Novel Approach to Optimized Cluster Head Selection in Hete...

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Innovations in High Speed Communication and Signal Processing (IHCSP), international conference on

作者： Amit Kumar Jain Megha Mudholkar Pankaj Mudholkar M. K. Sharma Mohit Tiwari Vandana Roy Department of Electrical & Electronics Engineering Poornima University Jaipur Rajasthan India Department of Computer Engineering Marwadi University Rajkot Gujarat India Faculty of Computer Applications Marwadi University Rajkot Gujarat India Mathematics Chaudhary Charan Singh University Meerut Uttar Pradesh India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi Department of Electronics Communication Gyan Ganga Institue of Technology and Sciences Jabalpur M.P. India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Despite the study proposing the LEACH methodology, its ineffectiveness has been shown. A non-deterministic polynomial (NP) is a difficult issue to optimise for CH. Using an Artificial Immune System (AIS) and Particle Swarm Optimisation (PSO), this study suggests a new routing algorithm. This paper also discusses the best route choice, which may increase network lifetime and reduce energy consumption. We make good use of a lot of metaheuristics techniques, including PSO and AIS. Using principles from neuroscience and social science, the PSO algorithm tackles challenges in a wide range of fields, with a focus on computing and engineering. Particles are creatures that move around the multi-dimensional search distance, where every particle represents a possible solution to the multi-dimensional optimisation issue. The most recent development in AI, AIS takes its cues from the workings of the immune response in living organisms. Immune systems control both the inborn and adapted immunity response's defence mechanisms. Since the latter includes metaphors like variety, self-control, recognition, and memory growth, it is more significant. The AIS outperforms the Heterogeneous Low Energy Adaptive Clustering Hierarchy (LEACH-HPR) and particle swarm optimisation (PSO) in terms of results from experiments. Artificial Immune System (AIS) retains an extensive amount of active nodes while maintaining its efficiency with a 98.5% accuracy rate. AIS is more energy efficient, which means nodes last longer, but it's not much better than PSO and LEACH-HPR in terms of accuracy.

关键词： Wireless sensor networks Accuracy Artificial immune systems Metaheuristics Signal processing algorithms Routing Polynomials Internet of Things Artificial intelligence Particle swarm optimization

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Fast-Fourier-Forecasting Resource Utilisation in distributed systems 29

Fast-Fourier-Forecasting Resource Utilisation in Distributed...

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29th international conference on Computer Communications and Networks (ICCCN)

作者： Pritz, Paul J. Perez, Daniel Leung, Kin K. Imperial Coll London Dept Comp London England

ISBN: (纸本)9781728166070

distributed computing systems often consist of hundreds of nodes (machines), executing tasks with different resource requirements. Efficient resource provisioning and task scheduling in such systems are non-trivial and require close monitoring and accurate forecasting of the state of the system, specifically resource utilisation at its constituent machines. Two challenges present themselves towards these objectives. First, collecting monitoring data entails substantial communication overhead. This overhead can be prohibitively high, especially in networks where bandwidth is limited. Second, forecasting models to predict resource utilisation should be accurate and also need to exhibit high inference speed. Mission critical scheduling and resource allocation algorithms use these predictions and rely on their immediate availability. To address the first challenge, we present a communication-efficient data collection mechanism. Resource utilisation data is collected at the individual machines in the system and transmitted to a central controller in batches. Each batch is processed by an adaptive data-reduction algorithm based on Fourier transforms and truncation in the frequency domain. We show that the proposed mechanism leads to a significant reduction in communication overhead while incurring only minimal error and adhering to accuracy guarantees. To address the second challenge, we propose a deep learning architecture using complex Gated Recurrent Units to forecast resource utilisation. This architecture is directly integrated with the above data collection mechanism to improve inference speed of the presented forecasting model. Using two real-world datasets, we demonstrate the effectiveness of our approach, both in terms of forecasting accuracy and inference speed. Our approach resolves several challenges encountered in resource provisioning frameworks and can also be generically applied to other forecasting problems.

关键词： Load Forecasting Data Collection Communication Efficient Fourier Transforms Complex Gated Recurrent Units Deep Learning

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