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Balancing Energy Efficiency and Portability: Assessing Domain-Specific Languages in Edge Platforms

Balancing Energy Efficiency and Portability: Assessing Domai...

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international conference on distributed computing in sensor systems (dcoss)

作者： Youssef Faqir-Rhazoui Luis Costero Carlos García Computer Science Faculty Complutense University of Madrid Spain

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Edge computing stands as a crucial component of the IoT revolution. IoT benefits significantly from reduced latency, enhancing real-time applications, and time-sensitive processes. However, since edge platforms must be energy-efficient while maintaining high performance in real-time environments, exploiting accelerators becomes pivotal to reduce energy consumption while increasing performance. CUDA or HIP languages are widely used to program GPUs; however, they are limited to specific vendor platforms. In contrast, solutions such as SYCL or Kokkos could benefit from their ability to run across parallel *** paper explores the trade-offs of CUDA, SYCL, and Kokkos in three Edge platforms: Jetson AGX Orin, Jetson Orin Nano, and UP Squared Pro 7000 (Intel). The work conducts an analysis of the power modes of each platform and compares CUDA, SYCL, and Kokkos on those *** experimental results revealed that, on average, there is an increased level of energy consumption compared to native solutions. Specifically, Kokkos demonstrated a 25% higher demand, whereas SYCL exhibited an average increase of 11%. These figures underscore the trade-off that programmers face in pursuit of achieving code portability when most performant solutions are non-crucial.

关键词： Energy consumption Graphics processing units Real-time systems Energy efficiency Internet of Things distributed computing Hip

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Design of Event-Based Resilient distributed Filtering Algorithm for Time-Varying Stochastic systems with Correlated Noises over sensor Networks 8

Design of Event-Based Resilient Distributed Filtering Algori...

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8th ieee international conference on Cloud computing and Intelligence systems, CCIS 2022

作者： Li, Zehao Hu, Jun Li, Jiaxing Gao, Peixia Dong, Ruijie Harbin University of Science and Technology Department of Applied Mathematics Harbin150080 China Harbin University of Science and Technology School of Automation Harbin150080 China

ISBN: (纸本)9781665477352

An event-triggered (ET) recursive distributed filtering approach is designed for a class of stochastic systems with correlated noises. The correlated noises are represented by known matrices and the Kronecker δ function. The ET mechanism that can regulate the sensor information is employed. In addition, the perturbation of the filter gain is considered to suppress the effects of the gain variation on filtering accuracies. An upper bound with respect to the filtering error covariance that can be minimized is obtained by properly choosing the filter gain. Finally, an illustrative example is given to verify the usefulness of the proposed filtering approach. © 2022 ieee.

关键词： sensor networks

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Multi-objective and Randomly distributed Fuzzy Logic-Based Unequal Clustering in Heterogeneous Wireless sensor Networks 16th

Multi-objective and Randomly Distributed Fuzzy Logic-Based U...

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16th international conference on Computational Collective Intelligence (ICCCI)

作者： Adnan, Mohd Ahmad, Tazeem Rafi, Saima Abdullah Vurity, Anudeep Inha Univ Incheon South Korea Univ Southern Queensland Brisbane Qld Australia Edinburgh Napier Univ Edinburgh Midlothian Scotland Univ Teknol Petronas Seri Iskandar Perak Malaysia George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9783031708152;9783031708169

Wireless sensor Networks (WSNs) are a crucial component in the fabric of the Internet of Things (IoT) ecosystem, enabling a myriad of applications ranging from environmental monitoring to precision agriculture and smart cities. However, these sensors are constrained in terms of energy, computing power, and storage which makes reliable communication a critical research challenge. To address these challenges, unequal clustering has emerged as a promising solution where clusters are intentionally formed with varying sizes to accommodate heterogeneous capabilities and energy demands across the network. In this paper, we introduce a novel Multi-Objective and Randomly distributed Fuzzy Logic-based Unequal Clustering (MORF-UC) scheme to address the challenge of energy management and hotspot issues in WSNs. By leveraging fuzzy logic to account for variables such as distance to the base station (BS), residual energy, node concentration, and data forwarding ratio of nodes, this scheme aims to extend network lifetime, energy use, and data transmission reliability while mitigating the hot spot issues. Simulation results demonstrate that the proposed methodology outperforms existing methods such as TTDFP and MOUOC in the energy conservation, network lifetime extension, and throughput enhancement, thereby offering a significant advancement in the field of WSN optimization.

关键词： Wireless sensor networks Unequal clustering Fuzzy logic Network lifetime Multi-objective Network scalability

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An LWE-Based Multi-Key Privacy-Preserving distributed Deep Learning∗ 23

An LWE-Based Multi-Key Privacy-Preserving Distributed Deep L...

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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： Antwi-Boasiako, Emmanuel Zhou, Shijie Liao, Yongjian Danso, Ebenezer Kwaku School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Ghana Institute of Management and Public Administration Computer Science Department Accra Ghana China University for Development Studies Ghana

ISBN: (纸本)9781665494571

Privacy preservation in distributed deep learning (DDL) has received a lot of attention recently. One key approach to preserving privacy for DDL is the use of Homomorphic Encryption (HE), which allows computation on ciphertexts without decryption. Although current applications of HE provide some level of privacy to DDL, they are not post-quantum resistant and multi-key compliant. The use of same key by all participants could lead to collusion attacks and possible leakage of sensitive information from a participant's local dataset in the distributed deep learning setting. Furthermore, most of the privacy solutions provided for the DDL assume that all learning participants must be present before the start of the protocol. Hence, such solutions do not allow a new participant to benefit from the utility of the joint learning of other participants, for which it was not present. To address the above problems, we propose a new privacy preserving solution for DDL that uses an LWE-based Multi-key approach. Our proposed privacy preserving DDL solution is multi-key compliant, post-quantum resistant and has an additional property of allowing a new participant to benefit from the utility of an already trained collaborative deep learning system. We prove that our privacy-preserving distributed deep learning system is secured. We note that our proposed solution generally provides a robust privacy-preserving solution for the DDL and at the same time has great potential to reduce communication and computation costs associated with similar systems. © 2021 ieee.

关键词： Deep learning Resistance Privacy Protocols Costs Collaboration Cryptography

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Message from the dcoss 2022 Workshop Chairs

Message from the DCOSS 2022 Workshop Chairs

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international conference on distributed computing in sensor systems (dcoss)

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Optimizing Federated Learning through Lightweight and Scalable Blockchain

Optimizing Federated Learning through Lightweight and Scalab...

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international conference on distributed computing in sensor systems (dcoss)

作者： Georgios Andronikidis George Niotis Charis Eleftheriadis Konstantinos Kyranou Sotiris Nikoletseas Panagiotis Sarigiannidis Sidroco Holdings Ltd Nicosia Cyprus Dpt. of Computer Engineering & Informatics University of Patras Patras Greece Dpt. of Electrical & Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In recent years, the increased focus on preserving data privacy, the advancements in machine learning, and the ongoing innovations in blockchain technology have sparked significant interest in implementing blockchain-based federated learning frameworks. Proposed solutions primarily focus on addressing the growing size of the blockchain in relation to machine learning algorithms, particularly the number of parameters involved in federated learning. However, the expanding blockchain scale results in increased time requirements, making many solutions impractical. In light of these issues, our research introduces a proof-of-concept for a lightweight, scalable, and modular blockchain network using Hyperledger Fabric to enhance federated learning. Performance evaluations using Hyperledger Caliper show that the proposed blockchain network effectively improves federated learning without compromising latency and throughput. Experimental results affirm blockchain’s proficiency in managing substantial transaction payloads from federated learning clients, especially with resource-intensive machine learning algorithms during training.

关键词： Training Machine learning algorithms Federated learning distributed ledger Computer architecture Throughput Fabrics

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Human Activity Recognition Using Spectrograms of Binary Motion sensor Data

Human Activity Recognition Using Spectrograms of Binary Moti...

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international conference on distributed computing in sensor systems (dcoss)

作者： Nima Seyedtalebi Simone Silvestri Department of Computer Science University of Kentucky Lexington USA

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Human activity recognition is at the basis of several applications in the smart living domain, such as energy management, elder care, and health management. Human activity recognition research can be divided into two categories, depending on the type of sensors used: wearable sensors, such as those found in mobile phones and smart watches, and ambient sensors, such as motion sensors or cameras placed in the environment. Among ambient sensors, binary sensors are often perceived as less invasive than sensors that collect video, audio, or biometric data. However, the performance of classifiers trained on binary sensor data is often lower since the data inherently contains less information. In this paper, we propose a non-intrusive human activity recognition framework that only exploits binary sensor data and results in high classification accuracy. Our approach is inspired by audio and image processing applied to binary sensors. Specifically, we exploit the Short-Time Fourier Transform (STFT) to extract features from binary data. These features are used to train a hybrid machine learning model which pairs Convolutional Neural Network (CNN) with a Long-Short-Term Memory (LTSM) architecture. We use a real dataset of human activities monitored through binary sensor data for evaluating the impact of the features on classifier performance. Results show that the proposed method significantly outperforms state-of-the-art solutions, requiring minimal training data needed to achieve a given level of accuracy.

关键词： Fourier transforms Accuracy Feature extraction Motion detection sensors Human activity recognition Biosensors

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An Improved Clone Node Detection System Based on Enhanced Neural Optimization Strategy Over Wireless sensor Network Environment

An Improved Clone Node Detection System Based on Enhanced Ne...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Sheela, D. Sagar, M. Vidya Rao, S Kondala Ranjith, S. Shahare, D.Y. Al Sherideh, A. Dept of Ece Chennai India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Wireless sensor Networks (WSNs) are widely used in diverse applications, including environmental monitoring and military surveillance. However, their open and distributed nature makes them vulnerable to clone node attacks, where an adversary replicates legitimate nodes to compromise network integrity. This paper presents an improved clone node detection system based on enhanced neural optimization strategies, utilizing various detection protocols such as Energy-efficient Ring-based Clone Detection (ERCD), distributed Hash Table (DHT), Randomly Directed Exploration (RDE), Random Multicast (RM), and Randomized, Efficient, and distributed (RED) methods. The performance of these protocols was evaluated based on detection accuracy, energy consumption, communication overhead, and memory usage across different network sizes. Results showed that the RED method achieved the highest detection accuracy of 99.3% in large networks (1000 nodes), followed closely by ERCD with 96.7%. The DHT and RDE protocols demonstrated lower energy consumption, making them suitable for large-scale, resource-constrained WSNs. The RM method had the lowest detection accuracy (94.3%) and the highest communication overhead. Overall, the proposed neural optimization strategy effectively balances accuracy and resource efficiency, enhancing the robustness of WSNs against clone node attacks. Future work will focus on integrating machine learning models to further optimize detection performance and minimize resource consumption. © 2024 ieee.

关键词： Network security

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Proceedings - 15th Annual international conference on distributed computing in sensor systems, dcoss 2019

Proceedings - 15th Annual International Conference on Distri...

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15th Annual international conference on distributed computing in sensor systems, dcoss 2019

ISBN: (纸本)9781728105703

The proceedings contain 108 papers. The topics discussed include: water in the cloud: understanding water chemistry via the Internet of things;a configurable distributed data analytics infrastructure for the industrial Internet of things;a hybridization of mobile crowdsensing, twitter analytics, and sensor data for the holistic approach of pollen onsets detection;collaborative agent-based detection of DDoS IoT botnets;security and privacy in the Internet of things using blockchain technology;utilizing mobile nodes for congestion control in wireless sensor networks;middleware for real-time event detection and predictive analytics in smart manufacturing;and a network architecture for high volume data collection in agricultural applications.

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distributed Service Discovery over Heterogeneous Robotic systems-of-systems

Distributed Service Discovery over Heterogeneous Robotic Sys...

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international conference on distributed computing in sensor systems (dcoss)

作者： Leonardo Montecchiari Angelo Trotta Lorenzo Gigli Lorenzo Bellone Marco Di Felice Enrico Natalizio Department of Computer Science and Engineering University of Bologna Italy Advanced Research Center on Electronic Systems “Ercole De Castro” University of Bologna Italy Technology Innovation Institute (TII) United Arab Emirates

In robotic swarms, nodes must cooperate in order to accomplish complex tasks. Robot interactions may occur at run-time based on the dynamic network topology and on mission-specific goals. For this reason, service discovery mechanisms are fundamental in order to inform each robot about the functionalities offered by the other peers. Although several network discovery protocols have been proposed so far, none of them fits the unique characteristics of robotic swarms in terms of dynamic topology, communication heterogeneity, and latency requirements. In this paper, we fill such gap by designing, implementing and evaluating a novel service discovery mechanisms for generic Robotic systems-of-systems (RSoS). The study proposes algorithmic and practical contributions. Regarding the first, we describe a novel distributed service discovery algorithm for RSoS which adapts to highly mobile robotic environments while limiting the network overhead and latency. Regarding the latter, we implemented the proposed algorithm within the Uhura swarm robotics framework; as a result, our solution is able to support multi-radio scenarios where robots are provided with heterogeneous Machine-to-Machine (M2M) communication technologies. In addition, we validate our solution through large-scale simulations and a test-bed in which ground robots are able to discover a Federated Learning (FL) task and join it at run-time to improve the accuracy.

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