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A reconfigurable hardware platform for low-power wide-area wireless sensor networks 1

A reconfigurable hardware platform for low-power wide-area w...

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1st international conference on Emerging Electrical Energy, Electronics and computing Technologies 2019, ICE4CT 2019

作者： Phong Truong, Tuyen Toan Le, Hai Thi Nguyen, Tram College of Engineering Technology Can Tho University Can Tho City Viet Nam

Low power, large-area wireless sensor networks (WSNs) have been experiencing rapid growth in a wide range of applications, such as environmental monitoring applications and natural disaster early warning systems. The WSN consists of numerous nodes with sensing, computation, actuation, and radio communication capabilities. Dynamically reconfigurable hardware and context-aware operation to optimize performance and functionality and to prolong the lifetime of sensor nodes are critical goals in the design of distributed sensing systems, especially for battery-based systems being deployed in complex geographic areas where are hard to approach. In recent years, Programmable System-on-Chip (PSoC) technology is emerging as an efficient solution for achieving the dynamic hardware reconfiguration of a sensor node in a single chip. That aims at optimizing the use of resources, minimizing of energy waste, and system size according to the requirements of the applications. This paper presents the implementation of a configurable hardware platform for remote sensing systems based on PSoC and LoRa technology. The proposed hardware design stands out for high performance, ultralow-power, providing long-range communication as well as reconfigurable interfaces in communicating and manipulating with various types of sensors and actuators. The experiments to evaluate the functional requirements and energy consumption of the design were investigated with positive results. © Published under licence by IOP Publishing Ltd.

关键词： sensor nodes

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Optimal DG Allocation in a Microgrid Using Droop-Controlled Load Flow 1st

Optimal DG Allocation in a Microgrid Using Droop-Controlled ...

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1st international conference on Intelligent computing Techniques for Smart Energy systems (ICTSES)

作者： Uniyal, Ankit Sarangi, Saumendra Natl Inst Technol Srinagar Uttarakhand India

ISBN: (纸本)9789811502149;9789811502132

With increasing autonomy of the microgrid and the growing load, DG allocation is a mandatory exercise. When output of DGs are droop controlled, their bus location and corresponding droops may affect the system voltage and frequency profile. To analyse such a system, droop-based load flow is required. In the present work, optimal placement of droop-based DGs is done in an islanded/autonomous radial microgrid system with an objective to minimise deviations of voltage and frequency from the nominal values. The problem is formulated as a bi-objective optimization problem and is solved using a multi-objective heuristic-based optimization technique, namely, NSGA-II and FSM. The proposed analysis is carried out for an ieee 33 bus radial distribution system modified as an autonomous microgrid. The results show the applicability of the proposed analysis.

关键词： distributed generation (DG) Fuzzy satisfying method (FSM) Load flow Microgrid Non-dominated sorted genetic algorithm (NSGA-II)

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HADES: a Hybrid Anomaly Detection System for Large-Scale Cyber-Physical systems 5

HADES: a Hybrid Anomaly Detection System for Large-Scale Cyb...

引用

5th international conference on Fog and Mobile Edge computing (FMEC)

作者： Alwan, Ahmed Abdulhasan Ciupala, Mihaela Anca Baravalle, Andres Falcarin, Paolo Univ East London Sch Architecture Comp Sci & Engn London England Akamai Technol Ltd London England

ISBN: (纸本)9781728172163

Smart cities rely on large-scale heterogeneous distributed systems known as Cyber-Physical systems (CPS). Information systems based on CPS typically analyse a massive amount of data collected from various data sources that operate under noisy and dynamic conditions. How to determine the quality and reliability of such data is an open research problem that concerns the overall system safety, reliability and security. Our research goal is to tackle the challenge of real-time data quality assessment for large-scale CPS applications with a hybrid anomaly detection system. In this paper we describe the architecture of HADES, our Hybrid Anomaly DEtection System for sensors data monitoring, storage, processing, analysis, and management. Such data will be filtered with correlation-based outlier detection techniques, and then processed by predictive analytics for anomaly detection.

关键词： Data Quality Quality of Service Cyber-Physical System Smart Cities Anomaly Detection Predictive Analysis Wireless sensor Network

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Gaia-Agstream: An Explainable AI Platform for Mining Complex Data streams in Agriculture 1

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1st international conference on Smart and Sustainable Agriculture (SSA)

作者： Schoenke, Jan Aschenbruck, Nils Interdonato, Roberto Kanawati, Rushed Meisener, Ann-Christin Thierart, Francois Vial, Guillaume Atzmueller, Martin LMIS AG Osnabruck Germany Osnabruck Univ Osnabruck Germany Cirad Montpellier France Univ Sorbonne Paris Nord Villetaneuse France MyEasyFarm Bezannes France

ISBN: (数字)9783030882594

ISBN: (纸本)9783030882594;9783030882587

We present a position paper about our concept for an artificial intelligence (AI) and data streaming platform for the agricultural sector. The goal of our project is to support agroecology in terms of carbon farming and biodiversity protection by providing an AI and data streaming platform called Gaia-Agstream that accelerates the adoption of AI in agriculture and is directly usable by farmers as well as agricultural companies in general. The technical innovations we propose focus on smart sensor networks, unified uncertainty management, explainable AI, root cause analysis and hybrid AI approaches. Our AI and data streaming platform concept contributes to the European open data infrastructure project Gaia-X in terms of interoperability for data and AI models as well as data sovereignty and AI infrastructure. Our envisioned platform and the developed AI components for carbon farming and biodiversity will enable farmers to adopt sustainable and resilient production methods while establishing new and diverse revenue streams by monetizing carbon sequestration and AI ready data streams. The open and federated platform concept allows to bring together research, industry, agricultural start-ups and farmers in order to form sustainable innovation networks. We describe core concepts and architecture of our proposed approach in these contexts, outline practical use cases for our platform and finally outline challenges and future prospects.

关键词： Explainable AI sensor networks distributed systems Uncertainty management Complex networks Machine learning Anomaly detection Root cause analysis Knowledge graph Data quality Agroecology Carbon farming Biodiversity Data fusion

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ADDAI: Anomaly Detection using distributed AI

ADDAI: Anomaly Detection using Distributed AI

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ieee international conference on Networking, Sensing and Control

作者： Maede Zolanvari Ali Ghubaish Raj Jain Computer Science and Engineering Washington University St. Louis MO USA

ISBN: (纸本)9781665440493

When dealing with the Internet of Things (IoT), especially industrial IoT (IIoT), two manifest challenges leap to mind. First is the massive amount of data streaming to and from IoT devices, and second is the fast pace at which these systems must operate. distributed computing in the form of edge/cloud structure is a popular technique to overcome these two challenges. In this paper, we propose ADDAI (Anomaly Detection using distributed AI) that can easily span out geographically to cover a large number of IoT sources. Due to its distributed nature, it guarantees critical IIoT requirements such as high speed, robustness against a single point of failure, low communication overhead, privacy, and scalability. Through empirical proof, we show the communication cost is minimized, and the performance improves significantly while maintaining the privacy of raw data at the local layer. ADDAI provides predictions for new random samples with an average success rate of 98.4% while reducing the communication overhead by half compared with the traditional technique of offloading all the raw sensor data to the cloud.

关键词： Cloud computing Data privacy Privacy Costs Scalability Robustness sensors

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A distributed XACML based Access Control Architecture for IoT systems 1

A Distributed XACML based Access Control Architecture for Io...

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1st international conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2020

作者： Charaf, Lalla Amina Alihamidi, Imam Addaim, Adnane Madi, Abdessalam Ait Ibn Tofail University National School of Applied Sciences Kenitra Morocco

ISBN: (数字)9781728149790

ISBN: (纸本)9781728149790

IoT (Internet of Things) is the new technological revolution that enables the high connectivity of intelligent objects or devices both to each other or/and to humans. However, the major difficulty found in the use of this IoT technology is the lack of networking reference architectures and standards for this new paradigm. Moreover, security and privacy protection are foremost important issue to gain trust of IoT users. In this paper, we firstly present a comparative study of IoT security architectures previously proposed in the literature, then we investigate different works done in the field, based on the CIA triad (Confidentiality, Integrity and Availability) and the associated algorithms. The last part of this paper is devoted to study the proposed distributed model in order to face the IoT access control problems using the XACML language. © 2020 ieee.

关键词： Access control

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A study on the Application of One Dimension Convolutional Neural Network for Classification of Gestures from Surface Electromyography Data

A Study on the Application of One Dimension Convolutional Ne...

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ieee distributed computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Praahas Amin Airani Mohammad Khan Mangalore University Mangalore India

ISBN: (纸本)9781665429528

Myoelectric control systems are gaining popularity with the availability of commercial, low-cost, surface electromyography sensors. These systems can be used for gesture recognition which finds application in human-machine interfaces. The gestures are recognized using pattern recognition algorithms. Machine learning or deep learning techniques can be applied for the classification of gestures. In this paper, a user-specific 1-Dimensional Convolution Neural Network is proposed for the classification of Surface Electromyography data recorded using a commercially available surface electromyography recording device to perform offline classification of 5 hand gestures using limited data of less than 400 samples. An average accuracy of 82%±3% was achieved during the study after cross-validation of the data using 5-fold stratified cross-validation.

关键词： Performance evaluation Machine learning algorithms distributed databases Gesture recognition Very large scale integration Electromyography sensor systems

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Maintaining Information Freshness in Power-Efficient status Update systems

Maintaining Information Freshness in Power-Efficient Status ...

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39th ieee international conference on Computer Communications (ieee INFOCOM)

作者： Rafiee, Parisa Zou, Peng Ozel, Omur Subramaniam, Suresh George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA

ISBN: (纸本)9781728186955

This paper is motivated by emerging edge computing systems which consist of sensor nodes that acquire and process information and then transmit status updates to an edge receiver for possible further processing. As power is a scarce resource at the sensor nodes, the system is modeled as a tandem computation-transmission queue with power-efficient computing. Jobs arrive at the computation server with rate A as a Poisson process with no available data buffer. The computation server can be in one of three states: (i) OFF: the server is turned off and no jobs are observed or processed, (ii) ON-Idle: the server is turned on but there is no job in the server, (iii) ON-Busy: the server is turned on and a job is processed in the server. These states cost zero, one and p(c) > 1 units of power, respectively. Under a long-term power constraint, the computation server switches from one state to another in sequence: first a deterministic T-o, time units in OFF state, then waiting for a job arrival in ON-Idle state and then in ON-Busy state for an independent identically distributed compute time duration. The transmission server has a single unit data buffer to save incoming packets and applies last come first serve with discarding as well as a packet deadline to discard a sitting packet for maintaining information freshness, which is measured by the Age of Information (AoI). Additionally, there is a monotonic functional relation between the mean time spent in ON-Busy state and the mean transmission time. We obtain closed-form expressions for average AoI and average peak AoI. Our numerical results illustrate various regimes of operation for best AoI performances optimized over packet deadlines with relation to power efficiency.

关键词： sensor nodes

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Building and Evaluating Federated Models for Edge computing 16

Building and Evaluating Federated Models for Edge Computing

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16th international conference on Network and Service Management (CNSM) / 2nd international Workshop on Analytics for Service and Application Management (AnServApp) / 1st international Workshop on the Future Evolution of Internet Protocols (IPFuture)

作者： Amannejad, Yasaman Mt Royal Univ Dept Math & Comp Calgary AB Canada

ISBN: (纸本)9783903176317

Today's state-of-the-art machine learning (ML) techniques, such as deep learning (DL) networks are typically trained using cloud platforms, leveraging elastic scalability of the cloud. For such processing, data from various sources need to be transferred to a cloud server. While this works well for some application domains, it is not suitable for all applications due to concerns about latency, connectivity, and privacy. For example, sharing life logging photos and videos from cellphones and wearable devices can cause privacy concerns for users, and transferring the unstructured data can burden the communication network. With the increase of such applications, federated learning (FL) is proposed as a distributed ML solution for learning on edge devices, such as cellphones and wearable devices. In FL, clients collaboratively train a model on their device without sharing their data. Each client trains a local model with their data and shares the model parameters with a FL server to aggregate and build a global model. Shifting from traditional ML techniques to federated solutions requires comparing these two approaches. Moreover, users need to study the performance of FL models to decide if federation is feasible for their learning task. In this paper, we propose an automated solution to compare centrally trained DL models with federated solutions. The tool allows users to easily analyze the accuracy of federated models for their learning task and study the effect of the federated parameters. We show the features of our tool building central and federated DL models from an input model structure for recognizing images in the MNIst benchmark dataset.

关键词： distributed Machine Learning Federated Learning Edge computing

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Security on the Farm: Safely Communicating with Legacy Agricultural Instrumentation 15

Security on the Farm: Safely Communicating with Legacy Agric...

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15th Annual international conference on distributed computing in sensor systems (DCOSS)

作者： Bell, Tim Chamberlain, Roger D. Chambers, Mike Rieck, Brian steinbrueck, Todd BECS Technol Inc St Louis MO 63132 USA Washington Univ Dept Comp Sci & Engn St Louis MO 63110 USA AGCO Corp Assumption IL USA

ISBN: (纸本)9781728105703

The notion of IoT has taken the farm by storm. Irrigation is controlled to the resolution of individual plants, fertilizer is dispensed based on yields from previous growing cycles, and livestock feed, water, and environment are all monitored and under automatic control. Much of the equipment that performs this monitoring and control, however, predates the Internet of Things, and integrating this legacy equipment into modern communication systems is fraught with issues, particularly security issues. We describe our approach to providing secure, ubiquitous connectivity to a variety of previously isolated systems on the farm, enabling these systems to safely become part of the IoT.

关键词： IoT agriculture

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