The advancements in IoT and Machine Learning have been a boon in solving day to day problems in the agricultural world. A robust monitoring and surveillance system to protect these farms from animals like elephants ha...
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In recent years businesses and organizations have experienced an increase in the occurrence of IT-security related threats, causing the compromise of sensitive information, disruption of everyday operations, and ultim...
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ISBN:
(纸本)9781665426060
In recent years businesses and organizations have experienced an increase in the occurrence of IT-security related threats, causing the compromise of sensitive information, disruption of everyday operations, and ultimately financial damage. Meanwhile, these attacks have become more varied and sophisticated, making them increasingly hard to detect. In order to address these issues we initiated the GLACIER 1 1 GLACIER = Attack detection through multidimensional analysis of security-relevant data streams-project [1]. As a part of the project we created an architecture, which can be realized as an in-house operated SIEM system for SMEs. In addition to SIEM-specific tasks like network data collection, normalization, enrichment and storage, the systems main purpose is to supply data to advanced multidimensional analysis algorithms. These provide a novel way to reliably detect security-related anomalies. Found anomalies are displayed in a GUI, which allows giving feedback for tuning the anomaly detection algorithm, while also providing access to network actors for quick incidence responses. The architecture can be implemented using exclusively free, open-source components and is suitable for both information technology (IT) and operational technology (OT) environments.
Denial of Service is the most common attack in Wireless sensor Networks (WSNs). Decision Trees (DT) and Artificial Neural Network (ANN) are used to detect attackers' signature. The most relevant features and the m...
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A wireless sensor network is made up of low-cost, energy-autonomous devices capable of monitoring physical or environmental conditions (temperature, humidity, noise, vibration, pressure, movement, pollution, etc.), pe...
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A wireless sensor network is made up of low-cost, energy-autonomous devices capable of monitoring physical or environmental conditions (temperature, humidity, noise, vibration, pressure, movement, pollution, etc.), performing specific calculations, and collaborate to transmit their data over wireless links to a recipient. With climate change, the occurrence of heavy rains becomes a danger that often leads to flooding. Heavy rains are characterized by their magnitude, duration, severity, and extent, controlled by sensor networks. This work will present a distributed decision support system that can be deployed to assist decision-makers in flood mitigation operations, namely a real-time flood prediction warning system featuring IBM web services. This system is based on the Bayesian approach, allowing decision-makers to take the necessary actions before the disaster occurs. A regression model is used to infer whether an alert is true or false.
Integration of distributed energy resources (DERs) into distribution networks offer various benefits, including lower greenhouse gas (GHG) emissions, loss reduction, and grid reliance reduction. However, as the number...
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ISBN:
(纸本)9781665428743
Integration of distributed energy resources (DERs) into distribution networks offer various benefits, including lower greenhouse gas (GHG) emissions, loss reduction, and grid reliance reduction. However, as the number of distributed energy resources grow, existing distribution networks face a variety of operational and market concerns, including voltage limit breaches, line congestion, visibility issues with DERs, and intermittent energy balances. It is difficult to construct a centralized market that serves several areas to handle such local concerns due to a variety of barriers. So, this work proposes a Peer-to-Peer (P2P) energy trading platform for participants with the capability of active participation in the market, where they can manage their demand and generation and participate as a buyer or seller considering network losses and network fees. For energy transfer, physical network or grid is considered, hence power losses cannot be ignored and grid-related costs are always present in the Peer-to-Peer trading. Grid-related cost consists of the network utilization fees, that is calculated using an electrical distance approach.
This present paper introduces an innovative approach to automatically monitor and control acidity or alkalinity in irrigation water. The system effectively calculated the amount of acidic or alkaline solution that had...
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The proceedings contain 12 papers. The topics discussed include: lightweight measurement and analysis of HPC performance variability;performance modeling of streaming kernels and sparse matrix-vector multiplication on...
ISBN:
(纸本)9781665422659
The proceedings contain 12 papers. The topics discussed include: lightweight measurement and analysis of HPC performance variability;performance modeling of streaming kernels and sparse matrix-vector multiplication on A64FX;evaluating the performance of NVIDIA's A100 ampere GPU for sparse and batched computations;developing models for the runtime of programs with exponential runtime behavior;evaluation of the communication motif for a distributed eigensolver using the SST network simulation tool;accelerating high-order stencils on GPUs;the performance and energy efficiency potential of FPGAs in scientific computing;and performance trade-offs in GPU communication: a study of host and device-initiated approaches.
Real-time performance is one of the most vital metrics for applications in Industrial Internet of Things (IIoTs), and the relative geographic relationship between data sink and wireless sensors has great influence on ...
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ISBN:
(纸本)9781665443326
Real-time performance is one of the most vital metrics for applications in Industrial Internet of Things (IIoTs), and the relative geographic relationship between data sink and wireless sensors has great influence on the real-time performance. Since the locations of wireless sensors are in generally fixed in IIoTs, setting reasonable location for data sink is an efficient way for improving the real-time performance. In this paper, we investigate Non-Orthogonal Multiple Access (NOMA) based IIoTs, and consider how to minimize average access delay by setting suitable location for data sink. We formulate the problem and present an algorithm by mapping the problem into the classic minimum chain covering problem, and make the problem algorithm-tractable. Simulation results reveal that due to the full exploitation of NOMA parallelism, average access delay decreases more than 60% for some typical settings, and it can even reach 70% for the linear network topology.
The dangers caused by fires are very great, causing property damage, casualties and environmental damage. Rapid detection of fire hazards and prompt response measures are the best means to reduce the damage caused by ...
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Biomedical Edge computing is an exciting area of interdisciplinary research involving the Internet of Medical Things (IoMT) sensors and devices with lightweight Artificial Intelligence (AI) logic. To address the rapid...
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ISBN:
(数字)9781728194486
ISBN:
(纸本)9781728194493
Biomedical Edge computing is an exciting area of interdisciplinary research involving the Internet of Medical Things (IoMT) sensors and devices with lightweight Artificial Intelligence (AI) logic. To address the rapidly growing need for smart and portable biomedical devices with localized decision-making capability, we present a proof-of-concept logic-in-sensor design with an arrhythmia analytics use-case. Existing signal processing techniques for arrhythmia analytics such as discrete wave transform (DWT) and non-linear delay differential equation (DDE) lead to high complexity and computational burden on biomedical edge devices due to expensive preprocessing steps. As a solution, we propose a deep learning-based lightweight arrhythmia classification method leveraging a customized one-dimensional (1-D) convolutional neural network (CNN). A rigorous analysis of the proposed method's performances and generalization potential are assessed using four publicly available datasets.
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