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Improving Information Freshness in Edge-Assisted Smart Grids: An AoI-Aware Routing Strategy for Neighborhood Area Networks

Improving Information Freshness in Edge-Assisted Smart Grids...

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IEEE international conference on Industrial Informatics (IndIN)

作者： Hossam Farag Mostafa Kotb Čedomir Stefanović Mikael Gidlund Department of Electronic Systems Aalborg University Denmark Department of Electrical Engineering Aswan University Egypt Department of Informatics Universität Hamburg Germany Mathematics Department Aswan University Egypt Department of Computer and Electrical Engineering Mid Sweden University Sweden

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

The integration of edge controllers into smart grid infrastructures facilitates advanced functionalities and high re-sponsiveness, thereby bolstering the overall efficiency of the energy grid. The freshness of the sensing information received at the edge controller, captured by the Age of Information (AoI) metric, is vital to maintain system stability, where outdated information may lead to incorrect responses to grid conditions, potentially causing inefficiencies or system disruptions. However, the timeliness of the transmitted updates is mainly compromised by the delay and congestion within the routing links in the Neighborhood Area Network (NAN). In this work, we develop an intelligent routing strategy to improve the AoI of the Routing Protocol for Low-power and lossy networks (RPL), which is the common routing protocol for smart grids. Our proposed method is based on an AoI-aware parent selection mechanism, by which a node becomes attached to the parent with the highest probability of delivering a packet within a predefined AoI threshold. The prediction is made based on a supervised machine learning model, trained using the collection of heterogeneous routing metrics. The performance of the proposed method is evaluated via extensive discrete-event simulations and the results show its potency to improve the peak AoI and the AoI violation probability compared to the standard RPL.

关键词： Measurement Computational modeling Predictive models Power system stability Routing Routing protocols Stability analysis Smart grids Sensors Standards

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Adaptively Scaffolding Cognitive Engagement with Batch Constrained Deep Q-Networks 1

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22nd international conference on Artificial Intelligence in Education (AIED) - Mind the Gap - AIED for Equity and Inclusion

作者： Fahid, Fahmid Morshed Rowe, Jonathan P. Spain, Randall D. Goldberg, Benjamin S. Pokorny, Robert Lester, James North Carolina State Univ Raleigh NC 27695 USA US Army CCDC STTC Orlando FL 32826 USA Intelligent Automat Inc Rockville MD 20855 USA

ISBN: (数字)9783030782924

ISBN: (纸本)9783030782924;9783030782917

Scaffolding student engagement is a central challenge in adaptive learning environments. The ICAP framework defines levels of cognitive engagement with a learning activity in terms of four different engagement modes-Interactive, Constructive, Active, and Passive-and it predicts that increased cognitive engagement will yield improved learning. However, a key open question is how best to translate the ICAP theory into the design of adaptive scaffolding in adaptive learning environments. Specifically, should scaffolds be designed to require the highest levels of cognitive engagement (i.e., Interactive and Constructive modes) with every instance of feedback or knowledge component? To answer this question, in this paper we investigate a data-driven pedagogical modeling framework based on batch-constrained deep Q-networks, a type of deep reinforcement learning (RL) method, to induce policies for delivering ICAP-inspired scaffolding in adaptive learning environments. The policies are trained with log data from 487 learners as they interacted with an adaptive learning environment that provided ICAP-inspired feedback and remediation. Results suggest that adaptive scaffolding policies induced with batch-constrained deep Q-networks outperform heuristic policies that strictly follow the ICAP model without RL-based tailoring. The findings demonstrate the utility of deep RL for tailoring scaffolding for learner cognitive engagement.

关键词： Deep reinforcement learning Cognitive engagement ICAP Adaptive learning environments

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Tackling the Credit Assignment Problem in Reinforcement learning-Induced Pedagogical Policies with Neural Networks 1

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22nd international conference on Artificial Intelligence in Education (AIED) - Mind the Gap - AIED for Equity and Inclusion

作者： Ausin, Markel Sanz Maniktala, Mehak Barnes, Tiffany Chi, Min North Carolina State Univ Raleigh NC 27695 USA

ISBN: (数字)9783030782924

ISBN: (纸本)9783030782924;9783030782917

intelligent Tutoring Systems (ITS) provide a powerful tool for students to learn in an adaptive, personalized, and goal-oriented manner. In recent years, Reinforcement learning (RL) has shown to be capable of leveraging previous student data to induce effective pedagogical policies for future students. One of the most desirable goals of these policies is to maximize student learning gains while minimizing the training time. However, this metric is often not available until a student has completed the entire tutor. For this reason, the reinforcement signal of the effectiveness of the tutor is delayed. Assigning credit for each intermediate action based on a delayed reward is a challenging problem denoted the temporal Credit Assignment Problem (CAP). The CAP makes it difficult for most RL algorithms to assign credit to each action. In this work, we develop a general Neural Network-based algorithm that tackles the CAP by inferring immediate rewards from delayed rewards. We perform two empirical classroom studies, and the results show that this algorithm, in combination with a Deep RL agent, can improve student learning performance while reducing training time.

关键词： Pedagogical agent Credit assignment problem Deep reinforcement learning

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A generation of dataset towards an Anomaly-Based Intrusion Detection System to detect Denial of Sleep Attacks in Internet of Things (IoT)

A generation of dataset towards an Anomaly-Based Intrusion D...

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international conference on Advances in ICT for Emerging Regions

作者： Ishara Dissanayake Hesiri Dhammika Weerasinghe Anuradhi Welhenge Dept. of Computer Engineering University of Sri Jayewardenepura Ratmalana Sri Lanka Dept. of Computer Systems Engineering University of Kelaniya Kelaniya Sri Lanka

In this digital era, Wireless Sensor networks (WSN), or else, Internet of Things (IoT) when these sensor networks are accessible via the internet, is a common and widely used technology. These tiny sensor nodes in these networks help to enhance and automate processes in various fields such as, smart homes and cities, healthcare, smart vehicular networks, Smart Grids and so on. Although this technology is widely used in many fields, these tiny devices are equipped with limited amounts of resources. Limited capacity of battery power is one of the major concerns with IoT when it comes to some specific applications. In addition to this, this limited battery capacity is vulnerable for power depletion attacks which can reduce the lifetime of these devices significantly. So, there is an urgent need of solid solution to preserve this battery power as much as possible. One proper solution for this is, a machine learning based intelligent intrusion detection system that can detect power depletion attacks against these IoT applications. However, the lack of a proper dataset to train and evaluate these machine learning models appears as a major obstacle for an intelligent intrusion detection system. So, in this paper, we are going to discuss how we can use COOJA simulator to generate a proper dataset that can be used to develop and evaluate machine learning models to detect Denial of Sleep attacks against Wireless Sensor Networks. Here, we are going to discuss 3 strategies (UDP flood attack, wormhole attack and externally generated legitimate request attack) to simulate Denial of Sleep attacks for WSNs, so that, any person can generate their own dataset according to their requirements. At the end of the paper, we have evaluated each strategy to deplete the power of the sensor nodes. Then, our conclusions are given.

关键词： Wireless sensor networks data security Urban areas Intrusion detection Machine learning Solids Batteries

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An Efficient Alignment Method for Scanning data of 77GHz mmWave Imaging Radar

An Efficient Alignment Method for Scanning Data of 77GHz mmW...

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international conference on Communication Technology (ICCT)

作者： Tongkai Xu Chunling Hao Qingshui Guo Kun Yin Tang Hu Lijie Yang Research Center for Engineering Technologies Zhejiang Lab Hangzhou China Research Center for Intelligent Chips and Devices Zhejiang Lab Hangzhou China Research Center for Scientific Technologies Zhejiang Lab Hangzhou China

Millimeter-wave (mmWave) imaging is becoming more necessary in security inspection as it penetrates fabrics, leathers, and other materials and is harmless at appropriate power levels. Currently, better restoration of the original scene from the radar echoes with interference is prime attention of researchers. However, with the short wavelength of mmWave, a slight positional bias can dramatically affect the focusing performance of imaging. Notably, mechanical scanning generally obtains a large virtual aperture in near-field mmWave high-resolution imaging applications. However, the mismatch bias between automated scanning and radar parameters accumulates and aggravates the image defocus. This paper studies the scanning bias of the 77 GHz mmWave imaging system based on the 3DRIED dataset, analyze the mismatch bias in both the beginning moment and velocity, and gives the corresponding model. Meanwhile, referring to the range alignment method of ISAR, this paper presents a scanning data alignment algorithm only using MISO radar echoes. The algorithm calculates the relative bias of two adjacent scanning by comparing the data of horizontal scanning to get the spatial distribution of all sampling points. By comparing the results before and after data alignment, the algorithm shows that as a pre-processing method, it can effectively improve the focusing performance of the high-resolution scene reconstruction.

关键词： Focusing Interference Radar imaging Millimeter wave radar MISO communication Inspection Image restoration

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Machine learning Approach for Cluster Head Selection in Internet of Things-Based Wireless Sensor Network

Machine Learning Approach for Cluster Head Selection in Inte...

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international Workshop on Research and Education in Mechatronics (REM)

作者： Ala' Khalifeh Belal Al Mohammad School of Electrical Engineering and Information Technology German Jordanian University Amman Jordan

ISBN: (数字)9798331505974

ISBN: (纸本)9798331505981

Wireless Sensor Network (WSN) which is considered a cost-effective wireless network that can be used with several Internet of Things (loT) applications, consists of several nodes equipped with sensors that measure various parameters of an Area of Interest (Aol), such as temperature, humidity, pressure, and air quality. These nodes collect data and transmit them to a remote center for further analysis and decision-making. In this paper, we have studied the energy model used in WSNs and created a new metric that uses the predictive remaining energy of the sensor nodes, named as Cluster Head-Machine learning (CH-ML) model, which predicts the remaining energy of the node that can be estimated based on the current remaining energy and the expected node activity/traffic in the future. This new metric will be used to determine the best node to serve as a cluster head, which will prolong the network lifetime. Furthermore, to show the effectiveness of the new approach, a comparison for the network performance with the standard Low-Energy Adaptive Clustering Hierarchy (LEACH) clustering model is depicted, where the proposed prediction-based model outperforms LEACH in terms of the First Node Dies (Fnd), Half of the Nodes Die (Hnd), and Last Node Dies (Lnd) metrics.

关键词： Temperature sensors Temperature measurement Wireless sensor networks Adaptation models Wireless networks Machine learning Predictive models Magnetic heads Sensors Sustainable development

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Performance of Sine Cosine Algorithm for ANN Tuning and Training for IoT Security 22nd

Performance of Sine Cosine Algorithm for ANN Tuning and T...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Bacanin, Nebojsa Zivkovic, Miodrag Hajdarevic, Zlatko Janicijevic, Stefana Dasho, Anni Marjanovic, Marina Jovanovic, Luka Singidunum University Danijelova 32 Belgrade11000 Serbia Luarasi University Rruga e Elbasanit 59 Tirana1000 Albania

ISBN: (纸本)9783031274084

Recent advances in Internet technology ensured that the World Wide Web is now essential for millions of users, offering them a variety of services. As the number of online transactions grows, the number of hostile users who are trying to manipulate with sensitive data and steal user’s private details, credit card data and money is also rising fast. To fight this threat, security companies developed a variety of security measures, aiming to protect both end user and business offering online services. Nowadays, machine learning methods are common part of the most of the contemporary security solutions. The research goal of this paper is proposal of the hybrid technique that uses multi-layer perceptron tuned by the well-known sine cosine algorithm. Sine cosine metaheuristics is utilized to determine the neural cell count within the hidden layer, and to obtain the weights and biases. The capabilities of the observed method were validated on a public web security benchmark dataset, and compared to the results obtained by other elite metaheuristics that have been tested under the same conditions. The simulation findings indicate that the introduced model surpassed other observed techniques, showing great deal of potential for practical use in this domain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sensitive data

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FPGA based Smart and Sustainable Agriculture

FPGA based Smart and Sustainable Agriculture

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international conference on VLSI Design

作者： Shyam Peraka SK Irfan Ali Durga Vasu Mogili Ashok Kumar Palivela Sudheer Reddy Jyotsna Bavisetti Dhanush Reddy Y Dept. of Electronics and Communication Engineering RGUKT-Nuzvid India Dept.of Computer Science and Engineering RGUKT-Nuzvid India

The agriculture sector is one of the key areas of concern interlinked with most of the Sustainable Development Goals (SDGs), as stated by most organizations. However, technological advancements and agricultural intervention, particularly in developing and underdeveloped countries, are still in their early stages. IoT, cloud computing, data analysis, and other technologies are being widely used in most areas, where they are bringing significant changes in outcomes compared to previous conventional practices. It is also observed that there have been some initiations in bringing such technologies into agriculture to assist farmers, but they are not comprehensive and real-time working prototypes. Hence, we have come up with a novel, comprehensive solution to address different issues. The focus of the proposed paper is to develop and design a real-time prototype model implementing the following: 1. Control of the irrigation system based on crop stage requirements and other factors such as soil nature, humidity levels, rainfall levels, and so on. 2. Detection of weeds and disease using image processing and machine learning algorithms 3. Identification and recommendation of suitable crops based upon the nature of the soil, fertility levels, climatic conditions, water availability, etc. 4. Voice-based responsive system-mobile application or web-based application. The above tasks were successfully implemented and tested on the prototype developed with the help of the DE10 Nano Cyclone FPGA board. The sensors used in the project include the NPK sensor, moisture sensor, pH sensor, electric conductivity sensor, and water level sensor. Azure cloud services are used for the deployment of the website. The farmer is able to get the voice-based response through a mobile application developed for this purpose.

关键词： Cloud computing Irrigation Crops Prototypes Soil Real-time systems Cyclones

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Multi-sensor-based Target Pose Estimation for Autonomous Precision Perching of Nano Aerial Vehicles 22

Multi-sensor-based Target Pose Estimation for Autonomous Pre...

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22nd international conference on Control, Automation and Systems, ICCAS 2022

作者： Do, Truong-Dong Xuan-Mung, Nguyen Nguyen, Ngoc-Phi Lee, Ji-Won Lee, Yong-Seok Lee, Seok-Tae Hong, Sung-Kyung Sejong University Department of Convergence Engineering for Intelligent Drone Seoul05006 Korea Republic of Sejong University Faculty of Mechanical and Aerospace Engineering Seoul05006 Korea Republic of Korea Institute of Robotics and Technology Convergence Human-Robot Interaction Research Center Pohang37553 Korea Republic of Sejong University Department of Aerospace Engineering Seoul05006 Korea Republic of

ISBN: (纸本)9788993215243

Nano Aerial Vehicles have become widely used for a variety of complex missions due to their mobility and the ability to access hard-to-reach areas. In most cases, these tasks require the vehicles to land on ground targets or perch on platforms mounted on diverse surfaces. Considering the surface the vehicle will reach, controlling perching is obviously a challenging task. Besides, the reliability of target position and direction estimation has a significant impact on perching performance. In this paper, a multi-sensor-based target pose estimation for autonomous precision perching of nano drones is proposed. First, the perching target, a cube cage containing a small marker inside a larger one, is designed to enhance pose estimation capability at a wide range of distances. Second, we constructed a nano drone with an upward monocular camera and a 5-direction multi-ranger deck. Next, the flying vehicle's pose toward the perching target is calculated, followed by a Kalman filter for filtering and estimating the missing data. Finally, we introduced an algorithm to merge the pose data from multiple sensors when drones approach close to the target. Real measurements are conducted on the testbed. The experimental results demonstrated the utility and potential of the adopted approach with millimeter-level precision. © 2022 ICROS.

关键词： Drones

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A Broadband Dual-Beam PIFA Antenna With Modified CPW Feed for Secure Sub-6 GHz 5G Applications

A Broadband Dual-Beam PIFA Antenna With Modified CPW Feed fo...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Saqer S. Alja'afreh Amjaad T. Altakhaineh Aser M. Matarneh Omar R. Daoud Electrical and Computer Engineering American International University Aljahra Kuwait Data Science and Machine Learning Department Pioneers International Academy Amman Jordan

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This work presents a broadband and low-profile printed Inverted-F antenna (PIFA). The design deploys a 1.5 mm thick FR-4 substrate with dimensions ( $\mathbf{4 0} \times \mathbf{2 5} \mathbf{ m m}^{2}$ ). A broadband operation is achieved through modifying a coplanar waveguide feed (CPW). Two $\lambda / 8$ stubs are created to enhance the entire operational frequency bandwidth. The simulation result of the reflection parameter achieves about 87 % fractional bandwidth ( $2.05-5.2 \text{GHz}$ ) based on the $\mathbf{- 1 0} \mathbf{d B}$ impedance bandwidth criterion. The design has total radiation efficiency above $\mathbf{7 2 \%}$ . Far-field radiation patterns results show a dualpattern (pattern diversity) among excited resonance modes frequencies. Such results make the proposed antenna an excellent candidate for current secure- and selective sub-6 GHz 5G communications systems.

关键词： 5G mobile communication Resonant frequency Bandwidth Directive antennas Resonance Broadband communication Coplanar waveguides Broadband antennas Impedance Antenna radiation patterns

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