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共 1274 条记录，以下是131-140 订阅

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CAVIaR Crayfish Algorithm Enabled Deep Kronecker Network for Wormhole Attack Mitigation on Network Control System

CAVIaR Crayfish Algorithm Enabled Deep Kronecker Network for...

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Emerging Technology (INCET), International conference for

作者： Avina. A M.R. Geetha Department of Computer science and engineering Ponjesly College of Engineering Nagercoil TamilNadu India Department of Electronics and Communication Engineering Ponjesly College of Engineering Nagercoil TamilNadu India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Wireless sensor Network (WSN) is expansively employed in the Network Control System (NCS). Wormhole (WH) attack detection is a burdensome process, specifically over WSN. A distributed passive state of WH attack makes the network extremely challenging to identify. Here, the CAVIaR crayfish algorithm enabled Deep Kronecker Network (CCA-DKN) is introduced for mitigation of WH attack on NCS. Firstly, the WSN model is simulated and routing is executed by Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. then, three stages namely in-band (IB)- WH detection, out-of-band (OB)-WH detection, and Neighbour Ratio threshold (NRT) are accomplished for WH detection. After the accomplishment of the NRT stage, the transmission range stage is performed in OB- WH detection. thereafter, the Packet Delivery Ratio (PDR) and Round Trip Time (RTT) are done on IB- WH detection. Next, the detection of WH attack is performed by ResNeXt-DSAE. Furthermore, ResNeXt-DSAE is designed by combining ResNeXt and Deep Stacked Autoencoder (DSAE). Lastly, attack mitigation is executed, whereupon a percentage of data reduction is carried out by exploiting the Deep Kronecker Network (DKN) and it is trained by the CAVIaR crayfish algorithm (CCA). Furthermore, CCA is an assimilation of Conditional Autoregressive Value at Risk (CAViaR) and Crayfish Optimization Algorithm (COA). Additionally, CCA-DKN achieved high PDR and throughput of 0.769 and 0.533Mbps as well as low delay of 0.712sec.

关键词： Wireless sensor networks Protocols networked control systems Prevention and mitigation Clustering algorithms throughput Routing

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Research and Design of Intelligent Inspection Robot for Large-Scale Chicken Farms

Research and Design of Intelligent Inspection Robot for Larg...

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Machine Learning and Human-Computer Interaction (MLHMI), International conference on

作者： Qingle Quan thelma D. Palaoag Hanqing Sun College of Information Technology and Computer Science University of the Cordilleras Baguio City Philippines

ISBN: (数字)9798350375763

ISBN: (纸本)9798350375770

In order to solve the problems of chicken status monitoring and farming decision making faced by intelligent livestock and poultry farming, this study aims to develop an intelligent inspection robot for chicken farms to explore the solution of intelligent inspection of chicken coops using Internet information technology and Internet of things (loT) technology. this study uses a function-driven development approach, using basic hardware such as LiDAR, remote sensing technology devices, sensor fusion hardware, communication devices, power supply, motors and actuators, and ROS (Robot Operating System) embedded development platform and Python language to complete the functional development of the system, and through the running test in the chicken farm to determine the system's usability and the system was tested in a chicken farm to determine the usability and reliability of the *** includes the use of LiDAR, infrared sensors, ultrasonic sensors, etc. to realize basic data acquisition in the chicken house, and a sensor fusion unit to integrate data from different sensors into a common environment model to create a comprehensive view of the robot's surroundings. the robot uses the sensor data to determine its current position, which is typically accomplished through LiDAR and map matching. the robot monitors the sensor data in real time to detect the presence of obstacles or changes in position for real-time obstacle avoidance. the robot can communicate with a remote operator or a centralized control system to transmit real-time data and receive task instructions. the robot can feedback the task execution and collected data to the operator or central system for further analysis and decision making. Users were satisfied with the usability, ease of use and reliability of the system through actual operational tests on the farm. the system realizes real-time monitoring of the chicken house, which can effectively enhance the level of intelligent farming, imp

关键词： Laser radar Inspection sensor fusion Robot sensing systems Real-time systems Reliability Usability

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Towards secure backscatter-based in-body sensor networks: Poster abstract 20

Towards secure backscatter-based in-body sensor networks: Po...

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18th acm conference on embedded networked sensor systems, SenSys 2020

作者： Voigt, thiemo Rohner, Christian Yan, Wenqing Joseph, Laya Hylamia, Sam Asan, Noor Badariah Mandal, Bappaditya Perez, Mauricio Augustine, Robin Uppsala University Sweden Universiti Teknikal Malaysia Melaka CeTRI Fkekk Malaysia

ISBN: (纸本)9781450375900

In the near future more and more people will have multiple implants to handle their diseases. the implants benefit from being connected using in-body sensor networks. We have previously shown that RF communication through human adipose (fat) tissue is feasible. In this poster, we argue why we believe that backscatter communication within this fat channel is possible. As security is of utmost importance for in-body communication, we also discuss how backscatter-based in-body networks can be secured. © 2020 acm.

关键词： Body sensor networks

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Capricorn: Towards Real-Time Rich Scene Analysis Using RF-Vision sensor Fusion 22

Capricorn: Towards Real-Time Rich Scene Analysis Using RF-Vi...

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Proceedings of the 20th acm conference on embedded networked sensor systems

作者： Ziqi Wang Ankur Sarker Jason Wu Derek Hua Gaofeng Dong Akash Deep Singh Mani Srivastava University of California

ISBN: (纸本)9781450398862

Video scene analysis is a well-investigated area where researchers have devoted efforts to detect and classify people and objects in the scene. However, real-life scenes are more complex: the intrinsic states of the objects (e.g., machine operating states or human vital signals) are often overlooked by vision-based scene analysis. Recent work has proposed a radio frequency (RF) sensing technique, wireless vibrometry, that employs wireless signals to sense subtle vibrations from the objects and infer their internal states. We envision that the combination of video scene analysis with wireless vibrometry form a more comprehensive understanding of the scene, namely "rich scene analysis". However, the RF sensors used in wireless vibrometry only provide time series, and it is challenging to associate these time series data with multiple real-world objects. We propose a real-time RF-vision sensor fusion system, Capricorn, that efficiently builds a cross-modal correspondence between visual pixels and RF time series to better understand the complex natures of a scene. the vision sensors in Capricorn model the surrounding environment in 3D and obtain the distances of different objects. In the RF domain, the distance is proportional to the signal time-of-flight (ToF), and we can leverage the ToF to separate the RF time series corresponding to each object. the RF-vision sensor fusion in Capricorn brings multiple benefits. the vision sensors provide environmental contexts to guide the processing of RF data, which helps us select the most appropriate algorithms and models. Meanwhile, the RF sensor yields additional information that is originally invisible to vision sensors, providing insight into objects' intrinsic states. Our extensive evaluations show that Capricorn real-timely monitors multiple appliances' operating status with an accuracy of 97%+ and recovers vital signals like respirations from multiple people. A video (https://***/b-5nav3Fi78) demonstrates the capabil

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Indoor Place Prediction on Smart Phones 22

Indoor Place Prediction on Smart Phones

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Proceedings of the 20th acm conference on embedded networked sensor systems

作者： Pritam Sen Xiaopeng Jiang Qiong Wu Manoop Talasila Wen-Ling Hsu Cristian Borcea New Jersey Institute of Technology AT&T Research Lab

ISBN: (纸本)9781450398862

High-accuracy and low-latency indoor place prediction for mobile users is crucial to enable applications for assisted living, emergency services, smart homes, and augmented reality. Previous studies on indoor place prediction use complex infrastructure with multiple visual/wireless anchors or multiple wireless access points. these localization techniques are difficult to deploy, may negatively impact user privacy through location tracking, and their data collection is not suitable for personalized place prediction. To solve these challenges, this paper proposes GoPlaces, a novel app that fuses inertial sensor data with WiFi-RTT estimated distances to predict the future indoor places visited by a user. GoPlaces does not require any infrastructure, except for one cheap off-the-shelf WiFi access point that supports ranging with RTT. In addition, it enables personalized place naming and prediction through its on-the-phone data collection and protects users' location privacy because user's data never leaves the phone. GoPlaces uses an attention-based bidirectional long short-term memory model to detect user's current trajectory, which is then used together with historical information stored in a prediction tree to infer user's future places. We implemented GoPlaces in Android and evaluated it in several indoor spaces. the experimental results demonstrate prediction accuracy as high as 92%, low latency, and low resource consumption on the phones.

关键词： human mobility sensor fusion time series analysis indoor place prediction wifi-RTT deep learning smart phones

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np-CECADA: Enhancing Ubiquitous Connectivity of LoRa Networks 18

<i>np</i>-CECADA: Enhancing Ubiquitous Connectivity of LoRa ...

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18th IEEE International conference on Mobile Ad hoc and Smart systems (IEEE MASS)

作者： Kouvelas, Nikolaos Prasad, R. Venkatesha Yazdani, Niloofar Lucani, Daniel E. Delft Univ Technol Embedded & Networked Syst Delft Netherlands Aarhus Univ DIGIT Aarhus Denmark Aarhus Univ Elect & Comp Engn Aarhus Denmark

ISBN: (纸本)9781665449359

Long Range Wide Area Networks (LoRaWAN) offer ubiquitous communications for the Internet of things (IoT). However, there are many challenges in rolling out LoRaWAN - mainly scalability, energy efficiency, Packet Reception Ratio (PRR), and keeping the channel access as simple as unslotted ALOHA. To this end, we design non-persistent Capture Effect Channel Activity Detection Algorithm (np-CECADA), which is a novel, distributed protocol for the MAC layer of LoRaWAN. It utilizes Channel Activity Detection (CAD), which is a built-in imperfect mechanism for channel sensing and minimal feedback from the gateways. In np-CECADA each device independently adapts backoff times based on the traffic in its vicinity and the transmission power based on the heuristically inferred probability of capturing the channel. To achieve this, first, we carried out an extensive on-field evaluation to measure the effectiveness of CAD and capture effect in LoRa. Using them we designed np-CECADA and developed ns-3 modules. Packet Reception Ratio of np-CECADA is 15.74x and 5.13x higher than vanilla LoRaWAN and p-CARMA, respectively. Channel utilization is 11.24x higher compared to LMAC. Further, on a testbed of 30 LoRa devices np-CECADA outperforms LoRaWAN up to 5 times.

关键词： Wide area networks Performance evaluation Protocols Scalability Logic gates throughput Media Access Protocol

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CIPhy: Causal Intervention with Physical Confounder from IoT sensor Data for Robust Occupant Information Inference 22

CIPhy: Causal Intervention with Physical Confounder from IoT...

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Proceedings of the 20th acm conference on embedded networked sensor systems

作者： Zhizhang Hu Tong Yu Ruiyi Zhang Shijia Pan University of California Carnegie Mellon University Duke University

ISBN: (纸本)9781450398862

Occupant information inference with IoT sensor data enables many smart applications, such as patients'/older adults' in-home monitoring. the difficulty of collecting labeled real-world IoT sensor data often leads to reliability and scalability issues for those systems. Extensive prior works (e.g., domain adaptation) focus on the domain shift issues, i.e., the inconsistent data feature and label relationship, and dataset bias is often neglected. Dataset bias is commonly caused by limited and varied accessibility to labeled data for each class, and it is inevitable for real-world datasets. the model trained with a biased dataset fits into the bias, hence cannot further generalize to the testing data for accurate *** propose CIPhy, a causal intervention scheme with physical confounders measured from the sensor data to achieve robust occupant information inference. We model the dataset bias as a confounding problem. there exists a confounder directly impacts both data feature and label, and each class's accessibility to labeled data varies when the confounder's condition changes. the model trained with biased data learns a spurious feature-label correlation conditioned on the confounder's condition in the training data. When testing data has a different condition, i.e., confounding shift, this correlation can not be applied. By using the causal intervention, e.g., backdoor adjustment, the confounding shift's negative impact on the data-driven models can be mitigated. the CIPhy decouples the sensor data to measure the confounder, then conducts the causal intervention for a de-biased occupant information inference. We use a public dataset on occupant identification as a case study, to investigate the feasibility of applying causal intervention to resolve the dataset bias issue. From the experiment, CIPhy achieves up to 11.42% identification accuracy improvement compared to baselines given the biased training data and confounding shift.

关键词： occupant information inference dataset bias causal intervention

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ePerceptive-Energy Reactive embedded Intelligence for Batteryless sensors 20

ePerceptive-Energy Reactive Embedded Intelligence for Batter...

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18th conference on embedded networked sensor systems

作者： Montanari, Alessandro Sharma, Manuja Jenkus, Dainius Alloulah, Mohammed Qendro, Lorena Kawsar, Fahim Nokia Bell Labs Cambridge England Univ Washington Seattle WA 98195 USA Newcastle Univ Newcastle Upon Tyne Tyne & Wear England Univ Cambridge Cambridge England Delft Univ Technol Delft Netherlands Nokia Bell Labs Cambridge Pervas Syst Dept Cambridge England

ISBN: (纸本)9781450375900

For long, we have studied tiny energy harvesters to liberate sensors from batteries. With remarkable progress in embedded deep learning, we are now re-imagining these sensors as intelligent compute nodes. Naturally, we are approaching a crossroad where sensor intelligence is meeting energy autonomy enabling maintenance-free swarm intelligence and unleashing a plethora of applications ranging from precision agriculture to ubiquitous asset tracking to infrastructure monitoring. One of the critical challenges, however, is to adapt intelligence fidelity in response to available energy to maximise the overall system availability. To this end, we present the design and implementation of ePerceptive: a novel framework for best-effort embedded intelligence, i.e., inference fidelity varies in proportion to the instantaneous energy supplied. ePerceptive operates on two core principles. First, it enables training a single deep neural network (DNN) to operate on multiple input resolutions without compromising accuracy or incurring memory overhead. Second, it modifies a DNN architecture by injecting multiple exits to guarantee valid, albeit lower-fidelity inferences in the event of energy interruption. the combination of these techniques offers a smooth adaptation between inference latency and recognition accuracy while matching the computational load to the available power budget. We report the manifestation of ePerceptive in designing batteryless cameras and microphones built with TI MSP430 MCU and off-the-shelf RF and solar energy harvesters. Our evaluation of these batteryless sensors with multiple vision and acoustic workloads suggest that the dynamic adaptation of ePerceptive can increase the inference throughput by up to 80% compared to a static baseline while ensuring a maximum accuracy drop of less than 6%.

关键词： Energy autonomous embedded intelligence batteryless devices

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Integration of Piezoelectric and Light-Dependent Resistor sensors for Efficient Generation and Utilization of Electricity for Pathways

Integration of Piezoelectric and Light-Dependent Resistor Se...

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Trends in Material Science and Inventive Materials (ICTMIM), International conference on

作者： Saranya G Kumaran K Daneshwaran M Shyam Venkatraman Jey Pradeep A Anish Anandhan A L School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the ever-increasing demand for energy in our modern world necessitates exploration of innovative and sustainable solutions. However, the research in the integration of piezoelectric materials has highlighted limitations with respect to scalability. this research investigates the potential of piezoelectric materials for harvesting energy from everyday human movement. We look into cost-effective system utilizing piezoelectric sensors embedded under flooring to capture the energy generated by footsteps. this harvested energy can be stored in and boosted for powering devices. this research explores the utilization of piezoelectric energy harvesting technology to create electricity-generating pathways, offering a sustainable solution for powering pathway lights in urban environments. Piezoelectric sensors strategically placed along the pathway surface capture kinetic energy from pedestrian movement, converting it into electrical energy. this energy is stored in rechargeable batteries and undergoes voltage conversion to power pathway lights effectively. A light-dependent resistor (LDR) sensor ensures lights only activate when necessary, optimizing energy usage. An Arduino Uno microcontroller orchestrates light operation based on ambient light conditions. the methodology focuses on efficient energy utilization and provides a practical solution for sustainable pathway lighting. Several prototypes and studies are discussed, highlighting the potential and limitations of piezoelectric energy harvesting technology. the results demonstrate consistent power generation, high energy conversion efficiency, and cost-effectiveness, underscoring the feasibility and effectiveness of the proposed approach.

关键词： Resistors Mechanical sensors Wireless sensor networks Pedestrians Costs Electricity Piezoelectric materials Lighting sensor systems Energy harvesting

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WCES 2021 - Proceedings of the Workshop on Control and embedded systems, Co-located with EASTON 100 - 5th International conference on Computing Sciences, ICCS 2021

WCES 2021 - Proceedings of the Workshop on Control and Embed...

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2021 Workshop on Control and embedded systems, WCES 2021

the proceedings contain 12 papers. the topics discussed include: performance evaluation of distributed networks for Internet of things;connectivity pattern analysis for virtual simulation design, based on high-performance game analysis;computational study for improvement of aerodynamic performance of airfoil by changing various aerodynamic properties;an experimental design approach to IoT enabled smart parallel irrigation system using embedded microcontrollers;to develop, test and record a 3 lead EMG electrode and flex sensor on a 3D prosthetic limb with different gait patterns using Arduino microcontroller;and low-cost system for the management of hospital services, applied to hospitalized patients through the use of IoT technology.

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