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Exact and Metaheuristic Approaches to Extend Lifetime and Maintain Connectivity in Wireless sensors Networks

5th International Network Optimization Conference (INOC)

作者： Raiconi, Andrea Gentili, Monica Univ Salerno I-84084 Fisciano SA Italy

ISBN: (纸本)9783642215278

Wireless sensor networks involve a large area of real-world contexts, such as national security, military and environmental control applications, traffic monitoring, among others. These applications generally consider the use of a large number of low-cost sensing devices to monitor the activities occurring in a certain set of target locations. One of the most important issue that is considered in this context is maximizing network lifetime, that is the amount of time in which this monitoring activity can be performed by opportunely switching the sensors from active to sleep mode. Indeed, the lifetime of the network can be maximized by individuating subset of sensors (i.e., covers) and switching among them. Two important aspects need to be taken into account among others: (i) coverage: each determined cover has to cover the entire set of targets, and (ii) connectivity: each cover should provide satisfactory network connectivity so that sensors can communicate for data gathering or data fusion (connected covers). In this paper we consider the problem of determining the maximum network lifetime to monitor all the targets by means of connected covers. We analyze the problem and propose an exact approach based on column generation and two heuristic approaches, namely a greedy algorithm and a GRASP algorithm, to solve it. We analyze the performance of the heuristic approaches by comparing the obtained solutions with those provided by the exact approach when available. Our preliminary experimental results show the proposed solution algorithms to be promising in terms of tradeoff between quality of solutions and computational effort.

关键词： Data fusion

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An Optimized fusion Method for Double-Wearable-Wireless-Band Platform on Cloud-Health Application 8th

An Optimized Fusion Method for Double-Wearable-Wireless-Band...

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8th International Symposium on Parallel architectures, algorithms, and Programming (PAAP)

作者： Xu, Wenchao Liu, Yanbo Yang, Yanqin Ning, Xiaoshuang Chu, Tianxing Song, Hongzhi East China Normal Univ Shanghai Peoples R China Texas A&M Univ Corpus Christi Corpus Christi TX 78412 USA

ISBN: (纸本)9789811064425;9789811064418

This paper presents a stable double-wireless-wearable-band platform that can detect hand gestures. The real-time monitoring and control system utilizes an MCU processor, a wireless transceiver, and a commercial three-axis, digital-output MEMS accelerometer. To detect the user's hand movements, a 3D virtual environment is created via a double-wearable-band controller. Compared with a single wearable band, double wearable bands can identify more gestures with improved stability. Performances in terms of control and detection are discussed in detail. This research development allows the user to specify desired two-hand postures using the multi-sensor information fusion technique for controlling a variety of robotic devices. In the system, the defined two-hand postures also allow the user to add freestyle control to various applications, which bridge the communication gap between humans and the systems. Moreover, the integration of the action recognition algorithm of the combination of two bracelets and the server brings out a real-time approach to analyze and make decisions based on the users' data. Therefore, the system can call for help in a timely manner under critical conditions.

关键词： Double wearable wireless band platform Multiple kalman filter fusion Cloud monitoring health platform

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Neural Architecture Search for Automotive Grid fusion Networks Under Embedded Hardware Constraints

Neural Architecture Search for Automotive Grid Fusion Networ...

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International Conference on Machine Learning and applications (ICMLA)

作者： Gabor Balazs Walter Stechele Technische Universität München Munich Germany

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

The goal of automotive sensor fusion is to generate an environmental model with low-cost, mass-produced sensors-typically camera and radar. The central fusion controller needs sophisticated post-processing algorithms to achieve this goal despite extensive pre-processing on the sensor side. Recent advances show the capabilities of deep convolutional auto-encoders for sensor fusion but are still limited to high-performance hardware. Limited memory and processing capabilities of embedded devices lead to the multi-objective optimization problem addressed in this work. The proposed evolutionary neural architecture search is configured to find novel sensor fusion network architectures optimized for embedded hardware. The discovered encoding and decoding cells improve fusion performance compared to that of corresponding cells from literature. These lightweight network architectures allow the deployment on constrained embedded devices, thus leverage environmental perception of level 2/2+ automated cars.

关键词： Training Computer architecture sensor fusion Hardware Encoding Decoding Automotive engineering

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Electromagentic diversity and EMI implications for multiple Co-sited radars and targeting applications

Electromagentic diversity and EMI implications for multiple ...

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IEEE International Symposium on Electromagnetic Compatibility, EMC 2007

作者： Drozd, Andrew L. Kasperovich, Irina Ruixin, Niu Varshney, Pramod K. ANDRO Computational Solutions LLC Beeches Technical Campus 7902 Turin Road Ste. 2-1 Rome NY United States Dept. of Electrical Engineering and Computer Science Syracuse University Link Hall Syracuse NY United States

ISBN: (纸本)1424413508

Real-time fusion of data collected from a variety of colocated radars that acquire information in a cooperative manner from multiple perspectives and/or different frequencies, is being shown to provide a more accurate and effective way of tracking complex targets in a multi-target scenario. This is more advantageous than employing a single radar or a group of radars operating independently. This paper describes a cooperative multi-sensor approach in which multiple radars operate together in a non-interference limited manner. A three-fold approach is presented: (i) applying multiobjective joint optimization algorithms to set limits on the operational parameters of the radars to preclude electromagnetic interference;(ii) measuring and processing radar returns in a shared manner for target feature extraction based on electromagnetic diversity principles in conjunction with target scattering cross sections;and (iii) employing feature-aided track/fusion algorithms to detect, discriminate, and follow real targets from clutter noise. The results of computer simulations are provided that demonstrate the advantages of this approach. © 2007 IEEE.

关键词： Extraction

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Streamlining River Flood Prevention with an Integrated AIoT Framework 20

Streamlining River Flood Prevention with an Integrated AIoT ...

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20th ACS/IEEE International Conference on Computer Systems and applications (AICCSA)

作者： Boulouard, Zakaria Ouaissa, Mariya Ouaissa, Mariyam Krichen, Moez Almutiq, Mutiq Algarni, Mohammad Hassan II Univ LIM Casablanca Morocco Cadi Ayyad Univ Comp Syst Engn Lab Marrakech Morocco Chouaib Doukkali Univ Lab Informat Technol El Jadida Morocco Al Baha Univ Dept Informat Technol Al Baha Saudi Arabia Qassim Univ Dept Management Informat Syst & Prod Management Coll Business & EconPOB 6640 Buraydah 51452 Saudi Arabia Al Baha Univ Comp Sci Dept Al Baha 65431 Saudi Arabia

ISBN: (纸本)9798350319439

River floods stand among the natural disasters with far-reaching impacts, affecting human lives, the economy, infrastructure, agriculture, and more. Substantial investments by organizations are directed toward innovative strategies for flood prevention. The concept of Artificial Intelligence of Things (AIoT), a fusion of Artificial Intelligence and Internet of Things technologies, has showcased its prowess across various domains. In this paper, we introduce an AIoT framework where river flood sensors, located in every region, transmit their data using LoRaWAN technology to local broadcast centers in proximity. These broadcast centers subsequently forward the data via 4G/5G networks to a centralized cloud server. The server employs efficient AI algorithms to analyze the data and predict river conditions nationwide, contributing to proactive flood prevention. This approach has demonstrated effectiveness on multiple fronts. LoRaWAN-based communication between sensor nodes and broadcast centers offers reduced energy consumption and expanded coverage, while AI-driven data analysis enhances the accuracy of river flood predictions.

关键词： Predictive River Flooding sensor Devices LoRaWAN Technology AI (Artificial Intelligence) IoT (Internet of Things) AIoT (Artificial Intelligence of Things)

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fusion of Data from Radar and Depth sensors Applied for Healthcare-Oriented Characterization of Persons’ Gait

Fusion of Data from Radar and Depth Sensors Applied for Heal...

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Signal Processing algorithms, architectures, Arrangements and applications (SPA)

作者： Paweł Mazurek Jakub Wagner Roman Z. Morawski Faculty of Electronics and Information Technology Institute of Radioelectronics and Multimedia Technology Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9781728139906

The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors applied in a system for unobtrusive monitoring of elderly persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared with respect to their potential for decreasing the uncertainty of estimation of several quantities, carrying information important for medical and healthcare services, viz. the state of being in motion, the direction of the movement, the travelled distance and the mean walking speed. The results of the numerical experimentation - performed on real-world data - show that the fusion of data enables more robust and accurate estimation of those parameters, and therefore more reliable monitoring of elderly persons.

关键词： Monitoring Legged locomotion sensor fusion Estimation Radar Biomedical monitoring

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Practical considerations in Bayesian fusion of point sensors

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Proceedings of SPIE - The International Society for Optical Engineering 2012年 8407卷

作者： Johnson, Kevin Minor, Christian Naval Research Laboratory Washington DC 20375 United States Nova Research Inc. Alexandria VA 22308 United States

ISBN: (纸本)9780819490858

sensor data fusion is and has been a topic of considerable research, but rigorous and quantitative understanding of the benefits of fusing specific types of sensor data remains elusive. Often, sensor fusion is performed on an ad hoc basis with the assumption that overall detection capabilities will improve, only to discover later, after expensive and time consuming laboratory and/or field testing that little advantage was gained. The work presented here will discuss these issues with theoretical and practical considerations in the context of fusing chemical sensors with binary outputs. Results are given for the potential performance gains one could expect with such systems, as well as the practical difficulties involved in implementing an optimal Bayesian fusion strategy with realistic scenarios. Finally, a discussion of the biases that inaccurate statistical estimates introduce into the results and their consequences is presented. © 2012 SPIE.

关键词： sensor data fusion

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A Constraint Sufficient Statistics based Distributed Particle Filter for Bearing Only Tracking

A Constraint Sufficient Statistics based Distributed Particl...

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IEEE International Conference on Communications (ICC)

作者： Mohammadi, Arash Asif, Amir York Univ Toronto ON M3J 1P3 Canada

ISBN: (纸本)9781457720536

A constrained sufficient statistic based distributed implementation of the particle filter (CSS/DPF) is proposed for angle/bearing-only tracking (BOT) applications. The CSS/DPF runs localized particle filters at each sensor node and computes the global sufficient statistics (GSS) of the overall system as a function (summation) of the local sufficient statistics (LSS). The CSS/DPF is, therefore, a two stage procedure: (i) First, the means of LSS at local nodes are computed by running average consensus algorithms to derive the GSS, and;(ii) Each node then updates its localized particle filter using the modified GSS. Simulation results show that the CSS/DPF is near-optimal with its performance almost identical to that of the centralized particle filter. The number of average consensus runs in the CSS/DPF are reduced by an order of magnitude of the dimension of the state vector, thereby, reducing the communication complexity and bandwidth requirement of the distributed implementation.

关键词： Consensus Algorithm Data fusion Distributed Estimation Bearing-only Tracking Particle Filters

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Lost in the Deep? Performance Evaluation of Dead Reckoning Techniques in Underwater Environments

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2023年第2期7卷 1-27页

作者： Radeta, Marko Rodrigues, Claudio Silva, Francisco Abreu, Pedro Pestana, Joao Nguyen, Ngoc Thi Zuniga, Agustin Flores, Huber Nurmi, Petteri Univ Madeira Fac Exact Sci & Engn Wave Labs Funchal Portugal Agencia Reg Desenvolvimento Invest Tecnol Inovaca ARNET Aquat Res Network MARE Marine & Environm Sci Ctr Funchal Portugal Univ Belgrade Dept Astron Fac Math Funchal Portugal Univ Madeira Wave Labs MARE ARDITIAR NET Funchal Portugal Univ Madeira Wave Labs MARE ARNETARDITI Funchal Portugal Univ Helsinki Dept Comp Sci Helsinki Finland Univ Tartu Inst Comp Sci Tartu Estonia

Computing research is increasingly addressing underwater environments and examining how computing can support diving and other activities. Unlike on land, where well-established positioning methods are widely available, underwater environments lack a common positioning mechanism, which is a prerequisite for many applications. Dead reckoning, the use of angle and distance estimates to track position changes from a known point of origin, is a promising candidate for underwater positioning as it does not rely on wireless signals (which decay rapidly in underwater environments) and as there is a wide range of literature and algorithms freely available. Yet, currently it is unclear whether the existing techniques can be adopted in underwater environments or whether the differences in medium and environment affect the performance of the dead reckoning techniques. We contribute by evaluating and systematically analyzing the performance and trade-offs associated with dead reckoning techniques in underwater environments. We present AEOLUS, a prototype unit comprising of a low-cost microcontroller and inertial measurement unit, to perform experiments on the ground and in underwater environments to assess how well the performance of different techniques translates from ground-based use cases to underwater environments. We benchmark 15 different algorithms and compare their performance in such environments to identify common patterns and dissimilarities, and identify root causes for these differences. The results show that displacement and turn errors can be estimated to within 5% error but that the best performing methods vary between land and underwater environments. We also show that the performance depends on the shape of the motion patterns with some algorithms performing better for hard turns whereas others perform better for gradual, more continuous turns.

关键词： sensor fusion Dead Reckoning Water Sensing Internet of Things AHRS Orientation Estimation Position Estimation Location Estimation IMU Underwater Positioning Review

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\"Found\" in Space - Helical Resonator Filters for Satellite Communication [Book\/Software Reviews]

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IEEE Microwave Magazine 2015年第11期16卷 80-82页

作者： James Chu Kennesaw State University Southern Polytechnic State University Marietta Georgia United States

This book's main objective is to offer a contemporary, comprehensive approach to the design of RF and microwave filters for satellite applications with an emphasis on helical resonator technology. By exploiting the synergies among available data, information fusion can reduce data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. The book examines the state of the art in information fusion. It presents the known methods, algorithms, architectures, and models of information fusion and discusses their applicability in the context of wireless sensor networks (WSNs). Paying particular attention to the wide range of topics that have been covered in recent literature, the text presents the results of a number of typical case studies. Complete with research-supported elements and comprehensive references, this teaching-oriented volume uses standard scientific terminology, conventions, and notations throughout. It applies recently developed convex-optimization theory and highly efficient algorithms in estimation fusion to open up discussion and provide researchers with an ideal starting point for further research on distributed estimation and fusion for WSNs. The book supplies a cohesive overview of the key results of theory and applications of information-fusion-related problems in networked systems in a unified framework. Providing advanced mathematical treatment of fundamental problems with information fusion, it will help readers broaden their understanding of prospective applications and how to address such problems in practice. After reading the book, readers will gain the understanding required to model parts of dynamic systems and use those models to develop distributed fusion control algorithms that are based on feedback control theory.

关键词： Book reviews Filtering theory Satellite communication Microwave filters Design methodology Wireless sensor networks Radio frequency Information filters Helical antennas

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