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Oppotunistic crowdsensing framework for Internet of things using Bluetooth low energy

JOURNAL OF ENGINEERING-JOE 2016年第5期2016卷 141-146页

作者： Basalamah, Anas Umm Al Qura Univ Dept Comp Engn Mecca 21955 Saudi Arabia

Researchers have leveraged modern day sensor rich smartphones to collect crowdsourced data and analyse it for different phenomenon of interests. Crowdsensing applications must bear with the limited energy, sensing, and computational resources available on the phone. To relief some burden from the smartphones, the authors envision a new crowdsourcing architecture where low-cost, low-energy sensors embedded in objects around us, i.e. walls, traffic lights, and billboards, providing a variety of sensors depending on their context, e.g. AirQuality, temperature. These devices would carry on the sensing, processing, and broadcasting of sensor data using wireless interfaces. Smartphones on the other hand, would opportunistically discover and collect data from these devices to provide a much better, richer, and energy-efficient sensing infrastructure. They discuss the usage of Bluetooth low energy (BLE) as a new energy-efficient sensing resource for crowdsensing. They focus on defining a unified BLE sensing framework, which provides a number of smart sensing schemes to ease the development of energy efficient and context aware crowdsensing applications. They also provide an open-source library that allows developers to utilise this framework and acts as a layer to support opportunistic crowdsensing out of the box.

关键词： Bluetooth Internet of Things intelligent sensors telecommunication power management energy conservation smart phones Internet open-source library context aware crowdsensing applications unified BLE sensing framework energy-efficient sensing resource energy-efficient sensing infrastructure data collection sensor data sensing sensor data broadcasting sensor data processing wireless interfaces crowdsourcing architecture crowdsourced data analysis smartphones Internet of Things opportunistic crowdsensing framework Bluetooth low-energy smart sensing

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GPU-Services: GPU Based Real-Time processing of 3D Point Clouds Applied to Robotic Systems and Intelligent Vehicles 12

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12th Latin American Robotics Symposium (LARS) / 3rd Brazilian Symposium on Robotics (SBR)

作者： Christino, Leonardo Osorio, Fernando Univ Sao Paulo Mobile Robot Lab LRM Ctr Robot CRob USP SC ICMC Trabalhador Sao Carlense 400 BR-13566590 Sao Carlos SP Brazil

ISBN: (纸本)9783319472478;9783319472461

The GPU-Services project fits into the context of research and development of methods for data processing of three-dimensional sensors data applied to mobile robotics and intelligent vehicles. The implemented methods are called services on this project, which provide 3D point clouds pre-processing algorithms, such as, data alignment, segmentation of safe/unsafe navigable zones (e.g. separating ground from obstacles and borders/curbs) and elements of interest detection. Due to the large amount of data provided by the sensors to be processed in a very short time, these services use the GPU (NVidia CUDA) to perform partial or complete parallel processing of these data. The project aims to provide data processing services to an autonomous car, forcing the services to approach real-time processing, which is defined as completing all data processing routines before the arrival of the sensor's next frame. This work was implemented considering 3D data acquired from a LIDAR, more specifically from a Velodyne HDL-32. The sensor data is structured in the form of a cloud of three-dimensional points, allowing for great parallel processing. However, the major challenge is the high rate of data received from this sensor (around 700,000 points/sec or 70.000 points/frame at 10 Hz), which gives the motivation of this project: to use the full potential of sensor and to efficiently use the parallelism of GPU programming. The GPU services are divided into four steps: The first step is an intelligent extraction, reorganization and spacial correction of the data provided by the Velodyne multi-layer laser sensor;The second stage is the segmentation of planar data;The third stage is object segmentation;The fourth stage is to develop a methodology that unite the results from the previous steps in order to better detect the curbs. The services were implemented and the performance was evaluated using traditional sequential data processing (CPU data processing) and parallel data processing (GPU

关键词： 3D point cloud sensor data processing Services GPU Parallel processing LIDAR

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ISO/IEC/IEEE 21451 Smart sensor Network for the Evaluation of Motorcycle Suspension Systems

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IEEE sensorS JOURNAL 2015年第5期15卷 2549-2558页

作者： Liguori, Consolatina Paciello, Vincenzo Paolillo, Alfredo Pietrosanto, Antonio Sommella, Paolo Univ Salerno Dept Ind Engn I-84084 Salerno Italy

Smart sensors and transducers are being widely employed in the development of electronically controlled systems, such as advanced suspensions, to improve the performance guaranteed by terrestrial vehicles. Nevertheless, a standard reference for the full exploitation of the smart features is not yet extensively adopted. The CAN-bus is only used for the communication functionalities within a sensor network, resulting in cost-effective and more reliable connections. An original data-driven approach, based on both the CAN-bus and the IEEE ISO/IEC/IEEE 21451 standard, is proposed for the characterization of suspension systems of two-wheel vehicles with regard to the comfort and vehicle handling. Because of the ISO/IEC/IEEE 21451, the data acquired from sensor are sent by means of a GPS-General Packet Radio Service module to PC software that implements the processing algorithm. The proposed methodology includes a low-cost measurement setup, data processing algorithms in both time and frequency domain, and the introduction of model-free synthetic indices to experimentally compare the on-road behavior influenced by different damping characteristics for the suspension system. The method is applied to the on-road verification of the riding comfort assured by different control strategies for semiactive suspensions.

关键词： ISO/IEC/IEEE 21451 sensor interface and architecture vehicle technology sensor data processing performance evaluation smart sensor communication protocol

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Enhancing Decision-Making by Leveraging Human Intervention in Large-Scale sensor Networks

Enhancing Decision-Making by Leveraging Human Intervention i...

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2015 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)

作者： Casini, Enrico Depree, Jessica Suri, Niranjan Bradshaw, Jeffrey M. Nieten, Teresa Florida Inst Human & Machine Cognit IHMC Pensacola FL USA Modus Operandi Melbourne FL USA US Army Res Lab Adelphi MD USA

ISBN: (纸本)9781479980154

Extensive deployment of sensor networks in recent years has led to the generation of large volumes of data. One approach to processing such large volumes of data is to rely on parallelized approaches based on architectures such as MapReduce. However, fully-automated processing without human intervention is error prone. Supporting human involvement in processing pipelines of data in a variety of contexts such as warfare, cyber security, threat monitoring, and malware analysis leads to improved decision-making. Although this kind of human-machine collaboration seems straightforward, involving a human operator into an automated processing pipeline presents some challenges. For example, due to the asynchronous nature of the human intervention, care must be taken to ensure that once a user-made correction or assertion is introduced, all necessary adjustment and reprocessing is performed. In addition, to make the best use of limited resources and processing capabilities, reprocessing of data in light of such corrections must be minimized. This paper introduces an innovative approach for human-machine integration in decision-making for large-scale sensor networks that rely on the popular Hadoop MapReduce framework.

关键词： sensor data processing sensor networks human-machine teamwork human-in-the-loop architectures human-assisted architectures data pipelines decision-making MapReduce Hadoop

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A data processing Method for Human Motion Estimation to Reduce Network and sensor Node Loads

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2013年第1期7卷 58-74页

作者： Imai, Shintaro Miyamoto, Mariko Cai, Mingrui Arai, Yoshikazu Inomata, Toshimitsu Iwate Prefectural Univ Grad Sch Software & Informat Sci Iwate Japan

Systems for estimating human motion using acceleration sensors present the following two problems: 1) advanced analysis and processing of sensor data are difficult because of resource limitations of sensor nodes;and 2) such analyses and processes burden the network because numerous sensor data are sent to the network. The authors' proposed method described herein for sensor data analysis and processing uses a host computer located near sensor nodes (neighborhood host). This method is intended to achieve a good balance between reduction of the network load and advanced sensor data analysis and processing. Moreover, this method incorporates reduction of the load to sensor nodes. To evaluate their method, the authors implement two prototype systems that use different machine learning methods. The authors conduct some experiments using these prototype systems. The experimentally obtained results demonstrate that the proposed method can resolve two problems.

关键词： Human Motion Estimation K-Means Clustering sensor data processing Support Vector Machine Symbiotic Computing Ubiquitous System

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SIPS: Solar Irradiance Prediction System

SIPS: Solar Irradiance Prediction System

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13th IEEE/ACM International Symposium on Information processing in sensor Networks (IPSN)

作者： Achleitner, Stefan Kamthe, Ankur Liu, Tao Cerpa, Alberto E. Penn State Univ Comp Sci & Engn University Pk PA 16802 USA Univ Calif Elect Engn & Comp Sci Merced 95344 CA USA

ISBN: (纸本)9781479931460

There is high interest in up-scaling capacities of renewable energy sources such as wind and solar. However, variability and uncertainty in power output is a major concern and forecasting is, therefore, a top priority. Advancements in forecasting can potentially limit the impact of fluctuations in solar power generation, specifically in cloudy days when the variability and dynamics are the largest. We propose SIPS, Solar Irradiance Prediction System, a novel sensing infrastructure using wireless sensor networks (WSNs) to enable sensing of solar irradiance for solar power generation forecasting. In this paper, we report the findings of a deployment of a hierarchical WSN system consisting of 19 TelosB nodes equipped with solar irradiance sensors, and 5 MicaZ nodes equipped with GPS boards, deployed in the vicinity of a 1 MW solar array. We evaluate different irradiance sensor types and the performance of different novel prediction methods using SIPS' data and show that the spatial-temporal cross-correlations between sensor node readings and solar array output power exists and can be exploited to improve prediction accuracy. Using this data for short-term solar forecasting for cloudy days with very high dynamics in solar output power generation -the worst case scenario for prediction-, we get an average of 97.24% accuracy in our prediction for short time horizon forecasting and 240% reduction of predicted normalized root mean square error (NRMSE) compared to state-of-the-art methods that do not use SIPS data.

关键词： wireless sensor networks solar energy forecasting sensor data processing forecasting algorithms

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Action Sequence Reproduction based on Automatic Segmentation and Object-Action Complexes 13

Action Sequence Reproduction based on Automatic Segmentation...

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13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

作者： Waechter, Mirko Schulz, Sebastian Asfour, Tamim Aksoy, Eren Woergoetter, Florentin Dillmann, Ruediger Karlsruhe Inst Technol Inst Anthropomat Adenauerring 2 D-76131 Karlsruhe Germany Univ Gottingen Phys Inst 3 Friedrich Hund Pl 1 D-37077 Gottingen Germany

ISBN: (纸本)9781479926190

Teaching robots object manipulation skills is a complex task that involves multimodal perception and knowledge about processing the sensor data. In this paper, we show a concept for humanoid robots in household environments with a variety of related objects and actions. Following the paradigms of Programming by Demonstration (PbD), we provide a flexible approach that enables a robot to adaptively reproduce an action sequence demonstrated by a human. The obtained human motion data with involved objects is segmented into semantic conclusive sub-actions by the detection of relations between the objects and the human actor. Matching actions are chosen from a library of Object-Action Complexes (OACs) using the preconditions and effects of each sub-action. The resulting sequence of OACs is parameterized for the execution on a humanoid robot depending on the observed action sequence and on the state of the environment during execution. The feasibility of this approach is shown in an exemplary kitchen scenario, where the robot has to prepare a dough.

关键词： automatic programming humanoid robots image motion analysis image segmentation image sequences motion control object detection robot programming teaching OAC PbD action sequence reproduction actions matching automatic segmentation household environments human actor human motion data multimodal perception object manipulation skills object-action complexes programming by demonstration robot teaching semantic conclusive subactions sensor data processing Humanoid robots Libraries Liquids Motion segmentation Robot sensing systems Trajectory

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A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic data Regular Paper

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2013年第8期10卷

作者： Hammoudi, Karim Dornaika, Fadi Soheilian, Bahman Vallet, Bruno McDonald, John Paparoditis, Nicolas Natl Univ Ireland Maynooth Dept Comp Sci Maynooth Kildare Ireland Univ Paris Est Inst Geog Natl Lab MATIS St Mande France Univ Basque Country Dept Comp Sci & Artificial Intelligence San Sebastian Spain Basque Fdn Sci IKERBASQUE Bilbao Spain

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information.

关键词： Mobile Platform Optical sensors Vision sensors Range sensors sensor Fusion sensor data processing Terrestrial-based Facade Modeling Facade Texturing Virtual Reality

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Efficient Resource Allocation for Attentive Automotive Vision Systems

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2012年第2期13卷 859-872页

作者： Matzka, Stephan Wallace, Andrew M. Petillot, Yvan R. AUDI AG D-85045 Ingolstadt Germany Heriot Watt Univ Sch Engn & Phys Sci ERPem Joint Res Inst Signal & Image Proc Edinburgh EH14 4AS Midlothian Scotland

We describe a novel architecture for automotive vision organized on five levels of abstraction, i.e., sensor, data, semantic, reasoning, and resource allocation levels, respectively. Although we implement and evaluate processes to detect and classify other participants within the immediate environment of a moving vehicle, our main emphasis is on the allocation of computational resource and attentive processing by the sensor suite. To that end, an efficient multiobjective resource allocation method is formalized and implemented. This includes a decision-making process dependent upon the environment, the current goal, the available sensors and computational resource, and the time available to make a decision. We evaluate our approach on road traffic test sequences acquired by a test vehicle provided by Audi. This vehicle includes lidar, video, radar, and sonar sensors, in addition to conventional global positioning system (GPS) navigation, but our evaluation is confined to lidar and video data alone.

关键词： Driver-assistance systems resource allocation safe navigation sensor data processing traffic participant classification

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Recovering Occlusion-Free Textured 3D Maps of Urban Facades by a Synergistic Use of Terrestrial Images, 3D Point Clouds and Area-Based Information

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Procedia Engineering 2012年 41卷 971-980页

作者： Karim Hammoudi Fadi Dornaika Bahman Soheilian Bruno Vallet John McDonald Nicolas Paparoditis Department of Computer Science National University of Ireland Maynooth Maynooth Co. Kildare Ireland Université Paris-Est Institut Géographique National Laboratoire MATIS Saint-Mandé France Department of Computer Science & Artificial Intelligence University of the Basque Country (UPV/EHU) San Sebastián Spain IKERBASQUE Basque Foundation for Science Bilbao Spain

关键词： Mobile Platform Optical sensors Vision sensors Range sensors sensor Fusion sensor data processing Terrestrial-based Facade Modeling Facade Texturing Virtual Reality

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