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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Shen, Zhishu Yokota, Kenji Tagami, Atsushi Higashino, Teruo KDDI Res Inc Saitama Japan Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka Japan

ISBN: (纸本)9781538632277

With the increasing demand to construct sensor networks for a smart IoT (Internet of Things) world, numerous sensors with sensing and communication capabilities are expected to be deployed in the future. Thanks to the development of hardware manufacture technology, relatively small IoT smart sensors are now commercially available and cost-effective. However, the total power required by operating these sensors is expected to be enormous, due to their large number and frequent activity. Removing "unneeded sensors" is the most direct way to reduce the power consumption of sensor networks. Here, "unneeded sensors" refers to those that can be placed in sleep mode, or even be removed from the network topology entirely, without serious impact on the overall networks data processing performance. In this paper, we report the development of an energy-efficient sensor network by using a machine learning model to determine the actual necessity of all the sensors in a sensor network. Machine learning model is introduced to identify unneeded sensors by comparing the data from neighboring sensors to that from the potentially unneeded ones. For identifying unneeded sensors, different strategies with different computational complexity are also proposed. Numerical experiments conducted in two real indoor environments verify that our proposed scheme can reduce the total number of active sensors by around 1/3, while maintaining more than 90% of the original high monitoring performance of the sensor network.

关键词： Internet of Things machine learning sensor data processing

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In-Network Self-Learning Algorithms for BEMS Through a Collaborative Fog Platform 32

In-Network Self-Learning Algorithms for BEMS Through a Colla...

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32nd IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Shen, Zhishu Yokota, Kenji Jin, Jiong Tagami, Atsushi Higashino, Teruo KDDI Res Inc Fujimino Saitama Japan Swinburne Univ Technol Sch Software & Elect Engn Hawthorn Vic Australia Osaka Univ Grad Sch Informat Sci & Technol Suita Osaka Japan

ISBN: (纸本)9781538621950

Building Energy Management System (BEMS) is a vital approach in constructing a global energy-efficient environment. It can be operated by analyzing data collected from sensors located in designated indoor areas. The key is to improve the data processing results while reducing the total data processing/communication volume required in the whole Internet of Things (IoT) networks as much as possible. In this work, a novel in-network self-learning algorithm for BEMS through a collaborative Fog platform is proposed. In particular, we devise an emerging Fog computing enabled IoT network architecture, where most of data can be processed in the sensor-to-Fog and Fog-to-Fog layers. data processing on Cloud is only required if anomalous sensor data are detected, and thus, the energy consumption due to heavy data processing on Cloud will be significantly reduced. The proposed algorithm makes the best use of Fog node capability to realize distributed data collection and processing. Via Fog-to-Fog connections, it can examine the sensor data by collecting them from different search ranges, whose values are meanwhile optimized. Numerical experiments conducted in a real indoor environment demonstrate that our algorithm achieve a high prediction accuracy for anomaly detection even with relatively small sensor data for processing. The effectiveness of Fog node placement is also verified. The overall scheme is expected to be a feasible solution to construct a cost-effective IoT network to minimize energy consumption while maximizing the indoor users comfort, from the perspective of achieving a high prediction accuracy in BEMS data monitoring.

关键词： Fog computing Internet of Things machine learning sensor data processing Building Energy Management System

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Investigations of rotor shaft position sensor signal processing in electric drive train systems

Investigations of rotor shaft position sensor signal process...

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IEEE Transportation Electrification Conference and Expo Asia-Pacific (ITEC Asia-Pacific)

作者： Datlinger, Christoph Hirz, Mario Graz Univ Technol FTG Inst Automot Engn Graz Austria

ISBN: (纸本)9781538657829

This paper deals with the evaluation of rotor shaft position sensor systems used in automotive electric powertrain applications. Those sensor systems comprise of an angular rotor position sensor as well as the powertrain's electronic control unit (ECU), processing the sensor data in terms of digitalization, filtering, position- and speed computation. The work introduces an evaluation method of influencing factors on the accuracy of rotor position measurement, e.g. quantization errors, filter settings and time latency of data processing chain. In this way, performance evaluation and investigation of electric powertrain sensor systems is enabled by combination of specific simulation and measurement procedures.

关键词： electric powertrain system rotor shaft position sensor system sensor data processing resolver electronic control unit ECU resolver-to-digital-conversion latency

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Assessment of Occupancy Estimators for Smart Buildings

Assessment of Occupancy Estimators for Smart Buildings

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IEEE International Conference on Intelligent data Acquisition and Advanced Computing Systems

作者： Claudia Chitu Grigore Stamatescu Iulia Stamatescu Valentin Sgarciu Department of Automatic Control and Industrial Informatics University Politehnica of Bucharest

ISBN: (纸本)9781728140704

Dense distributed sensor systems deployed to monitor and control the built environment open up wide application areas for improving the quality of life through smart spaces. By implementing these systems in residential or commercial buildings, more efficient, pervasive, real-time and finally more robust decision support is achieved. As one of the key tasks, high accuracy occupancy detection and estimation within buildings offers the potential to improve HVAC utilization while reducing maintenance needs, enhancing energy savings for building operators as well as a more suitable thermal comfort for occupants. In this context the main contribution of the paper consists of an assessment of supervised machine learning techniques namely random forests and extreme machine learning neural networks for indirect occupancy profile estimation. The methods are applied on a benchmarking dataset of indoor carbon dioxide measurements and ventilation damper positions as collected from the building information system. The impact of data preprocessing through filtering and smoothing as well as hardware constraints and cloud distributed processing for algorithm deployment are also discussed. We highlight key challenges and discuss how the learned models can be integrated for online operation to improve energy efficiency.

关键词： Statistical learning Occupancy estimation sensor data processing Smart buildings

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Monitoring and Control of Vehicles' Carbon Emissions 12th

Monitoring and Control of Vehicles' Carbon Emissions

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12th International Joint Conference on Software Technologies (ICSOFT)

作者： Tsokov, Tsvetan Petrova-Antonova, Dessislava Sofia Univ Dept Informat Technol 5 James Bourchier Blvd Sofia 1164 Bulgaria Sofia Univ Dept Software Engn 125 Tsarigradsko Shoes Blvd Sofia 1113 Bulgaria

ISBN: (纸本)9783319936413;9783319936406

Machine-to-machine communication, known as Internet of Things (IoT), allows machines to connect using variety of sensors and devices. Thus, feedbacks to govern energy, agriculture, transportation and environment are able to be obtained. The IoT provides opportunity not only for creation of new businesses and investments, but for reduction of carbon emissions. It enables production of highly automated and connected vehicles that change the global automotive market. Following the current IoT trends, this paper proposes a solution for real-time monitoring of vehicles and control of carbon emissions, called EcoLogic. The EcoLogic is composed of hardware module and several applications providing cloud services. The hardware module is based on Arduino and Raspberry Pi embedded systems and measures several physical parameters by sensors or extracts them from the onboard diagnostic system of the vehicle for further analysis. The cloud applications process the incoming data, store it into a SAP HANA database and analyze it to provide feedback and control of carbon emissions.

关键词： Clustering Internet of Things Reduction of carbon emissions sensor data processing

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Terrain assessment for precision agriculture using vehicle dynamic modelling

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BIOSYSTEMS ENGINEERING 2017年 162卷 124-139页

作者： Reina, Giulio Milella, Annalisa Galati, Rocco Univ Salento Dept Engn Innovat Via Arnesano I-73100 Lecce Italy CNR ISSIA Inst Intelligent Syst Automat Via Amendola 122 D-O I-70126 Bari Italy

Advances in precision agriculture greatly rely on innovative control and sensing technologies that allow service units to increase their level of driving automation while ensuring at the same time high safety standards. This paper deals with automatic terrain estimation and classification that is performed simultaneously by an agricultural vehicle during normal operations. Vehicle mobility and safety, and the successful implementation of important agricultural tasks including seeding, ploughing, fertilising and controlled traffic depend or can be improved by a correct identification of the terrain that is traversed. The novelty of this research lies in that terrain estimation is performed by using not only traditional appearance-based features, that is colour and geometric properties, but also contact-based features, that is measuring physics-based dynamic effects that govern the vehicle terrain interaction and that greatly affect its mobility. Experimental results obtained from an all-terrain vehicle operating on different surfaces are presented to validate the system in the field. It was shown that a terrain classifier trained with contact features was able to achieve a correct prediction rate of 85.1%, which is comparable or better than that obtained with approaches using traditional feature sets. To further improve the classification performance, all feature sets were merged in an augmented feature space, reaching, for these tests, 89.1% of correct predictions. (c) 2017 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Agricultural robotics Vehicle dynamics Terrain estimation and classification Vehicle-terrain interaction sensor data processing

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Assessing Motion Style Errors in Ski Jumping Using Inertial sensor Devices

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IEEE sensorS JOURNAL 2017年第12期17卷 3794-3804页

作者： Brock, Heike Ohgi, Yuji Keio Univ Grad Sch Media & Governance Tokyo 2520882 Japan Honda Res Inst Japan Saitama 3510188 Japan Keio Univ Fac Environm Informat Tokyo 2520882 Japan

Ski jumping is an expert sport that requires fine motor skills to guarantee the safe conduct of training and competition. In this paper, we therefore employed multiple inertial sensors to build and evaluate a framework for the assessment of jump errors and motion style. First, a large set of inertial ski jump motion captures were augmented, segmented, and transformed into multiple statistic and time-serial motion feature representations. All features were next used to learn and retrieve style error information from the jump segments under two classification strategies in a cross-validation cycle. Average accuracies of the error recognition indicated the applicability of the proposed system with error recognition rates between 60% and 75%, which should be considered sufficiently good under the present size and quality of the real life training data. Furthermore, the chosen signal-based motion features appeared to be better suited to extract and recognize style errors than the chosen kinematic induced features obtained using expensive post-processing. This assumption could constitute important information for many related application systems. Therefore, it should be investigated whether such result can be generalized under different extracted features or further feature set compositions in the future.

关键词： sensor data processing sensor applications inertial sensors motion analysis kinematics motor performance detection

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Monitoring Approach of Cyber-Physical Systems by Quality Measures 7th

Monitoring Approach of Cyber-Physical Systems by Quality Mea...

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7th EAI International Conference on sensor Systems and Software (S-CUBE)

作者： Laso, Pedro Merino Brosset, David Puentes, John Ecole Navale Chair Naval Cyber Def CC 600 F-29240 Brest 9 France Ecole Navale Naval Acad Res Inst CC 600 F-29240 Brest 9 France CNRS UMR Equipe DECIDE 6285 Telecom Bretagne Lab STICC Dept ITIInst Mines Telecom CS 83818 F-29238 Brest France

ISBN: (纸本)9783319615639;9783319615622

Modern cities, industrial plants, cars, trucks, and vessels, among others, make extensive use of cyber-physical systems and sensors. These systems are very critical and contribute to assist decision making. Large data streams are thus produced and analyzed to extract information that allows building knowledge through a set of principles called wisdom. However, because of multiple imperfections, as well as intrinsic, contextual, and extrinsic conditions that alter data, the quality of the generated streams must be evaluated, to determine how relevant they are for decision support. This paper presents a methodology to monitor cyber-physical systems by quality estimation, which defines suitable evaluation characteristics for pertinent analysis. Quality assessment is defined for data imperfections, information dimensions, knowledge factors, and wisdom aspects. The case study of a cyber-physical network of a liquid container training platform is presented in detail, to show how the approach can be applied. Obtained measures are multidimensional, heterogeneous, and variable.

关键词： Monitoring sensor data processing Multi-source sensor network Cyber-physical system data quality Information quality

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EcoLogic: IoT Platform for Control of Carbon Emissions 12

EcoLogic: IoT Platform for Control of Carbon Emissions

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12th International Joint Conference on Software Technologies (ICSOFT)

作者： Tsokov, Tsvetan Petrova-Antonova, Dessislava Sofia Univ Dept Informat Technol Sofia Bulgaria Sofia Univ Dept Software Engn Sofia Bulgaria

ISBN: (纸本)9789897582622

Today, the sensors and the Internet of Things (IoT) presence naturally in the people's lives. Billions of interactive devices exchange information about variety of objects in the physical world. The IoT technologies affect the business processes of all major industries such as transportation, manufacturing, healthcare, agriculture, etc. Despite the fact that the IoT has a positive impact to both people and industry, it also provides benefits for the environment. The IoT is recognized as a powerful tool in the fight against climate change. More specially, it has a significant potential in saving carbon emissions Taking into account the promising areas of IoT application, this paper proposes a solution for real-time monitoring of vehicles and detection of rising levels of carbon emissions, called EcoLogic. The EcoLogic consists of hardware module that collects sensor data related to vehicles' carbon emissions and cloud based applications for data processing, analysis and visualisation. Its primary purpose is to control the carbon emissions through smart notifications and vehicle's power limitations.

关键词： Clustering Internet of Things Reduction of Carbon Emissions sensor data processing

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sensor data processing for emergency response

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INTERNATIONAL JOURNAL OF EMERGENCY MANAGEMENT 2010年第3-4期7卷 233-248页

作者： Yang, Yanning May, Andrew Yang, Shuang-Hua Loughborough Univ Technol Comp Sci Dept Garendon WingHolywell Pk Loughborough LE11 3TU Leics England Loughborough Univ Technol Design Sch Loughborough LE11 3TU Leics England

A gap with current Emergency Response (ER) is the lack of real-time information on the status of an incident. Wireless sensor networks have shown great potential in addressing this gap. However, sensor data streams often contain 'noise', thus they are too difficult to be directly interpreted. In addition, sensor data are often meaningless unless it is associated with time and location information. This paper investigated the process of making sensor data work effectively for ER. The requirement analysis demonstrated that, in order to make sensor data work for ER, it needs to be properly pre-processed, it must be managed and stored efficiently, and meaning must be extracted from the data so that this can be conveyed to the emergency responders. A data cleaning approach and a data storage mechanism were proposed. Simulation results show that outliers were effectively reduced, and the query efficiency can be maintained as the database size increases.

关键词： emergency information management emergency response sensor data cleaning sensor data processing sensor data storage

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