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Utilizing IoT Sensors and Spatial data Mining for Analysis of Urban Space Actors' Behavior in University Campus Space Design

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SENSORS 2025年第5期25卷 1393-1393页

作者： Koszewski, Krzysztof Olszewski, Robert Palka, Piotr Walczak, Renata Korpas, Przemyslaw Dabrowska-Zoltak, Karolina Wyszomirski, Michal Czeranowska-Panufnik, Olga Manujlo, Andrzej Szczepankowska-Bednarek, Urszula Kuzmicz-Kubis, Joanna Szalwa, Anna Ejsmont, Krzysztof Czernic, Pawel Warsaw Univ Technol Fac Architecture Ul Koszykowa 55 PL-00659 Warsaw Poland Warsaw Univ Technol Fac Geodesy & Cartog Plac Politech 1 PL-00651 Warsaw Poland Warsaw Univ Technol Fac Elect & Informat Technol Ul Nowowiejska 15-19 PL-00665 Warsaw Poland Warsaw Univ Technol Fac Civil Engn Mech & Petrochem Ul Lukasiewicza 17 PL-09400 Plock Poland Warsaw Univ Technol Doctoral Sch Ul Rektorska 4 PL-00614 Warsaw Poland Warsaw Univ Technol Fac Power & Aeronaut Engn Nowowiejska 24 PL-00665 Warsaw Poland Warsaw Univ Technol Ctr Innovat Ul Rektorska 4 PL-00614 Warsaw Poland Warsaw Univ Technol Univ Social Responsibil Off Ul Mochnackiego 10 PL-02042 Warsaw Poland Warsaw Univ Technol Fac Mech & Ind Engn Ul Narbutta 85 PL-02524 Warsaw Poland

This paper discusses the use of IoT sensor networks and spatial data mining methods to support the design process in the revitalization of the university campus of the Warsaw University of Technology (WUT) in the spirit of universal design. The aim of the research was to develop a methodology for the use of IoT and edge computing for the acquisition of spatial knowledge based on spatial big data, as well as for the development of an open (geo)information society that shares the responsibility for the process of shaping the spaces of smart cities. The purpose of the article is to verify the hypothesis on whether it is possible to obtain spatial-temporal quantitative data that are useful in the process of designing the space of a university campus using low-cost Internet of Things sensors, i.e., already existing networks of CCTV cameras supported by simple installed beam-crossing sensors. The methodological approach proposed in the article combines two main areas-the use of IT technologies (IoT, big data, spatial data mining) and data-driven design based on analysis of urban space actors' behavior for participatory revitalization of a university campus. The research method applied involves placing a network of locally communicating heterogeneous IoT sensors in the space of a campus. These sensors collect data on the behavior of urban space actors: people and vehicles. The data collected and the knowledge gained from its analysis are used to discuss the shape of the campus space. The testbed of the developed methodology was the central campus of the WUT (Warsaw University of Technology), which made it possible to analyze the time-varying use of the selected campus spaces and to identify the premises for the revitalization project in accordance with contemporary trends in the design of the space of HEIs (higher education institutions), as well as the needs of the academic community and the residents of the capital. The results are used not only to optimize the process o

关键词： IoT networks data management and analytics smart cities urban design data-driven design spatial data mining

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Efficient data Collection for Connected Vehicles With Embedded Feedback-Based Dynamic Feature Selection

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 2509-2519页

作者： Wang, Zhuo Sahin, Alp Zeng, Xiangrui Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Lehigh Univ Dept Mech Engn & Mech Bethlehem PA 18015 USA

Collecting relevant and high-quality data is critical to machine-learning-based application development in automotive industry. It is highly desired to concentrate the connected vehicle data collection efforts on a set of useful data to avoid irrelevant or redundant data. For a given machine learning (ML) task, without a priori information, it can be challenging to determine what features to collect. In this work, we propose an efficient data collection workflow featuring a loop involving a connected vehicle fleet, a ML algorithm and a feedback-based dynamic feature selection strategy. The feature selection process is embedded in the data collection workflow, therefore it is more efficient than the traditional workflow where feature selection is applied after all data are collected. The proposed workflow reduces data transmission and storage cost. A feature selection algorithm that exploits and explores feature sets using an upper-confidence-bound-based method is designed for the trade-off between collecting known useful data and collecting potentially useful data. Validations are performed on two classification tasks to mimic the connected vehicle data collection: a synthetic problem based on a public ML dataset, and a robot ground surface classification problem with experiment data in a simulated data collection environment. The proposed method is tested against several baseline methods, including collecting all available data before feature selection, collecting random features, and running the data collection procedure episodically with embedded feature selection. Results show that the proposed algorithm in the closed-loop procedure achieves adequate performance with relatively few irrelevant and redundant data.

关键词： Feature extraction data collection Connected vehicles data models Costs Vehicle dynamics Task analysis Connected vehicles cyber-physical systems data management and analytics ML

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Blockchain-Enabled Lightweight Fine-Grained Searchable Knowledge Sharing for Intelligent IoT

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IEEE INTERNET OF THINGS JOURNAL 2023年第24期10卷 21566-21579页

作者： Wang, Jing Lin, Xi Wu, Yulei Wu, Jun Shanghai Jiao Tong Univ Collaborat Innovat Ctr Shanghai Ind Internet Sch Elect Informat & Elect Engn Shanghai Key Lab Integrated Adm Technol Informat S Shanghai 200240 Peoples R China Univ Exeter Fac Environm Sci & Econ Dept Comp Sci Exeter EX4 4RN England Waseda Univ Grad Sch Informat Prod & Syst Fukuoka 8080135 Japan

With the rapid development of the Internet of Things (IoT), millions of IoT devices are constantly generating massive amounts of data. The development of artificial intelligence (AI) and edge computing makes it possible to conduct data analysis and knowledge mining efficiently among IoT edge devices. Knowledge, including intermediate results and training models obtained by large amounts of redundant data, is the core and foundation of edge intelligence. However, the sharing and utilization of knowledge still face a series of security and privacy issues, such as illegal knowledge access, knowledge tampering, and privacy leakage. To address these issues, in this article, we propose a blockchain-based knowledge storage and sharing architecture that enables secure knowledge management in intelligent IoT. We first design a permissioned blockchain-based decentralized and trusted knowledge storage scheme, which includes the on-chain encrypted knowledge storage and an improved Delegated Proof of Stake (DPoS) consensus protocol. Besides, we propose a lightweight attribute-based searchable encrypted knowledge-sharing mechanism, in which fine-grained and privacy-preserving knowledge collaboration is achieved through smart contracts and keyword search. Moreover, we reduce the computing overhead of edge devices through the design of partial outsourcing decryption. Finally, we analyze the security performance of our system as well as verify its practicality and ability to reject dishonest servers by simulation.

关键词： data management and analytics secure communications security and privacy smart environment

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vContact: Private WiFi-Based IoT Contact Tracing With Virus Lifespan

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IEEE INTERNET OF THINGS JOURNAL 2022年第5期9卷 3465-3480页

作者： Li, Guanyao Hu, Siyan Zhong, Shuhan Tsui, Wai Lun Chan, S-H Gary Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

Covid-19 is primarily spread through contact with the virus, which may survive on surfaces with a lifespan of hours or even days if not sanitized. To curb its spread, it is hence of vital importance to detect those who have been in contact with the virus for a sustained period of time, the so-called close contacts. Most of the existing digital approaches for contact tracing focus only on direct face-to-face contacts. There has been little work on detecting indirect environmental contact, which is to detect people coming into a contaminated area with the live virus, i.e., an area last visited by an infected person within the virus lifespan. In this work, we study automatic Internet of Things (IoT) contact tracing when the virus has a lifespan, which may depend on the disinfection frequency at a location. Leveraging the ubiquity of WiFi signals, we propose vContact, a novel, private, pervasive, and fully distributed WiFi-based IoT contact tracing approach. Users carrying an IoT device (phone, wearable, dongle, etc.) continuously scan WiFi access points (APs) and store their hashed IDs. Given a confirmed case, the signals are then uploaded to a server for other users to match in their local IoT devices for virus exposure notification. vContact is not based on device pairing, and no information of other users is stored locally. The confirmed case does not need to have the device for it to work properly. As WiFi data are sampled sporadically and asynchronously, vContact uses novel and effective signal processing approaches and a similarity metric to align and match signals at any time. We conduct extensive indoor and outdoor experiments to validate vContact performance. Our results demonstrate that vContact is effective and accurate for contact detection. The precision, recall, and F1-score of contact detection are high (up to 90%) for close contact proximity (2 m). Its performance is robust against AP numbers, AP changes, and phone heterogeneity. Having implemented vCon

关键词： Coronaviruses Wireless fidelity Internet of Things COVID-19 Global Positioning System Servers Bluetooth Contact tracing COVID-19 data management and analytics exposure notification social impacts

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Multimodal Event Processing: A Neural-Symbolic Paradigm for the Internet of Multimedia Things

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IEEE INTERNET OF THINGS JOURNAL 2022年第15期9卷 13705-13724页

作者： Curry, Edward Salwala, Dhaval Dhingra, Praneet Pontes, Felipe Arruda Yadav, Piyush Natl Univ Ireland Galway Insight SFI Res Ctr Data Analyt Data Sci Inst Galway H91 AEX4 Ireland

With the Internet of Multimedia Things (IoMT) becoming a reality, new approaches are needed to process real-time multimodal event streams. Existing approaches to event processing have limited consideration for the challenges of multimodal events, including the need for complex content extraction, and increased computational and memory costs. This article explores event processing as a basis for processing real-time IoMT data. This article introduces the multimodal event processing (MEP) paradigm, which provides a formal basis for native approaches to neural multimodal content analysis (i.e., computer vision, linguistics, and audio) with symbolic event processing rules to support real-time queries over multimodal data streams using the multimodal event processing language to express single, primitive multimodal, and complex multimodal event patterns. The content of multimodal streams is represented using multimodal event knowledge graphs to capture the semantic, spatial, and temporal content of the multimodal streams. The approach is implemented and evaluated within a MEP engine using single and multimodal queries achieving near real-time performance with a throughput of frames processed per second (fps) and subsec- ond latency of 0.075-0.30 s for video streams of similar to 30 fps input rate. Support for high input stream rates (45 fps) is achieved through content-aware load-shedding techniques with a similar to 127X latency improvement resulting in only a minor decrease in accuracy.

关键词： data management and analytics event processing Internet of Multimedia Things (IoMT) service middleware and platform

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data Reduction Model for Balancing Indexing and Securing Resources in the Internet-of-Things Applications

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IEEE INTERNET OF THINGS JOURNAL 2021年第7期8卷 5953-5972页

作者： Younan, Mina Elhoseny, Mohamed Ali, Abd El-Mageid A. Houssein, Essam H. Minia Univ Fac Comp & Informat Al Minya Egypt Amer Univ Emirates Dept Comp Sci Dubai U Arab Emirates Mansoura Univ Fac Comp & Informat Mansoura 35516 Egypt

Evolution of the Internet of Things (IoT) makes a revolution in connecting, monitoring, controlling, and managing things, objects, and almost surroundings through the Internet. To reveal the potential of IoT, rich knowledge has to be extracted, indexed, and shared securely in real time. Recent comprehensive researches on IoT spot the light on main correlative challenges, such as security, scalability, heterogeneity, and big data. Due to the heterogeneity of IoT applications that produce a large volume of a variety of data streams in real time, mining, securing, and analyzing IoT data become tedious and challenging tasks. Indexing sensory data is one of data mining techniques, which ease information retrieval. But ordinary indexing methods are not fit with such massive and dynamic data;where indexes become out-of-date once they are built. Clustering, data reduction, and summarization present promising solutions for enabling low-power security and balanced indexing. This article presents a novel method for dynamic data reduction and summarization using dynamic time warping (DTW), which also presents a balanced architecture for enabling balanced indexing based on similarity data fusion. data reduction-based prediction models enable real-time search and secure discovery for Smart Things (SThs). The results of the proposed model were proved using real examples and data sets. Using the Szeged-weather data set similar SThs data is reduced by 95%. Thus, indexes sizes could be reduced, and using smart scheduling, crawling cycle length could be expanded.

关键词： Internet of Things Indexing Real-time systems Security data integration Big data Cloud computing Balanced indexing crowd sensing data management and analytics efficient communications industrial Internet of Things (IoT) similarity search time series trajectory clustering data sets

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Embedded machine learning of IoT streams to promote early detection of unsafe environments

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INTERNET OF THINGS 2024年 25卷

作者： Fernandez, Eduardo Illueca Valera, Antonio Jesus Jara Breis, Jesualdoas Fernandez Univ Murcia Dept Informat & Syst Murcia Spain Libelium LAB SL Ceuti Spain

Indoor particulate matter (PM) are small solid and liquid particles present in the air, and its monitoring is one of the key challenges regarding workplace safety because of its impact on human health. To address this issue, the Internet of Things (IoT) paradigm allows the implementation of hyperlocal monitoring systems, typically using traditional cloud architectures, which can be enhanced using edge computing architectures. For this reason, we propose an IoTEdge-Cloud architecture for a platform which promotes the early detection of unsafe environments through machine learning, composed of a sensing layer that collects all the data, an edge layer that performs the artificial intelligence tasks and a cloud layer orchestrating. This architecture is based on the FogFlow framework and the FIWARE components. Our solution proposes an embedded model that can predict the occurrence of PM values higher than the recommended ones-according to the Occupational Safety and Health Administration (OSHA) indicators-with an 87 % of accuracy and a reduction of latency of 26 %. Our platform is innovative because it is based on the FogFlow framework for edge computing and supported by the Smart Spot device validated in a field test. This step is missing from similar state-of-the-art platforms. Thus, we believe that this work contributes to demonstrating the usefulness of AIoT to monitor workplace safety and make trustable predictions, avoiding risky environments.

关键词： Embedded machine learning Edge computing Indoor air quality monitoring Real-time systems Sensor analysis data management and analytics

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MusQ: A Multi-Store Query System for IoT data Using a datalog-Like Language

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IEEE ACCESS 2020年 8卷 58032-58056页

作者： Ramadhan, Hani Indikawati, Fitri Indra Kwon, Joonho Koo, Bonyong Pusan Natl Univ Dept Big Data Busan 46241 South Korea Ahmad Dahlan Univ Dept Informat Engn Yogyakarta 55166 Indonesia Pusan Natl Univ Sch Comp Sci & Engn Busan 46241 South Korea Kunsan Natl Univ Sch Mech Syst Engn Gunsan 54150 South Korea

The growing number of connected Internet of Things (IoT) devices has increased the necessity for processing IoT data from multiple heterogeneous data stores. IoT data integration is a challenging problem owing to the heterogeneity of data stores in terms of their query language, data models, and schemas. In this paper, we propose a multi-store query system for IoT data called MusQ, where users can formulate join operation queries for heterogeneous data sources. To reconcile the heterogeneity between source schemas of IoT data stores, we extract a global schema from local source schemas semi-automatically by applying schema-matching and schema-mapping steps. In order to minimize the burden on the user to understand the finer details of various query languages, we define a unified query language called the multi-store query language (MQL), which follows a subset of the datalog grammar. Thus, users can easily retrieve IoT data from multiple heterogeneous sources with MQL queries. As the three MQL query-processing join algorithms are based on a mediator & x2013;wrapper approach, MusQ performs efficient data integration over significant volumes of IoT data from multiple stores. We conduct extensive experiments to evaluate the performance of the MusQ system using a synthetic and large real IoT data set for three different types of data stores (RDBMS, NoSQL, and HDFS). The experimental results demonstrate that MusQ is suitable, scalable, and efficient query processing for multiple heterogeneous IoT data stores. Those advantages of MusQ are important in several areas that involve complex IoT systems, such as smart city, healthcare, and energy management.

关键词： data management and analytics Internet of Things multi-store system query processing schema integration

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A Benchmark of data Stream Classification for Human Activity Recognition on Connected Objects

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SENSORS 2020年第22期20卷 6486.-6486.页

作者： Khannouz, Martin Glatard, Tristan Concordia Univ Dept Comp Sci & Software Engn Montreal PQ H3G 1M8 Canada

This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of Human Activity Recognition. We measure both the classification performance and resource consumption (runtime, memory, and power) of five usual stream classification algorithms, implemented in a consistent library, and applied to two real human activity datasets and three synthetic datasets. Regarding classification performance, the results show the overall superiority of the Hoeffding Tree, the Mondrian forest, and the Naive Bayes classifiers over the Feedforward Neural Network and the Micro Cluster Nearest Neighbor classifiers on four datasets out of six, including the real ones. In addition, the Hoeffding Tree and-to some extent-the Micro Cluster Nearest Neighbor, are the only classifiers that can recover from a concept drift. Overall, the three leading classifiers still perform substantially worse than an offline classifier on the real datasets. Regarding resource consumption, the Hoeffding Tree and the Mondrian forest are the most memory intensive and have the longest runtime;however, no difference in power consumption is found between classifiers. We conclude that stream learning for Human Activity Recognition on connected objects is challenged by two factors which could lead to interesting future work: a high memory consumption and low F1 scores overall.

关键词： application platform data management and analytics smart environment data streams classification power memory footprint benchmark human activity recognition MCNN Mondrian Hoeffding tree

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Context-Aware Computing, Learning, and Big data in Internet of Things: A Survey

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IEEE INTERNET OF THINGS JOURNAL 2018年第1期5卷 1-27页

作者： Sezer, Omer Berat Dogdu, Erdogan Ozbayoglu, Ahmet Murat TOBB Univ Econ & Technol Dept Comp Engn TR-06560 Ankara Turkey Cankaya Univ Dept Comp Engn TR-06790 Ankara Turkey

Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field;there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, "intelligence" becomes a focal point in IoT. Since data now becomes "big data," understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding "context," or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called "context-aware computing," and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.

关键词： Big data in Internet of Things (IoT) context awareness data management and analytics machine learning in IoT

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