The Sensable City: A Survey on the Deployment and Management for Smart City Monitoring

Du, Rong; Santi, Paolo; Xiao, Ming; Vasilakos, Athanasios V.; Fischione, Carlo

doi:10.1109/comst.2018.2881008

Cited by 235 publications

(101 citation statements)

References 194 publications

(214 reference statements)

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“…In smart homes, different motion and door sensors are installed at different locations and the primary objective is to recognize and assess activities but in smart homes, physical activities (i.e., running, cycling) cannot be performed due to the nature of activities.The most widely used sensors for recording physical activities data using a smartphone are the accelerometer, gyroscope, and position sensor [8,[14][15][16][17][18][19][20]]. An accelerometer is capable of tracking activity readings to infer complex user motions, such as tilt, swing, or rotation.Researchers showed that the accelerometer sensor is the most reliable and cheapest alternate of wearable sensors for physical activity recognition [19,[21][22][23]. They showed that the accelerometer can also be used in combination with other sensors such as a gyroscope, light, proximity, barometer, linear acceleration and magnetometer sensor for better activity recognition.…”

mentioning

confidence: 99%

“…Researchers showed that the accelerometer sensor is the most reliable and cheapest alternate of wearable sensors for physical activity recognition [19,[21][22][23]. They showed that the accelerometer can also be used in combination with other sensors such as a gyroscope, light, proximity, barometer, linear acceleration and magnetometer sensor for better activity recognition.…”

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confidence: 99%

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Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

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Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.2 of 18 emotional issues, and depression as well. Physical fitness can be tracked and analyzed by monitoring daily life physical activities.Physical activity recognition was initiated back in 2004 using on-body sensors. Researchers in [2] used the accelerometer's annotated data to recognize the physical activities. They made an Android-based system that collects raw data from the accelerometer and applied machine learning algorithms to predict physical activities. Authors in [3] recognized six basic activities, i.e., walking, jogging, sitting, standing, upstairs and downstairs. Authors in [4] used on-body sensors for activity recognition but found that it is very difficult to carry the sensors all the time. Many authors suggested that a smartphone is a non-obtrusive option for activity recognition [3,[5][6][7][8][9].The smartphone is playing a vital role in modern life. It provides services and applications such as health monitoring, early-stage disease detection, sports analysis, fitness tracking, and behavior analysis. Android-based smartphones have a built-in motion sensor that provides accurate and precise acceleration readings against physical activities. In early conditions, dedicated sensors were used for activity recognition. There exist several techniques for physical activity recognition such as on-body obtrusive and non-obtrusive sensors [10][11][12][13]. Non-obtrusive sensors are used in smart homes and smartphones. In smart homes, different motion and door sensors are installed at different locations and the primary objective is to recognize...

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confidence: 99%

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Javed

Sarwar

Khan

et al. 2020

Sensors

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“…While some attempts at integration has been done in the context of environmental monitoring, involving sensors for temperature, relative humidity, dew point, gas, wind, rain, and atmospheric pressure [49,50], the benefits of integration extend to traffic monitoring, where sensor readings are complemented with contextual data. Traffic monitoring is conducted by sensing capabilities, the GPS receivers installed on modern vehicles and a mixture of air quality sensor and acoustic sensors along the given road [51].…”

Section: Multiple Sensorsmentioning

confidence: 99%

An Interdisciplinary Review of Smart Vehicular Traffic and Its Applications and Challenges

Fiore

Florea

Lechuga

2019

JSAN

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Sensors and intelligent applications enabling smart vehicular traffic create an opportunity for improving the welfare of people, from the viewpoints of efficiency, sustainability, and social inclusivity. Like the opportunities, challenges of such an endeavour are multifaceted, including the scalable collection and processing of the hefty data volumes generated by sensors, and the coordinated operation between selfish agents. The purpose of this work is to survey recent literature with an emphasis on applications and a multidisciplinary eye, with the aim of stimulating discussion and reflection in the scientific communities. The principal application areas of smart traffic and smart mobility are discussed, synthesizing different perspectives. Many intriguing areas for future research exist besides those relative to connectivity, data fusion, and privacy. Some research challenges pertinent to sustainability, insurance, simulation and the handling of ambiguous information are highlighted.

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“…The IoT paradigm is also being deployed in several other contexts and applications areas, including different industry sectors [83,84], smart cities [85,86], and healthcare [87,88]. Besides data communication, an IoT system has to perform a multitude of other tasks.…”

Section: Iot Architecturementioning

confidence: 99%

Vehicle Electrification: Technologies, Challenges, and a Global Perspective for Smart Grids

Monteiro¹,

Afonso²,

Sousa³

et al. 2019

Innovation in Energy Systems - New Technologies for Changing Paradigms

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Nowadays, due to economic and climate concerns, the private transportation sector is shifting for the vehicle electrification. For this new reality, new challenges about operation modes are emerging, demanding a cooperative and dynamic operation with the power grid, guaranteeing a stable integration without omitting the power quality. Besides, new attractive and complementary technologies are offered by the vehicle electrification in the context of smart grids, valid for both on board and off board systems. In this perspective, this book chapter presents a global perspective and deals with challenges for the vehicle electrification, covering the key technologies toward a sustainable future. Among others, the flowing topics are covered: (1) Overview of battery charging systems, including on board and off board systems; (2) State of the art of communication technologies for application in the context of vehicular electrification, smart grids and smart homes; (3) Challenges and opportunities concerning wireless power transfer with bidirectional interface to the electrical grid; (4) Future perspectives about bidirectional power transfer between electric vehicles (vehicle to vehicle operation mode); (5) Unified technologies, allowing to combine functionalities of a bidirectional interface with the electrical grid and motor driver based on a single system; and (6) Smart grids and smart homes scenarios and accessible opportunities about operation modes.

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The Sensable City: A Survey on the Deployment and Management for Smart City Monitoring

Abstract: This is the accepted version of a paper published in IEEE Communications Surveys and Tutorials. This paper has been peer-reviewed but does not include the final publisher proofcorrections or journal pagination.

Cited by 235 publications

References 194 publications

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

An Interdisciplinary Review of Smart Vehicular Traffic and Its Applications and Challenges

Vehicle Electrification: Technologies, Challenges, and a Global Perspective for Smart Grids

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