EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Alablani, Ibtihal Ahmed; Alenazi, Mohammed J. F.

doi:10.3390/s20247191

Cited by 44 publications

(25 citation statements)

References 58 publications

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“…For example, the heart stroke rehabilitation system [95] in smart health care using LDA, MLP and SVM algorithms. Next-generation smart cities will have ultra-dense cellular IoT networks using high-performance machine learning algorithms [96][97][98][99][100].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma¹,

2021

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Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.

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Section: Discussionmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma¹,

2021

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“…There is a possibility of performance degradation in several nodes or even crashes. Researchers used many approaches to make the WSN system easily implemented, especially in smart city sensing [ 19 , 20 ]. The sensor nodes with Wi-Fi capability are mostly used on WSN that are widely already available in urban areas.…”

Section: Related Workmentioning

confidence: 99%

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications

Putra

Chen

Prayitno

et al. 2021

Sensors

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The sparse data in PM2.5 air quality monitoring systems is frequently happened on large-scale smart city sensing applications, which is collected via massive sensors. Moreover, it could be affected by inefficient node deployment, insufficient communication, and fragmented records, which is the main challenge of the high-resolution prediction system. In addition, data privacy in the existing centralized air quality prediction system cannot be ensured because the data which are mined from end sensory nodes constantly exposed to the network. Therefore, this paper proposes a novel edge computing framework, named Federated Compressed Learning (FCL), which provides efficient data generation while ensuring data privacy for PM2.5 predictions in the application of smart city sensing. The proposed scheme inherits the basic ideas of the compression technique, regional joint learning, and considers a secure data exchange. Thus, it could reduce the data quantity while preserving data privacy. This study would like to develop a green energy-based wireless sensing network system by using FCL edge computing framework. It is also one of key technologies of software and hardware co-design for reconfigurable and customized sensing devices application. Consequently, the prototypes are developed in order to validate the performances of the proposed framework. The results show that the data consumption is reduced by more than 95% with an error rate below 5%. Finally, the prediction results based on the FCL will generate slightly lower accuracy compared with centralized training. However, the data could be heavily compacted and securely transmitted in WSNs.

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“…Past research [29][30][31] already discussed the importance of IoT device deployment. In our system, the sensor location is particularly critical because it determines the details of the overall monitored structure's transformation and movement.…”

Section: Sensor Deploymentmentioning

confidence: 99%

Integration with 3D Visualization and IoT-Based Sensors for Real-Time Structural Health Monitoring

Chang

Shirazi

2021

Sensors

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Real-time monitoring on displacement and acceleration of a structure provides vital information for people in different applications such as active control and damage warning systems. Recent developments of the Internet of Things (IoT) and client-side web technologies enable a wireless microcontroller board with sensors to process structural-related data in real-time and to interact with servers so that end-users can view the final processed results of the servers through a browser in a computer or a mobile phone. Unlike traditional structural health monitoring (SHM) systems that deliver warnings based on peak acceleration of earthquake, we built a real-time SHM system that converts raw sensor results into movements and rotations on the monitored structure’s three-dimensional (3D) model. This unique approach displays the overall structural dynamic movements directly from measured displacement data, rather than using force analysis, such as finite element analysis, to predict the displacement statically. As an application to our research outcomes, patterns of movements related to its structure type can be collected for further cross-validating the results derived from the traditional stress-strain analysis. In this work, we overcome several challenges that exist in displaying the 3D effects in real-time. From our proposed algorithm that converts the global displacements into element’s local movements, our system can calculate each element’s (e.g., column’s, beam’s, and floor’s) rotation and displacement at its local coordinate while the sensor’s monitoring result only provides displacements at the global coordinate. While we consider minimizing the overall sensor usage costs and displaying the essential 3D movements at the same time, a sensor deployment method is suggested. To achieve the need of processing the enormous amount of sensor data in real-time, we designed a novel structure for saving sensor data, where relationships among multiple sensor devices and sensor’s spatial and unique identifier can be presented. Moreover, we built a sensor device that can send the monitoring data via wireless network to the local server or cloud so that the SHM web can integrate what we develop altogether to show the real-time 3D movements. In this paper, a 3D model is created according to a two-story structure to demonstrate the SHM system functionality and validate our proposed algorithm.

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EDTD-SC: An IoT Sensor Deployment Strategy for Smart Cities

Cited by 44 publications

References 58 publications

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications

Integration with 3D Visualization and IoT-Based Sensors for Real-Time Structural Health Monitoring

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