Intelligent Offloading for Collaborative Smart City Services in Edge Computing

Xu, Xiaolong; Huang, Qihe; Yin, Xiaochun; Abbasi, Mahdi; Khosravi, Mohammad R.; Qi, Lianyong

doi:10.1109/jiot.2020.3000871

Cited by 95 publications

(36 citation statements)

References 34 publications

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“…Smart city refers to the use of new-generation of information technologies such as IoT and cloud computing to fully perceive and effectively integrate and process various data and information within the physical city, to make objects smart and to coordinate various systems in the city through the smart management of the government, providing a good development space for enterprises in different fields in the city and a more convenient and high-quality living environment for urban residents, so that the urban economy, society, and environment can achieve sustainable development [ 18 , 19 ]. The characteristics of SCI system are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Smart city information processing under internet of things and cloud computing

Su¹,

Chen

Lu³

2021

J Supercomput

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This study is to explore the smart city information (SCI) processing technology based on the Internet of Things (IoT) and cloud computing, promoting the construction of smart cities in the direction of effective sharing and interconnection. In this study, a SCI system is constructed based on the information islands in the smart construction of various fields in smart cities. The smart environment monitoring, smart transportation, and smart epidemic prevention at the application layer of the SCI system are designed separately. A multi-objective optimization algorithm for cloud computing virtual machine resource allocation method (CC-VMRA method) is proposed, and the application of the IoT and cloud computing technology in the smart city information system is further analysed and simulated for the performance verification. The results show that the multi-objective optimization algorithm in the CC-VMRA method can greatly reduce the number of physical servers in the SCI system (less than 20), and the variance is not higher than 0.0024, which can enable the server cluster to achieve better load balancing effects. In addition, the packet loss rate of the Zigbee protocol used by the IoT gateway in the SCI system is far below the 0.1% indicator, and the delay is less than 10 ms. Therefore, the SCI system constructed by this study shows low latency and high utilization rate, which can provide experimental reference for the later construction of smart city.

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Section: Methodsmentioning

confidence: 99%

Smart city information processing under internet of things and cloud computing

Su¹,

Chen

Lu³

2021

J Supercomput

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“…Nowadays, many researchers are devoting themselves to technologies in the context of the Internet of Things (IoT) [8] [9] [10]. Based on IoT, Intelligent Transportation Systems (ITS) is emerged as a novel paradigm to manage urban traffic and bring convenience to the lives of residents [11].…”

Section: Related Workmentioning

confidence: 99%

6G-Enabled Short-Term Forecasting for Large-Scale Traffic Flow in Massive IoT Based on Time-Aware Locality-Sensitive Hashing

Wang

Zhu

Wang

et al. 2021

IEEE Internet Things J.

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With the advent of the Internet of Things (IoT) and the increasing popularity of the Intelligent Transportation System, a large number of sensing devices are installed on the road for monitoring traffic dynamics in real-time. These sensors can collect streaming traffic data distributed across different traffic sites, which constitute the main source of big traffic data.Analyzing and mining such a big traffic data in massive IoT can help traffic administrations to make scientific and reasonable traffic scheduling decisions, so as to avoid prospective traffic congestions in the future. However, the above traffic decisionmaking often requires frequent and massive data transmissions between distributed sensors and centralized cloud computing centers, which calls for lightweight data integrations and accurate data analyses based on large-scale traffic data. In view of this challenge, a big data-driven and non-parametric model aided by 6G is proposed in this paper to extract similar traffic patterns over time for accurate and efficient short-term traffic flow prediction in massive IoT, which is mainly based on time-aware LSH (Locality-Sensitive Hashing). We design a wide range of experiments based on a real-world big traffic dataset to validate the feasibility of our proposal. Experimental reports demonstrate that the prediction accuracy and efficiency of our proposal are increased by 32.6% and 97.3%, respectively, compared with the other two competitive approaches.

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“…But only the access control is not enough for IoT privacy improvement. Another recent research has discussed edge computing-based solutions for smart cities [16]. The authors have presented their intelligent offloading method with simulation results applied for a collaborative smart city.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Data Privacy in the Internet of Things (IoT) Using Edge Computing

Sadique

Rahmani

Johannesson

2020

Communications in Computer and Information Science

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The vast deployment of the Internet of Things (IoT) is improving human life standards every day. These IoT applications are producing a massive amount of data from the environment where it is deployed. The collected data are mostly including end-user private data or industrial data, which are transmitted over the internet to the cloud devices for storing, processing, and sharing with the connected applications. Recent IoT data privacy-related researches are mostly focused on data privacy within a particular location of the network or at a specific device, but as per our knowledge, none has pointed and listed all the places where the end-user or industrial data privacy risks exist. In this work, we have addressed both technical and management aspects to enhance the privacy of IoT data. We have identified and listed the places where IoT data privacy risks exist, followed by our proposed model for data privacy enhancement in the internet of things (IoT). A list of ten suggestions for avoiding data privacy leakage to enhance IoT data privacy enhancement is presented. The results of this work should be useful for both academic researchers and stakeholders from the industry while designing and implementing new IoT solutions for the enhancement of human society.

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Intelligent Offloading for Collaborative Smart City Services in Edge Computing

Cited by 95 publications

References 34 publications

Smart city information processing under internet of things and cloud computing

Smart city information processing under internet of things and cloud computing

6G-Enabled Short-Term Forecasting for Large-Scale Traffic Flow in Massive IoT Based on Time-Aware Locality-Sensitive Hashing

Enhancing Data Privacy in the Internet of Things (IoT) Using Edge Computing

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