SafeCity: Toward Safe and Secured Data Management Design for IoT-Enabled Smart City Planning

Zhang, Hui; Babar, Muhammad; Tariq, Muhammad Usman; Jan, Mian Ahmad; Menon, Varun G.; Li, Xingwang

doi:10.1109/access.2020.3014622

Cited by 51 publications

(21 citation statements)

References 30 publications

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“…On the other hand, data from crowdsensing projects would be incorporated into the public tram service to enhance the estimation of urban mobility trends. [59] Presents useful insights into a cleaner and clever City in an IoT world built on sensors. This paper imagined the essential role of safety and protection in IoT-enabled data calculation and communication in ensuring safe and stable decisions.…”

Section: Discussionmentioning

confidence: 99%

“…H. Zhang et al [59] The distinctive architecture showcasing the City's biodiversity, including cameras, sensors, and other physical equipment, was unmistakable. Safe City's three-tier architecture consists of a data security layer, a data computing layer, and a decision-making layer.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In particular, energy management and comfort regulation systems are in smart campus buildings and urban mass transit. On the other hand [59], Based on the data protected and the data calculation layer was processed. The decision-maker collects facts to assist you in making smart decisions.…”

Section: Semantic Web Technologiesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Data Mining Implementation in Smart City Applications

Ageed

Zeebaree

Sadeeq

et al. 2021

QAJ

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Many policymakers envisage using a community model and Big Data technology to achieve the sustainability demanded by intelligent city components and raise living standards. Smart cities use different technology to make their residents more successful in their health, housing, electricity, learning, and water supplies. This involves reducing prices and the utilization of resources and communicating more effectively and creatively for our employees. Extensive data analysis is a comparatively modern technology that is capable of expanding intelligent urban facilities. Digital extraction has resulted in the processing of large volumes of data that can be used in several valuable areas since digitalization is an essential part of daily life. In many businesses and utility domains, including the intelligent urban domain, successful exploitation and multiple data use is critical. This paper examines how big data can be used for more innovative societies. It explores the possibilities, challenges, and benefits of applying big data systems in intelligent cities and compares and contrasts different intelligent cities and big data ideas. It also seeks to define criteria for the creation of big data applications for innovative city services.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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A Survey of Data Mining Implementation in Smart City Applications

Ageed

Zeebaree

Sadeeq

et al. 2021

QAJ

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“…SSATP only uses primitive symmetric cryptography, allowing small devices with memory and low-end processing capacities to be easily implementable. However, DTLS must be excluded from the critical quality to suit the smart cities’ resource-intensive sensor nodes [ 12 , 35 , 36 ]. From the discussion above, we can see that most of the authentication schemes are still not satisfying the fog computing requirements.…”

Section: Related Workmentioning

confidence: 99%

SELAMAT: A New Secure and Lightweight Multi-Factor Authentication Scheme for Cross-Platform Industrial IoT Systems

Khalid

Hashim

Ahmad

et al. 2021

Sensors

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The development of the industrial Internet of Things (IIoT) promotes the integration of the cross-platform systems in fog computing, which enable users to obtain access to multiple application located in different geographical locations. Fog users at the network’s edge communicate with many fog servers in different fogs and newly joined servers that they had never contacted before. This communication complexity brings enormous security challenges and potential vulnerability to malicious threats. The attacker may replace the edge device with a fake one and authenticate it as a legitimate device. Therefore, to prevent unauthorized users from accessing fog servers, we propose a new secure and lightweight multi-factor authentication scheme for cross-platform IoT systems (SELAMAT). The proposed scheme extends the Kerberos workflow and utilizes the AES-ECC algorithm for efficient encryption keys management and secure communication between the edge nodes and fog node servers to establish secure mutual authentication. The scheme was tested for its security analysis using the formal security verification under the widely accepted AVISPA tool. We proved our scheme using Burrows Abdi Needham’s logic (BAN logic) to prove secure mutual authentication. The results show that the SELAMAT scheme provides better security, functionality, communication, and computation cost than the existing schemes.

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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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SafeCity: Toward Safe and Secured Data Management Design for IoT-Enabled Smart City Planning

Cited by 51 publications

References 30 publications

A Survey of Data Mining Implementation in Smart City Applications

A Survey of Data Mining Implementation in Smart City Applications

SELAMAT: A New Secure and Lightweight Multi-Factor Authentication Scheme for Cross-Platform Industrial IoT Systems

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

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