Security, Privacy and Risks Within Smart Cities: Literature Review and Development of a Smart City Interaction Framework

Ismagilova, Elvira; Hughes, Laurie; Rana, Nripendra P.; Dwivedi, Yogesh K.

doi:10.1007/s10796-020-10044-1

Cited by 202 publications

(87 citation statements)

References 187 publications

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“…In addition to protecting transparency and ensuring security for each smart sensor activity, a novel secure wireless mechanism utilizing Blockchain technology was proposed [6]. A review of security and privacy concerning the development of the smart city was investigated in [7].…”

Section: Related Workmentioning

confidence: 99%

Scalable and Secure Gradient Smart Dempster Shafer Reputation for Development of Smart Cities

Ayothi¹,

Duela²,

Ramesh³

et al. 2021

Preprint

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Over the last decade, blockchain has been considered an encouraging solution to secure distributed ledgers. Moreover, with the introduction of a pseudonymous payment method without a centralized database or authoritative person, blockchain has also evolved as the future generation for online payment system. However, with the eruption of a large scale database, scalability has also become a demanding issue. In addition to the obstacle mentioned above, challenges like security and scalability stop accelerated adjustments for the development of smart cities. Without directing this essential scalability and privacy issue, such an encouraging method may not help develop smart cities. This paper bestows a measure to analyze both scalability and security aspects of existing blockchain methods with applications of smart city networks. The proposed method is known as Gradient Smart Load Balancer and Blockchain Dempster Shafer Reputation (GSLB-BDSR). Gradient Smart Load Balancer is designed so that even though with the increase in the number of participating sensors, the load is said to balance by applying gradient function, therefore ensuring scalability. Next, to cover the security aspect, with the aid of scalable blocks in the blockchain network, a Blockchain Dempster Shafer Reputation model is proposed. Evaluation outcomes of proposed security solutions outperform conventional solutions.

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Section: Related Workmentioning

confidence: 99%

Scalable and Secure Gradient Smart Dempster Shafer Reputation for Development of Smart Cities

Ayothi¹,

Duela²,

Ramesh³

et al. 2021

Preprint

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“…Some ongoing smart city projects such as those initiated in Hong Kong and Masdar city in Abu Dhabi [ 46 ] have already been criticized because of the vulnerable urban development plan and consequently doubts about sustainability of the services. The management of several facets of sustainability programs inside smart cities are facilitated by IoT, and this exposes organizations to the risks of failure from the network unavailability, security breaches and damage of IoT infrastructure from natural disasters [ 47 ]. Further, sustainable operation of services such as intelligent transportation systems, smart buildings and sustainable usage of resources such as water and energy supply, garbage disposal, etc.…”

Section: Iot-based Smart City Frameworkmentioning

confidence: 99%

Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

Rashid

Kamruzzaman

Hassan

et al. 2020

IJERPH

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In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

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“…The energy that is required for domestic functions is evaluated with the help of a consumption-based point of view. This paper [6] proposes a theoretical agenda to study and examine two principal cases from the US and Asia. The main objective of this paper is to focus on the progress in building an efficient smart city by amalgamating a variety of practical viewpoints with contemplation of smart city characteristics.…”

Section: Comparative Study Of Different Models For Smart Citymentioning

confidence: 99%

Prognostic Modelling for Smart cities using Smart Agents and IoT: A Proposed Solution for Sustainable Development

Ahsaan¹,

Mourya²

2018

EAI Endorsed Transactions on Smart Cities

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Currently, Scientists are working on smart cities for providing artificial intelligence based smart facilities and services that can tackle different problems raised by rapid urbanization and boom in population growth. The Internet of things is also being used with other smart technologies to provide better solutions to the environment. It is also known as a network of networks. Mobile agent technology has also gained more attention. We are using it with different internet-based services. In this research, we have proposed two models with a sustainable approach. Our first model focused on monitoring air quality which consisted of several sensors, Arduino Uno board, and Raspberry Pi 3. The Second model explores the potential of the mobile agent technique to lever the Internet of Things in the modernization and personalized services for Smart Cities.

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Security, Privacy and Risks Within Smart Cities: Literature Review and Development of a Smart City Interaction Framework

Cited by 202 publications

References 187 publications

Scalable and Secure Gradient Smart Dempster Shafer Reputation for Development of Smart Cities

Scalable and Secure Gradient Smart Dempster Shafer Reputation for Development of Smart Cities

Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

Prognostic Modelling for Smart cities using Smart Agents and IoT: A Proposed Solution for Sustainable Development

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