City Internet Of Things Research Articles

Towards a conceptual framework for AI-driven anomaly detection in smart city IoT networks for enhanced cybersecurity

As smart cities advance, Internet of Things (IoT) devices present cybersecurity challenges that call for innovative solutions. This paper presents a conceptual model for using AI-enabled anomaly detection systems to identify anomalies and security threats in smart city IoT networks. The foundation is supported by the Complex Adaptive Systems (CAS) theory, Technology Acceptance Model (TAM), and Theory of Planned Behavior (TPB). In this framework, the importance of user engagement in ensuring effective AI-driven cybersecurity solutions is underlined with an emphasis on technological readiness and human interaction with AI. By fostering a security-conscious culture through continuous education and skills development, this research provides actionable insights for enhancing the resilience of smart cities against evolving cyber threats. The proposed framework lays the groundwork for future empirical studies and offers practical guidance for policymakers and urban planners dedicated to safeguarding the digital infrastructures of potentially tomorrow's cities – the smart cities.

Energy efficient reliable data transmission for optimizing IoT data transmission in smart city

The rapid proliferation of the internet of things (IoT) technology has significantly transformed urban landscapes, giving rise to smart city frameworks that leverage interconnected devices for enhanced efficiency and functionality. In these environments, vast amounts of data are generated by diverse sensors and devices, necessitating advanced strategies for effective data collection and transmission. This paper introduces a novel approach to address data collection and transmission challenges in IoT-enabled smart city frameworks. The proposed design integrates IoT-Cloud for efficient data collection and employs the energy efficient reliable data transmission (EERDT) model, optimizing IoT data transmission. The enhanced dragonfly routing algorithm, incorporating the firefly algorithm, enhances data routing efficiency. Experimental results demonstrate EERDT's superiority over energy-aware iot-routing (EAIR) and location-centric energy-harvesting aware-routing (LCEHAR), revealing significant improvements in communication overhead, data processing latency, and network lifetime. The EERDT exhibits substantial reductions in communication overhead, enhancing overall network performance. The EERDT model showcases lower data processing latency and energy consumption, highlighting its potential for resource-efficient IoT data transmission. This work contributes an innovative solution for smart city IoT networks, emphasizing performance enhancements and resource efficiency.

Comprehensive Review of Smart Cities using IOT in Coud

The rapid growth of urbanization necessitates smarter and more sustainable city management. The convergence of Internet of Things (IoT) devices and cloud computing offers a transformative solution for building Smart Cities. This review explores the integration of IoT and cloud technologies in creating intelligent urban infrastructure. It examines how data collected from a network of sensors and devices across the city can be leveraged to improve various aspects of urban life, including traffic management, energy efficiency, public safety, waste management, and parking. The paper discusses the key benefits of cloud computing for Smart City IoT applications, such as scalability, centralized data management, cost-effectiveness, security, and advanced analytics capabilities. Additionally, it explores the challenges associated with implementing Smart City IoT solutions, such as data privacy concerns, cybersecurity risks, and the need for interoperability between different devices and platforms. Finally, the review concludes by highlighting the potential of Smart City IoT powered by cloud computing to create a more efficient, sustainable, and livable future for our cities.

Empowering Smart City IoT Network Intrusion Detection with Advanced Ensemble Learning-based Feature Selection

This study presents an advanced methodology tailored for enhancing the performance of Intrusion Detection Systems (IDS) deployed in Internet of Things (IoT) networks within smart city environments. Through the integration of advanced techniques in data preprocessing, feature selection, and ensemble classification, the proposed approach addresses the unique challenges associated with securing IoT networks in urban settings. Leveraging techniques such as SelectKBest, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA), combined with the Gradient-Based One Side Sampling (GOSS) technique for model training, the methodology achieves high accuracy, precision, recall, and F1 score across various evaluation scenarios. Evaluation on the UNSW-NB15 dataset demonstrates the effectiveness of the proposed approach, with comparative analysis showcasing its superiority over existing techniques.

Governing Smart City IoT Interventions: A Complex Adaptive Systems Perspective

Urban infrastructure systems (UIS) encompass physical infrastructure and human stakeholders, delivering essential services through distributed flow networks. These systems exhibit properties of Complex Adaptive Systems (CAS), such as self-organization and adaptivity, which makes them resilient to perturbations from the outside. Smart city strategies advocate for external technology interventions to improve the efficiency and sustainability of UIS. Internet of Things (IoT) is a key enabler for such strategies which helps to gather data from various infrastructure components and optimize flow networks in UIS. Although beneficial, such interventions often give rise to ethical concerns, underscoring the need for appropriate governance measures. In this study, we adopt a CAS perspective to analyse the ethical concerns of a proposed smart city IoT platform in the cities of Karnataka, India. Based on our analysis, we present a framework to aid city authorities in formulating appropriate strategies to govern Smart City IoT interventions in general.

Smart City IoT Data Management with Proactive Middleware

With the increased emergence of cloud-based services, users are frequently perplexed as to which cloud service to use and whether it will be beneficial to them. The user must compare various services, which can be a time-consuming task if the user is unsure of what they might need for their application. This paper proposes a middleware solution for storing Internet of Things (IoT) data produced by various sensors, such as traffic, air quality, temperature, and so on, on multiple cloud service providers depending on the type of data. Standard cloud computing technologies become insufficient to handle the data as the volume of data generated by smart city devices grows. The middleware was created after a comparative study of various existing middleware. The middleware uses the concept of the federal cloud for the purpose of storing data. The middleware solution described in this paper makes it easier to distribute and classify IoT data to various cloud environments based on its type. The middleware was evaluated using a series of tests, which revealed its ability to properly manage smart city data across multiple cloud environments. Overall, this research contributes to the development of middleware solutions that can improve the management of IoT data in settings such as smart cities.

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia.

Towards an Integrated Methodology and Toolchain for Machine Learning-Based Intrusion Detection in Urban IoT Networks and Platforms

The constant increase in volume and wide variety of available Internet of Things (IoT) devices leads to highly diverse software and hardware stacks, which opens new avenues for exploiting previously unknown vulnerabilities. The ensuing risks are amplified by the inherent IoT resource constraints both in terms of performance and energy expenditure. At the same time, IoT devices often generate or collect sensitive, real-time data used in critical application scenarios (e.g., health monitoring, transportation, smart energy, etc.). All these factors combined make IoT networks a primary target and potential victim of malicious actors. In this paper, we presented a brief overview of existing attacks and defense strategies and used this as motivation for proposing an integrated methodology for developing protection mechanisms for smart city IoT networks. The goal of this work was to lay out a theoretical plan and a corresponding pipeline of steps, i.e., a development and implementation process, for the design and application of cybersecurity solutions for urban IoT networks. The end goal of following the proposed process is the deployment and continuous improvement of appropriate IoT security measures in real-world urban IoT infrastructures. The application of the methodology was exemplified on an OMNET++-simulated scenario, which was developed in collaboration with industrial partners and a municipality.

A Cross-Domain Trust Model of Smart City IoT Based on Self-Certification

Smart city refers to the information system with Internet of things and cloud computing as the core technology and government management and industrial development as the core content, forming a large-scale, heterogeneous and dynamic distributed Internet of things environment between different Internet of things. There is a wide demand for cooperation between equipment and management institutions in the smart city. Therefore, it is necessary to establish a trust mechanism to promote cooperation, and based on this, prevent data disorder caused by the interaction between honest terminals and malicious terminals. However, most of the existing research on trust mechanism is divorced from the Internet of things environment, and does not consider the characteristics of limited computing and storage capacity and large differences of Internet of things devices, resulting in the fact that the research on abstract trust mechanism cannot be directly applied to the Internet of things; On the other hand, various threats to the Internet of things caused by security vulnerabilities such as collision attacks are not considered. Aiming at the security problems of cross domain trusted authentication of Intelligent City Internet of things terminals, a cross domain trust model (CDTM) based on self-authentication is proposed. Unlike most trust models, this model uses self-certified trust. The cross-domain process of internet of things (IoT) terminal can quickly establish a trust relationship with the current domain by providing its trust certificate stored in the previous domain interaction. At the same time, in order to alleviate the collision attack and improve the accuracy of trust evaluation, the overall trust value is calculated by comprehensively considering the quantity weight, time attenuation weight and similarity weight. Finally, the simulation results show that CDTM has good anti collusion attack ability. The success rate of malicious interaction will not increase significantly. Compared with other models, the resource consumption of our proposed model is significantly reduced.

A Survey on Evolved LoRa-Based Communication Technologies for Emerging Internet of Things Applications

Survey/review study A Survey on Evolved LoRa-Based Communication Technologies for Emerging Internet of Things Applications Fang Yao 1,3, Yulong Ding 2,3,*, Shengguang Hong 2,4, and Shuang-Hua Yang 2,5,* 1 The Ta-tech company, Nanjing 210009, China 2 Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet, Department of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China 3 Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China 4 The School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150006, China 5 Department of Computer Science, University of Reading, West Berkshire RG6 6UR, United Kingdom * Correspondence: dingyl@sustech.edu.cn; Shuang-hua.yang@reading.ac.uk Received: 17 September 2022 Accepted: 17 October 2022 Published: 22 December 2022 Abstract: The concept of Internet of Things (IoT) greatly extends the coverage area that human being is able to perceive, access, and even control. By connecting various “Things” to the Internet, the IoT makes it possible to measure and manage the physical world as needed. As one of the most widely adopted Low Power Wide Area network technologies, the Long-Range-Radio (LoRa) has the features of long range, low power, and robustness, and thus plays an important role in building IoT applications where IoT objects are connected to the internet at affordable costs. Since the development of LoRa, many IoT applications have adopted LoRa and achieved success in the market. Currently, IoT technologies keep evolving towards different fields, giving rise to multifarious IoT applications including industrial IoT, smart city IoT, healthcare IoT, and direct-to-satellite IoT. In the meantime, LoRa also keeps developing and finding its position in various IoT applications either as a main or complementary player. The objective of this survey is to (1) provide a fundamental understanding of the LoRa technology; (2) explore research activities studying LoRa based communication systems for new IoT applications; and (3) demonstrate how the LoRa technology works together with other technologies to deliver better IoT services to end users.

Smart City IoT System Network Level Routing Analysis and Blockchain Security Based Implementation.

This paper demonstrates, network-level performance analysis and implementation of smart city Internet of Things (IoT) system with Infrastructure as a Service (IaaS) level cloud computing architecture. The smart city IoT network topology performance is analyzed at the simulation level using the NS3 simulator by extracting most of the performance-deciding parameters. The performance-enhanced smart city topology is practically implemented in IaaS level architecture. The intended smart city IoT system can monitor the principal parameters like video surveillance with a thermal camera (to identify the virus-like COVID-19 infected people), transport, water quality, solar radiation, sound pollution, air quality (O3, NO2, CO, Particles), parking zones, iconic places, E-suggestions, PRO information over low power wide area network in 61.88 km × 61.88 km range. Primarily we have addressed the IoT network-level routing and quality of service (QoS) challenges and implementation level security challenges. The simulation level network topology analysis is performed to improve the routing and QoS. Blockchain technology-based decentralization is adopted to enrich the IoT system performance in terms of security.

FARDA: A fog-based anonymous reward data aggregation security scheme in smart buildings

Nowadays the Internet of Things (IoT) for smart city is prevalent around the world, named as City IoT. Since building is the key facility in the city, smart building (SB) plays an important role in smart city. With the development of new AI technology and expansion of clients’ demand, smart buildings spread gradually in cities. Thereby, amounts of edge devices are accessed to the City IoT leading to the computation resources explosion and security issues in the smart environment. Here, we study the security of the data sensing in smart IoT and propose a novel data aggregation scheme, named Fog-based Anonymous Reward Data Aggregation scheme (later referred to as FARDA), to preserve the user privacy for developing a sustainable City IoT system. Specifically, this proposed scheme merges the AI based blockchain for enhancing the security on the edge devices and fog computing for the energy sustainability. More, FADRA attracts mobile sensing users and fog nodes by an anonymous reward mechanism in the blockchain framework. The framework leverages the provable Monero’s blockchain to endow two privacy requirements: (i) unlinkability between the payer’s address and the payee’s address and (ii) the non-traceability of the transaction sources. Moreover, two PKCs in the blockchain guarantee the benefits of sensing data participants theoretically. We verify the security properties of FARDA in theory and evaluate the efficiency for data sensing in smart environments on smartphones by experiments. Our study paves a way for broad researches on sustainable IoT in smart scenarios.

A Review of Using IoT for Energy Efficient Buildings and Cities: A Built Environment Perspective

Applications of the Internet of Things (IoT) are rapidly utilized in smart buildings and smart cities to reduce energy consumption. This advancement has caused a knowledge gap in applying IoT effectively by experts in the built environment to achieve energy efficiency. The study aims to provide an extensive review of IoT applications for energy savings in buildings and cities. This study contributes to the field of IoT by guiding and supporting built environment experts to utilize IoT technologies. This paper performed a thorough study using a systematic review that covered an overview of IoT concepts, models, applications, trends and challenges that can be encountered in the built environment. The findings indicated limitations in developing IoT strategies in buildings and cities by professionals in this field due to insufficient comprehension of technologies and their applied methods. Additionally, the study found an indefinite implementation and constraints on using IoT when integrated into the built environment. Finally, the study provides critical arguments and the next steps to effectively utilize IoT in terms of energy efficiency.

A Cluster based Intelligent Method to Manage Load of Controllers in SDN-IoT Networks for Smart Cities

Software Defined Network (SDN) is a programmable network which separates the control logic-plane and hardware data-plane. The SDN centrally manages different Internet of Things (IoT) enabled smart devices like, actuators and sensors connected in the networks. Smart city infrastructure is an application of IoT network which purpose is to manage the city network without human interventions. To collect the real time data, such smart devices generate large amount of data and increasing the traffic in network. To maintain the quality of services (QoS) of smart city IoT networks, the SDN needs to deploy the multi-controllers. But the communication performance reduces due to unbalance load distribution on controllers. To balance the traffic load of controller an intelligent cluster based Grey Wolf Optimization Affinity Propagation (GWOAP) Algorithm is proposed when deploying the multiple controllers in SDN-IoT enabled smart city networks. The proposed algorithm is simulated and the experimental results able to calculates the minimum overall communication cost in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Affinity Propagation (AP). The proposed GWOAP better balance the IoT enabled smart switches among clusters and node equalization is balanced for each controller in deployed topology. By using the proposed methodology, the traffic load of IoT enabled devices in smart city networks intelligently better balance among controllers.

Effective framework to tackle urban unemployment by e-government: an IoT solution for smart/metro cities in developing nation

PurposeWith the advancement in technology, the day-to-day life of people has gone through an immense transformation. The use of smart devices for day-to-day life is greater than before, and people are moving towards smart work rather than doing hard work. In this paper, a novel framework is proposed named Online Service Provider in Metro City (OSPMC) for IoT. The purpose of this study is to provide a theoretical framework for the E-Government in order to sustain or minimize the unemployment rate.Design/methodology/approachThe utilization of the Web in the upcoming years would create further opportunities for smart work. Internet of Things (IoT) plays an essential part in a system of multiple networks that aims to connect all things in the world that are capable of being connected through the internet. OSPMC framework can be developed on ASP.NET through (visual C#) 3.0 and Microsoft SQL Server with frontend and backend languages, respectively, under a web-based environment built on .Net platform. This lucrative facility is available mainly for the people living in the smart city.FindingsRural people are coming to smart cities in search of jobs, better education and a healthy lifestyle. India is also coping up with the modern world. The Government of India has taken an initiative for the making of 100 smart cities where the residents are relied upon to use Information and Communication Technology with the assistance of web.Social implicationsOSPMC promises to eliminate multiple evils like social injustice, crime, unemployment, tax fraud and would promote women empowerment. Also, provide opportunities to start-ups in order to grow and enhance their skill work.Originality/valueThe objective of OSPMC can be found useful for opening new job opportunities for urban/rural people while also encouraging people to learn skill work. For smart cities’ success in developing nations, it is important to recognize the elements influencing it. The motivation behind OSPMC is to identify those variables influencing the successful usage of allowing IoT in the smart cities by E-Government of India and to use IoT to help urban smart cities.

An intelligent way for optimal controller placements in software-defined–IoT networks for smart cities

Software Defined Networks (SDNs) is modern network paradigm that separately manages the control logic-plane and hardware data-plane. The programmable network SDN centrally manages different smart devices connected in the networks. These smart devices are facilitated by Internet of Things (IoT) and collect real time data for network devices. One of the emerging applications of IoT network is Smart City which purpose is to manage the city without human interventions. The rapid growths of IoT devices like actuators and sensors in smart city are generating large amount of data and increasing the traffic in network. To maintain the quality of services (QoS) of smart city IoT networks, the SDN characteristic is one way to provide better services to network users. Traditional IoT network having the challenges like, access delays, security and reliability issues. The SDN enabled IoT networks are capable to overcome these challenges and improves the network performance. One single controller is not sufficient to manage all IoT devices in smart city networks. So, multiple controllers are required to handle Software Defined-IoT Networks (SDN-IoT). Different metaheuristic algorithms are implemented to overcome controller placements problems (CPP) but as the number of controllers increases the performance of approaches degrades due to premature conversions. To better balance the exploration and exploitation features an intelligent Hybrid Differential Evolution and Whale Optimization (DEWO) Algorithm is proposed that optimizes the controller placements in SDN-IoT enabled smart city networks. The algorithm searches the optimal locations of controllers and balance the switch loads in respect of minimum latency. The algorithm also minimizes the link failure and evaluate the minimum end to end delay. The proposed hybrid algorithm is evaluated up to 40 controllers with other state of art method and able to achieve better results in comparison with other metaheuristic algorithms.

Analysis of Core Technologies and Implementation Services for Smart City

Smart City Platform is a software that optimally allocates resources and provides services to citizens by utilizing ICT technologies such as artificial intelligence, IoT, virtual reality, augmented reality, and 5G mobile communication to solve various urban problems occurring in the city.In order to implement the Smart City environment, basic platform technologies such as artificial intelligence, big data, network, Internet of Things(IoT) and Cloud computing are essential.In this paper, we used the big data analysis tool ‘BigKinds’ to lookup the topic 'Smart City Technology' and 'Smart City IoT' in news, editorials and quotations. After analyzing them, we then derived the core technologies needed in building a Smart City. Furthermore, we introduced the concept of the basic Smart City technologies and then deduced implications and drew a conclusion by analyzing both the local and foreign Smart City construction services. In addition, it compared and analyzed the core technologies for supporting services in various smart cities.The Smart City’s basic technologies and service analysis can be utilized by the local government to effectively establish policies and technology strategies in the Smart City industry.

Scalable and Efficient Hardware Architectures for Authenticated Encryption in IoT Applications

Internet of Things (IoT) is a key enabling technology, wherein sensors are placed ubiquitously to collect and exchange information with their surrounding nodes. Due to the inherent interconnectivity, IoT devices are vulnerable to cybersecurity attacks. To mitigate these vulnerabilities, cryptographic primitives can be employed, but they require significant computation, which restricts their adoption in IoT. Moreover, IoT systems have diverse requirements, ranging from high-throughput (TP) to the area constrained. This makes it hard to deploy appropriate security measures in a systematic manner. To address these issues, three generic implementation strategies (unrolled, round-based, and serialized) are proposed for developing highly efficient hardware architectures. They are applicable to all authenticated encryption schemes and are lightweight and fast, compared to conventional public key encryption. In this article, Ascon is implemented as an example based on those three strategies: 1) the unrolled architecture achieves TP of 766.9 Mb/s (Ascon-128) and 1389.2 Mb/s (Ascon-128a), which are suitable for high-throughput IoT applications; 2) the round-based architecture achieves 0.153 (Ascon-128) and 0.244 (Ascon-128a) TP-to-area ratio, which are, respectively, 73.8% and 40.2% better than state-of-the-art results; and 3) a novel serialized implementation technique is proposed wherein the substitution-box (S-box) is processed in multiple-bit-per-cycle, in contrast to the conventional one-bit-per-cycle approach. The TP of the two-bits-per-clock-cycle implementation is increased by 230.8% with only 36.8% additional hardware area. The proposed strategies allow us to scale the number of rounds (round-based) and bits-per-clock-cycle (serialized) to meet differing requirements in TP and area which are demonstrated for smart city IoT applications.

Essence of Scalability in Wireless Sensor Network for Smart City Applications

Internet of Things (IoT) is the emerging research area. Along with many physical objects, Wireless Sensor Networks (WSNs) play an impeccable role in the smart city applications. It is learned from the retrospective study that the interaction between different types of objects, gathering of data from all the zones, their analyses etc., are very difficult to handle without energy conserving algorithms and protocols. Furthermore, applications of IoT specifically smart cities require a fault tolerant, scalable and reliable framework for real time processing of collected data. In this paper, we have proposed a cluster-based routing methodology which could be used at the sensing layer of smart city IoT. We select Cluster Head (CH) based on energy, distance, network energy and node density. The consideration of above parameters helps in acquiring the optimized performance of the protocol Zone-Stable Election Protocol (Z-SEP). We have performed simulations in MATLAB to emphasize the role of scalability of WSN in smart city application. We found that this protocol under performs when it is encountered for scalablity.

LPGA: Location Privacy-Guaranteed Offloading Algorithm in Cache-Enabled Edge Clouds

The computation offloading, where Internet of Things (IoT) devices transfers their task to an external cloud, has several advantages such as low energy consumption of IoT devices and fast response time. To maximize these advantages, IoT devices can exploit the nearest edge cloud. However, frequent offloadings to the nearest edge cloud can cause a location privacy vulnerability due to the proximity of the edge cloud from IoT devices, which is a critical issue in smart city IoT applications. To address this problem, we propose a location privacy-guaranteed offloading algorithm (LPGA) in cache-enabled edge cloud environments. In LPGA, an IoT device decides where to offload the task (i.e., edge cloud or central cloud) with the consideration of the privacy level on its location and the cache hit probability. To minimize the generated traffic volume while maintaining low energy outage probability and providing a sufficient level of location privacy, a constrained Markov decision process (CMDP) problem is developed and it is converted into an equivalent linear programming (LP) model to achieve the optimal policy for offloading. Evaluation results demonstrate LPGA can reduce the traffic volume up to 39 percent compared to a central cloud-based offloading scheme while maintaining the energy outage probability below a certain level and providing required location privacy level.